CN117745267A - Remote sensing service method and system for railway passenger station elevator based on Internet of things - Google Patents

Abstract

The present invention provides a method and system for remote sensing service of railway passenger station elevators based on the Internet of Things. The method includes: based on the locations of elevators and passenger transport areas, associating all elevators in the station with corresponding passenger transport areas, and collecting elevator operations in real time. and receive surrounding environment data, regularly receive train operation and passenger flow statistical information in different passenger transport areas, and generate elevator dispatching strategies based on real-time operation data, train operation and passenger flow statistical information in different passenger transport areas, and correlation relationships; obtain historical operation and surrounding environment data and historical fault data. The long-short-term memory network of the attention mechanism performs fault prediction and alarm based on historical operation and surrounding environment data, historical fault data and real-time operation and surrounding environment data, and generates elevator fault warning results; obtains historical fault and maintenance data. , the convolutional neural network with attention mechanism generates an elevator maintenance plan based on historical fault and maintenance data and elevator fault warning results.

Description

Railway passenger station elevator remote sensing service method and system based on Internet of Things

Technical field

The present invention relates to the technical field of the Internet of Things, and in particular to a method and system for remote sensing service of railway passenger station elevators based on the Internet of Things.

Background technique

In public places such as railway passenger stations, elevators play a key role in the passenger transportation process. They are crucial to passengers' safety and travel experience. Elevator shutdowns or problems will seriously interfere with passengers' travel. At the same time, railway passenger stations usually face complex passenger flow conditions. Passenger congestion levels vary greatly at different times and regions, resulting in highly uneven demand for elevators.

The railway industry usually uses elevator equipment produced by famous brand companies such as Thyssen, Mitsubishi, Hitachi, and Kone. These brand elevators have elevator monitoring systems for monitoring and managing elevator equipment, but these monitoring systems are only applicable to the elevators of their respective brands. It cannot be used for elevators of other brands, and railway stations need to maintain multiple monitoring systems of different brands at the same time, which increases the complexity of management and maintenance. In addition, the monitoring data obtained by the monitoring system is relatively limited and cannot provide comprehensive elevator status and environmental information, making it more difficult to detect potential problems in a timely manner and implement preventive maintenance. The most important thing is that due to the failure to integrate business data in the railway field, the elevator system is difficult to intelligently schedule according to actual passenger demand and passenger flow, and cannot effectively reduce the pressure during peak hours and improve the utilization rate of elevators. These problems limit the performance and availability of elevator systems in railway passenger stations, posing challenges to passengers’ travel experience and safety.

Contents of the invention

In view of this, embodiments of the present invention provide a railway passenger station elevator remote sensing service method and system based on the Internet of Things to eliminate or improve one or more defects existing in the existing technology.

One aspect of the present invention provides a remote sensing service method for railway passenger station elevators based on the Internet of Things, including:

Based on the location of the elevators and passenger transport areas, all elevators in the station are associated with the corresponding passenger transport areas, and the station-level sensor network collects the operation data and surrounding environment data of all elevators in the station in real time;

The comprehensive data platform receives real-time operation data and surrounding environment data of all elevators from the station-level sensor network, and regularly receives train operation information and passenger flow statistical information in different passenger transport areas pre-configured by the passenger service and control platform. According to the real-time elevator Operation data, train operation information and passenger flow statistical information in different passenger transport areas, as well as the correlation between elevators and passenger transport areas generate elevator dispatching strategies;

The comprehensive data platform obtains the historical operating data, historical surrounding environment data, and historical fault data of all elevators, and the preset long-short-term memory network model based on the attention mechanism uses the historical operating data, historical surrounding environment data, and historical fault data of all elevators. Use data, real-time operating data, and real-time surrounding environment data to perform fault prediction and alarm, and generate elevator fault warning results;

The comprehensive data platform obtains the historical fault data and historical maintenance data of all elevators, and the preset convolutional neural network model based on the attention mechanism generates the elevator maintenance plan based on the historical fault data, historical maintenance data and elevator fault warning results of all elevators. ;

The real-time operation data, the elevator dispatching strategy, the elevator failure warning results and the elevator maintenance plan are displayed in real time by the remote monitoring terminal.

In some embodiments of the present invention, the step of generating an elevator dispatching strategy based on the real-time operation data of the elevator, train operation information and passenger flow statistics information of different passenger transport areas, and the association between the elevator and the passenger transport area includes:

The comprehensive data platform determines the available elevators and the current operating status of the available elevators based on the real-time operation data of the elevators, analyzes the regularly received train operation information and passenger flow statistical information in different passenger transport areas, and obtains the peak passenger flow time period and passenger transport area;

According to the time period of peak passenger flow and the relationship between passenger transport area, elevator and passenger transport area, adjust and optimize the dispatch sequence, service time period, service area, operating speed and group configuration of available elevators, and optimize according to the current operating status of available elevators Intensity of use of available elevators.

In some embodiments of the present invention, the preset long short-term memory network model based on the attention mechanism is based on the historical operating data, historical surrounding environment data, historical fault data and real-time operating data and real-time surrounding environment data of all elevators. The steps to perform fault prediction and alarm and generate elevator fault warning results include:

Perform data cleaning and feature engineering processing on the historical operating data, historical surrounding environment data, historical fault data, real-time operating data, and real-time surrounding environment data of all elevators;

The preset long-short-term memory network model based on the attention mechanism is trained using the processed historical operating data, historical surrounding environment data and historical fault data, and the long-short-term memory network model is strengthened to predict the operating status, equipment status and possible consequences of the elevator. concern about the relationship between faults;

The trained long-short-term memory network model based on the attention mechanism predicts and warns elevator faults based on the processed real-time operating data and real-time surrounding environment data, and generates elevator fault warning results. The elevator fault warning results at least include possible occurrences. The faulty elevator number, fault type, fault level, fault occurrence probability and the impact of the fault on the elevator operation.

In some embodiments of the present invention, the steps of performing data cleaning and feature engineering processing on the historical operating data, historical surrounding environment data, historical fault data, real-time operating data, and real-time surrounding environment data of all elevators include:

The historical operating data, historical surrounding environment data, historical fault data and real-time operating data of all elevators, and real-time surrounding environment data include elevator non-vibration data, elevator vibration data and elevator image data. For elevator non-vibration data and elevator vibration data, The data is cleaned to remove noise, fill in missing values and process outliers;

Convert the cleaned elevator non-vibration data into statistical indicators and standardize them. Use wavelet changes to identify and extract wavelet components of different frequencies from the cleaned elevator vibration data. Perform feature extraction, recognition and classification on the elevator image data to achieve feature extraction. Engineering processing.

In some embodiments of the present invention, the step of generating an elevator maintenance plan based on historical fault data, historical maintenance data and elevator fault warning results of all elevators using a preset attention mechanism-based convolutional neural network model includes:

Perform data cleaning on the historical fault data, historical maintenance data and elevator fault warning results of all elevators, vectorize the cleaned data through word embedding, and perform standardization and normalization processing;

The preset convolutional neural network model based on the attention mechanism is trained using the standardized and normalized historical fault data and historical maintenance data to strengthen the convolutional neural network model's attention to elevator maintenance information;

The trained convolutional neural network model based on the attention mechanism generates an elevator maintenance plan based on the standardized and normalized elevator fault warning results. The elevator maintenance plan at least includes the elevator number, maintenance type, and maintenance resources that need to be maintained. , maintenance frequency, maintenance time and priority.

In some embodiments of the invention, the method further includes:

The communication gateway receives the operating data and surrounding environment data of all elevators from the station-level sensor network and forwards it to the comprehensive data platform. During the forwarding process, the formats and protocols of all elevator operating data and surrounding environment data are converted and unified.

In some embodiments of the present invention, the passenger transport area at least includes at least one entrance, platform, exit, waiting room and ticket gate.

In some embodiments of the present invention, the operating data at least includes operating status, operating speed, elevator door switch status, operating position, temperature, vibration, pressure, load, number of runs, number of starts and stops, accumulated elevator running time and each floor dwell time;

The surrounding environment data at least include the flow of people riding the elevator and non-standard elevator riding behaviors including door-picking and fighting in the elevator;

The train operation information at least includes the train number, as well as the delay status of different train numbers, estimated departure time, actual departure time, estimated arrival time, actual arrival time, ticket check-in time, platform, station exit and ticket gate;

The passenger flow statistical information at least includes the number of people in the station at different time periods, the number of people getting on and off different trains, and the passenger flow distribution in different passenger transport areas at different time periods.

Another aspect of the present invention provides a railway passenger station elevator remote sensing service system based on the Internet of Things, the system comprising: a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps of the aforementioned method.

Another aspect of the present invention provides a computer-readable storage medium on which a computer program is stored, which implements the steps of the foregoing method when executed by a processor.

The remote sensing service method and system for railway passenger station elevators based on the Internet of Things of the present invention applies the Internet of Things technology to railway passenger station elevators, and includes all elevators in the railway passenger station in a network that can realize interconnection within the scope of the railway passenger station. Improve the monitoring efficiency of all elevators, realize intelligent elevator failure warning and operation and maintenance, and integrate railway business, optimize the intelligent dispatching and rational use of elevators, effectively reduce the operating pressure of elevators during peak hours, and improve the utilization rate of elevators in stations.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the specification and drawings.

Those skilled in the art will understand that the objectives and advantages that can be achieved with the present invention are not limited to the specific description above, and the above and other objectives that can be achieved with the present invention will be more clearly understood from the following detailed description.

Description of drawings

The drawings described here are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention.

Figure 1 is a schematic flow chart of an embodiment of the railway passenger station elevator remote sensing service method based on the Internet of Things of the present invention;

FIG2 is a flow chart of another embodiment of the railway passenger station elevator remote sensing service method based on the Internet of Things of the present invention;

Figure 3 is a schematic flow chart of another embodiment of the railway passenger station elevator remote sensing service method based on the Internet of Things of the present invention;

Figure 4 is a schematic structural diagram of an embodiment of the railway passenger station elevator remote sensing service system based on the Internet of Things of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the embodiments and drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not used to limit the present invention.

Here, it should also be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, and the details related to them are omitted. Other details are less relevant to the invention.

It should be emphasized that the term "comprising" when used herein refers to the presence of features, elements, steps or components but does not exclude the presence or addition of one or more other features, elements, steps or components.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

In order to effectively deal with the complex passenger flow situation in railway passenger stations, passengers in different time periods and in different passenger transport areas can smoothly reach the designated locations they want to reach and travel safely and efficiently, while avoiding elevator shutdowns or malfunctions and need for maintenance. To solve the problem of interfering with passenger travel, embodiments of the present invention provide a remote sensing service method and system for railway passenger station elevators based on the Internet of Things. The Internet of Things technology is applied to railway passenger station elevators, and all elevators in the railway passenger station are included in the railway passenger station. In a network that is interconnected within the passenger station, the monitoring efficiency of all elevators can be improved, intelligent elevator failure warning and operation and maintenance can be realized, railway business can also be integrated, the intelligent dispatching and rational use of elevators can be optimized, and elevators can be effectively alleviated during peak hours. Reduce operating pressure and improve the utilization rate of elevators in stations.

Referring to Figures 1 and 3, the remote sensing service method for railway passenger station elevators based on the Internet of Things according to the embodiment of the present invention includes the following steps:

Step S110: Based on the positions of the elevators and passenger transport areas, all elevators in the station are associated with the corresponding passenger transport areas, and the station-level sensor network collects the operation data and surrounding environment data of all elevators in the station in real time.

In one embodiment of the present invention, the passenger transport area at least includes at least one entrance, platform, exit, waiting room and ticket gate. One or more elevators, including straight elevators and escalators, are set up between these different passenger transport areas so that passengers can smoothly go from one designated passenger transport area to another, such as from the first waiting area. Arrive at the sixth platform to take a certain train. According to the adjacent position relationship between the elevator and different passenger transport areas, whether the elevator is set up in a certain passenger transport area, and the passenger transport area that the elevator can reach, the passenger transport area that the elevator can reach and the passenger transport area that is adjacent to the elevator is Area, the passenger area where the elevator is located in a certain passenger area are associated, and the elevator is numbered.

In one embodiment of the present invention, the station-level sensor network includes various types of physical sensors that are reasonably configured according to the characteristics of elevator equipment and monitoring requirements, specifically including switch sensors for monitoring elevator door switches, speed sensors for monitoring elevator operating speed, Position sensor for monitoring the position of the elevator, temperature sensor for monitoring the ambient temperature of the elevator, human body sensor for monitoring whether there is someone riding in the elevator, vibration sensor for monitoring whether the elevator is vibrating, and monitoring the pressure of the elevator. Pressure sensor, load sensor for monitoring the elevator load, counting sensor for monitoring the number of elevator runs, the number of up and down trips and the number of starts and stops, time sensor for monitoring the dwell time and cumulative running time of the elevator on each floor, and acquisition Visual cameras with image data in the elevator car or the elevator environment can monitor and perceive the elevator operation and environmental conditions in real time. The operating data of all elevators include but are not limited to operating status, operating speed, elevator door opening and closing status, operating position, temperature, vibration, pressure, load, number of runs, number of starts and stops, accumulated elevator running time and dwell time on each floor. The status can be used to determine whether the elevator is stopped, running, going up or down based on the characteristics of the elevator's running speed and acceleration. The elevator door switch status includes the normal and abnormal status of the door opening and closing. The surrounding environment data includes but is not limited to the flow of people on the elevator and the status of the elevator. This includes non-standard riding behaviors such as door-picking and fighting in the elevator.

Step S130: The comprehensive data platform receives real-time operation data and surrounding environment data of all elevators from the station-level sensor network, and regularly receives train operation information and passenger flow statistical information from different passenger transport areas pre-configured by the passenger service and management control platform. According to The real-time operation data of elevators, train operation information and passenger flow statistical information in different passenger transport areas, and the relationship between elevators and passenger transport areas generate elevator dispatching strategies.

In one embodiment of the present invention, the train operation information includes, but is not limited to, train numbers, and the on-time and late status of trains of different numbers (including on-time arrival and late arrival), estimated departure time, actual departure time, estimated arrival time, and actual arrival time. Station time, ticket check time, platform, exit and ticket gate. Passenger flow statistical information includes but is not limited to the number of people in the station at different time periods, the number of people getting on and off different trains, the passenger flow distribution in different passenger transport areas at different time periods, and the number of trains taken by different passengers. The comprehensive data platform can receive and integrate multi-source data collected in real time from various physical sensors in the station-level sensor network and from the railway business data platform (passenger service and control platform).

In one embodiment of the present invention, please refer to Figure 3. In step S130, the step of generating an elevator dispatching strategy based on the real-time operation data of the elevator, the train operation information and passenger flow statistical information of different passenger transport areas, and the association between the elevator and the passenger transport area includes: : The comprehensive data platform determines the available elevators and the current operating status of the available elevators based on the real-time operation data of the elevators, analyzes the regularly received train operation information and passenger flow statistical information in different passenger transport areas, and obtains the peak passenger flow time period and passenger transport area. ; According to the time period of peak passenger flow and the relationship between passenger transport area, elevator and passenger transport area, adjust and optimize the scheduling sequence, service time period, service area, operating speed and group configuration of available elevators, and based on the current operating status of available elevators Optimize usage intensity of available elevators.

In the embodiment of the present invention, the comprehensive data platform determines the available elevators and their number based on the received elevator running speed and the elevator door opening and closing status of whether the door is opened normally, and obtains the current operating status of these available elevators (stopped or normal operation). ), analyze the regularly received train operation information and passenger flow statistical information from different waiting rooms, different ticket gates and different platforms, based on the expected and actual departure and arrival operation information of each train in different time periods. Analyze and obtain the peak passenger flow time period and passenger transportation area. For example, the two trains corresponding to each platform in the first waiting room from the first to the sixth platform all depart within the same time period, and the number of each train If more passengers board the train, then this time period is the peak passenger flow period. During this time period, the first waiting room, the ticket gates corresponding to these departure trains and the first to sixth platforms are the passenger transportation areas with peak passenger flow. , it is necessary to implement at least one of the following intelligent dispatching strategies for the available elevators going to the first waiting room, the ticket gates corresponding to these departures, and the first to sixth platforms, or the available elevators that can lead to adjacent locations of these passenger areas. Prioritize the use of these available elevators and the scheduling of their up and down operation sequences, schedule these available elevators to serve passengers in these passenger transport areas during the peak time period, and appropriately speed up the operation of the elevators under the premise of safe operation, it will be possible Multiple elevators located in the same passenger area or adjacent locations leading to the same passenger area are configured as an elevator group. Multiple elevators configured as an elevator group can respond to the same elevator request at the same time and based on the available elevators. The current operating status optimizes the usage intensity of available elevators, increases the usage intensity (frequency of use and passenger occupancy rate) of elevators that are idle or with low usage intensity, and performs dynamic dispatching of elevators based on the peak passenger flow and different time periods in different passenger transport areas. , improve the utilization rate of idle elevators to reduce the waiting time of passengers, ensure that each elevator reaches the designated passenger area at different times to meet the needs of passengers, and significantly improve the efficiency of passengers entering, boarding and exiting the station. It provides a unique travel experience and improves the efficiency and rational utilization of elevators.

Step S140, the comprehensive data platform obtains the historical operation data, historical surrounding environment data and historical fault data of all elevators, and uses the preset long-short-term memory network model based on the attention mechanism based on the historical operating data, historical surrounding environment data of all elevators. , historical fault data, real-time operation data, and real-time surrounding environment data for fault prediction and alarm, and generate elevator fault warning results.

In one embodiment of the present invention, please refer to Figure 3. In step S140, the preset long short-term memory network model based on the attention mechanism is based on the historical operation data, historical surrounding environment data, historical fault data and real-time operation data of all elevators. The steps for performing fault prediction and warning based on real-time surrounding environment data and generating elevator fault warning results include: data cleaning and characterization of historical operating data, historical surrounding environment data, historical fault data and real-time operating data, and real-time surrounding environment data for all elevators. Engineering processing; use the processed historical operating data, historical surrounding environment data and historical fault data to train the preset long-short-term memory network model based on the attention mechanism, and strengthen the long-short-term memory network model to the elevator's operating status and equipment status. Pay attention to the relationship between the fault and the possible fault; the trained long-short-term memory network model based on the attention mechanism predicts and warns the elevator fault based on the processed real-time operating data and real-time surrounding environment data, and generates an elevator fault early warning. As a result, the elevator fault early warning result at least includes the number of the elevator that may fail, the fault type, the fault level, the probability of the fault and the degree of impact of the fault on the elevator operation.

Among them, the steps of data cleaning and feature engineering processing of historical operating data, historical surrounding environment data, historical fault data, real-time operating data, and real-time surrounding environment data of all elevators include: historical operating data, historical surrounding environment data of all elevators Environmental data, historical fault data and real-time operation data, and real-time surrounding environment data include elevator non-vibration data, elevator vibration data and elevator image data. The elevator non-vibration data and elevator vibration data are used to remove noise, fill in missing values and Data cleaning for outlier processing; convert the cleaned elevator non-vibration data into statistical indicators and standardize them, identify and extract wavelet components of different frequencies from the cleaned elevator vibration data through wavelet changes, and characterize the elevator image data Extract, identify and classify, and implement feature engineering processing. On the one hand, the above preprocessing process can improve the accuracy and consistency of the data. On the other hand, specific feature engineering processing is performed on different types of data, which can accurately extract corresponding data features from a large amount of various types of data and improve deep learning. The performance of the model in efficiently and accurately identifying data.

In the embodiment of the present invention, the historical fault data at least includes the fault type, fault level, and the impact of the fault on the elevator operation in the past time period, such as the past two or several years.

Step S150, the comprehensive data platform obtains the historical fault data and historical maintenance data of all elevators, and generates it based on the historical fault data, historical maintenance data and elevator fault warning results of all elevators by the preset convolutional neural network model based on the attention mechanism. Elevator maintenance plan.

In one embodiment of the present invention, please refer to Figure 3. In step S150, the preset convolutional neural network model based on the attention mechanism generates the elevator maintenance plan based on the historical fault data, historical maintenance data and elevator fault warning results of all elevators. The steps include: cleaning the historical fault data, historical maintenance data and elevator fault warning results of all elevators, vectorizing the cleaned data through word embedding, and performing standardization and normalization processing; by standardization and normalization The processed historical fault data and historical maintenance data train the preset convolutional neural network model based on the attention mechanism, and strengthen the convolutional neural network model's attention to the elevator maintenance information; the trained attention-based convolutional neural network model The cumulative neural network model generates an elevator maintenance plan based on the standardized and normalized elevator fault warning results. The elevator maintenance plan at least includes the elevator number that needs to be maintained, maintenance type, maintenance resources, maintenance frequency, maintenance time and priority. .

In the embodiment of the present invention, the historical maintenance data includes at least the past time period, such as the maintenance type of faults that occurred in the elevator in the past two or several years (including routine maintenance, faults that have a large impact on the operation of the elevator) Maintenance and emergency maintenance), required maintenance resources (maintenance personnel and equipment), maintenance frequency, maintenance time and priority.

Step S160, the remote monitoring terminal displays the real-time operation data, the elevator dispatching strategy, the elevator fault warning result and the elevator maintenance plan in real time.

In the embodiment of the present invention, the real-time operation data of the elevator as well as the generated dispatching strategy, fault warning results and maintenance plan are displayed in real time by the remote monitoring terminal, so that the actual operation status of the elevator in the station can be understood in a timely manner and clearly understood. , and perform macro-control and auxiliary decision-making on the dynamic scheduling of elevators to make up for the shortcomings of the generated scheduling strategy. At the same time, the abnormal information of predicted elevator faults (elevator number that may cause a fault, fault type, fault level, fault occurrence probability) can be and the degree of impact of the fault on the elevator operation) and the corresponding elevator maintenance plan are promptly sent or notified to the corresponding maintenance personnel to perform maintenance on the corresponding elevator, so as to avoid the occurrence of major elevator faults that may cause serious impact or interference on passenger travel. The forms of delivery or notification include but are not limited to text messages, emails, push delivery notifications and sound alerts.

Through the remote sensing service method and system for railway passenger station elevators based on the Internet of Things disclosed in the embodiments of the present invention, various functions of real-time status monitoring, intelligent dispatching, fault prediction, abnormal alarm and preventive maintenance of elevators in the station can be realized, and early detection can be realized Potential faults and risks of elevators in the station require timely and targeted maintenance in advance to eliminate potential faults and risks, thereby building a safer and more reliable elevator operating environment for railway passenger stations, ensuring safe travel for passengers and improving the travel experience.

In another embodiment of the present invention, please refer to Figures 2 and 3. The railway passenger station elevator remote sensing service method based on the Internet of Things also includes the following steps: Step S120, the communication gateway receives the information of all elevators from the station-level sensor network. The operating data and surrounding environment data are forwarded to the comprehensive data platform. During the forwarding process, the formats and protocols of all elevator operating data and surrounding environment data are converted and unified.

In the embodiment of the present invention, according to steps S120 and S130, the integrated data platform receives the multi-source data collected from various physical sensors in the station-level sensor network forwarded by the communication gateway. In order to process the multi-source data of different protocols and different formats, Unified management allows the multi-source data to be reliably transmitted to the Internet of Things platform - the comprehensive data platform for efficient analysis and use. During the process of forwarding and transmission by the communication gateway through wired or wireless communication, the multi-source data also needs to be processed Conversion of formats and protocols, converting data from different types of sensors into highly compatible formats, including converting data protocols of different sensors, such as Modbus, CAN, BACnet, OPC-UA, etc., into a unified MQTT protocol to achieve Unified transmission and processing of different sensor data to interact with IoT platforms. Wired communication technologies include but are not limited to Ethernet, RS485, RS232, CAN bus, etc. Wireless communication technologies include but are not limited to Wi-Fi, Bluetooth, LoRa, Zigbee, mobile communication networks (such as 3G, 4G, 5G), NB-IoT wait.

Corresponding to the above method, a railway passenger station elevator remote sensing service system based on the Internet of Things according to the embodiment of the present invention includes a computer device. The computer device includes a processor and a memory. Computer instructions are stored in the memory. The processing The processor is used to execute computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the aforementioned railway passenger station elevator remote sensing service method based on the Internet of Things.

Please refer to Figure 4. The railway passenger station elevator remote sensing service system based on the Internet of Things includes a station-level sensor network, communication gateway, comprehensive data platform and remote monitoring terminal, forming a complete railway passenger station elevator Internet of Things monitoring service system. The station-level sensor network is used to collect the operating data and surrounding environment data of all elevators in the station in real time. The station-level sensor network includes various physical sensors that are reasonably configured according to the characteristics of the elevator equipment and monitoring needs, including specifically those used to monitor elevator door switches. Switch sensor, speed sensor used to monitor the running speed of the elevator, position sensor used to monitor the position of the elevator, temperature sensor used to monitor the ambient temperature of the elevator, human body sensor used to monitor whether someone is riding the elevator, Vibration sensors with vibration, pressure sensors used to monitor elevator pressure, load sensors used to monitor elevator load, counting sensors used to monitor elevator operation times, up and down times and starts and stops, and monitoring elevators on each floor Time sensors for dwell time and accumulated running time, visual cameras used to obtain image data in the elevator car or the elevator environment, etc., can monitor and perceive the elevator operation and environmental conditions in real time. The communication gateway is used to receive and forward the operating data and surrounding environment data of all elevators from the station-level sensor network. During the forwarding and transmission process, the formats and protocols of all elevator operating data and surrounding environment data are converted and unified. The comprehensive data platform is used to receive real-time operation data and surrounding environment data of all elevators forwarded and transmitted from the communication gateway, and regularly receives train operation information and passenger flow statistical information from different passenger transport areas pre-configured by the passenger service and control platform. According to the elevator The real-time operation data, train operation information and passenger flow statistical information of different passenger transport areas, as well as the relationship between elevators and passenger transport areas are used to generate elevator dispatching strategies. The comprehensive data platform is also used to obtain the historical operating data, historical surrounding environment data and historical fault data of all elevators. The preset long-short-term memory network model based on the attention mechanism is based on the historical operating data, historical surrounding environment data, Historical fault data, real-time operation data, and real-time surrounding environment data are used for fault prediction and alarm, and elevator fault warning results are generated. The comprehensive data platform is also used to obtain historical fault data and historical maintenance data of all elevators. The preset convolutional neural network model based on the attention mechanism generates elevators based on the historical fault data, historical maintenance data and elevator fault warning results of all elevators. Maintenance plan. The remote monitoring terminal is used to display the real-time operation data, the elevator dispatching strategy, the elevator failure warning results and the elevator maintenance plan in real time.

A data interface is established between the remote monitoring terminal and the comprehensive data platform to realize two-way transmission and interaction of data through the standardized mqtt communication protocol. The remote monitoring terminal can send data requests to the comprehensive data platform to obtain various operating data of the elevator, train operation and passenger flow statistical information, and various planning data (scheduling strategies, fault prediction and warning results, maintenance plans, etc.). At the same time, the comprehensive data platform can also integrate the elevator operation data and train operation and passenger flow information processed and analyzed by the comprehensive data platform. It can also integrate the elevator dispatching strategies, fault prediction and warning results, maintenance plans and other data generated by the comprehensive data platform. Transmitted to the remote monitoring terminal for visual display, relevant maintenance and support personnel can promptly discover potential risks and faults of the entire system and resolve them in a timely manner, allowing passengers to travel smoothly in various passenger transport areas within the station, giving them a smooth boarding and alighting experience. More safe and reliable.

Embodiments of the present invention also provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the aforementioned railway passenger station elevator remote sensing service method based on the Internet of Things are implemented. The computer readable storage medium may be a tangible storage medium such as random access memory (RAM), memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, floppy disk, hard disk, removable storage disk, CD-ROM, or any other form of storage medium known in the art.

Those of ordinary skill in the art should understand that each exemplary component, system and method described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of both. Whether it is implemented in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered to be beyond the scope of the present invention. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, or the like. When implemented in software, elements of the invention are programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted over a transmission medium or communications link via a data signal carried in a carrier wave.

It is to be understood that this invention is not limited to the specific arrangements and processes described above and illustrated in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications and additions, or change the order between steps after understanding the spirit of the present invention.

In the present invention, features described and/or illustrated with respect to one embodiment may be used in the same or in a similar manner in one or more other embodiments and/or may be combined with or substituted for features of other embodiments. Features of Embodiments.

The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and changes may be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims (10)

1. The remote perception service method for the railway passenger station elevator based on the Internet of things is characterized by comprising the following steps of:

based on the positions of the elevators and the passenger areas, associating all the elevators in the station with the corresponding passenger areas, and acquiring operation data and surrounding environment data of all the elevators in the station in real time by a station-level sensor network;

receiving operation data and surrounding environment data of all elevators from a station-level sensor network in real time by a comprehensive data platform, regularly receiving train operation information and passenger flow statistical information of different passenger transport areas pre-configured by a passenger service and management and control platform, and generating an elevator dispatching strategy according to the real-time operation data of the elevators, the train operation information and passenger flow statistical information of the different passenger transport areas and the association relation between the elevators and the passenger transport areas;

acquiring historical operation data, historical surrounding environment data and historical fault data of all elevators by a comprehensive data platform, carrying out fault prediction and warning by a preset long-short-time memory network model based on an attention mechanism according to the historical operation data, the historical surrounding environment data, the historical fault data, the real-time operation data and the real-time surrounding environment data of all elevators, and generating an elevator fault early warning result;

acquiring historical fault data and historical maintenance data of all elevators by a comprehensive data platform, and generating an elevator maintenance plan according to the historical fault data, the historical maintenance data and elevator fault early warning results of all elevators by a preset convolutional neural network model based on an attention mechanism;

and the remote monitoring terminal displays the real-time operation data, the elevator dispatching strategy, the elevator fault early warning result and the elevator maintenance plan in real time.

2. The method of claim 1, wherein the step of generating an elevator dispatch strategy based on real-time elevator operation data, train operation information and passenger flow statistics for different passenger areas, and an association between an elevator and a passenger area comprises:

determining available elevators and the current running states of the available elevators according to real-time running data of the elevators by a comprehensive data platform, and analyzing train running information and passenger flow statistical information of different passenger flow areas which are received regularly to obtain a passenger flow peak time period and a passenger flow area;

and adjusting and optimizing the scheduling sequence, service time period, service area, running speed and group configuration of the available elevators according to the time period of the passenger flow peak, the association relation between the passenger transport area and the elevators and the passenger transport area, and optimizing the use intensity of the available elevators according to the current running state of the available elevators.

3. The method according to claim 1, wherein the step of generating elevator fault warning results by performing fault prediction and warning according to historical operation data, historical ambient environment data, historical fault data, real-time operation data and real-time ambient environment data of all elevators by a preset long-short-time memory network model based on an attention mechanism comprises:

carrying out data cleaning and characteristic engineering processing on the historical operation data, the historical surrounding environment data, the historical fault data, the real-time operation data and the real-time surrounding environment data of all elevators;

training a preset long-short-time memory network model based on an attention mechanism by the processed historical operation data, the historical surrounding environment data and the historical fault data, and enhancing the attention of the long-short-time memory network model to the operation state, the equipment state and the possible fault of the elevator;

and predicting and alarming the elevator fault according to the processed real-time operation data and real-time surrounding environment data by a trained long-short-time memory network model based on the attention mechanism to generate an elevator fault early-warning result, wherein the elevator fault early-warning result at least comprises an elevator number, a fault type, a fault grade, a fault occurrence probability and the influence degree of the fault on elevator operation, wherein the elevator number, the fault type, the fault grade and the fault occurrence probability possibly generate faults.

4. A method according to claim 3, characterized in that the step of data cleaning and feature engineering of all elevators' historical operating data, historical surroundings data, historical fault data and real-time operating data, real-time surroundings data comprises:

all the historical operation data, historical surrounding environment data, historical fault data and real-time operation data of the elevators, wherein the real-time surrounding environment data comprises non-vibration data, vibration data and image data of the elevators, and the non-vibration data and the vibration data of the elevators are subjected to data cleaning for removing noise, filling missing values and abnormal value processing;

the cleaned elevator non-vibration data is converted into statistical indexes and standardized, wavelet components with different frequencies are identified and extracted through wavelet change, and the elevator image data is subjected to characteristic extraction, identification and classification, so that characteristic engineering processing is realized.

5. The method of claim 1, wherein the step of generating an elevator maintenance plan from the historical fault data, the historical maintenance data and the elevator fault pre-warning results of all elevators by the preset attention-mechanism-based convolutional neural network model comprises:

carrying out data cleaning on the historical fault data, the historical maintenance data and the elevator fault early warning results of all elevators, embedding the cleaned data into vectorization through words, and carrying out standardization and normalization processing;

training a preset convolutional neural network model based on an attention mechanism by using the standardized and normalized historical fault data and the historical maintenance data, and enhancing the attention of the convolutional neural network model to elevator maintenance information;

generating an elevator maintenance plan according to the standardized and normalized elevator fault early warning result by the trained convolutional neural network model based on the attention mechanism, wherein the elevator maintenance plan at least comprises an elevator number, a maintenance type, maintenance resources, maintenance frequency, maintenance time and priority which need to be maintained.

6. The method according to claim 1, wherein the method further comprises:

and the communication gateway receives the operation data and the surrounding environment data of all the elevators from the station-level sensor network and forwards the operation data and the surrounding environment data to the comprehensive data platform, and the formats and protocols of the operation data and the surrounding environment data of all the elevators are converted and unified in the forwarding process.

7. A method according to any one of claims 1 to 6, wherein the passenger transport area comprises at least one of an entrance, a platform, an exit, a waiting room and a ticket gate.

8. The method according to any one of claims 1 to 6, characterized in that the operating data comprises at least operating status, operating speed, elevator door opening and closing status, operating position, temperature, vibration, pressure, load, number of times of operation, number of times of start and stop, accumulated operating time of elevator and residence time of floors;

the surrounding environment data at least comprises elevator riding personnel flow and nonstandard elevator riding behaviors including elevator inner door-pulling and alarm;

the train operation information at least comprises train times, the right-late state of trains with different train times, predicted departure time, actual departure time, predicted arrival time, actual arrival time, ticket checking time, a platform, a station outlet and a ticket checking port;

the passenger flow statistical information at least comprises the number of people in stations in different time periods, the number of people getting on and off different times and the passenger flow distributed in different time periods in different passenger transport areas.

9. A railway passenger elevator remote perception service system based on the internet of things, comprising a processor and a memory, characterized in that the memory has stored therein computer instructions for executing the computer instructions stored in the memory, which system, when executed by the processor, implements the steps of the method according to any one of claims 1 to 8.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 8.