CN108147242A - Elevator faults analysis method and device based on big data - Google Patents

Abstract

The present invention relates to an elevator failure analysis method and device based on big data. The method comprises the following steps: obtaining elevator failure time, taking the failure time as a base point, and selecting elevator operation status data within a preset time period from an elevator operation status database; The data-driven method is used to extract the key information in the elevator running state data, and the analysis result of the elevator running state is obtained; the cause of the fault is determined according to the analysis result. The above-mentioned elevator fault analysis method based on big data uses a large amount of elevator operating status data to analyze elevator faults when analyzing elevator faults, which can improve the accuracy of the analysis, thereby facilitating elevator research and development or maintenance personnel to comprehensively and accurately understand elevator faults cause, and then repaired.

Description

Elevator fault analysis method and device based on big data

technical field

The invention relates to the technical field of elevator fault analysis and maintenance, in particular to a big data-based elevator fault analysis method and device.

Background technique

With the acceleration of the pace of life and the popularity of high-rise buildings, elevators have been widely used in various specific occasions, including residences, shopping malls, office buildings, hospitals and so on. While elevators bring convenience to people, there are also potential safety hazards, especially when elevators break down, it may lead to safety accidents, and will threaten the lives of passengers in severe cases. It can be seen that it is particularly important to analyze the fault of the elevator and repair it in time.

At present, when analyzing an elevator failure, usually only the instantaneous state of the elevator when the elevator fails can be obtained, and the cause of the elevator failure cannot be accurately analyzed.

Contents of the invention

Based on this, it is necessary to provide an elevator fault analysis method and device based on big data for the problem that the current elevator fault analysis method cannot accurately analyze the cause of the elevator fault.

A method for analyzing elevator faults based on big data, comprising the following steps:

Obtaining the failure time of the elevator, taking the failure time as a base point, selecting the elevator operation status data within a preset time period from the elevator operation status database;

Using a data-driven method to extract key information in the elevator operating state data to obtain an analysis result of the elevator operating state;

Determine the cause of the fault according to the analysis result.

An elevator fault analysis device based on big data, comprising

The failure time acquisition module is used to obtain the elevator failure time;

The operation state data selection module is used to select the elevator operation state data within a preset time period from the elevator operation state database based on the failure time;

The analysis result obtaining module is used to extract the key information in the elevator operation state data by using the data-driven method, and obtain the analysis result of the elevator operation state;

The failure cause determination module is used to determine the cause of the failure according to the analysis result.

A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the following steps when executing the program:

Obtaining the failure time of the elevator, taking the failure time as a base point, selecting the elevator operation status data within a preset time period from the elevator operation status database;

Using a data-driven method to extract key information in the elevator operating state data to obtain an analysis result of the elevator operating state;

Determine the cause of the fault according to the analysis result.

A computer storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented:

Obtaining the failure time of the elevator, taking the failure time as a base point, selecting the elevator operation status data within a preset time period from the elevator operation status database;

Using a data-driven method to extract key information in the elevator operating state data to obtain an analysis result of the elevator operating state;

Determine the cause of the fault according to the analysis result.

The present invention first obtains the time of elevator failure when carrying out elevator failure analysis, and takes the elevator failure time as a base point to select a preset time period (within a period of time before and after an elevator failure or a period of time before an elevator failure) from the elevator operation state database. Internal) elevator running status data, analyze these running status data, and judge the cause of the elevator failure according to the analysis results. Using a large amount of elevator operating status data to analyze elevator faults can improve the accuracy of the analysis, so that elevator research and development or maintenance personnel can comprehensively and accurately understand the cause of elevator faults, and then perform maintenance.

Description of drawings

Fig. 1 is the schematic flow chart of the elevator fault analysis method based on big data in one of the embodiments of the present invention;

Fig. 2 is a schematic flow chart of an elevator fault analysis method based on big data in one of the embodiments of the present invention;

Fig. 3 is the schematic flow chart that elevator running status database is set up in the elevator failure method based on big data of the present invention;

Fig. 4 is the schematic flow chart of data packet processing method in the elevator fault analysis method based on big data of the present invention;

Fig. 5 is a schematic flow chart of an elevator fault analysis device based on big data in one of the embodiments of the present invention;

FIG. 6 is a schematic structural diagram of a computer device of the present invention in an embodiment.

Detailed ways

The content of the present invention will be further described in detail below in conjunction with preferred embodiments and accompanying drawings. Apparently, the embodiments described below are only used to explain the present invention, not to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention. It should be noted that, for the convenience of description, only parts related to the present invention are shown in the drawings but not all content.

Fig. 1 is the schematic flow chart of the elevator fault analysis method based on big data of the present invention in an embodiment, as shown in Figure 1, the elevator fault analysis method based on big data in the embodiment of the present invention, comprises the following steps:

In step S110, the failure time of the elevator is acquired, and the elevator operation status data within a preset period of time is selected from the elevator operation status database based on the failure time.

When an elevator fails, R&D personnel or elevator maintenance personnel need to understand the cause of the failure in detail, and formulate a detailed and accurate solution to the cause of the failure. In this embodiment, when the research and development personnel or elevator maintenance personnel need to know the cause of the elevator failure at a certain moment, the time of the elevator failure is obtained, and the time of the elevator failure is taken as the base point, and the time period within the preset time period is selected from the elevator operation database. Elevator running status data. Wherein, in the process of selecting the elevator operation status data within the preset time period from the elevator operation database, the elevator operation status data before the elevator failure and within the preset time period after the failure can be selected based on the time when the elevator failure occurs. Optionally, the elevator running state data within a preset time period before the elevator failure can also be selected based on the time when the elevator failure occurs. In addition, the preset time refers to any period of time, which can be more than ten minutes (for example, 15 minutes), dozens of minutes (for example, 30 minutes), or several hours (for example, one hour), etc., the R&D personnel or Elevator maintenance personnel can choose a preset time period according to their actual needs during the research process.

Elevator running status data is mainly data related to the running status of the elevator, which can include elevator identification information data, elevator running floor data, elevator running direction data, elevator running speed data, elevator running acceleration data, elevator door status data, elevator leveling data Data, whether the elevator has passenger status data, whether the elevator has electrical status data, etc., wherein the elevator door status data also includes elevator door opening data and elevator door closing data. R&D personnel or maintenance personnel can choose the data to be monitored according to their own needs.

Step S120, using a data-driven method to extract key information in the elevator running state data, and obtain an analysis result of the elevator running state.

Specifically, the data-driven method is to extract useful information from system process data, and diagnose system faults based on these useful information. Data-driven methods can generally be divided into: machine learning method, information fusion method, signal processing method, rough set method and statistical analysis. The main idea of the machine learning method is to use historical data to train learning machines, and then use the trained machines to diagnose system faults. The main representative methods are neural network methods and support vector machine methods. The main idea of the information fusion method is to integrate multi-source data, complete decision-making under certain criteria through the complementarity of information between data, and improve the reliability of fault diagnosis. According to the difference of fusion information, it can be divided into data layer fusion method and feature layer fusion method. Methods and Decision-Level Fusion Methods. The main idea of the signal processing method is to use the theoretical methods and technical means of signal processing to analyze the process data and extract relevant information in the time domain or frequency domain to diagnose system faults. The representative methods include wavelet transform method and spectral analysis method . The main idea of the rough set method is to find hidden knowledge and distinguish some characteristics of the system from a large amount of process data, so as to achieve the purpose of fault diagnosis. The statistical analysis method is mainly based on the system process data, by constructing the corresponding statistical information, and comparing it with the fault threshold of the statistical data, to diagnose the fault of the system, generally divided into univariate and multivariate statistical analysis methods. In this embodiment, the data-driven method is mainly used to extract the key information in the elevator running state data, and the key information is analyzed to obtain the analysis result of the elevator running state.

It should be understood that when using the data-driven method to extract key information in the elevator operating state data, it is not only possible to use one of the above-mentioned data-driven methods, and those skilled in the art can choose two or more according to actual needs. A variety of data-driven methods are used to comprehensively process the elevator running status data.

Step S130, determining the cause of the fault according to the analysis result.

The above-mentioned elevator failure analysis method based on big data, when performing elevator failure analysis, first obtains the time of elevator failure, and takes the elevator failure time as a base point, selects the preset time period (a period before and after the elevator breaks down) from the elevator operation status database. Time or a period of time before the elevator fails) the elevator running status data, analyze these running status data, and judge the cause of the elevator failure according to the analysis results. Using a large amount of elevator operating status data to analyze elevator faults can improve the accuracy of the analysis, so that elevator research and development or maintenance personnel can comprehensively and accurately understand the cause of elevator faults, and then perform maintenance.

In one of the embodiments, in the step of extracting the key information in the elevator running state data using the data-driven method, including:

Step S121, taking time as the abscissa and elevator running status data as the vertical coordinate to generate a waveform diagram of the elevator running status; wherein, the elevator running status data includes elevator door status data, elevator leveling data, elevator running speed data, and elevator running acceleration One or more of data and elevator running direction data.

Step S122, extracting key information from the waveform diagram of the elevator running state to obtain an analysis result of the elevator running state.

Specifically, take time as the abscissa and the elevator running state data as the vertical coordinate to generate the elevator running state waveform diagram, extract key information from the waveform diagram, obtain the elevator running state analysis result, and generate the elevator running state data into a waveform diagram (such as Curve), very intuitive, convenient for elevator R&D personnel or maintenance personnel to quickly and accurately extract key information from the waveform diagram, obtain the analysis results of the elevator running status, and then analyze the cause of the elevator failure.

In one of the embodiments, the elevator state data is elevator door opening data and elevator leveling data, and the elevator door opening data and elevator leveling data are signal data composed of 0 and 1; in the step of extracting key information from the elevator running state waveform diagram , including:

Step S1221, when the elevator door opening data and the elevator leveling data do not correspond at the same time point, call the waveform diagram of the elevator door opening data, and extract the elevator door opening data from the elevator door opening data and the elevator leveling data waveform diagram Key Information.

The elevator status data may be elevator door opening data and elevator leveling data. The door opening data of the elevator and the elevator leveling data are signal data composed of 0 and 1. Specifically, when the elevator opens the door, the data is recorded as 1, and when the elevator does not open the door, the data is recorded as 0; when the elevator reaches the leveling floor, The data is recorded as 1, and when the elevator does not reach the leveling floor, the data is recorded as 0. Compare the elevator door opening data with the elevator leveling data. At the same time point, when the elevator door opening data and the elevator leveling data do not correspond (that is, the elevator door opening data 1 and the elevator leveling data 1 do not appear at the same time, for example, the elevator door opening data 1 , the elevator leveling data is 0; the elevator door opening data is 0, and the elevator leveling data is 1), indicating that the elevator is in the middle position (when the elevator has not reached the leveling floor) and the elevator has opened the door, or when the elevator reaches the leveling floor, the elevator has not opened the door. In both cases the elevator fails. In order to quickly query the cause of the elevator failure, call the waveform diagram of the elevator door opening data, and extract the key information in the elevator door opening data from the waveform diagram of the elevator door opening data and elevator leveling data, so as to analyze the cause of the elevator failure. The above-mentioned failure is one of the most common failures of elevators. Using the above-mentioned method can ensure that the user can quickly find out the cause of the elevator failure, so that it is easy to find out the solution to the failure.

For ease of understanding, a detailed example is given. In a period of time, the door opening data is all 1, and the elevator leveling data is all 0. The R&D personnel can judge that there is an abnormality in the system, and then further call the waveform diagram that generates the elevator door opening data, so as to gradually find the root of the problem.

In one of the embodiments, as shown in Figure 2, before the step of selecting the elevator running state data in the preset time period from the elevator running state database, it also includes:

Step S140, regularly collecting elevator running state data, compressing the elevator running state data and storing it in the elevator running state database.

Specifically, at present, when the elevator fails, usually only the instantaneous state of the elevator when the elevator fails can be obtained, which is not conducive to the analysis of the elevator failure. And stored in the elevator operation status database, it is convenient for R&D personnel or researchers to extract a large amount of elevator operation status data from the elevator operation status database when the elevator fails, and use big data analysis technology to accurately find out the cause of the elevator failure. Wherein, the timed collection of the elevator running state data may be at any time, such as collecting the elevator running state data once every 30 seconds or 1 minute. The frequency of specific data collection can be determined according to the needs of actual research. In addition, the elevator running state database may be a local database or a cloud database. The local database is used to store the elevator running status data, which is convenient for researchers to quickly call the elevator running status data, process the data, and analyze the elevator failure. Use the cloud database to store the elevator running status data. It is convenient for rescuers to call the elevator running status data anytime and anywhere. As shown in FIG. 3 , the elevator control system 31 is used to regularly collect the elevator running state data, and then the collected elevator running state data is sent to the elevator running state data 33 through the communication terminal 32 .

In one of the embodiments, in the step of regularly collecting elevator running state data, including:

Step S141, in each collection period, regularly collect the elevator running status data, and record the elevator status data in each collection period as a data packet.

Step S142, respectively load the elevator state data in the data packets corresponding to each collection period into the data buffer, and perform cyclic comparison on the elevator state data in the data packets, and delete repeated data in consecutive time points.

Specifically, the time T (T is any time, and the value of T should not be too large or too small) can be used as a collection cycle, within a time T, the elevator running state data is regularly collected, and the data collected within a time T The elevator running state data is loaded into the data buffer, and then the data in the data buffer (such as buffer) is cyclically compared. When the data in the continuous time point is repeated, the repeated data is deleted (because the elevator is in normal working state) Or when working abnormally, most of the time data is in a constant state. Therefore, during periodic data collection, a large amount of repeated data will be generated). By analogy, the data collected at each time T is respectively recorded as multiple data packets, and the same processing is performed on the data in the multiple data packets.

For ease of understanding, a detailed example is given. As shown in Figure 4, there are M collection times T1, T2...TN, and the elevator running status data collected at collection times T1, T2...TM are respectively recorded as data packet 1, data packet 2... Data packets M, wherein each data packet contains M data, recorded as data 1, data 2, ... data N. Compare data 1 and data 2 in data packet 1. If data 1 and data 2 are exactly the same, you can delete data 2, and then compare data 1 and data 3. If they are the same, delete data 3. If the data is different, keep the data 3. Compare in turn and delete duplicate data. In the same way, do the same processing for data packet 2, ..... data packet M, and finally save all data packet data in the elevator running status database. Using the above-mentioned data storage method, a large amount of repeated data can be deleted, which reduces the data storage space on the one hand, and improves the speed of data transmission on the other hand.

In one of the embodiments, the step of cyclically comparing the elevator status data in the data packet also includes:

Step S1411, when the length of the data packet is less than the preset byte, save all the elevator status data in the data packet.

When the length of the data packet is the preset byte, it indicates that the data packet is relatively small (that is, less elevator operation status data is collected within a collection period T), and there is no need to delete the data, and all the elevator status data in the data packet are directly saved. In order to ensure that enough data can be selected for later elevator failure analysis, so as to ensure the accuracy of the analysis results.

In one of the embodiments, as shown in Figure 2, the elevator fault analysis method based on big data also includes:

Step S150, reconstruct the running state of the elevator according to the running state data of the elevator, and obtain an animation image of the running process of the elevator.

Specifically, use the data of the elevator running state to reconstruct the running state to obtain the animated image of the elevator running state (the elevator running animation image can be simulated according to the elevator running data for a certain period of time, for example, from 15:00-15:10, The elevator runs to the 3rd floor, then to the 10th floor, and finally stops at the 5th floor (this animation), which is convenient for researchers and maintenance personnel to understand the operating status of the elevator more intuitively.

Fig. 5 is a schematic structural diagram of an embodiment of the big data-based elevator fault analysis device of the present invention. As shown in Figure 5, the elevator failure analysis device based on big data in this embodiment includes:

Failure time acquisition module 10, used to obtain elevator failure time;

The running state data selection module 20 is used to select the elevator running state data in the preset time period from the elevator running state database based on the failure time;

The analysis result obtaining module 30 is used to extract the key information in the elevator operation state data by using the data-driven method, and obtain the analysis result of the elevator operation state;

The failure cause determination module 40 is configured to determine the cause of the failure according to the analysis result.

In one of the embodiments, the analysis result obtaining module 30 includes: a running state waveform diagram generating module 31;

Running state waveform diagram generating module 31 is used to take time as the abscissa and the elevator running state data as the vertical coordinate to generate the elevator running state waveform diagram; wherein, the elevator running state data includes elevator door state data, elevator leveling data , one or more of elevator running speed data, elevator running acceleration data and elevator running direction data;

The analysis result obtaining module 30 is configured to extract the key information from the waveform diagram of the elevator operation state, and obtain the analysis result of the elevator operation state.

In one of the embodiments, the elevator state data is the elevator door opening data and the analysis result obtaining module 30, which also includes: a key information extraction module 32;

The key information extraction module 32 is used to call the waveform diagram of the elevator door opening data when the elevator door opening data and the elevator leveling data do not correspond at the same time point, and extract the data from the waveform diagram of the elevator door opening data and the elevator leveling data. Key information in the elevator door opening data.

In one of the embodiments, the elevator failure analysis device based on big data also includes:

The elevator running state data collection module 50 is used for regularly collecting elevator running state data.

The data compression storage module 60 is used for compressing the elevator running state data and storing it in the elevator running state database.

In one of the embodiments, the elevator failure analysis device based on big data also includes:

The elevator running state data collection module 50 is also used for regularly collecting elevator running state data in each collection cycle;

Data packet builds module 70, is used for recording the elevator state data in each acquisition cycle as a data packet;

The data packet processing module 80 is used to respectively load the elevator state data in the data packets corresponding to each acquisition cycle into the data buffer, and carry out cyclic comparison with the elevator state data in the data packets, and delete repeated time points in consecutive time points. The data.

In one of the embodiments, the elevator failure analysis device based on big data also includes:

The data packet processing module 80 is also used for saving all elevator status data in the data packet when the length of the data packet is less than a preset byte.

In one of the embodiments, the elevator failure analysis device based on big data also includes:

The animation image obtaining module 90 is used for reconstructing the operation state of the elevator according to the elevator operation state data, and obtaining the animation image of the elevator operation process.

The above-mentioned elevator fault analysis device based on big data can execute the elevator fault analysis method based on big data provided by the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. As for the processing methods performed by each functional module, such as the failure time acquisition module 10, the operation status data selection module 20, and the animation image acquisition module 90, refer to the descriptions in the above method embodiments, and will not be repeated here.

According to the big data-based elevator fault analysis method and device of the present invention, the present invention also provides a computer device. The computer device of the present invention will be described in detail below in conjunction with the accompanying drawings and preferred embodiments.

FIG. 6 is a schematic structural diagram of a computer device of the present invention in an embodiment. As shown in Figure 6, the computer device 600 in this embodiment includes a memory 601, a processor 602, and a computer program stored in the memory and operable on the processor, wherein the processor can implement the method of the present invention when executing the program All method steps in the example.

The processor 602 in the above-mentioned computer device 600 can execute the elevator fault analysis method based on big data provided by the embodiment of the present invention, and has corresponding beneficial effects of executing the method. Reference may be made to the descriptions in the foregoing method embodiments, and details are not repeated here.

According to the elevator fault analysis method, device and computer equipment based on big data of the present invention, the present invention also provides a computer-readable storage medium. The computer-readable storage medium of the present invention will be described in detail below in conjunction with the accompanying drawings and preferred embodiments. illustrate.

The computer-readable storage medium in the embodiment of the present invention stores a computer program thereon, and when the program is executed by a processor, all method steps in the method embodiment of the present invention can be realized.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware, and the programs can be stored in a computer-readable storage medium. , may include the flow of the embodiments of the above-mentioned methods. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random Access Memory, RAM), etc."

The above-mentioned computer-readable storage medium is used to store the program (instruction) of the elevator fault analysis method based on big data provided by the embodiment of the present invention, wherein executing the program can perform the elevator fault analysis based on big data provided by the embodiment of the present invention The method has the corresponding beneficial effect of executing the method. Reference may be made to the descriptions in the foregoing method embodiments, and details are not repeated here.

The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be It is considered to be within the range described in this specification.

The above examples only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as limiting the scope of the patent for the invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (10)

1. a kind of elevator fault analysis method based on big data is characterized in that, comprises the following steps: Obtaining the failure time of the elevator, taking the failure time as a base point, selecting the elevator operation status data within a preset time period from the elevator operation status database; Using a data-driven method to extract key information in the elevator operating state data to obtain an analysis result of the elevator operating state; Determine the cause of the fault according to the analysis result. 2. the elevator fault analysis method based on big data according to claim 1, is characterized in that, utilizes data-driven method to extract in the step of the key information in the described elevator running status data, comprises: Taking time as the abscissa and elevator running state data as the vertical coordinate, generate the elevator running state waveform diagram; wherein, the elevator running state data includes elevator door state data, elevator leveling data, elevator running speed data, and elevator running acceleration data One or more of the elevator running direction data; The key information is extracted from the waveform diagram of the elevator operating state to obtain an analysis result of the elevator operating state. 3. the elevator fault analysis method based on big data according to claim 2, is characterized in that, described elevator status data is elevator door opening data and elevator leveling data, and described elevator door opening data and elevator leveling data are 0 and 1 composed of signal data; in the step of extracting the key information from the elevator running state waveform diagram, including: When the door-opening data of the elevator does not correspond to the leveling data of the elevator at the same time point, call the waveform diagram of the door-opening data of the elevator, and from the waveform diagram of the door-opening data of the elevator and the leveling data of the elevator Extract the key information in the elevator door opening data. 4. the elevator failure analysis method based on big data according to claim 1, is characterized in that, before the step of selecting the elevator running state data in the preset time period from the elevator running state database, it also includes: The elevator running state data is regularly collected, and the elevator running state data is compressed and stored in the elevator running state database. 5. The elevator failure analysis method based on big data according to claim 4, characterized in that, in the step of regularly collecting the elevator running state data, comprising: In each collection cycle, regularly collect the elevator running state data, and record the elevator state data in each collection cycle as a data packet; Respectively load the elevator state data in the data packets corresponding to each acquisition cycle into a data buffer, and perform a cyclic comparison of the elevator state data in the data packets, and delete repeated time points The data. 6. the elevator fault analysis method based on big data according to claim 4, is characterized in that, in the step that the elevator state data in the data packet is carried out cyclic comparison, also comprises: When the length of the data packet is less than a preset byte, all the elevator state data in the data packet are saved. 7. the elevator fault analysis method based on big data according to claim 1-6, is characterized in that, also comprises: According to the elevator running state data, the running state of the elevator is reconstructed to obtain an animation image of the elevator running process. 8. A kind of elevator fault analysis device based on big data, it is characterized in that, comprising: The failure time acquisition module is used to obtain the elevator failure time; The operation state data selection module is used to select the elevator operation state data within a preset time period from the elevator operation state database based on the failure time; The analysis result obtaining module is used to extract the key information in the elevator operation state data by using the data-driven method, and obtain the analysis result of the elevator operation state; The failure cause determination module is used to determine the cause of the failure according to the analysis result. 9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the method of claims 1-7 is implemented when the processor executes the program A step of. 10. A computer storage medium, on which a computer program is stored, characterized in that, when the program is executed by a processor, the steps of the method according to claims 1-7 are implemented.