Security prediction management method and system based on big data

Abstract

The invention belongs to the technical field of rail transit, and particularly relates to a safety prediction management method and system based on big data, the system comprises a total early warning system, the output end of the total early warning system is connected with a fault acquisition transceiver module and a server, and the output end of the fault acquisition transceiver module is connected with a regional sub-control module. According to the invention, a total early warning system is divided into a plurality of node detection modules from whole to zero, each node detection module is provided with an equipment fault acquisition module and a line fault acquisition module, and the equipment fault acquisition module passes through an operation environment detection module and an operation condition detection module; the environment and condition states in the operation process of the equipment are monitored in real time, and the line connection between the equipment is monitored in real time in cooperation with the line fault acquisition module, so that active and automatic early warning can be immediately performed when problems occur, fault points, fault places and fault causes can be determined, and the working efficiency is improved. Maintenance is facilitated for operation and maintenance personnel, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of rail transit, and in particular to a safety prediction management method and system based on big data. Background Art

[0002] With the acceleration of urbanization, rail transit has developed rapidly due to its safety, high speed and environmental protection. Urban rail has become the preferred mode of transportation for most citizens. Urban rail transit itself is a complex system engineering involving multiple professions and disciplines. It has many branches and is very scattered. Each branch is different and has great differences. Therefore, it leads to geometric growth of urban rail transit data and massive data generated in management, production and services.

[0003] However, the following problems were found in the implementation of relevant technologies: In the existing technology, in the process of safety management of rail transit, multiple interrelated steps such as monitoring, personnel investigation, on-site maintenance, etc. are used to process and predict risks to avoid risks. However, in the process of processing, various aspects cannot be connected with each other in a timely manner, and it is impossible to predict and deal with possible risks in the entire city in a timely manner, resulting in chaotic management in this aspect. In view of this, a safety prediction management method and system based on big data is provided to overcome the above-mentioned defects. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a safety prediction management method and system based on big data.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a safety prediction management method and system based on big data, including a system terminal, the output end of the system terminal is connected to a regional subsystem, and one end of the output end of the regional subsystem is connected to an information feedback module, the other end of the output end of the regional subsystem is connected to a platform housekeeping system, and the output end of the platform housekeeping system is connected to a risk assessment module and a risk identification module, and the output end of the risk identification module is connected to a section monitoring local system; first, a regional subsystem is formed according to dividing the city into multiple areas, and it is directly associated with the platform housekeeping system of each station, and reported to the system terminal, forming a point-to-surface form, each operating but managed by the system terminal, each platform housekeeping system is equipped with a risk assessment module and a risk identification module, possible and existing problems are evaluated and identified and reported to the system terminal, so as to avoid affecting the timeliness of the information and causing the inability to mobilize quickly, and the information feedback module logs the location, information, driver and other information corresponding to each route vehicle for convenient and quick query.

[0006] As a further description of the above technical solution, the road section monitoring local area system includes a road section classification module, a dangerous area warning module and a road section monitoring module;

[0007] The road section classification module is used to assess the danger level of the areas covered by rail transit and external influences such as road turns and soft sleeping soil layers;

[0008] The dangerous area warning module is used to sense and remind the main station and vehicle maintenance personnel when entering a relatively dangerous area;

[0009] The road section monitoring module is used to start specific monitoring videos when entering a dangerous level area, shoot and upload for backup, and divide the routes into three levels: safe, ordinary and dangerous through the road section classification module according to multiple factors such as the bends, turns, settlements, soft sleeper soil layers, underground river water, passenger density, regional importance, and even past accident sites of each route. When entering the dangerous level, the platform and crew are reminded to pay attention through the dangerous area warning module, and the dangerous section and vehicle-mounted monitoring are turned on in advance through the road section monitoring module, so that the road conditions can be viewed in advance to achieve a predictive effect. At the same time, the dangerous road conditions are photographed and uploaded for backup, so that the road conditions can be viewed and compared regularly.

[0010] As a further description of the above technical solution, the output end of the risk identification module is also connected to an environmental monitoring local area system, and the output end of the environmental monitoring local area system is connected to a vehicle monitoring module;

[0011] The vehicle monitoring module includes a vehicle condition monitoring module, a vehicle equipment monitoring module and a message transmission module;

[0012] Wherein, the vehicle condition monitoring module is used to monitor the vehicle condition before or during startup;

[0013] The vehicle-mounted equipment monitoring module is used to monitor the vehicle's internal monitoring, auxiliary equipment, etc. before or during startup;

[0014] The message transmission module is used to upload the vehicle condition information for timely backup before the vehicle arrives at a station for comparison and timely discovery of problems. The vehicle condition monitoring module and the on-board equipment monitoring module can monitor the vehicle body and the equipment carried by the vehicle, and timely warn of problems. The information is automatically transmitted to the nearest station's internal affairs system through the message transmission module for quick response and maintenance.

[0015] As a further description of the above technical solution, the environmental monitoring local area system also includes a platform monitoring module, and the platform monitoring module includes a maintenance record library and a passenger monitoring module;

[0016] The maintenance record library is used to back up and record each maintenance operation data, so as to facilitate timely comparison of vehicle condition data, etc.

[0017] The passenger monitoring module is used to monitor the number of passengers on the platform in the station. When the number of passengers is large, additional personnel will be dispatched in time to maintain order. Maintenance information will be updated in real time through the maintenance record library. It can determine in advance whether there are problems based on the data and play a predictive effect. At the same time, the passenger monitoring module monitors the number of passengers on the platform. When congestion occurs locally, it can remind the platform to dispatch additional personnel for fixed-point maintenance.

[0018] As a further description of the above technical solution, another output end of the risk identification module is connected to a personnel monitoring local area system, and a transmission end of the personnel monitoring local area system is connected to a vehicle personnel management module;

[0019] The vehicle personnel management module includes a motion monitoring module, a sound capture module and an emergency warning module.

[0020] The action monitoring module is used to monitor the crew members and give timely reminders when they show any lax actions;

[0021] The sound capture module is used to monitor the sound in the passenger compartment and issue a timely warning when encountering sensitive words;

[0022] The emergency warning module is used to issue a warning in time when the motion monitoring module and the sound capture module have monitoring situations. The motion monitoring module is used to monitor the movements and demeanor of the train personnel. If they are in a daze, sleepy or other state, the emergency warning module will promptly remind them. At the same time, the sound capture module monitors the sound inside the car and monitors similar sensitive words such as drugs and gunpowder. After discovery, they are captured and uploaded in time, and screenshots are uploaded in conjunction with the in-car monitoring to identify personal information and avoid danger in advance. If there is a problem, a warning will be issued in time through the emergency warning module.

[0023] As a further description of the above technical solution, the delivery end of the personnel monitoring local area system is also connected to an in-station personnel management module, and the in-station personnel management module includes a duty entry module and a personnel update module;

[0024] The duty entry module is used to replace the duty table in real time to ensure that everyone is on duty.

[0025] The personnel update module is used to timely replace and record the circulation personnel to avoid data confusion and inconvenient management. The duty entry module is set to update the duty, personnel attendance and on-the-job status in real time, so that when problems occur, the corresponding person in charge can be queried in time and called and reminded in time through the mobile phone APP. The personnel update module timely replaces information such as personnel flow, and strengthening personnel management can effectively avoid danger.

[0026] As a further description of the above technical solution, the in-station personnel management module also includes a regional inspection module and an equipment acceptance module;

[0027] Among them, the area inspection module is used to correspond and randomly divide the platform area management personnel and areas, so that they can supervise each other;

[0028] The equipment acceptance module is used to unify the acceptance equipment personnel and equipment man-machine to ensure the acceptance quality. The regional inspection module can randomly assign and correspond personnel and inspection areas, which can improve the inspection quality and supervise each other. The equipment acceptance module allows the equipment and operators to directly correspond to each other, making it convenient to quickly find staff when problems occur.

[0029] As a further description of the above technical solution, the output end of the platform housekeeping system is also connected to a risk processing module, and the risk processing module includes a determination module and a maintenance sorting module;

[0030] Wherein, the determination module is used to upload the collected data to the database for comparison and determination;

[0031] The maintenance sorting module is used to match the problems encountered with the corresponding responsible groups or individuals, and to quickly handle and deal with the aftermath. When the above problems occur on vehicles, they are uploaded to the judgment module, and the internal information of the database is used to quickly determine the solution to the problem. The maintenance sorting module is used to promptly notify the targeted technical, management or police personnel of nearby stations for convenient and quick processing.

[0032] As a further description of the above technical solution, the risk processing module also includes a platform warning module and an emergency risk avoidance module;

[0033] Among them, the platform warning module is used for timely processing and maintenance by internal staff when problems occur;

[0034] The emergency avoidance module is used to automatically activate various escape channels when there is a major problem. When the problem is controllable, the platform warning module reminds the staff to deal with it to avoid panic and unnecessary danger. If the problem is uncontrollable, the emergency avoidance module can quickly activate the evacuation exit and report it to the system terminal for quick response.

[0035] A safety prediction management system method based on big data includes the following steps:

[0036] S1. The system terminal is divided into multiple regional subsystems, and then divided into specific station housekeeping systems, so that direct management can be achieved to avoid problems that affect timeliness;

[0037] S2. The risk assessment module and risk identification module are used to organize, identify and evaluate the information. At the same time, the local road monitoring system can predict and warn of dangers in advance.

[0038] S3, real-time monitoring and early warning of vehicle and equipment conditions through the vehicle monitoring module;

[0039] S4. The platform monitoring module can predict vehicle problems based on maintenance data;

[0040] S5. The vehicle management module can monitor the vehicles in motion to avoid dangerous situations;

[0041] S6. The station personnel management module can start from the management aspect and strengthen the management of personnel and equipment;

[0042] S7. The risk processing module collects the problems, determines the solutions, and notifies the corresponding personnel in time to handle them, thereby improving the efficiency of problem solving.

[0043] The present invention has the following beneficial effects:

[0044] The present invention designs a safety prediction management method and system based on big data. First, the entire system is divided from top to bottom into system terminals, regional subsystems and platform housekeeping systems to form a total-point-point form. Problems that occur in the form of points to surfaces can be reported and processed in a timely manner to avoid affecting the timeliness of problem processing. At the same time, the route is divided into three levels according to the route conditions through the section classification module. When the most dangerous level is reached, the dangerous area warning module is first used to remind the nearby platform staff and crew members to provide timely warnings, and problems can be responded to quickly. The dangerous section and vehicle-mounted monitoring are opened in advance through the section monitoring module, which can effectively predict and avoid the occurrence of danger in advance.

[0045] The present invention designs a safety prediction management method and system based on big data. Through the vehicle condition monitoring module and the vehicle equipment monitoring module of the on-board monitoring module, the vehicle and the vehicle assembly equipment are timely monitored in real time. When a problem occurs, it is automatically transmitted to the nearest platform housekeeping system through the message transmission module, which can quickly respond and maintain. At the same time, the vehicle data is compared through the maintenance record library to determine the vehicle condition, avoid problems and make safety predictions in advance, and the passenger monitoring module allows the platform to adjust the passenger status in time to avoid dangerous situations.

[0046] The present invention designs a safety prediction management method and system based on big data. First, the crew members are monitored through the action monitoring module. When a safety violation occurs, the emergency warning module is used to promptly remind them. At the same time, the passengers in the car are monitored through the sound capture module. When sensitive words appear, they are promptly photographed and uploaded for face recognition through the built-in monitoring. If a problem occurs, the emergency warning module is used to remind them. Two emergency warning modules are set, one is located at the crew for convenient monitoring, and the other is connected to the platform housekeeping system through signals to facilitate timely uploading and information exchange, so as to avoid danger in time. Through the duty entry module and the personnel update module, personnel management is strengthened, and in conjunction with the regional inspection module and the equipment acceptance module, patrol and equipment maintenance can be strengthened to avoid the occurrence of danger.

[0047] The present invention designs a safety prediction management method and system based on big data. After the judgment module identifies the problem according to the database information, the maintenance sorting module retrieves the appropriate processing station point and corresponding personnel, which facilitates rapid processing and improves timeliness, and issues warnings for different situations through the platform warning module and emergency avoidance module. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the present invention;

[0049] Figure 2 This is a schematic diagram of the vehicle-mounted monitoring module of the present invention;

[0050] Figure 3 This is a schematic diagram of a platform monitoring module of the present invention;

[0051] Figure 4 This is a schematic diagram of a vehicle service personnel management module of the present invention;

[0052] Figure 5 This is a schematic diagram of the personnel management module within the station of the present invention;

[0053] Figure 6 This is a schematic diagram of the risk processing module of the present invention;

[0054] Figure 7 It is a schematic diagram of the working process of the present invention;

[0055] Legend:

[0056] 1. System terminal; 2. Regional subsystem; 3. Information feedback module; 4. Platform housekeeping system; 5. Risk assessment module; 6. Risk identification module;

[0057] 7. Road section monitoring local system; 701. Road section classification module; 702. Dangerous area warning module; 703. Road section monitoring module;

[0058] 8. Environmental monitoring local system;

[0059] 9. Vehicle monitoring module; 901. Vehicle condition monitoring module; 902. Vehicle equipment monitoring module; 903. Message transmission module;

[0060] 10. Platform monitoring module; 1001. Maintenance record library; 1002. Passenger monitoring module

[0061] 11. Personnel monitoring local system;

[0062] 12. Vehicle personnel management module; 1201. Action monitoring module; 1202. Sound capture module; 1203. Emergency warning module;

[0063] 13. In-station personnel management module; 1301. Duty entry module; 1302. Personnel update module; 1303. Regional inspection module; 1304. Equipment acceptance module;

[0064] 14. Risk handling module; 1401. Judgment module; 1402. Maintenance and sorting module; 1403. Platform warning module; 1404. Emergency avoidance module. DETAILED DESCRIPTION

[0065] Reference Figure 1-7 The present invention provides a safety prediction management method and system based on big data: comprising a system terminal 1, wherein the output end of the system terminal 1 is connected to a regional subsystem 2, and one output end of the regional subsystem 2 is connected to an information feedback module 3, and the other output end of the regional subsystem 2 is connected to a platform housekeeping system 4, and the output end of the platform housekeeping system 4 is connected to a risk assessment module 5 and a risk identification module 6, and the output end of the risk identification module 6 is connected to a section monitoring local system 7;

[0066] First, the city is divided into multiple areas to form a regional subsystem 2, which is directly linked to the platform housekeeping system 4 of each station and reported to the system terminal 1, forming a point-to-surface form. Each station operates independently but is managed by the system terminal 1. Each platform housekeeping system 4 is equipped with a risk assessment module 5 and a risk identification module 6 to evaluate and identify possible and existing problems and report them to the system terminal 1 to avoid affecting the timeliness of the information and causing the inability to mobilize quickly. The information feedback module 3 logs the location, information, driver and other information corresponding to each route vehicle for quick and easy query.

[0067] As a further implementation scheme of the above technical scheme: the section monitoring local system 7 includes a section classification module 701, a dangerous area warning module 702 and a section monitoring module 703; wherein the section classification module 701 is used to assess the danger level of the areas and roads covered by rail transit according to external influences such as turning points and soft sleeping soil layers; the dangerous area warning module 702 is used to sense and remind the main station and vehicle maintenance personnel when entering a relatively dangerous area; the section monitoring module 703 is used to start a specific monitoring video when entering a dangerous area, shoot and upload it for backup;

[0068] According to the factors of each route such as bends, turns, subsidence, soft sleeper coating, underground river water, passenger density, regional importance, and even past accident sites, the route is divided into three levels: safe, ordinary and dangerous through the section classification module 701. When entering the dangerous level, the platform and crew are reminded to pay attention through the dangerous area warning module 702, and the dangerous section and vehicle-mounted monitoring are turned on in advance through the section monitoring module 703, so that the road conditions can be viewed in advance to achieve predictive effects. At the same time, the dangerous road conditions can be photographed and uploaded for backup so that the road conditions can be viewed and compared regularly.

[0069] During the specific implementation, firstly, the whole system is divided from top to bottom into system terminal 1, regional subsystem 2 and platform housekeeping system 4, forming a total-division-division form, and problems can be reported and processed in time in the form of points to surfaces, so as to avoid affecting the timeliness of problem processing. At the same time, the road section classification module 701 divides the route into three levels according to the route conditions. When the most dangerous level is reached, the dangerous area warning module 702 will first remind the nearby platform staff and crew members to serve as a timely warning, and they can respond quickly when problems occur. The dangerous road section and vehicle-mounted monitoring will be opened in advance through the road section monitoring module 703, which can effectively predict and avoid the occurrence of danger in advance.

[0070] As a further implementation scheme of the above technical scheme: the output end of the risk identification module 6 is also connected to the environmental monitoring local system 8, and the output end of the environmental monitoring local system 8 is connected to the vehicle monitoring module 9; the vehicle monitoring module 9 includes a vehicle condition monitoring module 901, a vehicle equipment monitoring module 902 and a message transmission module 903; wherein, the vehicle condition monitoring module 901 is used to monitor the vehicle condition before or during startup; the vehicle equipment monitoring module 902 is used to monitor the internal monitoring, auxiliary equipment, etc. of the vehicle before or during startup; the message transmission module 903 is used to upload the vehicle condition information before the vehicle arrives at a station for timely backup for comparison and timely discovery of problems;

[0071] The vehicle condition monitoring module 901 and the onboard equipment monitoring module 902 can monitor the vehicle body and the equipment carried by the vehicle, and issue a timely warning when a problem occurs. The information is automatically transmitted to the nearest platform housekeeping system 4 through the information transmission module 903 for quick response and maintenance.

[0072] As a further description of the above technical solution, the environmental monitoring local system 8 also includes a platform monitoring module 10, and the platform monitoring module 10 includes a maintenance record library 1001 and a passenger monitoring module 1002; wherein, the maintenance record library 1001 is used to back up and record the data of each maintenance operation, so as to facilitate timely comparison of vehicle condition data, etc.; the passenger monitoring module 1002 is used to monitor the number of passengers on the platform in the station, and to promptly dispatch additional personnel to maintain order when the number of passengers is large; by updating the maintenance information in real time through the maintenance record library 1001, it is possible to determine in advance whether there is a problem based on the data, thereby achieving a predictive effect. At the same time, the passenger monitoring module 1002 monitors the number of passengers on the platform, and when congestion occurs locally, it can remind the platform to dispatch additional personnel for fixed-point maintenance.

[0073] During specific implementation, the vehicle condition monitoring module 901 and the vehicle equipment monitoring module 902 of the on-board monitoring module 9 are used to timely monitor the vehicle and the vehicle-mounted equipment in real time. If a problem occurs, it is automatically transmitted to the nearest platform housekeeping system 4 through the message transmission module 903, which can quickly respond and maintain. At the same time, the vehicle data is compared through the maintenance record library 1001 to determine the vehicle condition, avoid problems and make safety predictions in advance, and the passenger monitoring module 1002 allows the platform to adjust the passenger status in time to avoid dangerous situations.

[0074] As a further description of the above technical solution, another output end of the risk identification module 6 is connected to a personnel monitoring local system 11, and the transmission end of the personnel monitoring local system 11 is connected to a vehicle personnel management module 12; the vehicle personnel management module 12 includes a motion monitoring module 1201, a sound capture module 1202 and an emergency warning module 1203, wherein the motion monitoring module 1201 is used to monitor the crew members and give timely reminders when slack actions occur; the sound capture module 1202 is used to monitor the sound of the passenger compartment and give timely warnings when encountering sensitive words; the emergency warning module 1203 is used to give timely warnings when the motion monitoring module 1201 and the sound capture module 1202 have monitoring conditions;

[0075] The motion monitoring module 1201 monitors the motion and demeanor of the train personnel. If they are dazed or sleepy, the emergency warning module 1203 will promptly remind them. At the same time, the sound capture module 1202 monitors the sound inside the car, and monitors sensitive words such as drugs and gunpowder. Once discovered, they are captured and uploaded in time, and screenshots are uploaded in conjunction with the in-car monitoring to identify personal information and avoid danger in advance. If there is a problem, a warning will be issued in time through the emergency warning module 1203.

[0076] As a further description of the above technical solution, the delivery end of the personnel monitoring local system 11 is also connected to the station personnel management module 13, and the station personnel management module 13 includes a duty entry module 1301 and a personnel update module 1302; wherein, the duty entry module 1301 is used to replace the duty table in real time to achieve the situation of responsibility on duty; the personnel update module 1302 is used to replace the circulation personnel in time to avoid data confusion and difficulty in management;

[0077] The duty entry module 1301 is set to update the duty, personnel attendance and on-the-job status in real time, so that when problems occur, the corresponding person in charge can be queried in time and called and reminded in time through the mobile phone APP. The personnel update module 1302 timely updates the information such as personnel flow, and strengthening personnel management can effectively avoid danger.

[0078] As a further description of the above technical solution, the station personnel management module 13 also includes a regional inspection module 1303 and an equipment acceptance module 1304; wherein the regional inspection module 1303 is used to correspond and randomly divide the station area management personnel and the area, and can supervise each other; the equipment acceptance module 1304 is used to unify the acceptance of equipment personnel and equipment man-machine to ensure the acceptance quality;

[0079] The regional inspection module 1303 can randomly assign and match personnel and inspection areas, which can improve the inspection quality and mutual supervision. The equipment acceptance module 1304 allows direct human-machine correspondence between equipment and operators, making it convenient to quickly find staff when problems arise.

[0080] During the specific implementation; first, the crew members are monitored through the action monitoring module 1201. When any safety violation occurs, the emergency warning module 1203 will promptly remind them. At the same time, the passengers in the car are monitored through the sound capture module 1202. When sensitive words appear, they are promptly photographed and uploaded for face recognition through the built-in monitoring. If there is a problem, a reminder will be given through the emergency warning module 1203. Two emergency warning modules 1203 are set, one is located at the crew for convenient monitoring, and the other is connected through signals and the platform housekeeping system 4 for convenient and timely uploading and information exchange, which can avoid danger in time. Through the duty entry module 1301 and the personnel update module 1302, personnel management is strengthened, and in conjunction with the regional inspection module 1303 and the equipment acceptance module 1304, patrol and equipment maintenance can be strengthened to avoid danger.

[0081] As a further description of the above technical solution, the output end of the platform housekeeping system 4 is also connected to a risk processing module 14, and the risk processing module 14 includes a determination module 1401 and a maintenance sorting module 1402; wherein, the determination module 1401 is used to upload the collected data to the database for comparison and determination of problems; the maintenance sorting module 1402 is used to match the encountered problems with the corresponding responsible groups or individuals, and to quickly handle and deal with the aftermath;

[0082] When the above problems occur in vehicles, they are uploaded to the determination module 1401, and the solution to the problem is quickly determined through the internal information of the database. The maintenance sorting module 1402 is used to promptly notify the targeted technical, management or police personnel of nearby stations for convenient and quick processing.

[0083] As a further description of the above technical solution, the risk handling module 14 also includes a platform warning module 1403 and an emergency avoidance module 1404; wherein, the platform warning module 1403 is used for internal staff to promptly handle and maintain when problems occur; the emergency avoidance module 1404 is used to automatically activate various escape passages when major problems occur; when the problem is controllable, the platform warning module 1403 reminds the staff to handle it to avoid causing panic and unnecessary danger. If the problem is uncontrollable, the emergency avoidance module 1404 is used to quickly activate the evacuation exit and report it to the system terminal 1 for quick response.

[0084] During specific implementation, after the judgment module 1401 identifies the problem based on the database information, the maintenance sorting module 1402 retrieves the appropriate processing station point and corresponding personnel to facilitate rapid processing and improve timeliness, and issues warnings for different situations through the platform warning module 1403 and the emergency avoidance module 1404.

[0085] A safety prediction management system method based on big data includes the following steps:

[0086] S1, the system terminal 1 is divided into multiple regional subsystems 2, and then divided into specific station housekeeping systems 4, so that direct management can be achieved to avoid problems that affect timeliness;

[0087] S2, the risk assessment module 5 and the risk identification module 6 are used to sort, identify and evaluate the information, and at the same time, the road section monitoring local area system 7 can predict and warn of dangers in advance;

[0088] S3, real-time monitoring and early warning of vehicle and equipment conditions through the vehicle monitoring module 9;

[0089] S4. The platform monitoring module 10 can predict vehicle problems based on maintenance data;

[0090] S5. The vehicle management module 12 can monitor the moving vehicle to avoid dangerous situations;

[0091] S6, the station personnel management module 13 can start from the management aspect and strengthen the management of personnel and equipment;

[0092] S7. The risk processing module 14 collects the problems, determines the solutions, and notifies the corresponding personnel in time to handle them, thereby improving the efficiency of problem solving.

[0093] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A safety prediction management system based on big data, characterized by: The system comprises a system terminal (1), wherein an output end of the system terminal (1) is connected to a regional subsystem (2), and one output end of the regional subsystem (2) is connected to an information feedback module (3), and the other output end of the regional subsystem (2) is connected to a platform housekeeping system (4), and the output end of the platform housekeeping system (4) is connected to a risk assessment module (5) and a risk identification module (6), and the output end of the risk identification module (6) is connected to a road section monitoring local area system (7).

2. The big data-based safety prediction management system according to claim 1, characterized in that: The road section monitoring local area system (7) comprises a road section classification module (701), a dangerous area warning module (702) and a road section monitoring module (703); The road section classification module (701) is used to assess the danger level of the area covered by rail transit and external influences such as road turns and soft sleeping soil layers; The dangerous area warning module (702) is used to sense and remind the main station and vehicle maintenance personnel when entering a relatively dangerous area; The road section monitoring module (703) is used to start a specific monitoring video when entering a dangerous level area, shoot and upload it for backup.

3. The big data-based safety prediction management system according to claim 1, characterized in that: The output end of the risk identification module (6) is also connected to an environmental monitoring local area system (8), and the output end of the environmental monitoring local area system (8) is connected to a vehicle-mounted monitoring module (9); The vehicle-mounted monitoring module (9) comprises a vehicle condition monitoring module (901), a vehicle-mounted equipment monitoring module (902) and a message transmission module (903); Wherein, the vehicle condition monitoring module (901) is used to monitor the vehicle condition before or during startup; The vehicle-mounted equipment monitoring module (902) is used to monitor the vehicle's internal monitoring, auxiliary equipment, etc. before or during startup; The message transmission module (903) is used to upload vehicle condition information before the vehicle arrives at a station for timely backup for comparison and timely discovery of problems.

4. The big data-based safety prediction management system according to claim 3 is characterized by: The local environment monitoring system (8) further comprises a platform monitoring module (10), wherein the platform monitoring module (10) comprises a maintenance record library (1001) and a passenger monitoring module (1002); The maintenance record library (1001) is used to back up and record each maintenance operation data, so as to facilitate timely comparison of vehicle condition data, etc.; The passenger monitoring module (1002) is used to monitor the number of passengers on the platform in the station, and to promptly dispatch additional personnel to maintain order when the number of passengers is large.

5. The big data-based safety prediction management system according to claim 1, characterized in that: Another output end of the risk identification module (6) is connected to a local personnel monitoring system (11), and a transmission end of the local personnel monitoring system (11) is connected to a vehicle personnel management module (12); The vehicle service personnel management module (12) comprises a motion monitoring module (1201), a sound capturing module (1202) and an emergency warning module (1203). The action monitoring module (1201) is used to monitor the crew members and give timely reminders when slack actions occur; The sound capture module (1202) is used to monitor the sound in the passenger compartment and issue a timely warning when encountering sensitive words; The emergency warning module (1203) is used to issue a timely warning when the action monitoring module (1201) and the sound capturing module (1202) encounter a monitoring situation.

6. The big data-based safety prediction management system according to claim 5, characterized in that: The transport end of the personnel monitoring local area system (11) is also connected to an in-station personnel management module (13), and the in-station personnel management module (13) includes a duty entry module (1301) and a personnel update module (1302); The duty entry module (1301) is used to replace the duty table in real time to ensure that everyone is on duty; The personnel update module (1302) is used to timely update and replace the circulation personnel to avoid data confusion and difficulty in management.

7. The big data-based safety prediction management system according to claim 6, characterized in that: The station personnel management module (13) further includes an area inspection module (1303) and an equipment acceptance module (1304); The area inspection module (1303) is used to correspond and randomly divide the platform area management personnel and areas, so that they can supervise each other; The equipment acceptance module (1304) is used to unify the equipment acceptance personnel and equipment man-machine to ensure the acceptance quality.

8. The big data-based safety prediction management system according to claim 1, characterized in that: The output end of the platform housekeeping system (4) is also connected to a risk processing module (14), and the risk processing module (14) includes a determination module (1401) and a maintenance sorting module (1402); Wherein, the determination module (1401) is used to upload the collected data to a database for comparison and determination; The maintenance sorting module (1402) is used to match the problems encountered with the corresponding responsible groups or individuals, and to quickly handle and deal with the aftermath.

9. The big data-based safety prediction management system according to claim 8, characterized in that: The risk processing module (14) further includes a platform warning module (1403) and an emergency risk avoidance module (1404); Wherein, the platform warning module (1403) is used for timely processing and maintenance by internal staff when problems occur; The emergency escape module (1404) is used to automatically activate various escape routes in case of major problems.

10. A safety prediction management method and system based on big data, characterized by: The following steps are involved: S1. The system terminal (1) is divided into multiple regional subsystems (2), and then divided into specific station housekeeping systems (4), so that direct management can be achieved to avoid problems that affect timeliness; S2, the risk assessment module (5) and the risk identification module (6) are used to organize, identify and evaluate the information, and at the same time, the road section monitoring local area system (7) can predict and warn of dangers in advance; S3, real-time monitoring and early warning of vehicle and equipment conditions through the vehicle-mounted monitoring module (9); S4, the platform monitoring module (10) can predict vehicle problems based on maintenance data; S5. The vehicle management module (12) can monitor the moving vehicle to avoid dangerous situations; S6. The in-station personnel management module (13) can start from the management aspect to strengthen the management of personnel and equipment; S7. The risk processing module (14) collects the problems, determines the solutions and promptly notifies the corresponding personnel to handle them, thereby improving the efficiency of problem solving.

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