CN111247078B - Elevator Analysis System and Elevator Analysis Method - Google Patents

Abstract

An elevator analysis system with a processor and a storage device connected with the processor, the storage device is used to store the number of passengers, the number of passengers is at the elevator of each floor of the elevator group as the control object, because the elevator is to be used As for the number of users appearing, the processor predicts the number of passengers in the future according to the number of passengers stored in the storage device, and determines the operation control of each car belonging to the elevator group according to the predicted number of passengers in the future. Applicable operation rules and control parameters set in each operation rule are further outputted as determined operation rules and control parameters.

Description

Elevator Analysis System and Elevator Analysis Method

This application claims priority to Japanese Patent Application No. 2017-209721 filed on October 30, 2017, and the entire contents of this application are incorporated herein by reference.

technical field

The present invention relates to a technique for analyzing group control elevators.

Background technique

In larger-scale buildings, multiple elevators are installed in parallel to increase the transport capacity of the elevators, and a system is introduced that selects the best car for service when an elevator is called at the boarding point. Also, as the building scale increases, the number of elevators installed in parallel increases, and the group control device can appropriately control the plurality of elevators to improve service quality such as reducing the waiting time of users. At this time, the group control device attempts to perform optimal control by predicting the usage status of the elevator using operation data or the like.

In Patent Document 1, based on the number of people riding in a car, the feature data of the proportion of passengers getting off the elevator in the ascending direction, the feature amount of getting off the elevator in the ascending direction, the feature value of the proportion of getting on the elevator in the descending direction, and the feature amount of getting off the elevator in the descending direction are used. Predictive analytics usage needs.

In Patent Document 2, a camera is installed in an elevator hall, and the number of people at the boarding place is counted. When predicting the number of people waiting for an elevator in a certain period of time, the average of the waiting times at the same time in the past specific period is used as the predicted value.

In Patent Document 3, the number of passengers on the car is used. Use the building's current congestion status and past occupancy history to predict congestion status.

In Patent Document 4, a camera is installed so as to take an image from the front of the hall toward the entrance of the building, and when a person is detected approaching the hall, the car is dispatched.

prior art literature

Patent Literature

Patent Document 1: Japanese Patent Laid-Open No. 2014-172718

Patent Document 2: Japanese Patent Laid-Open No. 2015-9909

Patent Document 3: International Publication No. 2017/006379

Patent Document 4: Japanese Patent Laid-Open No. 2000-26034

SUMMARY OF THE INVENTION

Problem to be solved by the present invention

Since the boarding time, the boarding floor, the landing floor, and the number of boarding persons of the user at the boarding place are all unknown, the control over the group elevator is limited.

In Patent Literature 1 and Patent Literature 3, since the number of passengers using the elevator car is used, the situation of the boarding floor and the landing floor is known, but the situation of the boarding place is not known. Therefore, it is difficult to control according to the change of the boarding place.

In addition, in Patent Document 2 and Patent Document 4, since the camera is installed at the boarding place, the situation at the boarding place is known, but the boarding floor and the descending floor are not known. Therefore, it is difficult to control according to the conditions of the boarding floor and the landing floor. In addition, the cost of installing the camera is calculated separately.

The object of the present invention is to implement control so that when the usage time of the user at the boarding place, the boarding floor, the landing floor and the number of boarding persons are obtained from the data, and the future congestion situation is further predicted, by shortening the time at the boarding place user's waiting time to alleviate user dissatisfaction.

solutions to problems

In order to solve at least one of the above-mentioned problems, the present invention is an elevator analysis system having a processor and a storage device connected to the processor, wherein the storage device is used in an elevator group to be controlled in an elevator group. The number of passengers that is the number of users appearing due to the use of the elevator is stored at the elevators on each floor of the group, and the processor predicts the number of passengers in the future according to the number of passengers stored in the storage device, and according to the predicted The number of passengers in the future is determined to determine the operation rules applicable to the operation control of the cars belonging to the elevator group, and the control parameters set in each operation rule, and further output the determined Run rules and control parameters.

Invention effect

According to one aspect of the present invention, by realizing optimal elevator control, it is possible to alleviate the dissatisfaction of elevator-related users. Problems, structures, and effects other than those described above will be clarified by the description of the following embodiments.

Description of drawings

1A is a block diagram showing the overall configuration of a control system for a group elevator according to an embodiment of the present invention.

FIG. 1B is a block diagram showing the hardware configuration of the analysis server of the embodiment of the present invention.

Fig. 2 is an explanatory diagram showing the relationship between processing and data of the control system for a group control elevator according to the embodiment of the present invention.

3 is a sequence diagram showing an outline of the number of occupants predicted and the destination floor predicted in the process of the control system for group control elevators according to the embodiment of the present invention.

4 is a sequence diagram showing an outline of effective rules and parameter selection in the processing of the control system for group control elevators according to the embodiment of the present invention.

5 is a flowchart showing the processing of the occupant number estimation model generation unit according to the embodiment of the present invention.

FIG. 6 is a flowchart showing the processing of the occupant number estimation unit according to the embodiment of the present invention.

7 is a flowchart showing the processing of the occupant number prediction unit according to the embodiment of the present invention.

8 is a flowchart showing the processing of the destination floor estimation unit according to the embodiment of the present invention.

9 is a flowchart showing the processing of the destination floor prediction unit according to the embodiment of the present invention.

FIG. 10 is a flowchart showing the processing of the control selector unit according to the embodiment of the present invention.

11 is a flowchart showing the processing of the rule/parameter evaluation unit according to the embodiment of the present invention.

FIG. 12 is an explanatory diagram showing building basic information stored in the analysis server according to the embodiment of the present invention.

FIG. 13 is an explanatory diagram showing a random seed stored by the analysis server according to the embodiment of the present invention.

FIG. 14 is an explanatory diagram showing the number of people getting on and off the elevator stored in the analysis server according to the embodiment of the present invention.

15 is an explanatory diagram showing an elevator operation log stored in an analysis server according to an embodiment of the present invention.

16 is an explanatory diagram showing external information (weather) stored by the analysis server according to the embodiment of the present invention.

FIG. 17 is an explanatory diagram showing external information (camera) stored in the analysis server according to the embodiment of the present invention.

18 is an explanatory diagram showing external information (building information) stored by the analysis server according to the embodiment of the present invention.

FIG. 19 is an explanatory diagram showing the occupant number estimation input held by the analysis server according to the embodiment of the present invention.

20 is an explanatory diagram showing an occupant number estimation model stored in an analysis server according to an embodiment of the present invention.

FIG. 21 is an explanatory diagram showing an estimation result of the number of occupants stored in the analysis server according to the embodiment of the present invention.

FIG. 22 is an explanatory diagram showing an occupant number prediction result stored in the analysis server according to the embodiment of the present invention.

FIG. 23 is an explanatory diagram showing the occupant number prediction result 2 stored in the analysis server according to the embodiment of the present invention.

24 is an explanatory diagram showing destination floor estimates for different time periods held by the analysis server according to the embodiment of the present invention.

FIG. 25 is an explanatory diagram showing destination floor prediction results for different time periods stored by the analysis server according to the embodiment of the present invention.

FIG. 26 is an explanatory diagram showing a rule/control template stored by an analysis server according to an embodiment of the present invention.

FIG. 27 is an explanatory diagram showing a KPI list stored by the analysis server according to the embodiment of the present invention.

FIG. 28 is an explanatory diagram showing simulation inputs and results stored by the analysis server according to the embodiment of the present invention.

FIG. 29 is an explanatory diagram showing valid rules/parameters held by the analysis server according to the embodiment of the present invention.

30 is an explanatory diagram showing a valid rule/parameter subdivision list held by an analysis server according to an embodiment of the present invention.

FIG. 31 is an explanatory diagram showing a rule/parameter list stored by the analysis server according to the embodiment of the present invention.

32 is an explanatory diagram showing a building personalized report output by an analysis server according to an embodiment of the present invention.

Detailed ways

Next, the embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to the following embodiments, and various modifications and changes can be made within the scope of the technical idea of the present invention, and these modifications and modifications should be included in the within the scope of this protection. Hereinafter, one embodiment of the present invention will be explained with reference to FIG. 1 .

1A is a block diagram showing the overall configuration of a control system for a group elevator according to an embodiment of the present invention.

Analysis server SA, client terminal CL, external information adjacent building information EXN, external information database EXD, external information camera EXC, control cabinet CA, car 1CA1, car 2CA2 and car 8CA8 and network NW in open or close state connected.

The analysis server SA constitutes an elevator analysis system for performing analysis related to the control of the group elevator. The analysis server SA includes a database SA0, a display unit SA1, a request unit SA2, and an execution unit SA3.

The database SA0 handles input/output data used in the analysis server SA. Specifically, the database SA0 includes information preliminarily set in the analysis server SA, information acquired via the network NW, information generated by the processing of the execution unit SA3, and the like. Although not shown in FIG. 1A , the database SA0 includes, for example, building basic information SA00, random seed SA01, number of people taking elevators SA02, elevator operation log SA03, external information (weather) SA04, external information (camera) SA05, external information (building information) SA06, occupant number estimation input SA07, occupant number estimation model SA08, occupant number estimation result SA09, occupant number prediction result SA10, occupant number prediction result SA11, destination floor estimation at different time periods SA12, purpose in different time periods Ground floor prediction result SA13, rule/control template SA14, KPI list SA15, simulation input and result SA16, valid rule/parameter SA17, and rule/parameter list SA19 (see Figure 2, Figure 12 to Figure 31).

The execution part SA3 is a part that actually performs the analysis, and consists of the measurement processing part SA31, the occupant number estimation model generation part SA32, the occupant number estimation part SA33, the occupant number prediction part SA34, the destination floor estimation part SA35, and the destination floor prediction part SA36 , a control selector unit SA37, and a rule/parameter evaluation unit SA38.

The client terminal CL is a terminal used by the administrator to browse the analysis status. External information The adjacent building information EXN, the external information database EXD, the external information camera ExC and the control cabinet CA provide information not related to the operation of the elevator. This information is recorded as external information. In addition to the above-mentioned information, the external information may include public information such as railway traffic data and road condition data. The car 1CA1, the car 2CA2, and the car 8CA8 are elevator cars, and the control cabinet CA is a control device for controlling the cars 1CA1 to 8CA8.

Although a part of illustration is abbreviate|omitted, in the example of FIG. 1A, 8 cages of cage|basket|car 1CA1 - cage|basket|car 8CA8 are controlled by the control cabinet CA. The cars 1CA1 to 8CA8 are, for example, cars of an elevator group that is installed in the same building and faces the same elevator boarding area (hall), is composed of 8 elevators, and is the target of group control. However, 8 is only an example, and the present invention can also be applied to an elevator group consisting of two or more elevators.

In addition, in the present embodiment, the number of occupants refers to the number of users who appear in the hall in order to take the elevator.

FIG. 1B is a block diagram showing the hardware configuration of the analysis server SA of the embodiment of the present invention.

The analysis server SA is, for example, a computer having an interface (I/F) 101 , an input device 102 , an output device 103 , a processor 104 , a main storage device 105 , and an auxiliary storage device 106 that are connected to each other.

The interface 101 is connected to the network NW, and communicates with the client terminal CL, the external information database EXD, the external information camera EXC, and the control cabinet CA via the network NW, and obtains external information neighboring building information EXN and the like. The input device 102 is a device used by the user of the analysis server SA to input information to the analysis server SA, and may include at least one of a keyboard, a mouse, a touch sensor, and the like. The output device 103 is a device for outputting information to a user of the analysis server SA, and may include a display device that displays, for example, text and images.

The processor 104 executes various processes according to programs stored in the main storage device 105 . The main storage device 105 is, for example, a semiconductor storage device such as a DRAM, and stores programs executed by the processor 104 , data necessary for processing by the processor, and the like. The auxiliary storage device 106 is a large-capacity storage device such as a hard disk drive or a flash memory, and stores data and the like that are referred to during processing performed by the processor.

The main storage device 105 of the present embodiment stores therein a measurement processing unit SA31, an occupant estimation model generation unit SA32, an occupant estimation unit SA33, an occupant estimation unit SA34, and a destination floor included in the execution unit SA3. Programs of the estimation unit SA35, the destination floor prediction unit SA36, the control selector unit SA37, and the rule/parameter evaluation unit SA38. Therefore, in the following description, the processing executed by each part included in the execution unit SA3 is actually executed by the processor 104 according to the program corresponding to each part stored in the main storage device 105 .

Further, the processing of the request unit SA2 may be realized by the processor 104 controlling the interface 101 or the input device 102 according to a program corresponding to the request unit SA2 stored in the main storage device 105 . The processing of the display unit SA1 may be realized by the processor 104 controlling the output device 103 according to a program corresponding to the display unit SA1 stored in the main storage device 105 .

The auxiliary storage device 106 of the present embodiment stores the database SA0. Furthermore, the program corresponding to each part included in the execution unit SA3 may be stored in the auxiliary storage device 106, and may be copied to the main storage device 105 as necessary. In addition, at least a part of the database SA0 may be copied to the main storage device 105 as needed.

Fig. 2 is an explanatory diagram showing the relationship between processing and data of the control system for a group control elevator according to the embodiment of the present invention.

By referring to this figure, the input data and output data related to each process are made clearer. In addition, an overall overview of processing and data can be performed. Thick-lined boxes represent processing, and thin-lined boxes represent data. Furthermore, the range enclosed by the solid line is preferably processed in real time, while the range enclosed by the dashed line is preferably performed offline.

Specifically, the occupant number estimation model generation unit SA32 executes the occupant number model process SP01 ( FIG. 5 ) based on the building basic information SA00 ( FIG. 12 ) and the random seed SA01 ( FIG. 13 ), and outputs the occupant number estimation model SA08 ( FIG. 20 ) ). In the example of FIG. 2, this occupant number model processing SP01 is executed as off-line processing SZ1.

The number of passengers estimating unit SA33 is based on the number of passengers SA02 ( FIG. 14 ), the elevator operation log SA03 ( FIG. 15 ), the external information (weather) SA04 ( FIG. 16 ), the external information (camera) SA05 ( FIG. 17 ), the external information ( Building information) SA06 (FIG. 18), building basic information SA00, and occupant estimation model SA08 execute occupant estimation processing SP02 (FIG. 6), and input the result to occupant prediction unit SA34. If there is available external information other than the above-mentioned information, the occupant number estimation unit SA33 may use the external information.

The occupant number prediction unit SA34 executes the occupant number prediction process SP03 ( FIG. 7 ) based on the result of the occupant number estimation process SP02 , and inputs the result to the destination floor prediction unit SA36 and the control selector unit SA37 . In addition, this result is stored by storage processing SP07 (FIG. 9).

The destination floor estimation unit SA35 executes the destination floor estimation process SP04 ( FIG. 8 ) based on the number of people getting on and off the elevator SA02 , and inputs the result to the destination floor estimation unit SA36 .

The destination floor prediction unit SA36 executes the destination floor prediction process SP05 ( FIG. 9 ) based on the results of the occupant number prediction process SP03 and the destination floor estimation process SP04. This result is stored by storage processing SP07.

The control selector unit SA37 executes the control selector SP06 ( Figure 10), and output this result to the control cabinet CA.

In the example of FIG. 2 , the above-described occupant number estimation processing SP02 to storage processing SP07 are executed as real-time processing SZ0.

The rule/parameter evaluation unit SA38 executes the KPI simulation process SP11, the effective rule/parameter selection SP12, and the termination judgment based on the data stored in the storage process SP07, the rule/control template SA14 (FIG. 26), and the KPI list SA15 (FIG. 27). Processing SP13, valid rule/parameter subdivision processing SP14, and display/control data generation processing SP15 (FIG. 11). During this process, simulation inputs and results SA16 (FIG. 28) and valid rules/parameters SA17 (FIG. 29) are generated, and finally a list of rules/parameters SA19 (FIG. 31) and a building personalization report SA20 (FIG. 32) are generated.

In the example of FIG. 2, the above-described KPI simulation processing SP11 to display/control data generation processing SP15 are executed as offline processing SZ2.

3 is a sequence diagram showing an outline of the number of occupants predicted and the destination floor predicted in the process of the control system for group control elevators according to the embodiment of the present invention.

The sequence diagram in FIG. 3 uses related data (the number of elevators SA02, elevator operation log SA03, external information (weather) SA04, external information (camera) SA05, etc.), analysis server SA, control cabinet CA, and client terminal CL in Each of the corresponding 4 axes is represented.

The data collection S01 is a process of periodically sending data to the analysis server SA from an external system such as the external information database EXD. The measurement processing unit SA31 of the execution unit SA3 of the analysis server SA receives these data, executes the database registration S02, and stores the received data in each table of the database SA0. The processes of the occupant estimation model generation unit SA32, the occupant estimation unit SA33, the occupant estimation unit SA34, the destination floor estimation unit SA35, the destination floor estimation unit SA36, and the control selector unit SA37 of the execution unit SA3 are periodically executed, and the Database registration S03, this result data is stored in each table of database SA0. Finally, the analysis server SA transmits the control parameter selected by the control selector section SA37 to the control cabinet CA as an input command CA0.

4 is a sequence diagram showing an outline of effective rules and parameter selection in the processing of the control system for group control elevators according to the embodiment of the present invention.

The timing chart of FIG. 4 is represented using the same four axes as those of FIG. 3 .

In the client terminal CL, the manager executes the operator information input S04, and inputs, for example, KPI and time period as the operator information. The client terminal CL sends a request command including the input information to the analysis server SA.

The analysis server SA executes data acquisition S05, and acquires the data sent from the client terminal CL. Then, the analysis server SA executes the rule/parameter evaluation section SA38, selects useful rules and control parameters while acquiring the corresponding data from the database SA0, and generates content using the results. The analysis server SA transmits display data including the generated content to the client terminal CL.

The client terminal CL executes display processing S06 and displays the content. An example of the displayed content will be described later with reference to FIG. 30 . Also, since KPIs and time periods, etc. are used in the analysis, it is best to record them in advance.

FIG. 5 is a flowchart showing the processing of the occupant number estimation model generation unit SA32 according to the embodiment of the present invention.

The occupant number model processing SP01 consists of two parts: the occupant number data generation SP010 and the occupant number estimation model generation SP011. In the passenger number data generation SP010, the passenger number estimation model generation unit SA32 obtains the number of passengers (that is, the number of passengers in each car) by simulation (second simulation) based on the building basic information SA00 and the random seed SA01. and the number of people getting off the elevator) and the status of the car.

For example, in the simulation, the number of occupants estimation model generation unit SA32 randomly generates a plurality of users who want to take the elevator in the hall on each floor. Specifically, the passenger number estimation model generation unit SA32 uses the random seed SA01 to randomly determine the hall floor and the appearance time of each user. Furthermore, the occupant number estimation model generation unit SA32 randomly specifies the destination floor for each user from the floors selected from the building basic information SA00.

Then, the passenger number estimation model generation unit SA32 simulates the operation of each car based on the determined appearance time, appearance floor, and destination floor of each user, and obtains the number of passengers and the car for each car at each time. status and generate it as occupancy estimate input into SA07. The car state is, for example, the floor where each car is located, the traveling direction (upward or downward direction) of each car, and the number of passengers in each car. The values recorded in log SA03 are the same. However, although the actual measurement value is recorded in the elevator operation log SA03, the occupant estimation model generation unit SA32 may generate the value by simulation.

At this time, the occupant number estimation model generation unit SA32 may execute a simulation based on, for example, any one of the operation rules/control parameters recorded in the rule/control template SA14 ( FIG. 26 ) described later.

In the occupant number estimation model generation SP011, the occupant number estimation model generation unit SA32 generates the occupant number estimation model SA08 by executing the two-stage process consisting of Step 1 and Step 2. In step 1, the occupant estimation model generation unit SA32 determines the occupant number at each time, the number of people getting on and off the elevator, and the state of the car, which are obtained in correspondence with each other, based on the simulation result. Then, in step 2, the occupant estimation model generation unit SA32 obtains a model for estimating the occupant, that is, the occupancy, based on the state of each car, the number of boardings and the number of people getting off the elevator for each car on each floor The number of people = f (the number of people taking the elevator, the state of the car) is a function f that holds. As will be described later, referring to FIG. 20, for example, multiple regression analysis may be performed using the number of passengers and the state of the car as explanatory indicators, and the number of passengers as a target variable.

At this time, if at least any one of the elevator operation log SA03, external information (weather) SA04, external information (camera) SA05, and external information (building information) SA06 (or other external information) can be used, these values can be As an external variable, a function f that satisfies the establishment of the number of passengers=f (the number of people taking the elevator, the state of the car, and the external variable) is obtained. When the function f is obtained, only the inverse conversion from the number of passengers determined in step 1 to the number of passengers taking elevators, etc. is required. Furthermore, if the function f is to be evaluated, other methods can be used.

FIG. 6 is a flowchart showing the processing of the occupant number estimation unit SA33 according to the embodiment of the present invention.

In the passenger number estimation processing SP02, the passenger number estimation unit SA33 substitutes the current number of passengers SA02 and the actual number of passengers, the number of people getting off, and the car state acquired from the elevator operation log SA03 into FIG. 5 . In the obtained occupant number estimation model SA08 , a current occupant number estimation result SA09 is obtained, and stored in the main storage device 105 or the auxiliary storage device 106 . In this way, the riding status of the user who is about to take the elevator can be estimated from the riding status of each car and the state of each car, such as the position and traveling direction of the user.

The number of people getting on and off each car can be estimated from, for example, a change in the weight of each car measured by the control cabinet CA. In addition, the position, running direction, etc. of each car depend on the control of the control cabinet CA. Therefore, according to the above-mentioned occupancy model processing SP01 and occupant estimation processing SP02, even if no external information is obtained, it is possible to estimate the occupancy of the user who is about to take the elevator based on the information obtained by the elevator itself.

In addition, the current number of passengers SA02 and the elevator operation log SA03 may also contain operation rules/control parameters for identifying the operation rules/control parameters applicable to the elevator when acquiring the data contained in these (that is, the control cabinet CA executes each car according to this. The operating rules/control parameters of the control, for example, see Figure 26) information. In this case, the number of passengers estimating part SA33 simulates the operation rules/control parameters by comparing the current number of passengers SA02 and the actual number of passengers, the number of people getting off the elevator and the actual number of passengers obtained from the elevator operation log SA03. The car state is substituted into the occupant estimation model SA08 generated by the occupant estimation model generation unit SA32, and the current occupant estimation result SA09 is obtained. Thereby, highly accurate estimation can be performed.

In addition, at this time, if at least one of the elevator operation log SA03, external information (weather) SA04, external information (camera) SA05, and external information (building information) SA06 (or other external information) can be used, then Substitute to use these.

FIG. 7 is a flowchart showing the processing of the occupant number prediction unit SA34 according to the embodiment of the present invention.

The occupant number prediction processing SP03 consists of the occupant number prediction SP030 and the format conversion SP031.

In the occupant number prediction SP030, the occupant number prediction unit SA34 uses the occupant number estimation result SA09 for each time point (for example, each time slot having a predetermined time width) obtained in the process of FIG. 6 to predict the self-storage The number of occupants at a future time from the time in the occupant number estimation result SA09, and the result is output as the occupant number prediction result SA10. At this time, if at least one of the elevator operation log SA03, external information (weather) SA04, external information (camera) SA05, and external information (building information) SA06 (or other external information) can be used, it can be used instead. These. For example, when the external information (camera) SA05 can be used, the number of people (SA057) contained in the external information (camera) SA05 can be used in place of the number of occupants acquired from the occupant number estimation result SA09; when the number of occupants obtained from other external information can be used These can also be used when the number of occupants determined in .

In format conversion SP031, using the occupant number prediction result SA10 obtained in occupant number prediction SP030, it is converted into the occupancy probability of different occupants per unit time using Poisson distribution, and this result is used as occupant number prediction result 2_SAll. output. The KPI simulation described later can be performed using this riding probability.

FIG. 8 is a flowchart showing the processing of the destination floor estimation unit SA35 according to the embodiment of the present invention.

The destination floor estimating unit SA35 estimates the destination floor by finding the trend of people getting off the elevator from the car in each time slot based on the number of people getting on and off the elevator SA02, and uses the result as the destination floor estimation SA12 for different time slots to output.

FIG. 9 is a flowchart showing the processing of the destination floor prediction unit SA36 according to the embodiment of the present invention.

The destination floor prediction unit SA36 executes destination floor prediction processing SP05 and storage processing SP07.

In the destination floor prediction processing SP05, the destination floor prediction unit SA36 predicts the destination floor of the boarding user, that is, the floor to which the boarding user will go. Specifically, the destination floor prediction unit SA36 differentiates between the occupant population prediction result 2_SA11, which is the processing result of the occupant number prediction unit SA34 in FIG. 7, and the time difference, which is the processing result of the destination floor estimation unit SA35 in FIG. The segment destination floor estimation SA12 is multiplied to predict the destination floor, and the result is output as the destination floor prediction result SA13 for different time segments.

The storage process SP07 is a process of storing, for example, the occupant number prediction result SA10 and the destination floor prediction result SA13 in different time zones, etc. obtained before, in the database SA0, for example. The reason for performing this processing is that a large amount of data in the past is required when performing offline processing.

FIG. 10 is a flowchart showing the processing of the control selector unit SA37 according to the embodiment of the present invention.

The control selector SP06 executed by the control selector section SA37 is a process for selecting a rule/parameter list that satisfies the occupancy number prediction result SA10 and the destination floor prediction result SA13 for different time periods. The selected parameters are sent to the control cabinet CA as input command CA0.

FIG. 11 is a flowchart showing the processing of the rule/parameter evaluation unit SA38 according to the embodiment of the present invention.

The rule/parameter evaluation unit SA38 includes KPI simulation processing SP11, valid rule/parameter selection SP12, termination judgment processing SP13, valid rule/parameter subdivision processing SP14, and display/control data generation processing SP15.

In the KPI simulation processing SP11, the rule/parameter evaluation unit SA38 executes while changing the operation rule/control parameter using the rule/control template SA14, KPI list SA15, destination floor prediction result SA13 for different time periods, and passenger number prediction result 2_SA11 Multiple simulations (1st simulation) and output KPI values.

Specifically, the rule/parameter evaluation unit SA38 estimates the destination floor probability entered in SA12 and the occupancy probability entered in the occupant prediction result 2_SA11 according to the destination floor in different time periods, and the occupancy probability of each occupant entered in the occupant number prediction result 2_SA11 is estimated at each floor. The user appears in the hall of the , and accordingly, the simulation for controlling each car is executed according to the operation rule/control parameter selected from the rule/control template SA14. This simulation is performed multiple times while changing the applicable operating rules/control parameters, as described later.

In the valid rule/parameter selection SP12, the rule/parameter evaluation unit SA38 selects the valid rule/parameter from the value substituted in the KPI simulation processing SP11 and the result thereof.

In the end judgment processing SP13, the rule/parameter evaluation unit SA38 judges whether or not the improvement effect can be seen as a result of the effective rule/parameter selection SP12, and if the improvement effect is seen, it goes to "Yes"; "No".

In the effective rule/parameter subdivision processing SP14, the rule/parameter evaluation unit SA38 determines a range for subdividing more effective feature amounts based on the result of the effective rule/parameter selection SP12.

The rule/parameter evaluation unit SA38 repeats the loop by substituting this result into the KPI simulation process SP11 until the improvement effect is seen in the end judgment process SP13.

In the display/control data generation processing SP15, the rule/parameter evaluation section SA38 generates a rule/parameter list SA19 and a building personalized report SA20 based on the valid rule/parameter SA17.

FIG. 12 is an explanatory diagram showing the building basic information SA00 stored in the analysis server SA according to the embodiment of the present invention.

Building basic information SA00 is a table in which basic building information is recorded. The elevators in each building are made up of multiple cars, which are called elevator groups. The building basic information table is a table for managing each elevator group in order to perform control (FIG. 12). For example, cars 1CA1 to 8CA8 in FIG. 1A belong to one elevator group. An elevator group is equivalent to the elevator group that is the object of the control cabinet CA group control. When there are multiple elevator groups in a building, there are many combinations of control cabinets and multiple cars.

The building ID (SA000) is identification information (ID) of the building in which the elevator is installed. Use different IDs to identify individual buildings. The elevator group ID (SA001) is an ID for distinguishing the elevator group in the building. Group Name (SA002) is the name of the elevator group. The number of cars (SA003) is the number of cars constituting the elevator group. The target floor (SA004) indicates the floor where the cars constituting the elevator group stop. The latitude (SA005) and the longitude (SA006) are the latitude and longitude indicating the location of the elevator group, respectively. When the area of the elevator group is large, the latitude and longitude of its center of gravity can also be used. Further, it is only necessary that there is information indicating the position of the elevator group in absolute coordinates covering the entire earth, and values other than latitude and longitude may be used. The building name (SA007) is the official name of the building where the elevator is located.

FIG. 12 shows an example, and if there is data required for the building basic information at the time of analysis, the building basic information SA00 may be changed to add the data.

FIG. 13 is an explanatory diagram showing the random seed SA01 stored in the analysis server SA according to the embodiment of the present invention.

The random seed SA01 is a table describing seeds used when generating random numbers. Random seed No (SA010) is the ID of the random seed. Use a different ID to identify each random seed.

Random Seed (SA011) is the random seed value. By using this table, when specifying the random seed No, you can refer to its corresponding value.

An example shown in FIG. 13 is that when generating a random number, if necessary data exists, the random seed SA01 can be changed to add the data.

FIG. 14 is an explanatory diagram showing the number of passengers SA02 stored in the analysis server SA according to the embodiment of the present invention.

The number of people getting on and off the elevator SA02 is a table showing the actual number of people getting on and getting off the elevator for each car on each floor of the elevator.

The building ID (SA020) is an ID for identifying the building. The elevator group ID (SA021) is an ID for identifying each of a plurality of elevator groups in the building. Date (SA022) is a date for indicating the operating status of the elevator. The time (SA023) is the time for indicating the operation status of the elevator.

The day of the week (SA024) is the day of the week for indicating the operation status of the elevator. The time range (SA025) is a time range for summarizing the operating conditions of the elevator. Car 1 (SA026) indicates that a car belonging to the elevator group identified by the elevator group ID (SA021) has been identified. The floor (SA027) is the floor where the car 1 (SA026) is located within the time period specified by the date (SA022), time (SA023), day of the week (SA024), and time range (SA025). The number of passengers in the car (SA028) is the number of users who take the car (that is, the car) during the time period specified by the date (SA022), time (SA023), week (SA024) and time range (SA025). number of people inside).

The number of passengers (SA02A) in the upward direction (SA029) indicates that in the time period specified by the date (SA022), time (SA023), day of the week (SA024) and time range (SA025), when the car 1 (SA026) In the upward direction, the number of users who take the car (that is, the number of passengers on the elevator). The number of people getting off the elevator (SA02B) in the upward direction (SA029) indicates that in the time period specified by the date (SA022), time (SA023), day of the week (SA024) and time range (SA025), when the car 1 (SA026) The number of users who get off the elevator from the car in the upward direction (that is, the number of people getting off the elevator).

The number of passengers (SA02D) in the downward direction (SA02C) indicates that when the car is in the downward direction during the time period specified by the date (SA022), time (SA023), day of the week (SA024) and time range (SA025) The number of users taking this car. The number of people getting off the elevator (SA02E) in the downward direction (SA02C) indicates that when the car is in the downward direction during the time period specified by the date (SA022), time (SA023), day of the week (SA024) and time range (SA025) number of people descending the stairs.

The number of people getting on and off the elevator SA02 includes information on all the cars constituting the elevator group. The information about the car 1 (SA026) shown in FIG. 14 is one of them. Although not shown in FIG. 14 , data on other cars is also stored as the number of passengers SA02 .

The timing of filling the data in the number of passengers SA02 may be every event (for example, when an actual change occurs, etc.), or may be every predetermined period (for example, every 1 millisecond, every 1 second, every 1 minute, etc. ). Date (SA022), time (SA023), and day of the week (SA024) can be used to indicate the date and time actually filled in. When filling in information every predetermined period, this period may also be filled in as a time range (SA025). In addition, all data specified in this form need not be stored.

For example, the first row of the table in Figure 14 indicates that: Car 1 (SA026) belonging to the elevator group identified with the elevator group ID "01" of the building identified by the building ID "B001", from June 2017 At least one stop on the 3rd floor within 5 minutes starting from 10:00:01 a.m. on Tuesday, 27th, when the number of people in the car (SA028) was 10, the number of passengers at the stop during the upward movement (SA02A) and the number of people descending The number of people on the elevator (SA02bB) is 15 and 1, respectively, and the number of passengers (SA02D) and the number of people getting off the elevator (SA02E) at the stop during the downward movement are 0 and 10, respectively. The number of people in the car (SA028) is the number of people who have finished taking the elevator on the landing floor. If the car 1 (SA026) stops on the 3rd floor for many times within the above 5 minutes, the number of people can be the sum of these times, or the number of people each time it stops on the 3rd floor can be filled in. In addition, in the same 5 minutes, when the car 1 (SA026) stops more than once on another floor, the same information as above is filled in the table of the floor.

Fig. 14 shows an example, and when the number of people getting on and off the elevator on different floors is shown, if there is necessary data, the number of people getting on and off the elevator SA02 can be changed to add the data.

FIG. 15 is an explanatory diagram showing the elevator operation log SA03 stored in the analysis server SA according to the embodiment of the present invention.

The elevator operation log SA03 is a table for showing the actual elevator operation log. In this table, data can be stored in terms of both data aggregated by each elevator group and data of cars belonging to the elevator group.

Building ID (SA030) is an ID for identifying the building. The elevator group ID (SA031) is an ID for identifying a plurality of elevator groups in the building. Date (SA032) is a date for indicating the operation status of the elevator. The time (SA033) is the time for indicating the operation status of the elevator.

The day of the week (SA034) is the day of the week for indicating the operation status of the elevator. The time width (SA035) is the time width for summarizing the operating conditions of the elevator. The long waiting rate (SA036) is the waiting time (that is, the calling car) generated by the elevator group during the time period specified by the date (SA032), time (SA033), week (SA034) and time range (SA035). In the waiting time of the users before the car arrives), it indicates the proportion of the waiting time greater than or equal to a predetermined time length (for example, 60 seconds). The predetermined length of time can be changed by prespecifying.

The number of car calls (SA037) is the number of times the car call button is pressed in the elevator group within the time period specified by date (SA032), time (SA033), day of the week (SA034), and time range (SA035). The traffic flow pattern (SA038) is the operating pattern of the elevator group.

The long-time waiting rate (SA036), the number of car calls (SA037), and the traffic flow pattern (SA038) are aggregated values for each elevator group, but if necessary data exists, the above can be changed and information other than the above can be added.

Car 1 (SA039) indicates that a car belonging to the elevator group ID (SA031) has been identified. The floor (SA0A) is the position (floor) where the car 1 (SA039) is located at the time point specified by the date (SA032), the time (SA033), and the day of the week (SA034). The direction (SA03B) is the running direction of the car 1 (SA039) at the time point specified by the date (SA032), time (SA033) and day of the week (SA034). For example, up indicates the upward direction, and lower indicates the downward direction.

The state (SA03C) indicates the state of the car 1 (SA039) at the time point designated by the date (SA032), time (SA033), and day of the week (SA034). For example, "action" means that car 1 (SA039) is actually running, while "stop" means that it has stopped. The number of passengers (SA03D) indicates the number of people who took the car 1 (SA039) at the time specified by the date (SA032), the time (SA033), and the day of the week (SA034).

The elevator operation log SA03 includes information on all the cars constituting the elevator group. Car 1 (SA039) shown in FIG. 15 is one of them. Although not shown in FIG. 15, data related to other cars is also stored as an elevator operation log SA03.

The timing of filling data in the elevator operation log SA03 may be every event (eg, when a change actually occurs, etc.), or may be every predetermined period (eg, every 1 millisecond, every 1 second, every 1 minute, etc.) . Date (SA032), time (SA033), and day of the week (SA034) can be used to indicate the date and time actually filled in. When filling in information every predetermined period, this period may also be filled in as a time range (SA035). In addition, all data specified in this form need not be stored.

Fig. 15 shows an example, and when the elevator operation log is displayed, if necessary data exists, the elevator operation log SA03 can be changed and the data can be added.

FIG. 16 is an explanatory diagram showing external information (weather) SA04 stored in the analysis server SA according to the embodiment of the present invention.

The external information (weather) SA04 is a table for summarizing data related to weather, which is one of the external information.

The external information ID (SA040) is the identification ID of the external information. Date (SA041) is the date on which the external information was acquired. Time (SA042) is the time when the external information is acquired. The day of the week (SA043) is the week of the acquisition of the external information. The location (SA044) is the location where the external information is acquired. Latitude (SA045) is the latitude at which the external information is acquired. Longitude (SA046) is the longitude at which the external information was acquired. Weather (SA047), Air Temperature (SA048), and Rainfall (SA049) are the weather, temperature, and rainfall at the location specified by Location (SA044) at the time specified by Date (SA041) and Time (SA042), respectively .

The timing of filling data in the external information (weather) SA04 may be every event (for example, when a change actually occurs, etc.), or may be every predetermined period (for example, every 1 millisecond, every 1 second, every 1 minute) Wait). Date (SA041), time (SA042), and place (SA044) can be used to indicate the date and time actually filled in, and the place where the data was acquired. In addition, all data specified in this form need not be stored.

An example shown in FIG. 16 is that when data related to weather, which is one of the external information, is displayed, if necessary data exists, the external information (weather) SA04 can be changed to add the data.

FIG. 17 is an explanatory diagram showing external information (camera) SA05 stored in the analysis server SA according to the embodiment of the present invention.

External information (camera) SA05 is a table for summarizing data related to information identified by camera measurement, which is one of external information.

The external information ID (SA050) is the identification ID of the external information. Date (SA051) is the date on which this information was acquired. Time (SA052) is the time when this information was acquired. The day of the week (SA053) is the day of the week for which this information was obtained. The building ID (SA054) is an ID for identifying the building that has acquired this information. Floor (SA055) is the floor where this information is acquired. The installation location (SA056) is the location where the camera is installed to obtain this information.

The number of people (SA057) is the number of people detected by the cameras installed at the location designated by the installation location (SA056) at the time point designated by the date (SA051) and time (SA052). Children (SA058), adults (SA059), males (SA05A), females (SA05B), wheelchairs (SA05C) and trolleys (SA05D) at the point of time specified by the date (SA051) and time (SA052), respectively, at the installation location (SA056) The number of children, adults, men, women, wheelchairs and trolleys detected by the cameras installed at the location specified. In this way, it is possible to detect not only the total number of users, but also the detailed contents of different user attributes such as age group and gender, as well as objects other than users.

Anger (SA05E) is among the users detected by the camera based on the results detected by the camera installed at the location specified by the installation site (SA056) at the point in time specified by date (SA051) and time (SA052). , the number of users who are judged to be angry. In this way, it is not only possible to detect the number of users, but also to use the camera to detect the user's emotions through facial expressions and behaviors, and further calculate the number of people who have detected a specific emotion.

The timing of filling data in the external information (camera) SA05 may be every event (for example, when a change actually occurs, etc.), or may be every predetermined period (for example, every 1 millisecond, every 1 second, every 1 minute) Wait). Date (SA051), time (SA052), and installation location (SA056) can be used to indicate the date and time actually filled in, and the installation location of the camera that acquired this data. In addition, all data specified in this form need not be stored.

FIG. 17 shows an example. When data related to information identified by camera measurement, which is one of the external information, is displayed, if necessary data exists, the external information (camera) SA05 can be changed to add the data.

FIG. 18 is an explanatory diagram showing external information (building information) SA06 stored in the analysis server SA according to the embodiment of the present invention.

The external information (building information) SA06 is a table for summarizing data related to a building which is one of the external information.

The external information ID (SA060) is the identification ID of the external information. The building ID (SA061) is an ID for identifying the building that has acquired this information. Date (SA062) is the date on which this information was acquired. Time (SA063) is the time when this information was acquired. The day of the week (SA064) is the day of the week for which this information was obtained. The east side of the 3rd floor (SA065) indicates the floor (3rd floor) where this information is acquired and the area (east side) where this information is acquired in the area dividing the floor. Stores values aggregated per floor and per area. Floors and areas can be added arbitrarily, and when adding, the data collected on the floors and areas can be stored in the same way as on the east side of the third floor (SA065).

The electricity consumption (SA066) and the water consumption (SA067) are the electricity consumption and the water consumption on the east side of the 3rd floor (SA065) at the time specified by the date (SA062) and the time (SA063), respectively. The temperature (SA068) and the humidity (SA069) are the temperature and humidity of the east side of the 3rd floor (SA065) at the time point specified by the date (SA062) and the time (SA063), respectively. The number of people staying (SA06A) is the number of people staying on the east side of the 3rd floor (SA065) at the time specified by the date (SA062) and time (SA063).

The timing of filling data in the external information (building information) SA06 may be every event (for example, when a change actually occurs, etc.), or may be every predetermined period (for example, every 1 millisecond, every 1 second, every 1 minute, etc.). Date (SA062), time (SA062), and the east side of the third floor (SA065) can be used to indicate the date and time actually filled in, and the place where this data was acquired. In addition, all data specified in this form need not be stored.

An example shown in FIG. 18 is that when data related to a building, which is one of the external information, is displayed, if necessary data exists, the external information (building information) SA06 can be changed to add the data.

FIG. 19 is an explanatory diagram showing the occupant number estimation input SA07 held by the analysis server SA according to the embodiment of the present invention.

The occupant number estimation input SA07 is a table in which data generated by the occupant number data generation SP010 in the occupant number model processing SP01 is stored. The data generated includes the number of passengers on each floor, the number of people taking elevators in different cars, and the status of the car.

The occupant estimation input ID (SA070) is an ID for identifying the occupant estimation input value. The time (SA071), the day of the week (SA072), and the time width (SA073) are the time, day of the week, and time width generated by the passenger number data generation SP010, respectively. The number of passengers (SA074) is the number of passengers generated by the number of passengers data generation SP010. Calculate the number of passengers by floor. In FIG. 19, the number of passengers on the third floor is represented by the third floor (SA075). Although not shown in FIG. 19 , the number of passengers on other floors is also filled in the same way. The number of passengers may be generated for each floor, each area, and each hall, and in this case, the generated number of passengers is stored in the number of passengers (SA074).

The number of people getting on and off the elevator by car (SA076) is the number of people getting on and off the elevator in the time period specified by time (SA071), day of the week (SA072), and time range (SA073), generated by the number of passengers data generation SP010. number of ladders. The number of people on elevators (SA076) by car is obtained for each car. In Fig. 19 , the number of people riding on and off the elevator in different cars of the car 1 is represented by car 1 (SA077). In the car 1 (SA077), the information related to the number of people taking the elevator in the car 1 is stored, the floor (SA078) is the floor where the car is located, and the upward direction (SA079) is when the upward traveling car stops at the floor. The number of people getting on the elevator and the number of people getting off the elevator, the downward direction (SA07A) is the number of people who get on the elevator and the number of people getting off the elevator when the car running down is parked on the floor. In addition to the above, other information related to the car 1 may also be stored in the car 1 (SA077). The number of people riding on and off the elevator by car (SA076) For a car other than the car 1, information related to the car may be stored.

The car state (SA07B) stores the data related to the car state, and stores the data related to the car 1 in the car 1 (SA07C). The floor is the floor where the car 1 (SA07C) is located in the time period specified by the time (SA071), the day of the week (SA072), and the time range (SA073). The direction is the direction in which car 1 (SA07C) travels for the time period specified by time (SA071), day of the week (SA072), and time amplitude (SA073). For example, "up" indicates the upward direction, and "down" indicates the downward direction.

The state indicates the state of the car 1 (SA07C) in the time period specified by the time (SA071), the day of the week (SA072), and the time width (SA073) of the car 1 (SA07C). For example, "action" means it's actually running, and "dock" means it's docked. The number of passengers represents the number of people who took the car 1 (SA07C) during the time period specified by the time (SA071), the day of the week (SA072), and the time range (SA073). In addition to the above, information related to the state of the car 1 may be stored in the car 1 (SA07C). The car state (SA07B) may store information related to the state of the car other than the car 1 .

The timing of filling data in the occupancy estimation input SA07 may be every event (eg, when a change actually occurs, etc.), or may be every predetermined period (eg, every 1 millisecond, every 1 second, every 1 minute, etc.) ). The date and time actually filled in can be represented by time (SA071) and day of the week (SA072). In addition, all data specified in this form need not be stored.

Fig. 19 shows an example, and when the data generated in the occupant data generation SP010 is shown, if necessary data exists, the occupant estimation input SA07 can be changed to add the data.

FIG. 20 is an explanatory diagram showing the occupant number estimation model SA08 stored in the analysis server SA according to the embodiment of the present invention.

The occupant number estimation model SA08 is a table in which data generated by the occupant number estimation model generation SP011 is stored in the occupant number model processing SP01. The generated data is a function f satisfying "the number of passengers = f (the number of people getting on and off the elevator, the state of the car, and external information)". The number of occupants, the number of people getting on the elevator, and the state of the car are acquired as the number of passengers estimated input SA07, and external information is acquired from external information (weather) SA04, external information (camera) SA05, and external information (building information) SA06.

The occupant number estimation ID (SA080) is an ID for identifying the occupant number estimation model. The floor (SA081) is the floor used as the object of the generated estimation model. The direction (SA082) is the direction of the object used as the generated estimation model. Time (SA083) is the time used as the object of the generated estimation model. Week (SA084) is the week used as the object of the generated estimation model. The time magnitude (SA082) is the time magnitude of the object used as the generated estimation model.

Store the coefficients of the function f in the back column. Select from the number of people in different cars (SA076), car status (SA07B), or external information (weather) SA04, external information (camera) SA05, and external information (building information) SA06 entered in SA07 for estimating the number of passengers One or more pieces of data are used as feature quantities. Then, using these feature amounts as an explanatory index and the number of passengers (SA074) as a target index, the coefficients of the feature amounts can be obtained by multiple regression analysis. The number of passengers coefficient 1 (SA085), the car state coefficient 1 (SA087), and the external variable coefficient 1 (SA088) are feature quantity coefficients obtained by analysis. Since the coefficient of each feature amount is obtained, it is preferable to store the coefficient of each feature amount.

As a method for generating a model for estimating the number of occupants, analysis methods other than multiple regression analysis can also be used.

The timing of filling data in the occupancy estimation model SA08 may be every event (for example, when a change actually occurs, etc.), or may be every predetermined period (for example, every 1 millisecond, every 1 second, every 1 minute, etc. ). The date and time actually filled in can be represented by time (SA083) and day of the week (SA084). In addition, all data specified in this form need not be stored.

Fig. 20 shows an example, and when the model generated in the occupant estimation model generation SP011 is shown, if necessary data exists, the occupant estimation model SA08 may be changed to add the data.

FIG. 21 is an explanatory diagram showing the occupant number estimation result SA09 stored in the analysis server SA according to the embodiment of the present invention.

The occupant number estimation result SA09 is a table in which the data generated by the occupant number estimation SP020 is stored in the occupant number estimation processing SP02. In the passenger number estimation SP020, the stored passenger number estimation model (function f), the number of passengers taking the elevator at the current time, the state of the car, and external variables are input, and the number of passengers to be taken on each floor is estimated. This result is stored in the occupant number estimation result SA09 shown in FIG. 21 .

The estimated occupant number ID (SA090) is an ID for identifying the estimated number of occupants. Date (SA092) is a date for estimating the number of occupants. Time (SA093) is the time for estimating the number of passengers. Week (SA094) is the week used to estimate the number of occupants. The time span (SA092) is the time span for estimating the number of passengers. The floor (SA093) is a floor for estimating the number of passengers. The location (SA094) is a location for estimating the number of occupants. The number of passengers (SA095) is the number of passengers used to estimate the number of passengers.

The timing of filling the data in the occupancy estimation result SA09 may be every event (for example, when a change actually occurs, etc.), or may be every predetermined period (for example, every 1 millisecond, every 1 second, every 1 minute, etc. ). Date (SA092), time (SA093), and day of the week (SA094) can be used to indicate the date and time actually filled in. In addition, all data specified in this form need not be stored.

Fig. 21 shows an example, and when the occupant number generated in the occupant number estimation SP020 is shown, if necessary data exists, the occupant number estimation result SA09 can be changed to add the data.

FIG. 22 is an explanatory diagram showing the occupant number prediction result SA10 stored in the analysis server SA according to the embodiment of the present invention.

The occupant number prediction result SA10 is a table in which the data generated by the occupant number estimation SP020 is stored in the occupant number prediction processing SP03. In the occupant number prediction SP030, the occupant number prediction unit SA34 executes processing for estimating the future occupant number using the occupant number estimation result SA09 obtained in the occupant number estimation process SP02 and external information. This result is stored in the occupant number prediction result SA10 shown in FIG. 22 .

As explained below with reference to FIG. 7 , the input of the occupant number prediction SP030 includes the time range for analysis (for example, the past 10 minutes), the occupant number estimation result SA09, the external information (weather) SA04, the external information (camera) SA05 And external information (building information) SA06, and the output is the number of passengers in the future.

As a method of predicting the number of passengers, for example, an AR model (autoregressive model) or the like can be used, but an analysis method other than the AR model can also be used.

The occupant number prediction ID ( SA100 ) is an ID for identifying that the occupant number prediction has been performed. Date ( SA101 ), time ( SA102 ), and day of the week ( SA103 ) are the date, time, and week of the analysis object (ie, at the point in time when the analysis is performed), respectively. The predicted time ( SA104 ) is the predicted time to be analyzed (ie, the predicted number of passengers at that time). The time range (SA105) is the time range of the analysis object. Floor (SA106) is the floor to be analyzed. The location (SA107) is the location of the analysis object. The number of occupants (SA108) is the number of occupants at the time of prediction analysis.

For example, the first row of the passenger number prediction result SA10 in Figure 22 indicates that within 5 minutes from 10:6:1 on Tuesday, June 27, 2017, the process of predicting the number of passengers on the elevator floor on the third floor is performed on that day. Executed at 10:00 a.m. 1 second, the result shows that the predicted number of passengers is 12 people.

The timing to be substituted into the occupant number prediction result SA10 may be every event (eg, when a change actually occurs) or every predetermined period (eg, every 1 millisecond, every 1 second, every 1 minute, etc.). Date (SA101), time (SA102), and day of the week (SA103) can be used to indicate the date and time actually filled in. In addition, all data specified in this form need not be stored.

An example shown in FIG. 22 is that when the occupant number prediction is indicated in the occupant number prediction SP030, if necessary data exists, the occupant number prediction result SA10 can be changed to add the data.

FIG. 23 is an explanatory diagram showing the occupant number prediction result 2_SA11 stored in the analysis server SA according to the embodiment of the present invention.

The occupant number prediction result 2_SA11 is a table for storing the result of format conversion of the occupant number prediction generated by the format conversion SP031 in the occupant number prediction processing SP03.

In the format conversion SP031, the occupant number prediction unit SA34 uses the occupant number prediction result SA10 to obtain the occupancy probability of the different number of people per unit time using the Poisson distribution. This result is stored in the occupant number prediction result 2_SA11 shown in FIG. 23 .

The formula of the Poisson distribution is shown in the following formula (1). By substituting the number of passengers on different floors into λ in Equation (1), the riding probability P(k) of more than k passengers can be obtained.

【Number 1】

The above example is a method of calculating the occupancy probability for each occupant, assuming that the probability distribution of the occupants is a Poisson distribution. However, in order to obtain the probability of the number of passengers, an analysis method other than the Poisson distribution method may be used.

The occupant number prediction ID ( SA110 ) is an ID for identifying the occupant number prediction that has been performed. Date (SA111), time (SA112), and day of the week (SA113) are the date, time, and day of the week that are the objects of analysis (ie, at the point in time when the analysis is performed). The predicted time ( SA114 ) is the time at which the analysis target is predicted (ie, the predicted ride probability at that time). The time range (SA115) is the time range of the analysis object. Floor (SA116) is the floor to be analyzed. The location (SA117) is the location of the analysis object. The riding probability of more than one person ( SA118 ) is the riding probability of one or more users per unit time. The riding probability of two or more people ( SA119 ) is the riding probability of two or more users per unit time. As the unit time, the time width (SA115) can also be used.

For example, the first row of the occupant number prediction result 2_SA11 in FIG. 23 shows an example corresponding to the prediction result filled in the first row of the occupant number prediction result SA10 in FIG. 22 . That is, the first row of the number of occupants prediction result 2_SA11 in Figure 23 indicates that from the number of people "12 people" who are expected to take the elevator on the elevator floor on the 3rd floor, the occupancy of more than one user on the elevator floor on the 3rd floor per unit time is predicted. The probability is 90%, and the probability of riding for two or more users is 75%. Although not shown in FIG. 23 , similarly, the riding probability of three or more users and the riding probability of four or more users may be calculated and filled in the occupant number prediction result 2_SA11.

An example shown in FIG. 23 is that when the occupant number prediction is indicated in the format conversion SP031, if necessary data exists, the occupant number prediction result 2_SAl1 can be changed and the data can be added.

FIG. 24 is an explanatory diagram showing the destination floor estimation SA12 for different time periods held by the analysis server SA according to the embodiment of the present invention.

The destination floor estimation SA12 for different time periods is a table in which data generated by the destination floor estimation processing SP04 is stored. In the destination floor estimation processing SP04, the destination floor estimation unit SA35 generates a model for estimating the destination floor in a different time zone using the number of people getting on and off the elevator at the different floors (SA02). Specifically, the destination floor estimation unit SA35 counts the number of people getting off the elevators on different floors in each time zone, and obtains the trend of the number of people getting off the elevators on different floors. Then, convert it to an estimate that is 100% overall. This result is stored in the destination floor estimation SA12 for different time periods shown in FIG. 24 .

The destination floor estimation ID ( SA120 ) is an ID for identifying the executed destination floor estimation. Date (SA121), time (SA122), day of the week (SA123), and time range (SA124) are the date, time, day of the week, and time range of the analysis object, respectively. The boarding floor (SA125) is the boarding floor of the user. The direction (SA126) is the direction in which the car travels. The destination floor (SA127) is the landing floor of the user. Fill in an estimate of 100% overall relative to the floor where the elevator stops.

For example, the first row in Figure 24 indicates that within 60 minutes from 10:1:1 a.m. on Tuesday, June 27, 2017, 10% of the users who took the car in the upward direction on the 3rd floor were in The 26th floor gets off the elevator, and another 10% gets off the elevator on the 27th floor, which is estimated based on the number of people taking the elevator SA02. Although the proportion of users who get off the elevator on other floors is omitted in FIG. 24 , the sum of the proportions of all destination floors of users who take the elevator on the third floor is calculated to be 100%. This ratio is also calculated for the destination floors of users who take elevators on other floors. In this embodiment, these ratios can be used as destination floor probabilities, that is, the probability that the destination floor of the user who appears at the boarding place of each floor is the floor.

In addition, for example, when it can be determined whether the user who rides the car on each floor and the user who gets off the elevator from the car on each floor are the same person based on external information (camera) SA05, etc., it is determined to ride on each floor according to the judgment result. On which floor the elevator users get off the elevator, and based on this, the proportion of the destination floor of the users who take the elevator on each floor is calculated. For example, among the users who take the elevator on the 3rd floor, the proportion of users who get off the elevator on the 26th floor 10% etc. However, when the above-mentioned external information cannot be used, for example, when estimating the number of elevator users on each floor based on the car weight, etc., each of the elevator users cannot be identified, the destination floor ratio can be approximated based on certain assumptions.

For example, the number of users who get off the elevator on each floor can be summarized in the time period specified by the date (SA121), time (SA122), week (SA123) and time range (SA124), and the number of users who get off the elevator on the 26th floor is calculated. The proportion of the number of users who got off the elevator on floors other than the 3rd floor, to be used as the proportion of users who got off the elevator on the 26th floor to the users who took the elevator on the 3rd floor (i.e. the destination floor of users who took the elevator on the 3rd floor was 26 probability of building). In this case, the same method can be used to calculate the proportion of users who get off the elevator on other floors, and the proportion of users who get off the elevator on each floor after taking the elevator from other floors.

An example shown in FIG. 24 is that when the destination floor estimation is indicated in the destination floor estimation processing SP04, if there is necessary data, the time zone destination floor estimation SA12 can be changed to add the data.

FIG. 25 is an explanatory diagram showing the destination floor prediction result SA13 for different time periods held by the analysis server SA according to the embodiment of the present invention.

The time zone destination floor prediction result SA13 is a table for storing data generated by the different time zone destination floor prediction SP051 in the destination floor prediction processing SP05. In the different time period destination floor prediction SP051, the destination floor prediction section SA36 uses the different time period destination floor estimation SA12 and the occupant number prediction result 2_SA11 as input data, and by combining these, it is possible to predict that the boarding user will go to Which floor to go to. Specifically, the boarding probability of the predicted boarding time can be multiplied by the estimated destination floor at the same time for each floor.

The forecasting method for destination floors in different time periods shown above is an example, and other methods can also be used. This result is stored in the destination floor prediction result SA13 for different time periods shown in FIG. 25 .

The destination floor prediction ID ( SA130 ) is an ID for identifying that the destination floor prediction has been performed. The date (SA131), the time (SA132), and the day of the week (SAl33) are the date, time, and week of the analysis object (ie, at the time point at which the analysis was performed), respectively. The predicted time ( SA134 ) is the time at which the analysis target is predicted (ie, the predicted ride probability at that time). The time range (SA135) is the time range of the analysis object. The boarding floor (SA136) is the boarding floor to be analyzed. The destination floor (SA137) is the destination floor to be analyzed. The direction (SA138) is the running direction of the car to be analyzed. The riding probability of more than one person (SA139) is the riding probability of one or more users per unit time. The riding probability of two or more people (SA13A) is the riding probability of two or more users per unit time. As the unit time, the time width (SA135) can also be used.

For example, the first row of the destination floor prediction result SA13 in different time periods in FIG. 25 shows the prediction results filled in the first row of the passenger number prediction result 2_SA11 in FIG. 23 and the purpose of different time periods in FIG. 24 . An example corresponding to the estimation result filled in the first row of the ground floor estimation SA12. That is, the first line of the destination floor prediction result SA13 in different time periods in Fig. 25 indicates that, in unit time, among the users who take the elevator car in the upward direction on the elevator floor on the 3rd floor and plan to get off the elevator on the 26th floor, it is estimated that one user The probability of riding with more than one person is 9%, and the probability of riding with more than two people is 7.5%.

In this example, "9%" is calculated by multiplying the ratio value "10%" of the 26th floor in the first row of FIG. 24 to the destination floor (SA127) by the probability of riding as more than one person in the first row of FIG. 23 (SA118) after "90%". "7.5%" is obtained by multiplying the ratio value "10%" of the 26th floor in the first row in Fig. 24 to the destination floor (SA127) by the first row in Fig. 23, which is the occupancy probability (SA119) of two or more people "75%" after that.

Fig. 25 shows an example. When the destination floor prediction for different time periods is indicated in the destination floor prediction for different time periods SP051, if necessary data exists, the destination floor prediction result for different time periods SA13 can be changed to add the data. .

FIG. 26 is an explanatory diagram showing the rule/control template SA14 held by the analysis server SA according to the embodiment of the present invention.

The rule/control template SA14 is a table for storing the operation rule/control parameter template of the elevator. Here, the operation rule refers to a rule applied by the control cabinet CA in order to control the operation of a plurality of cars of elevators as group control objects, and the control parameter refers to a parameter that can be changed in each operation rule. In the present embodiment, the operation rule and the control parameters included therein are described as operation rule/control parameter. In addition, an operation rule may be described individually as a rule, and a control parameter may be described as an individual parameter.

By using the rule/control template SA14, it is possible to search for the best operating rule/control parameters. The search method consists of two steps. Step 1 is to search for rule/control No (SA140). This is the step to select a control parameter from a number of run rules/control parameters that is suitable for improving the KPI. Step 2 is to search for parameter values (initial values) (SA144). The search object is the controllable parameter value in the control parameter. By searching for it, better control parameters can be found.

Rule/Control No (SA140) is an ID for identifying an operation rule/control parameter. Rule name (SA141) is the name of the run rule/control parameter. Condition (SA142) is an action condition for running the rule/control parameter. The parameter value (initial value) (SA143) is a controllable parameter in the operation rule/control parameter. For example, in the rule "After 5 minutes, direct from the ○ floor" corresponding to the rule/control No "Ru01", the ○ part (the number of floors in this example) is a controllable parameter. The coefficient (initial coefficient) (SA145) is a coefficient for obtaining a regression equation or the like. The parameter value (initial value) (SA143) and the coefficient (initial coefficient) (SA145) can be changed by repeatedly performing the optimization process to the stored numerical value.

An example shown in FIG. 26 is to change the rule/control template SA14 to add the data if necessary data exists when the operation rule/control parameter of the elevator is realized.

FIG. 27 is an explanatory diagram showing the KPI list SA15 held by the analysis server SA according to the embodiment of the present invention.

The KPI list SA15 is a table in which KPIs (key performance indicators), which are evaluation indicators when searching for optimal operation rules/control parameters, are stored. Since there are cases where the KPI of each building is different, the KPI of each building is preset by using the flag (SA155). At this time, the target value of the KPI can also be set (SA154).

KPIID (SA150) is an ID for identifying a KPI. Classification (SA151) is the classification of KPIs. Specifically, a classification (SA151) indicates that the quasi would benefit from improving this KPI.

Name (SA152) is the name of the KPI. The condition (SA153) indicates the content of the KPI. The target value ( SA154 ) represents the target value of the variable parameter value portion (the portion of 0 in the example of FIG. 27 ) under the condition ( SA153 ). Since the situation of each building is different, set it before using it. Use a flag (SA155) to specify the KPI from among several KIPs to use when performing the current optimization. When the use flag (SA155) is 1, it indicates that it has been designated. Additionally, multiple KPIs can be specified.

In the example of FIG. 27 , as KPIs, the waiting time of users who appear at the boarding place before boarding the car, the congestion rate at the boarding place, and the power consumption of the floor (that is, the consumption including the car running) are shown. power). In these examples, the following can be evaluated as appropriate operating rules/control parameters: for example, operating rules/control parameters that can reduce the longest waiting time, operating rules/control parameters that can reduce the congestion rate at the ride, and reduce Operating rules/control parameters for electricity consumption.

However, the above is just an example, and KPIs other than the above may also be specified. For example, it is also possible to use a KPI that gives a higher evaluation as the ratio of a plurality of users taking the elevator on different floors in the same car is smaller. Thus, the control of the car can be implemented according to the needs of the elevator related users (eg, users or managers, etc.), so that the related users are satisfied.

An example shown in FIG. 27 is that when the operation rules and control parameters of the elevator are realized, if necessary data exists, the KPI list SA15 can be changed to add the data.

FIG. 28 is an explanatory diagram showing simulation input and result SA16 held by the analysis server SA according to the embodiment of the present invention.

The simulation input and result SA16 is a table for storing the result of the processing performed by the KPI simulation processing SP11. In the KPI simulation processing SP11, as an input, a KPI list SA15 is used, in which the occupant number prediction result 2_SA11 representing the occupancy situation, the destination floor prediction result SA13 for different time periods, the rule/control template SA14 representing the control parameter, and Optimization target KPIs. By using these data, operation rules and control parameters for improving the user's KPI in the riding state can be obtained.

In the KPI simulation processing SP11, while changing the operation rule/control parameter, the output processing of KPIs when a certain operation rule/control parameter is used in the state where the user is riding the elevator is executed multiple times. The result is the analog input and the result SA16.

The KPI Simulation ID (SA160) is an ID that identifies the KPI simulation. The number of times (SA161) is the number of times when the KPI simulation is executed multiple times. Rule Control List 1 (SA162) represents a set of run rules/control parameters used in each simulation. Rule/Control No (SA163) is an ID for identifying an operation rule/control parameter. The parameter value (SA164) is the control parameter for the current control. The coefficient (SA165) is a coefficient for obtaining the regression equation and the like. In one simulation, multiple rule control lists can be stored. KPIID (SA166) is an ID for identifying a KPI. The KPI simulation result (SA167) is the KPI value obtained as a result when the KPI simulation is performed using the rule control list.

Fig. 28 shows an example, and if necessary data exists when realizing the operation rules and control parameters of the elevator, the analog input and result SA16 can be changed to add the data.

FIG. 29 is an explanatory diagram showing a valid rule/parameter SA17 held by the analysis server SA according to the embodiment of the present invention.

The effective rule/parameter SA17 is a table for storing the result of finding the operating rule/control parameter that contributes to optimization (that is, effective) from the simulation input and result SA16 shown in FIG. 28 . The rule/parameter evaluation unit SA38 uses the simulation input and result SA16 shown in FIG. 28 as input, uses the target variable as the KPI simulation result, and uses the explanatory variable as the rule control list, and can perform multiple regression analysis using the results of multiple times. However, methods other than multiple regression analysis methods can also be used, as long as the rule control parameters that contribute to the optimization are specified.

Valid rule/parameter ID (SA170) is an ID for identifying a valid operating rule/control parameter. Valid rule control list 1 (SA171) is the rule control parameter that contributes the most when performing multiple regression analysis. Rule/Control No (SA172) is an ID that identifies an operation rule/control parameter. The parameter value (SA173) is the control parameter value used in this processing. The coefficient (SA174) is a coefficient obtained by multiple regression analysis, and is a value indicating a degree of contribution to optimization. By referring to this, it is possible to specify valid (that is to say, help to improve KPIs) operational rule/control parameters. Multiple valid rule control lists can be stored. KPIID (SA175) is an ID for identifying a KPI. The predicted value (SA176) is a KPI value predicted using the regression equation obtained by the multiple regression analysis.

Fig. 29 shows an example. When the operation rule/control parameter of the elevator is realized, if necessary data exists, the valid rule/parameter SA17 can be changed to add the data.

FIG. 30 is an explanatory diagram showing a valid rule/parameter subdivision list SA18 held by the analysis server SA according to the embodiment of the present invention.

Further optimization can be achieved by subdividing the control parameter values for the operating rules/control parameters specified from the effective rules/parameters SA17 shown in FIG. 29 that are very helpful for optimization. In the valid rule control list of valid rule/parameter SA17, select the operation rule/control parameter with the larger coefficient (SA174). Then, the rule/parameter evaluation unit SA38 executes the subdivision processing SP14 of the effective rule/parameter for the selected operation rule/control parameter. Specifically, the rule/parameter evaluation unit SA38 can search for a more optimal operating rule/control parameter by increasing or decreasing the control parameter value included in the selected operating rule/control parameter.

Valid rule/parameter refinement ID (SA180) is an ID used to identify valid rule/parameter refinement. The valid rule/parameter ID (SA181) is an ID for identifying a valid operation rule/control parameter. Effective rule control list 1 (SA182) is the rule control parameter with the largest contribution estimated by multiple regression analysis. Rule/Control No (SA183) is an ID that identifies an operation rule/control parameter. The parameter value (SA184) is the control parameter value used in this processing. The coefficient (SA185) is a coefficient obtained by multiple regression analysis, and is a value that contributes to optimization. The parameter value subdivision range (SA186) is a value obtained by the valid rule/parameter subdivision processing SP14. Multiple valid rule control lists can be stored. KPIID (SA187) is an ID for identifying a KPI. The predicted value (SA188) is a KPI value predicted using the regression equation obtained by the multiple regression analysis. Furthermore, the rule/parameter evaluation section SA38 may randomly select several operating rule/control parameters from the rule/control template SA14.

Fig. 30 shows an example, when the operation rule/control parameter of the elevator is realized, if necessary data exists, the valid rule/parameter subdivision list SA18 can be changed to add the data.

FIG. 31 is an explanatory diagram showing the rule/parameter list SA19 held by the analysis server SA according to the embodiment of the present invention.

The rule/parameter list SA19 is a table in which operation rules/control parameters to be used in actual operation are selected from the valid rules/parameters SA17 of FIG. 29 . Among the operation rules/control parameters in the effective rule control list, the part with a larger value of the judgment coefficient (SA174) is an operation rule/control parameter with a high contribution rate.

The rule/parameter ID (SA190) is an ID that identifies the operating rule/control parameter. The first effective rule control (SA191) is to estimate the rule control parameter with the largest contribution according to the result of multiple regression analysis. Rule/Control No (SA192) is an ID that identifies an operation rule/control parameter. The parameter value (SA193) is the control parameter value used in this processing. The coefficient (SA194) is a coefficient obtained by multiple regression analysis, and is a value that contributes to optimization.

The second effective rule control (SA195) is to estimate the operating rule/control parameter with the second highest contribution degree based on the results of the multiple regression analysis. Rule/Control No (SA196) is the control parameter value used in this processing. The parameter value (SA197) is the control parameter value used in this processing. The coefficient (SA198) is a coefficient obtained by multiple regression analysis, and is a value that contributes to optimization.

KPIID (SA199) is an ID for identifying a KPI. The predicted value (SA19A) is a value predicted using the regression equation obtained by the multiple regression analysis.

The rule/parameter list SA19 is sent to the control selector SP06. The control selector SP06 generates an input command CA0 indicating an operation rule/control parameter for improving the KPI based on the rule/parameter list SA19, and sends it to the control cabinet CA. The control cabinet CA changes the already set operation rule/control parameter to the instructed operation rule/control parameter according to the input command CA0, and controls the car according to the changed operation rule/control parameter. In this way, the control of the elevator with improved KPI is realized.

Fig. 31 shows an example. When implementing the operation rule/control parameter of the elevator, if necessary data exists, the rule/control parameter list SA19 can be changed to add the data.

The processing described in this embodiment is executed by the execution unit SA3 of the analysis server SA, but a part or all of the processing may be executed by the control cabinet CA. For example, the control cabinet CA may have hardware similar to the analysis server SA shown in FIG. 1B , and at least a part of the functions of the analysis server SA may be implemented by these hardwares.

FIG. 32 is an explanatory diagram showing the building personalized report SA20 output by the analysis server SA according to the embodiment of the present invention.

The building personalized report SA20 is generated by the rule/parameter evaluation unit SA38 in the display/control data generation process SP15, and sent to the display unit SA1. The display unit SA1 (eg, a display device installed as the output device 103 ) displays the received building personalized report SA20 .

For example, as shown in FIG. 32 , the building personalized report SA20 includes building name 3201 , elevator group name 3202 , time period 3203 , KPI 3204 , and result 3205 .

The elevator group name 3202 and the building name 3201 are the elevator group and the building name in which the elevator group is installed, which are the execution objects of the various processes shown in FIG. 2, and are the same as the group name (SA002) and the building name shown in FIG. (SA007) corresponds. A period 3203 is a period that is an object of simulation. The KPI 3204 is an evaluation index selected as an evaluation target in the process of the rule/parameter evaluation section SA38, and corresponds to the KPI for which the usage flag ( SA155 ) shown in FIG. 27 is valid. The result 3205 is the valid operation rule/control parameter selected according to the processing result of the rule/parameter evaluation section SA38, and corresponds to the operation rule/control parameter recorded in the rule/parameter list SA19.

By referring to the building personalized report SA20, the elevator manager can grasp the change content of the operation rules/control parameters, which is required to improve the evaluation index displayed as KPI3204. The administrator can manually set the change of the mastered operation rules/control parameters in the control cabinet CA. In this way, the control of the elevator with improved KPI is realized.

As described above, according to the present embodiment, the number of passengers in the hall is predicted based on the number of people in the elevator hall, a control method suitable for the prediction result is generated, and the optimal performance can be achieved by evaluating using an index related to user dissatisfaction. The best elevator control. For example, near the time when congestion is expected to occur in the future, by smoothly deploying the car at the boarding place, it is possible to reduce the situation of users waiting for a long time at the boarding place, improve the transportation capacity of users, and further improve user satisfaction.

In addition, the present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-mentioned embodiments have been described in detail for better understanding of the present invention, but are not necessarily limited to having all the structures described.

In addition, each of the above-described structures, functions, processing units, processing devices, and the like can be designed by, for example, an integrated circuit, and a part or all of them can be realized by hardware. In addition, each of the above-described structures, functions, and the like can be realized by software by interpreting and executing programs for realizing the various functions by a processor. Information such as programs, forms, and files that realize various functions can be stored in storage devices such as non-volatile semiconductor memory, hard disk drives, and SSD (Solid State Drive), or in computers such as IC cards, SD cards, and DVDs. readable non-transitory data storage medium.

Furthermore, for better illustration, control lines and information lines are thus shown, but not necessarily all control lines and information lines on the product. In fact, it can also be considered that almost all structures are interconnected.

Claims (14)

1. An elevator analysis system, which is an elevator analysis system with a processor and a storage device connected with the processor, It is characterized by: The storage device saves: information on the number of people getting on and off the elevator, which is used to indicate the actual number of people on each floor and the number of people getting off the elevator in each car belonging to the elevator group that is the control object; The actual state of each car of the elevator group; and the number of passengers, wherein the number of passengers refers to the number of users who want to use the elevator at the boarding place of each floor of the elevator group, The processor performs the following operations: Estimating the number of passengers according to the information on the number of people taking the elevator and the operation log information, predicting future ridership based on the estimated ridership, and according to the predicted number of passengers in the future to determine the running rules applicable to the running control of the cars belonging to the elevator group, and the control parameters set in the respective running rules, The determined operation rules and control parameters are further output. 2. The elevator analysis system according to claim 1, It is characterized by: The processor calculates the probability of the destination floor according to the information on the number of people taking the elevator, and the users who appear at the elevator on each floor according to each floor may become the destination floor. The destination floor of the user appearing at the elevator is the probability of that floor; Based on the number of future passengers and the destination floor probability, an operation rule applicable to the operation control of each car belonging to the elevator group and a control parameter set in each operation rule are determined. 3. The elevator analysis system according to claim 2, It is characterized by: The storage device further stores information specifying an evaluation index, wherein the evaluation index is used to evaluate the running status of each car in the elevator group; the processor A first simulation is performed a plurality of times while changing the applicable operating rules and the control parameters, wherein the simulation generates users at the rides on each floor based on the future occupants and the destination floor probability then run each car in the elevator group; According to the result of the first simulation, the specified evaluation index is calculated, According to the calculated evaluation index, the operation rule and the control parameter which are helpful for improving the evaluation index are determined, as the operation control applied to the cars belonging to the elevator group. Operation rules, and control parameters set in each operation rule. 4. The elevator analysis system according to claim 3, It is characterized by: According to the predicted number of passengers in the future, the processor calculates the riding probability of the number of users according to each number of passengers when the probability distribution of the number of passengers is assumed to be Poisson distribution; The first simulation is performed by generating users according to the occupancy probability for each passenger number and the destination floor probability for each destination floor. 5. The elevator analysis system according to claim 3, It is characterized in that: it further has a display device connected with the processor, The processor specifies the operation rule and the control parameter that help to improve the contribution of the evaluation index and satisfy a predetermined condition, The display device displays the designated operation rules and control parameters. 6. The elevator analysis system according to claim 3, It is characterized by: further having the processor and an interface connected to an external network of the elevator analysis system; A control device is connected to the network, wherein the control device is used to control each car belonging to the elevator group; The processor specifies the operation rule and the control parameter that are helpful to improve the contribution of the evaluation index and satisfy a predetermined condition; The specified operating rules and control parameters are sent to the control device through the interface. 7. elevator analysis system according to claim 3, is characterized in that: The evaluation index includes any one of the waiting time before the boarding user gets on any car, the congestion rate at the boarding place, and the power consumption for running each car in the elevator group. 8. The elevator analysis system according to claim 1, It is characterized by: The processor generates a plurality of users who appear at the elevator group boarding place because they want to use the elevator, and randomly determines the floor of the boarding place where each user is located, the appearance time of each user, and the For each user's destination floor, a second simulation of operating the cars belonging to the elevator group is performed based on the appearance time of the each user, the boarding floor at which the user appeared, and the destination floor, thereby According to the state of each car, the number of people getting on the elevator in each car on each floor, and the number of people getting off the elevator in each car on each floor, an estimate for estimating that a user appears at the boarding place on each floor is generated. The number of passengers is the passenger number estimation model of the number of passengers, By applying the actual number of people getting on the elevator, the number of people getting off the elevator, and the state of each car obtained according to the information on the number of people getting on and off the elevator and the information on the operation log into the number of passengers estimation model, the number of passengers on each floor is estimated, storing the estimated number of occupants in the storage device, The future occupant number is predicted based on the estimated occupant number stored in the storage device. 9. elevator analysis system according to claim 8, is characterized in that: In the second simulation, the processor uses the number of occupants of each floor in each predetermined time width as a target index, and uses the state of each car, each The occupant number estimation model is generated by performing a multiple regression analysis using the number of people getting on the car in each floor and the number of people getting off the elevator in each car in each floor as explanatory indexes. 10. The elevator analysis system according to claim 8, wherein: The information on the number of people getting on and off the elevator and the information on the running log include the number of people getting on the elevator, the number of people getting off the elevator, and each car belonging to the elevator group when the actual state of each car is obtained. Information on the operating rules applicable to the operating control of the car and the control parameters set in the operating rules; The processor performs the second simulation by operating the cars according to the applicable operating rules and the set control parameters. 11. An elevator analysis method performed by an elevator analysis system having a processor and a storage device connected to the processor, characterized in that: The storage device saves: information on the number of people getting on and off the elevator, which is used to indicate the actual number of people on each floor and the number of people getting off the elevator for each car belonging to the elevator group that is the control object; The actual state of each car of the elevator group; and the number of passengers, wherein the number of passengers refers to the number of users who want to use the elevator at the boarding place of each floor of the elevator group; The elevator analysis method includes the following steps: Sequence 1, the processor estimates the number of passengers according to the information on the number of passengers on and off the elevator and the operation log information; Sequence 2, the processor estimates the number of passengers according to the estimated number of passengers to predict the number of occupants in the future; in sequence 3, the processor determines, according to the predicted number of occupants in the future, the operation rules applicable to the operation control of each car belonging to the elevator group and the parameters in each operation rule. The set control parameters; in sequence 4, the processor outputs the determined operation rules and control parameters. 12. elevator analysis method according to claim 11 is characterized in that: In said sequence 3, The processor calculates the probability of the destination floor according to the information on the number of people taking the elevator, and the users who appear at the elevator on each floor according to each floor may become the destination floor, wherein the probability of the destination floor is the The destination floor of the user that appears at the floor boarding place is the probability of that floor; Based on the number of future passengers and the destination floor probability, an operation rule applicable to the operation control of each car belonging to the elevator group and a control parameter set in each operation rule are determined. 13. elevator analysis method according to claim 12 is characterized in that: The storage device further stores information specifying an evaluation index, wherein the evaluation index is used to evaluate the running status of each car in the elevator group; In said sequence 3, The processor executes a first simulation a plurality of times while changing the applicable operating rules and the control parameters, wherein the simulation is based on the future occupant number and the destination floor probability, multiplying at each floor. After the user is generated in the elevator, each car in the elevator group is run; According to the result of the first simulation, the specified evaluation index is calculated, The operation rule and the control parameter that contribute to the improvement of the evaluation index are determined according to the calculated evaluation index, as the operation applicable to the operation control of the respective cars belonging to the elevator group rules, and the control parameters set in each operation rule. 14. elevator analysis method according to claim 11 is characterized in that: The elevator analysis method further includes the following sequence: The processor generates a plurality of users who appear at the elevator group boarding place because they want to use the elevator, and randomly determines the floor of the boarding place where each user is located, the appearance time of each user, and the For each user's destination floor, a second simulation of operating the cars belonging to the elevator group is performed based on the appearance time of the each user, the boarding floor at which the user appeared, and the destination floor, thereby According to the state of each car, the number of people getting on the elevator in each car on each floor, and the number of people getting off the elevator in each car on each floor, an estimate for estimating that a user appears at the boarding place on each floor is generated. The number of passengers is the passenger number estimation model of the number of passengers; The processor estimates the number of passengers on each floor by applying the actual number of passengers, the number of people getting off the elevator, and the state of each car obtained from the information on the number of passengers on and off the elevator and the operation log information into the passenger number estimation model. the number of passengers; the processor saves the estimated number of occupants in the storage device; In the sequence 2, the processor predicts the future occupant based on the estimated occupant stored in the storage device.

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