Rail transit operation safety event monitoring system and method based on the Internet of Things

Abstract

The present invention discloses a rail transit operation safety incident monitoring system and method based on the Internet of Things, which relates to the technical field of rail transit operation safety incident monitoring, including multiple sensor units, multiple edge preliminary analysis modules, multiple self-organizing network modules and a rail transit operation central safety analysis module. The present invention adopts an edge-cloud combined main architecture to realize the collection of sensor and visual data. Multiple sensor units can realize real-time monitoring of different monitoring items. The visual monitor has a generalized monitoring capability and realizes visual recognition of different abnormal features based on a visual feature comparison algorithm; an edge preliminary analysis module is set, and the preliminary screening of the edge preliminary analysis module will greatly reduce the data processing volume of the rail transit operation central safety analysis module, reduce the computing power requirements of the server and the hardware layout cost, and finally the rail transit operation central safety analysis module only performs security assessment on data with higher risks, and the security assessment efficiency is high.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit operation safety event monitoring technology, and in particular to a rail transit operation safety event monitoring system and method based on the Internet of Things. Background Art

[0002] The existing rail transit operation safety incident monitoring system system architecture includes sensor module, network communication module, rail transit operation safety assessment and analysis module and monitoring display module.

[0003] The sensor module typically includes various sensors (such as temperature, humidity, vibration, displacement, and video) and a communication module (such as Wi-Fi, Bluetooth, and ZigBee). The network communication module is responsible for transmitting the data collected by the sensor module to the rail transit operation safety assessment and analysis module deployed in the cloud. The rail transit operation safety assessment and analysis module analyzes and processes the collected data and ultimately displays it through the monitoring display module to achieve safety event monitoring.

[0004] The above technical solution suffers from the following drawbacks: sensor data silos and a lack of effective data fusion. The monitoring system is slow to respond. The massive amount of sensor data, especially visual data, required to be aggregated in real time for the operational safety assessment and analysis module requires enormous computing power and is unable to process large amounts of data in real time. The hardware equipment is energy-intensive and centralized, resulting in high maintenance costs and a high failure rate.

[0005] Therefore, how to design a rail transit operation safety incident monitoring system based on the Internet of Things that can efficiently realize the monitoring of rail transit operation safety incidents, require relatively low hardware equipment computing power, equalize energy consumption, and reduce failure rate is a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides a rail transit operation safety event monitoring system and method based on the Internet of Things. The following technical solutions are adopted:

[0007] The Internet of Things-based rail transit operation safety event monitoring system includes multiple sensor units, multiple edge preliminary analysis modules, multiple self-organizing network modules, and a rail transit operation central safety analysis module. The multiple sensor units divide the rail transit operation safety area into multiple regional grids based on rail transit safety operation-related parameters within the sensor monitoring area. The multiple edge preliminary analysis modules and the multiple self-organizing network modules are respectively arranged at the physical center of the regional grid. The sensor units are communicatively connected to the edge preliminary analysis modules of the regional grids. The edge preliminary analysis modules perform preliminary analysis and screening of the rail transit safety operation-related parameters of the sensor units based on anomaly thresholds, cache rail transit safety operation-related parameters below the anomaly threshold for a set time, package rail transit safety operation-related parameters that are higher than or equal to the anomaly threshold and threshold exceedance results, and forward them to the rail transit operation central safety analysis module through the self-organizing network module at the physical center of the same regional grid. The rail transit operation central safety analysis module sorts abnormal events within the rail transit operation safety area based on the threshold exceedance results, performs safety assessment and analysis on the abnormal events based on the sorting results based on a data fusion security event assessment algorithm, and outputs the assessment results.

[0008] Optionally, the sensor unit includes a temperature sensor, a vibration sensor, a displacement sensor and a visual monitor, and within the grid area, the temperature sensor, the vibration sensor, the displacement sensor and the visual monitor are respectively connected to the edge preliminary analysis module at the physical center of the grid area by direct connection via communication lines.

[0009] By adopting the above technical solution and combining edge cloud with the main architecture to realize the collection of sensor and visual data, multiple sensor units can realize real-time monitoring of different monitoring items, such as the temperature value in the distribution box of important rail transit facilities, the vibration value and displacement value of rail transit tracks, etc. The visual monitor has a generalized monitoring capability and can realize visual recognition of different abnormal features based on the visual feature comparison algorithm. Although it does not have the monitoring accuracy of sensors, it can detect some items such as moving pedestrians or animals entering places where pedestrians are prohibited from entering, water accumulation, and whether the indicator lights are normal.

[0010] These monitoring items are forwarded to the rail transit operation central safety analysis module through the self-organizing network module at the physical center of the same regional grid. The purpose of dividing the monitored area into regional grids is to maintain stable wireless communication effects within the physical range. The monitored area of rail transit operation is generally more complex. If the entire communication network adopts direct connection of communication lines, the complexity and stability of its wiring are both poor. Therefore, a short-line direct connection within the regional grid is adopted, and then a self-organizing network module is used for unified forwarding in one regional grid. This can more stably and reliably transmit edge sensor data to the rail transit operation central safety analysis module efficiently.

[0011] Before the sensor data is transmitted, an edge preliminary analysis module is set up in regional grid units, which can perform preliminary screening based on abnormal thresholds. In actual operation, the abnormal proportion of monitoring data is not large in most cases. Therefore, the preliminary screening of the edge preliminary analysis module will greatly reduce the data processing volume of the rail transit operation central safety analysis module, reduce the computing power requirements of the server and the hardware layout cost. Finally, the rail transit operation central safety analysis module only performs security assessment on data with higher risks. The security assessment is efficient, and the monitoring energy consumption is evenly distributed to the grid area, reducing the impact of small area failures on the stability of the entire monitoring system.

[0012] Optionally, the edge preliminary analysis module includes a first cache, a data analysis chip, a visual analysis chip, a second cache and a memory, the first cache is respectively communicated with the temperature sensor, vibration sensor, displacement sensor and visual monitor of the sensor unit within the grid area, the data analysis chip and the visual analysis chip are respectively communicated with the first cache and the memory, and the data stored in the first cache is compared with the abnormal threshold based on the time series, and any rail transit safety operation related parameters and threshold exceeding results that are higher than or equal to the abnormal threshold are packaged and stored in the second cache, and the second cache is communicated with the data input end of the self-organizing network module at the physical center of the grid in the same area.

[0013] By adopting the above technical solution, the data received by the edge preliminary analysis module is first stored in the first buffer, and the data analysis chip and the visual analysis chip can be synchronously called for analysis. The data analysis chip mainly performs analysis based on the set abnormality threshold, so the computing power requirement is relatively low. The analysis of the visual analysis chip is mainly based on the abnormal feature database stored in the memory for similarity comparison. The precision and accuracy requirements of the comparison here are not very high, and it can also be implemented with a low-cost visual analysis chip, thereby achieving the goal of significantly reducing the maximum computing power of the entire rail transit operation safety event monitoring system, reducing the high hardware cost brought by high-computing power servers, and evenly distributing the concentrated high power consumption of high-computing power servers.

[0014] Optionally, multiple self-organizing network modules implement self-organizing networking based on LoRaWAN technology, and multiple self-organizing network modules are wirelessly networked with the rail transit operation central safety analysis module respectively. When the self-organizing network module cannot directly communicate wirelessly with the rail transit operation central safety analysis module, it is forwarded through the adjacent self-organizing network module.

[0015] By adopting the above technical solutions, a wireless ad hoc network can be implemented within a long-range grid area based on LoRaWAN. Select nodes with LoRaWAN-compatible modules and sufficient processing power, storage, and power. Configure the node software to support the LoRaWAN protocol and ad hoc networking capabilities.

[0016] Utilizing the gateway relay function of LoRaWAN, when data cannot be sent wirelessly directly to the central server, multi-hop communication between nodes is allowed through the gateway.

[0017] Configure the nodes to support self-organizing network functionality, enabling them to automatically discover neighbor nodes.

[0018] Optionally, the rail transit operation central safety analysis module includes a central self-organizing network module, a computer server and a rail transit monitoring screen. The computer server is wirelessly connected to multiple self-organizing network modules through the central self-organizing network module. The computer server performs safety assessment and analysis on abnormal events based on the security event assessment algorithm of data fusion according to the sorting results, and outputs the assessment results through the rail transit monitoring screen.

[0019] By adopting the above technical solution, a backup module can be set up in the central self-organizing network module during specific applications to avoid failures that cause data to be unable to be received. The computer server receives abnormal data packets from the central self-organizing network module and performs security assessment and analysis on abnormal events based on the data fusion security event assessment algorithm, and finally outputs the assessment results through the rail transit monitoring screen to realize intelligent security event assessment.

[0020] Optionally, the computer server includes a first computer, a second computer and a third computer. The first computer is communicatively connected to the data output end of the central self-organizing network module, and parses abnormal data packets sent by multiple self-organizing network modules in a time sequence. Based on the set emergency safety event standards, the abnormal data packets are analyzed to see whether they meet the emergency safety event standards. The abnormal data packets that meet the emergency safety event standards are sent to the third computer, and the abnormal data packets that do not meet the emergency safety event standards are sent to the second computer in a time sequence. The second computer and the third computer respectively communicate with the rail transit monitoring screen to interact with the evaluation results.

[0021] By adopting the above technical solution, three ordinary industrial control computers can be used to achieve relatively reliable and stable abnormal data packet processing. The first computer parses the abnormal data packets sent by multiple self-organizing network modules in a time sequence, and analyzes whether the abnormal data packets meet the emergency safety event standards based on the set emergency safety event standards. Those that meet the emergency safety event standards can be sent to the third computer for immediate processing to avoid affecting the response time of the emergency event due to the processing time. The second computer is responsible for performing safety assessments in a time sequence. The second computer and the third computer communicate and interact with the rail transit monitoring large screen to evaluate the results, and can also set up sound and light alarms or network alarms to alarm safety events.

[0022] The rail transit operation safety incident monitoring method adopts the rail transit operation safety incident monitoring system based on the Internet of Things to monitor and analyze rail transit operation safety incidents within the region, including the following steps:

[0023] Step 1: Mark the multiple regional grids of the rail transit operation safety area as A1, A2, ..., A n , where n is the number of regional grids, regional grid A n The monitoring temperature sensor, vibration sensor, displacement sensor and visual monitor in the area grid A every two seconds n The first buffer of the edge preliminary analysis module arranged at the physical center transmits temperature values, vibration values, displacement values and visual images;

[0024] Step 2: The data analysis chip calls the temperature value, vibration value, and displacement value every two seconds, and determines whether there is an abnormality based on the temperature abnormality threshold, vibration abnormality threshold, and displacement abnormality threshold;

[0025] The visual analysis chip matches the visual image with abnormal features based on the abnormal feature database stored in the memory. If there is a successfully matched abnormal feature, it is determined that an abnormality exists;

[0026] If there is no anomaly in the data uploaded at the same time, the data uploaded at the same time will be packaged and cached for one week; otherwise, the data uploaded at the same time will be packaged, and the upload time and sensor number will be marked in the data packet header to form an abnormal data packet, which will be forwarded to the rail transit operation central safety analysis module through the self-organizing network module at the physical center of the grid in the same area;

[0027] Step 3: The first computer parses the abnormal data packets sent by the multiple ad hoc network modules in a time sequence, analyzes whether the abnormal data packets meet the emergency security event criteria based on the set emergency security event criteria, and directly sends the abnormal data packets that meet the emergency security event criteria to the third computer for security event assessment;

[0028] Otherwise, the abnormal data packet is forwarded to the second computer according to the time sequence, and the second computer performs a security event assessment on the abnormal data packet based on the time sequence;

[0029] In step 4, the second computer and the third computer respectively send the security event assessment results to the rail transit monitoring screen, and the rail transit monitoring screen displays the security event assessment results.

[0030] Optionally, in step 3, the emergency safety event standard is: whether there is at least one monitoring value exceeding the abnormal threshold and at least one abnormal feature matching is successful.

[0031] By adopting the above technical solution, generally speaking, the urgency brought about by a single monitoring value exceeding the abnormal threshold is usually not high, and it can be evaluated through the normal safety assessment process. If the abnormal threshold exceeds the standard and the abnormal feature is successfully matched, the probability of a safety incident will be greatly increased. For example, if the temperature of the distribution cabinet exceeds the standard and smoke is detected by visual monitoring, then there is a high probability that the distribution cabinet has a circuit breaker and a fire, so emergency handling is required. Of course, in specific applications, the emergency safety event standard can be flexibly set according to actual conditions, such as a higher abnormal threshold.

[0032] Optionally, in step 3, the security incident assessment includes the following steps:

[0033] The monitoring data provided by each sensor is X m , the abnormal feature items whose matching similarity of abnormal features is greater than the similarity threshold are treated as monitoring items and are uniformly processed. The labels are arranged after the last sensor monitoring value. The security event assessment value is Y, and the total number of abnormal features with sensor monitoring values and matching similarity greater than the similarity threshold is recorded as m;

[0034] Y=X1·W1·α1+X2·W2·α2+…+X m W m α m ;

[0035] Where W m is the weight of the mth monitoring value, α m is the monitoring item setting weight of the mth monitoring value;

[0036]

[0037] By adopting the above technical solution, IF(W m <0,0,W m ) is to set W less than 0 m The weight is set to 0. If the monitoring value does not exceed the threshold, it is considered to have no impact on the assessment of the security incident.

[0038] In summary, the present invention includes at least one of the following beneficial technical effects:

[0039] The present invention can provide a rail transit operation safety event monitoring system and method based on the Internet of Things. It adopts the edge-cloud combined main architecture to realize the collection of sensor and visual data. Multiple sensor units can realize real-time monitoring of different monitoring items. The visual monitor has a generalized monitoring capability and can realize visual recognition of different abnormal features based on the visual feature comparison algorithm.

[0040] These monitoring items are forwarded to the rail transit operation central safety analysis module through the self-organizing network module at the physical center of the same regional grid. The monitored area is divided into regional grids to maintain stable wireless communication within the physical range, which can more stably and reliably transmit edge sensor data to the rail transit operation central safety analysis module efficiently.

[0041] Before the sensor data is transmitted, an edge preliminary analysis module is set up in units of regional grids, which can perform preliminary screening based on abnormal thresholds. The preliminary screening of the edge preliminary analysis module will greatly reduce the data processing volume of the rail transit operation central safety analysis module, reduce the computing power requirements of the server and the hardware layout cost. Finally, the rail transit operation central safety analysis module only performs security assessment on data with higher risks. The security assessment is highly efficient, and the monitoring energy consumption is evenly distributed to the grid area, reducing the impact of small area failures on the stability of the entire monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the electrical component connection principle of the rail transit operation safety event monitoring system based on the Internet of Things of the present invention;

[0043] Figure 2 It is a schematic diagram of the hardware connection principle within a regional grid of the rail transit operation safety event monitoring system based on the Internet of Things of the present invention.

[0044] Explanation of the accompanying symbols: 11. Temperature sensor; 12. Vibration sensor; 13. Displacement sensor; 14. Visual monitor; 2. Edge preliminary analysis module; 21. First cache; 22. Data analysis chip; 23. Visual analysis chip; 24. Second cache; 25. Memory; 3. Self-organizing network module; 4. Central safety analysis module for rail transit operations; 41. Central self-organizing network module; 42. Computer server; 421. First computer; 422. Second computer; 423. Third computer; 43. Rail transit monitoring screen. DETAILED DESCRIPTION

[0045] The present invention will be further described in detail below with reference to the accompanying drawings.

[0046] The embodiments of the present invention disclose a rail transit operation safety event monitoring system and method based on the Internet of Things.

[0047] Reference Figure 1 and Figure 2 , Example 1, a rail transit operation safety event monitoring system based on the Internet of Things, including multiple sensor units, multiple edge preliminary analysis modules 2, multiple self-organizing network modules 3 and a rail transit operation central safety analysis module 4, multiple sensor units respectively divide the rail transit operation safety area into multiple regional grids based on the rail transit safety operation related parameters in the sensor monitoring area, multiple edge preliminary analysis modules 2 and multiple self-organizing network modules 3 are respectively arranged at the physical center of the regional grid, the sensor unit is communicated with the edge preliminary analysis module 2 of the regional grid, the edge preliminary analysis module 2 performs preliminary analysis and screening on the rail transit safety operation related parameters of the sensor unit based on the abnormal threshold, caches the rail transit safety operation related parameters below the abnormal threshold for a set time, packages any rail transit safety operation related parameters that are higher than or equal to the abnormal threshold and the threshold exceeding result, and forwards them to the rail transit operation central safety analysis module 4 through the self-organizing network module 3 at the physical center of the same regional grid, the rail transit operation central safety analysis module 4 sorts the abnormal events in the rail transit operation safety area based on the threshold exceeding result, performs security assessment and analysis on the abnormal events based on the data fusion security event assessment algorithm according to the sorting result, and outputs the assessment result.

[0048] In Example 2, the sensor unit includes a temperature sensor 11, a vibration sensor 12, a displacement sensor 13 and a visual monitor 14, and within the grid area, the temperature sensor 11, the vibration sensor 12, the displacement sensor 13 and the visual monitor 14 are respectively connected to the edge preliminary analysis module 2 at the physical center of the grid area by direct connection via communication lines.

[0049] The edge-cloud combined with the main architecture is used to realize the collection of sensor and visual data. Multiple sensor units can realize real-time monitoring of different monitoring items, such as the temperature value in the distribution box of important rail transit facilities, the vibration value and displacement value of rail transit tracks, etc. The visual monitor 14 has a generalized monitoring capability and can realize visual recognition of different abnormal features based on the visual feature comparison algorithm. Although it does not have the monitoring accuracy of sensors, it can detect some items such as moving pedestrians or animals entering places where pedestrians are prohibited from entering, water accumulation, and whether the indicator lights are normal.

[0050] These monitoring items are forwarded to the rail transit operation central safety analysis module 4 through the self-organizing network module 3 at the physical center of the same regional grid. The purpose of dividing the monitored area into regional grids is to maintain a stable wireless communication effect within the physical range. The monitored area of rail transit operation is generally more complex. If the entire communication network adopts direct connection of communication lines, the complexity and stability of its wiring are both poor. Therefore, a short-line direct connection within the regional grid is adopted, and then a self-organizing network module 3 is used for unified forwarding in one regional grid. This can more stably and reliably transmit the edge sensor data to the rail transit operation central safety analysis module 4 efficiently.

[0051] Before the sensor data is transmitted, an edge preliminary analysis module 2 is set up in units of regional grids, which can perform preliminary screening based on abnormal thresholds. In actual operation, the abnormal proportion of monitoring data is not large in most cases. Therefore, through the preliminary screening of the edge preliminary analysis module 2, the data processing volume of the rail transit operation central safety analysis module 4 will be greatly reduced, and the computing power requirements and hardware layout costs of the server will be reduced. Finally, the rail transit operation central safety analysis module 4 only performs security assessments on data with higher risks. The security assessment efficiency is high, and the monitoring energy consumption is averaged to the grid area, reducing the impact of small area failures on the stability of the entire monitoring system.

[0052] Example 3, the edge preliminary analysis module 2 includes a first cache 21, a data analysis chip 22, a visual analysis chip 23, a second cache 24 and a memory 25. The first cache 21 is respectively communicated with the temperature sensor 11, the vibration sensor 12, the displacement sensor 13 and the visual monitor 14 of the sensor unit within the grid area, the data analysis chip 22 and the visual analysis chip 23 are respectively communicated with the first cache 21 and the memory 25, and the data stored in the first cache 21 is compared with the abnormal threshold based on the time series, and any rail transit safety operation-related parameters and threshold-exceeding results that are higher than or equal to the abnormal threshold are packaged and stored in the second cache 24. The second cache 24 is communicated with the data input end of the self-organizing network module 3 at the physical center of the grid in the same area.

[0053] The data received by the edge preliminary analysis module 2 is first stored in the first buffer 21. The data analysis chip 22 and the visual analysis chip 23 can be synchronously called for analysis. The data analysis chip 22 mainly performs analysis based on the set abnormality threshold, so the computing power requirement is relatively low. The analysis of the visual analysis chip 23 is mainly based on the abnormal feature database stored in the memory 25 for similarity comparison. The precision and accuracy requirements of the comparison here are not very high, and the lower-cost visual analysis chip 23 can also be used for implementation, thereby achieving the goal of significantly reducing the maximum computing power of the entire rail transit operation safety event monitoring system, reducing the high hardware cost brought by high-computing power servers, and evenly distributing the concentrated high power consumption of high-computing power servers.

[0054] In embodiment 4, multiple self-organizing network modules 3 realize self-organizing networking based on LoRaWAN technology. Multiple self-organizing network modules 3 are wirelessly networked with the rail transit operation central safety analysis module 4 respectively. When the self-organizing network module 3 cannot directly communicate with the rail transit operation central safety analysis module 4 wirelessly, it is forwarded through the adjacent self-organizing network module 3.

[0055] To implement a wireless ad hoc network within a long-range mesh area based on LoRaWAN, select nodes with LoRaWAN-compatible modules and sufficient processing power, storage, and power. Configure the node software to support the LoRaWAN protocol and ad hoc networking capabilities.

[0056] Utilizing the gateway relay function of LoRaWAN, when data cannot be sent wirelessly directly to the central server, multi-hop communication between nodes is allowed through the gateway.

[0057] Configure the nodes to support self-organizing network functionality, enabling them to automatically discover neighbor nodes.

[0058] Example 5, the rail transit operation central safety analysis module 4 includes a central self-organizing network module 41, a computer server 42 and a rail transit monitoring screen 43. The computer server 42 is wirelessly connected to multiple self-organizing network modules 3 through the central self-organizing network module 41. The computer server 42 performs safety assessment and analysis on abnormal events based on the security event assessment algorithm of data fusion according to the sorting results, and outputs the assessment results through the rail transit monitoring screen 43.

[0059] The central self-organizing network module 41 can also be equipped with a backup module in specific applications to avoid failures that result in data failure. The computer server 42 receives abnormal data packets from the central self-organizing network module 41 and performs security assessment and analysis on abnormal events based on the data fusion security event assessment algorithm, and finally outputs the assessment results through the rail transit monitoring screen 43 to realize intelligent security event assessment.

[0060] Example 6, the computer server 42 includes a first computer 421, a second computer 422 and a third computer 423, the first computer 421 is communicatively connected to the data output end of the central self-organizing network module 41, and analyzes the abnormal data packets sent by multiple self-organizing network modules 3 in a time sequence, analyzes whether the abnormal data packets meet the emergency safety event standards based on the set emergency safety event standards, sends the abnormal data packets that meet the emergency safety event standards to the third computer 423, and sends the abnormal data packets that do not meet the emergency safety event standards to the second computer 422 in a time sequence, and the second computer 422 and the third computer 423 communicate and interact with the rail transit monitoring screen 43 to evaluate the results.

[0061] The use of three ordinary industrial control computers can achieve relatively reliable and stable abnormal data packet processing. The first computer 421 parses the abnormal data packets sent by multiple self-organizing network modules 3 in a time sequence, and analyzes whether the abnormal data packets meet the emergency safety event standards based on the set emergency safety event standards. Those that meet the emergency safety event standards can be sent to the third computer 423 for immediate processing to avoid affecting the response time of the emergency event due to the processing time limit. The second computer 422 is responsible for performing security assessments in a time sequence. The second computer 422 and the third computer 423 communicate and interact with the rail transit monitoring screen 43 to obtain the evaluation results. They can also set up sound and light alarms or network alarms to alarm security events.

[0062] Example 7, a rail transit operation safety incident monitoring method, uses an Internet of Things-based rail transit operation safety incident monitoring system to monitor and analyze rail transit operation safety incidents within a region, including the following steps:

[0063] Step 1: Mark the multiple regional grids of the rail transit operation safety area as A1, A2, ..., A n , where n is the number of regional grids, regional grid A n The monitoring temperature sensor 11, vibration sensor 12, displacement sensor 13 and visual monitor 14 in the area grid A are sent to the area grid A every two seconds. n The first buffer 21 of the edge preliminary analysis module 2 arranged at the physical center transmits temperature values, vibration values, displacement values and visual images;

[0064] Step 2: The data analysis chip 22 calls the temperature value, vibration value, and displacement value every two seconds, and determines whether there is an abnormality based on the temperature abnormality threshold, vibration abnormality threshold, and displacement abnormality threshold;

[0065] The visual analysis chip 23 performs abnormal feature matching on the visual image based on the abnormal feature database stored in the memory 25. If there is a successfully matched abnormal feature, it is determined that an abnormality exists;

[0066] If the data uploaded at the same time does not contain any anomalies, the data uploaded at the same time will be packaged and cached for one week; otherwise, the data uploaded at the same time will be packaged and the upload time and sensor number will be marked in the data packet header to form an abnormal data packet, which will be forwarded to the rail transit operation central safety analysis module 4 through the self-organizing network module 3 at the physical center of the grid in the same area;

[0067] Step 3: The first computer 421 analyzes the abnormal data packets sent by the multiple ad hoc network modules 3 in a time sequence, analyzes whether the abnormal data packets meet the emergency security event criteria based on the set emergency security event criteria, and directly sends the abnormal data packets that meet the emergency security event criteria to the third computer 423 for security event assessment;

[0068] Otherwise, the abnormal data packet is forwarded to the second computer 422 according to the time sequence, and the second computer 422 performs a security event assessment on the abnormal data packet based on the time sequence;

[0069] In step 4, the second computer 422 and the third computer 423 respectively send the security event assessment results to the rail transit monitoring screen 43, and the rail transit monitoring screen 43 displays the security event assessment results.

[0070] In Example 8, in step 3, the emergency safety event standard is: whether there is at least one monitoring value exceeding the abnormal threshold and at least one abnormal feature matching is successful.

[0071] Generally speaking, if a monitoring value exceeds the abnormal threshold, the urgency it brings is usually not high and can be evaluated through the normal safety assessment process. However, if the abnormal threshold exceeds the standard and the abnormal feature is successfully matched, the probability of a safety incident will increase significantly. For example, if the temperature of the distribution cabinet exceeds the standard and smoke is detected by visual monitoring, there is a high probability that the distribution cabinet has a circuit breaker and a fire, so emergency handling is required. Of course, in specific applications, the emergency safety event standard can be flexibly set according to actual conditions, such as a higher abnormal threshold.

[0072] In Example 9, in step 3, the security incident assessment includes the following steps:

[0073] The monitoring data provided by each sensor is X m , the abnormal feature items whose matching similarity of abnormal features is greater than the similarity threshold are treated as monitoring items and are uniformly processed. The labels are arranged after the last sensor monitoring value. The security event assessment value is Y, and the total number of abnormal features with sensor monitoring values and matching similarity greater than the similarity threshold is recorded as m;

[0074] Y=X1·W1·α1+X2·W2·α2+…+X m W m αm ;

[0075] Where W m is the weight of the mth monitoring value, α m is the monitoring item setting weight of the mth monitoring value;

[0076]

[0077] IF(W m <0,0,W m ) is to set W less than 0 m The weight is set to 0. If the monitoring value does not exceed the threshold, it is considered to have no impact on the assessment of the security incident.

[0078] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. The rail transit operation safety incident monitoring system based on the Internet of Things is characterized by: The invention comprises a plurality of sensor units, a plurality of edge preliminary analysis modules (2), a plurality of self-organizing network modules (3) and a rail transit operation central safety analysis module (4); the plurality of sensor units respectively divide the rail transit operation safety area into a plurality of regional grids based on rail transit safety operation related parameters in the sensor monitoring area; the plurality of edge preliminary analysis modules (2) and the plurality of self-organizing network modules (3) are respectively arranged at the physical center position of the regional grid; the sensor units are connected to the edge preliminary analysis modules (2) of the regional grids; the edge preliminary analysis modules (2) analyze the rail transit safety operation related parameters of the sensor units based on abnormal thresholds; Perform preliminary analysis and screening, cache rail transit safety operation related parameters that are lower than the abnormal threshold for a set time, package any rail transit safety operation related parameters that are higher than or equal to the abnormal threshold and the threshold exceeding result, and forward them to the rail transit operation central safety analysis module (4) through the self-organizing network module (3) at the physical center of the grid in the same area. The rail transit operation central safety analysis module (4) sorts the abnormal events in the rail transit operation safety area based on the threshold exceeding result, and performs safety assessment analysis on the abnormal events based on the safety event assessment algorithm of data fusion according to the sorting result, and outputs the assessment result; Security incident assessment includes the following steps: Each sensor provides monitoring data for X m , the abnormal feature items whose matching similarity of abnormal features is greater than the similarity threshold are treated as monitoring items and are uniformly processed. The labels are arranged after the last sensor monitoring value. The security event assessment value is Y, and the total number of abnormal features with sensor monitoring values and matching similarity greater than the similarity threshold is recorded as m; Y=X1·W1·a1+X2·W2·a2+…+X m ·W m ·a m ; Where W m is the weight of the mth monitoring value, α m is the monitoring item setting weight of the mth monitoring value; 2. The rail transit operation safety event monitoring system based on the Internet of Things according to claim 1 is characterized by: The sensor unit comprises a temperature sensor (11), a vibration sensor (12), a displacement sensor (13) and a visual monitor (14), and within the range of the grid area, the temperature sensor (11), the vibration sensor (12), the displacement sensor (13) and the visual monitor (14) are respectively connected to the edge preliminary analysis module (2) at the physical center of the grid area by direct connection via communication lines.

3. The rail transit operation safety event monitoring system based on the Internet of Things according to claim 2 is characterized by: The edge preliminary analysis module (2) comprises a first buffer (21), a data analysis chip (22), a visual analysis chip (23), a second buffer (24) and a memory (25); the first buffer (21) is respectively connected to the temperature sensor (11), the vibration sensor (12), the displacement sensor (13) and the visual monitor (14) of the sensor unit within the grid area; the data analysis chip (22) and the visual analysis chip (23) are respectively connected to the first buffer (21) and the memory (25); an abnormality threshold comparison is performed on the data stored in the first buffer (21) based on a time series; and any rail transit safety operation-related parameter and threshold exceeding result that is higher than or equal to the abnormality threshold are packaged and stored in the second buffer (24); the second buffer (24) is connected to the data input end of the self-organizing network module (3) at the physical center of the grid in the same area.

4. The rail transit operation safety event monitoring system based on the Internet of Things according to claim 3 is characterized by: Multiple self-organizing network modules (3) realize self-organizing network based on LoRaWAN technology, and multiple self-organizing network modules (3) are wirelessly networked with the rail transit operation central safety analysis module (4) respectively. When the self-organizing network module (3) cannot directly communicate with the rail transit operation central safety analysis module (4) wirelessly, the self-organizing network module (3) forwards the communication through the adjacent self-organizing network module (3).

5. The rail transit operation safety event monitoring system based on the Internet of Things according to claim 4 is characterized in that: The rail transit operation central safety analysis module (4) includes a central self-organizing network module (41), a computer server (42) and a rail transit monitoring screen (43). The computer server (42) is wirelessly connected to the plurality of self-organizing network modules (3) through the central self-organizing network module (41). The computer server (42) performs safety assessment and analysis on abnormal events based on a safety event assessment algorithm of data fusion according to the sorting results, and outputs the assessment results through the rail transit monitoring screen (43).

6. The rail transit operation safety event monitoring system based on the Internet of Things according to claim 5 is characterized by: The computer server (42) includes a first computer (421), a second computer (422) and a third computer (423). The first computer (421) is communicatively connected to the data output terminal of the central self-organizing network module (41), and analyzes abnormal data packets sent by multiple self-organizing network modules (3) in a time sequence. Based on the set emergency safety event standard, the abnormal data packets are analyzed to see whether they meet the emergency safety event standard. The abnormal data packets that meet the emergency safety event standard are sent to the third computer (423), and the abnormal data packets that do not meet the emergency safety event standard are sent to the second computer (422) in a time sequence. The second computer (422) and the third computer (423) respectively communicate with the rail transit monitoring screen (43) to exchange evaluation results.

7. A rail transit operation safety incident monitoring method, characterized in that: The rail transit operation safety incident monitoring system based on the Internet of Things according to claim 6 is used to monitor and analyze rail transit operation safety incidents within a region, comprising the following steps: Step 1: Mark the multiple regional grids of the rail transit operation safety area as A1, A2, ..., A n , where n is the number of regional grids, regional grid A n The monitoring temperature sensor (11), vibration sensor (12), displacement sensor (13) and visual monitor (14) in the area grid A are sent to the area grid A every two seconds. n The first buffer (21) of the edge preliminary analysis module (2) arranged at the physical center transmits temperature values, vibration values, displacement values and visual images; Step 2, the data analysis chip (22) calls the temperature value, vibration value, and displacement value every two seconds, and determines whether they are abnormal based on the temperature abnormality threshold, vibration abnormality threshold, and displacement abnormality threshold; The visual analysis chip (23) performs abnormal feature matching on the visual image based on the abnormal feature database stored in the memory (25), and if there is an abnormal feature that is successfully matched, it is determined that an abnormality exists; If there is no abnormality in the data uploaded at the same time, the data uploaded at the same time will be packaged and cached for one week; otherwise, the data uploaded at the same time will be packaged, and the upload time and sensor number will be marked in the data packet header to form an abnormal data packet, which will be forwarded to the rail transit operation central safety analysis module (4) through the self-organizing network module (3) at the physical center of the grid in the same area; Step 3, the first computer (421) analyzes the abnormal data packets sent by the plurality of ad hoc network modules (3) in a time sequence, analyzes whether the abnormal data packets meet the emergency security event standards based on the set emergency security event standards, and directly sends the abnormal data packets that meet the emergency security event standards to the third computer (423) for security event assessment; otherwise forwarding the abnormal data packet to a second computer (422) according to the time sequence, and the second computer (422) performing a security event assessment on the abnormal data packet based on the time sequence; In step 4, the second computer (422) and the third computer (423) respectively send the security event assessment results to the rail transit monitoring screen (43), and the rail transit monitoring screen (43) displays the security event assessment results.

8. The rail transit operation safety incident monitoring method according to claim 7, characterized in that: In step 3, the emergency safety event standard is: whether there is at least one monitoring value exceeding the abnormal threshold and at least one abnormal feature matching is successful.

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