Subway station large passenger flow emergency command linkage method and device and storage medium

Abstract

The invention relates to a subway station large passenger flow emergency command linkage method and device and a storage medium. The method comprises the steps that station passenger flow information is received in real time; the station passenger flow information is preprocessed and analyzed, and real-time passenger flow data are extracted; the historical passenger flow data and the real-time passenger flow data are utilized, the future passenger flow condition and the number of passengers getting off a train about to arrive are predicted through a machine learning algorithm, if it is predicted that the future passenger flow exceeds a station bearing capacity threshold value, the abnormal passenger flow condition is judged, and if it is judged that the abnormal passenger flow is generated, a large passenger flow alarm is generated; alarm information is automatically generated according to large passenger flow alarm, and is pushed to an operation control center for confirmation; and after the alarm information is confirmed, an emergency plan is started for station management and control. Compared with the prior art, real-time monitoring, early warning, evaluation and emergency command of the large passenger flow of the subway station are realized, and the emergency response speed and the disposal efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart stations, and in particular to a method, device and storage medium for emergency command linkage for large passenger flow in a subway station. Background Art

[0002] In the urban subway transportation system, subway stations are key nodes for passenger flow distribution, and their operational efficiency and safety are crucial to ensuring the travel experience of passengers. However, in the face of large passenger flow, the existing subway station emergency command methods still have many shortcomings. The traditional manual monitoring and dispatching mode has limitations in response speed, dispatching accuracy, and resource allocation, and it is difficult to effectively respond to sudden large passenger flow situations. Especially during peak hours or special events, the passenger flow at subway stations increases sharply. If there is a lack of an effective emergency command linkage mechanism, it is easy to cause station congestion, passenger detention and other problems, and even safety accidents in serious cases. Therefore, how to use intelligent and automated technical means to achieve rapid response and efficient management of large passenger flows at subway stations has become an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a subway station large passenger flow emergency command linkage method, equipment and storage medium, so as to realize real-time monitoring, early warning, evaluation and emergency command of large passenger flow in subway stations, and improve emergency response speed and handling efficiency.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] A subway station large passenger flow emergency command linkage method, the method comprising:

[0006] Receive station passenger flow information in real time, including station video images, station AFC passenger flow data and train weighing data;

[0007] Preprocess and analyze station passenger flow information to extract real-time passenger flow data, including station passenger flow density, passenger movement speed, inbound and outbound passenger flow, train load, and carriage congestion;

[0008] Using historical and real-time passenger flow data, the machine learning algorithm is used to predict future passenger flow and the number of passengers getting off the trains that are about to arrive. If the predicted future passenger flow exceeds the station's carrying capacity threshold, an abnormal passenger flow situation is determined. If an abnormal passenger flow is determined, a large passenger flow alarm is generated.

[0009] Automatically generate alarm information based on high passenger flow alarms and push it to the operation control center for confirmation;

[0010] After the alarm information is confirmed, the emergency plan is activated to carry out station control.

[0011] Furthermore, the process of extracting real-time passenger flow data includes:

[0012] Identify station video images and extract station passenger flow density and personnel movement speed through data cleaning, abnormal data processing, data formatting, data normalization and text processing;

[0013] Statistical analysis is performed on the station AFC passenger flow data and vehicle weighing data to obtain the inbound and outbound passenger flow, train load and carriage congestion.

[0014] Furthermore, the abnormal passenger flow situation includes:

[0015] The station is severely congested;

[0016] The movement of people has almost come to a standstill;

[0017] The passenger flow in and out of the station continues to grow and exceeds a certain threshold;

[0018] The number of passengers getting off the upcoming train is expected to exceed the current platform capacity threshold.

[0019] Furthermore, any abnormal passenger flow situation is judged to generate abnormal passenger flow and a large passenger flow alarm is generated.

[0020] Furthermore, the heavy passenger flow alarm includes a red warning, an orange warning and a yellow warning.

[0021] Furthermore, the abnormal passenger flow situation that generates the red warning is:

[0022] The future passenger flow in the next 15 minutes exceeds 80% or more of the station's carrying capacity, and the passenger flow in and out of the station continues to grow; or the number of passengers getting off the train that is about to arrive will exceed 120% of the current maximum platform capacity; or the passenger flow at the station is severely congested and the movement of people is almost stagnant;

[0023] The orange warning is generated when:

[0024] The future passenger flow in the next 15 minutes exceeds 60%-80% of the station's carrying capacity, and the passenger flow in and out of the station increases; or the number of passengers getting off the train that is about to arrive will exceed 100% of the maximum number of people on the current platform of the station; or the passenger flow at the station is moderately congested, and the speed of people moving is slow;

[0025] The yellow warning is generated when:

[0026] The future passenger flow in the next 15 minutes exceeds the station's carrying capacity by 40%-60%, and the passenger flow in and out of the station is relatively stable; or the number of passengers getting off the arriving train is close to the maximum capacity of the station's current platform; or the station passenger flow is slightly congested, but people can still move normally.

[0027] Furthermore, if the predicted future passenger flow does not exceed the station carrying capacity threshold, or the predicted future passenger flow exceeds the station carrying capacity threshold but it is not judged that abnormal passenger flow is generated, the station is judged to be in a green state, the station is maintained in normal operation, and the station passenger flow information continues to be monitored to ensure smooth operation of the station.

[0028] Furthermore, the alarm information includes the alarm level, alarm time, alarm location and estimated impact range.

[0029] Furthermore, the process of pushing to the operation control center for confirmation includes:

[0030] The alarm information is pushed to the operation control center, and the large passenger flow alarm icon is displayed on the panoramic view of the dispatcher or station attendant's workstation, and the dispatcher or station attendant confirms the alarm information.

[0031] Furthermore, the process of activating the emergency plan to control the station includes:

[0032] Issue alarm information through the station passenger information system and public broadcasting system to remind passengers to pay attention to safety;

[0033] The station passenger information system or platform door PIS displays the congestion information of the trains that are about to arrive in real time, providing passengers with a reference for boarding.

[0034] Automatically link key area monitoring equipment to monitor passenger flow in real time;

[0035] The station structure is shown to the dispatcher through BIM modeling. The passenger flow heat map and the congestion information of the trains that are about to arrive are displayed in real time based on the information collected by the monitoring equipment. The dispatcher is provided with intuitive passenger flow distribution and feedback is received from the dispatcher.

[0036] Provide real-time passenger flow data to station attendants and receive feedback from them;

[0037] Provide real-time passenger flow data and station attendant feedback to station attendants, and receive station attendant feedback;

[0038] Combined with passenger flow monitoring and feedback from dispatchers, station attendants and station attendants, the traffic organization adjustment strategy is automatically generated and pushed, and the strategy is automatically executed after the central dispatcher selects and confirms it.

[0039] Furthermore, the process of generating the traffic organization adjustment strategy includes:

[0040] Combine station passenger flow information with feedback from dispatchers, station attendants and station attendants to determine whether additional trains are needed. If necessary, analyze the time it takes for spare cars to arrive and the number of passengers transported based on the available spare cars on the line that day, and evaluate and draw a plan for the running interval and number of trains.

[0041] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the subway station large passenger flow emergency command linkage method as described above are implemented.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the subway station large passenger flow emergency command linkage method as described above.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention processes and analyzes station video images, station AFC passenger flow data and train weighing data to predict future passenger flow and the number of passengers getting off the trains that are about to arrive, thereby achieving real-time monitoring and accurate early warning of subway station passenger flow, effectively improving the accuracy and efficiency of emergency command of large passenger flows, reducing the problem of inaccurate scheduling caused by human factors, and improving the operational safety of subway stations;

[0045] 2. The present invention automatically generates emergency command strategies through automated and intelligent technical means, coordinates and commands dispatchers, station attendants, station attendants and other relevant station staff, simplifies the emergency command process, improves the response speed, and ensures that subway stations can quickly take effective measures to deal with sudden large passenger flow situations;

[0046] 3. The present invention provides dispatchers and passengers with intuitive and comprehensive passenger flow information through multi-dimensional information display methods such as BIM modeling, heat map display, carriage congestion information display, and mini-program passenger guidance, thereby improving the decision-making efficiency of central dispatchers, while also ensuring the travel safety of passengers and optimizing their travel experience;

[0047] 4. The present invention divides the large passenger flow alarm into red warning, orange warning and yellow warning according to the actual situation, so that the station management personnel can quickly identify the current passenger flow situation and take corresponding countermeasures according to the warning level, thereby improving the overall response efficiency. Through the grading system, the station can allocate resources more effectively and avoid waste of resources;

[0048] 5. The alarm information automatically generated by the present invention needs to be pushed to the operation control center for confirmation before the emergency plan is activated for station management and control. The authenticity of the alarm information is further verified manually to prevent the system from misjudgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 This is an information processing flow chart of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0052] Example 1

[0053] A subway station large passenger flow emergency command linkage method, the method is as follows Figure 1 As shown, including:

[0054] Step S1, receiving station passenger flow information in real time, including station video images, station AFC passenger flow data and train weighing data.

[0055] In this embodiment, the video recognition system, the entry and exit data comparison system (AFC) and the vehicle weighing system installed in the subway station are used to receive the station video images, the station AFC passenger flow data and the train weighing data in real time.

[0056] CCTV monitoring equipment is used in the video recognition system.

[0057] Step S2, pre-processing and analyzing the station passenger flow information, extracting real-time passenger flow data, including station passenger flow density, passenger movement speed, inbound and outbound passenger flow, train load and carriage congestion.

[0058] The process of extracting real-time passenger flow data includes:

[0059] The station video images are recognized by Figure 2 The information processing flow of the invention shown extracts the passenger flow density and personnel movement speed of the station through data cleaning, abnormal data processing, data formatting, data normalization and text processing;

[0060] S201, data cleaning: remove noise and redundant information in the data to ensure the accuracy and completeness of the data;

[0061] S202, abnormal data processing: identifying and processing abnormal values ​​in the data, such as a sudden surge or decrease in passenger flow data, to avoid interference with subsequent analysis;

[0062] S203, data formatting: converting the data into a unified format to facilitate subsequent analysis and processing;

[0063] S204, data normalization: normalize the data so that the data from different sources have the same dimension and range, which is convenient for comparison and analysis;

[0064] S205, text processing: for data containing text information (such as text descriptions in video recognition results), text parsing and extraction are performed to facilitate subsequent analysis and judgment;

[0065] Statistical analysis is performed on the station AFC passenger flow data and vehicle weighing data to obtain the inbound and outbound passenger flow, train load and carriage congestion.

[0066] Step S3, using historical passenger flow data and real-time passenger flow data, predict the future passenger flow and the number of passengers getting off the train that is about to arrive through a machine learning algorithm. If it is predicted that the future passenger flow exceeds the station's carrying capacity threshold, an abnormal passenger flow situation is judged. If it is determined that an abnormal passenger flow is generated, a large passenger flow alarm is generated.

[0067] The process by which the machine learning algorithm predicts future passenger flow and the number of people getting off the upcoming trains includes:

[0068] Collect historical passenger flow data, including passenger flow data from the past few years or months, and perform data feature processing to extract useful features from historical data and real-time data, such as time, weather conditions, holidays, train types, station types, etc. Normalize the data so that feature values ​​of different dimensions have similar scales to facilitate processing by machine learning algorithms;

[0069] Choose appropriate machine learning algorithms. For passenger flow forecasting, commonly used algorithms include time series forecasting methods (such as ARIMA and exponential smoothing), regression analysis methods (such as linear regression and multivariate regression), and machine learning algorithms (such as support vector machines, random forests, and neural networks). For the prediction of the number of passengers getting off the train that is about to arrive, use algorithms that can process time series data, such as recurrent neural networks (such as LSTM);

[0070] Use the extracted features and machine learning algorithms to build a prediction model and optimize the model parameters;

[0071] Use historical data to train the model so that it can learn the rules and patterns of passenger flow changes. During the training process, methods such as cross-validation can be used to evaluate the performance of the model to avoid overfitting or underfitting.

[0072] Use the trained model to predict future passenger flow and the number of people getting off trains that are about to arrive;

[0073] Continuously collect new passenger flow data and real-time data over time to update and optimize the forecasting model;

[0074] Models are regularly evaluated and adjusted to ensure their accuracy and reliability in real-world applications.

[0075] When using machine learning algorithms to predict the number of people getting off a train that is about to arrive, the data used include: historical and real-time train load and carriage crowding, and the historical ratio of the number of people getting off at the next stop to the number of people getting off.

[0076] Abnormal passenger flow situations include:

[0077] The station is severely congested;

[0078] The movement of people has almost come to a standstill;

[0079] The passenger flow in and out of the station continues to grow and exceeds a certain threshold;

[0080] The number of passengers getting off the upcoming train is expected to exceed the current platform capacity threshold.

[0081] If any of the above abnormal passenger flow situations occurs, it will be judged as abnormal passenger flow and a large passenger flow alarm will be generated.

[0082] The heavy passenger flow alarm generates three levels of warnings based on the degree of abnormality, including red warning, orange warning and yellow warning.

[0083] The abnormal passenger flow conditions that generate the red warning (level 1 alarm) are:

[0084] The future passenger flow in the next 15 minutes exceeds 80% or more of the station's carrying capacity, and the passenger flow in and out of the station continues to grow; or the number of passengers getting off the arriving train will exceed 120% of the current platform's maximum capacity; or the station passenger flow is severely congested and the movement of people is almost stagnant.

[0085] The orange warning (secondary alarm) is generated when:

[0086] The future passenger flow in the next 15 minutes will exceed 60%-80% of the station's carrying capacity, and the passenger flow in and out of the station will increase; or the number of passengers getting off the arriving train will exceed 100% of the maximum capacity of the station's current platform; or the station passenger flow is moderately congested and people move slowly.

[0087] The yellow warning (level 3 alarm) is generated when:

[0088] The future passenger flow in the next 15 minutes exceeds the station's carrying capacity by 40%-60%, and the passenger flow in and out of the station is relatively stable; or the number of passengers getting off the arriving train is close to the maximum capacity of the station's current platform; or the station passenger flow is slightly congested, but people can still move normally.

[0089] In this embodiment, the system will automatically give treatment suggestions for different warnings and serve as a reference for subsequent generation of alarm information.

[0090] The recommended handling methods for a red alert are: immediately activate the emergency evacuation plan, guide passengers to exit the station or go to other carriages / platforms in an orderly manner through broadcasting, display screens, etc.; dispatch additional staff to provide guidance and assistance at key locations; consider suspending or restricting the entry of some trains to reduce pressure on the station.

[0091] The suggestions for handling orange warnings are: strengthen passenger flow information prompts on broadcasts and display screens in stations to guide passengers to distribute reasonably; increase staff to provide guidance and assistance in areas with high passenger flow; communicate with the train dispatching department to adjust the train arrival intervals in a timely manner to relieve platform pressure.

[0092] The handling suggestions for the yellow warning are: strengthen passenger flow monitoring in the station and prepare for possible passenger flow peaks; adjust the signs in the station in a timely manner to guide passengers to enter and exit the station quickly; maintain communication with the train dispatching department to ensure smooth train operation.

[0093] If the predicted future passenger flow does not exceed the station carrying capacity threshold, or the predicted future passenger flow exceeds the station carrying capacity threshold but no abnormal passenger flow is judged to be generated, the station is judged to be in a green state, the station maintains normal operation status, and continues to monitor the station passenger flow information to ensure smooth operation of the station.

[0094] Step S4, automatically generate alarm information based on the large passenger flow alarm and push it to the operation control center for confirmation.

[0095] The alarm information includes the alarm level, alarm time, alarm location and estimated impact range.

[0096] The process of pushing to the Operation Control Center for confirmation includes:

[0097] The alarm information is pushed to the operation control center, and the large passenger flow alarm icon is displayed on the panoramic view of the dispatcher or station attendant's workstation, and the dispatcher or station attendant confirms the alarm information.

[0098] Step S5: After the alarm information is confirmed, the emergency plan is activated to control the station.

[0099] The process of launching the emergency plan for station control includes:

[0100] Issue alarm information through the station passenger information system and public broadcasting system to remind passengers to pay attention to safety;

[0101] The station passenger information system or platform door PIS displays the congestion information of the trains that are about to arrive in real time, providing passengers with a reference for boarding.

[0102] Automatically link key area monitoring equipment to monitor passenger flow in real time;

[0103] The station structure is shown to the dispatcher through BIM modeling. The passenger flow heat map and the congestion information of the trains that are about to arrive are displayed in real time based on the information collected by the monitoring equipment. The dispatcher is provided with intuitive passenger flow distribution and feedback is received from the dispatcher.

[0104] Provide real-time passenger flow data to station attendants and receive feedback from them;

[0105] Provide real-time passenger flow data and station attendant feedback to station attendants, and receive station attendant feedback;

[0106] Combined with passenger flow monitoring and feedback from dispatchers, station attendants and station attendants, the traffic organization adjustment strategy is automatically generated and pushed, and the strategy is automatically executed after the central dispatcher selects and confirms it.

[0107] The process of generating the traffic organization adjustment strategy includes:

[0108] Combine station passenger flow information with feedback from dispatchers, station attendants and station attendants to determine whether additional trains are needed. If necessary, analyze the time it takes for spare cars to arrive and the number of passengers transported based on the available spare cars on the line that day, and evaluate and draw a plan for the running interval and number of trains.

[0109] When executing the strategy, the station attendant workstation will automatically confirm the location of the station attendant through the Bluetooth positioning system, and only the station attendant at the right position will assist in the control of large passenger flow at the station. At the same time, it will intelligently coordinate with relevant operating personnel including station attendants, station attendants and central dispatchers to form a group, and send emergency instructions to relevant operating personnel, so that the corresponding personnel can quickly reach the designated location for deployment and guidance.

[0110] According to the passenger flow situation, the station's environmental control equipment, such as ventilation equipment and lighting equipment, can also be adjusted automatically or manually to improve the station environment and enhance passenger comfort.

[0111] In practical applications, if this method is applied to emergency command of large passenger flow in a large subway station, the specific process is as follows:

[0112] When the passenger flow of a station exceeds the preset threshold, the system will automatically trigger the early warning mechanism and display a large passenger flow alarm icon on the panoramic view of the OCC dispatcher / station attendant workstation. At the same time, the system links the CCTV monitoring equipment in key areas to monitor the large passenger flow in real time. By clicking to enter the station interface, the dispatcher can intuitively see the station's BIM modeling (Building Information Modeling) and the large passenger flow status and carriage congestion information displayed by the heat map. Based on this information, the dispatcher can quickly make emergency command decisions, such as adjusting the train departure interval and activating temporary evacuation channels. At the same time, the station PIS (Passenger Information System) or platform door PIS displays the carriage congestion information in real time to provide passengers with a reference for riding. The station attendant receives the large passenger flow information through the handheld terminal and confirms the input. After the station attendant confirms the large passenger flow based on the CCTV viewing results and the station attendant's feedback, the central dispatcher workstation automatically pushes the traffic organization adjustment plan. The system analyzes the time for the standby vehicles to arrive and the number of passengers transported based on the availability of spare vehicles on the line that day, and evaluates and lays out the plan for adjusting the running interval and the number of trains. After the central dispatcher selects and confirms, the system automatically executes the operation plan adjustment and adds trains to relieve the passenger flow pressure. In addition, the station duty workstation also automatically generates a large passenger flow emergency response plan, and confirms the location of the station attendant through the Bluetooth positioning system to assist in the station's large passenger flow control. The system intelligently collaborates with relevant operating personnel to build a group and push emergency instructions such as closing entrances and exits and entry / exit gate operation requirements. The station PA (Public Address, public broadcasting system) broadcasts large passenger flow information, the PIS prompts large passenger flow information, and guides passengers to exits, transfers within the station, and various locations within the station through mini-programs to ensure the safe and orderly evacuation of passengers.

[0113] Example 2

[0114] Based on Example 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the aforementioned emergency command linkage method based on large passenger flow in subway stations.

[0115] At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and of course may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above-mentioned subway station large passenger flow emergency command linkage method. Of course, in addition to the software implementation method, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0116] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0118] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A subway station large passenger flow emergency command linkage method, characterized in that: The method comprises: Receive station passenger flow information in real time, including station video images, station AFC passenger flow data and train weighing data; Preprocess and analyze station passenger flow information to extract real-time passenger flow data, including station passenger flow density, passenger movement speed, inbound and outbound passenger flow, train load, and carriage congestion; Using historical and real-time passenger flow data, the machine learning algorithm is used to predict future passenger flow and the number of passengers getting off the trains that are about to arrive. If the predicted future passenger flow exceeds the station's carrying capacity threshold, an abnormal passenger flow situation is determined. If an abnormal passenger flow is determined, a large passenger flow alarm is generated. Automatically generate alarm information based on high passenger flow alarms and push it to the operation control center for confirmation; After the alarm information is confirmed, the emergency plan is activated to carry out station control.

2. A subway station large passenger flow emergency command linkage method according to claim 1, characterized in that: The process of extracting real-time passenger flow data includes: Identify station video images and extract station passenger flow density and personnel movement speed through data cleaning, abnormal data processing, data formatting, data normalization and text processing; Statistical analysis is performed on the station AFC passenger flow data and vehicle weighing data to obtain the inbound and outbound passenger flow, train load and carriage congestion.

3. The subway station large passenger flow emergency command linkage method according to claim 1, characterized in that: The abnormal passenger flow situations include: The station is severely congested; The movement of people has almost come to a standstill; The passenger flow in and out of the station continues to grow and exceeds a certain threshold; The number of passengers getting off the upcoming train is expected to exceed the current platform capacity threshold.

4. A subway station large passenger flow emergency command linkage method according to claim 3, characterized in that: Any abnormal passenger flow situation will be judged as abnormal passenger flow and a large passenger flow alarm will be generated.

5. The subway station large passenger flow emergency command linkage method according to claim 1, characterized in that: The large passenger flow alarm includes red warning, orange warning and yellow warning.

6. A subway station large passenger flow emergency command linkage method according to claim 5, characterized in that: The abnormal passenger flow conditions that generate the red warning are: The future passenger flow in the next 15 minutes exceeds 80% or more of the station's carrying capacity, and the passenger flow in and out of the station continues to grow; Or the number of passengers getting off the upcoming train will exceed 120% of the current platform's maximum capacity; or the station is severely congested and passenger movement is almost stagnant; The orange warning is generated when: The future passenger flow in the next 15 minutes exceeds 60%-80% of the station's carrying capacity, and the passenger flow in and out of the station increases; or the number of passengers getting off the train that is about to arrive will exceed 100% of the maximum number of people on the current platform of the station; or the passenger flow at the station is moderately congested, and the speed of people moving is slow; The yellow warning is generated when: The future passenger flow in the next 15 minutes exceeds the station's carrying capacity by 40%-60%, and the passenger flow in and out of the station is relatively stable; Either the number of passengers getting off the arriving train is close to the maximum capacity of the current platform; or the station is slightly congested, but people can still move normally.

7. The subway station large passenger flow emergency command linkage method according to claim 1, characterized in that: If the predicted future passenger flow does not exceed the station carrying capacity threshold, or the predicted future passenger flow exceeds the station carrying capacity threshold but no abnormal passenger flow is judged to be generated, the station is judged to be in a green state, the station maintains normal operation status, and continues to monitor the station passenger flow information to ensure smooth operation of the station.

8. The subway station large passenger flow emergency command linkage method according to claim 1, characterized in that: The alarm information includes the alarm level, alarm time, alarm location and estimated impact range.

9. The subway station large passenger flow emergency command linkage method according to claim 1, characterized in that: The process of pushing to the operation control center for confirmation includes: The alarm information is pushed to the operation control center, and the large passenger flow alarm icon is displayed on the panoramic view of the dispatcher or station attendant's workstation, and the dispatcher or station attendant confirms the alarm information.

10. The subway station large passenger flow emergency command linkage method according to claim 1, characterized in that: The process of launching the emergency plan to control the station includes: Issue alarm information through the station passenger information system and public broadcasting system to remind passengers to pay attention to safety; The station passenger information system or platform door PIS displays the congestion information of the trains that are about to arrive in real time, providing passengers with a reference for boarding. Automatically link key area monitoring equipment to monitor passenger flow in real time; The station structure is shown to the dispatcher through BIM modeling. The passenger flow heat map and the congestion information of the trains that are about to arrive are displayed in real time based on the information collected by the monitoring equipment. The dispatcher is provided with intuitive passenger flow distribution and feedback is received from the dispatcher. Provide real-time passenger flow data to station attendants and receive feedback from them; Provide real-time passenger flow data and station attendant feedback to station attendants, and receive station attendant feedback; Combined with passenger flow monitoring and feedback from dispatchers, station attendants and station attendants, the traffic organization adjustment strategy is automatically generated and pushed, and the strategy is automatically executed after the central dispatcher selects and confirms it.

11. A subway station large passenger flow emergency command linkage method according to claim 10, characterized in that: The process of generating the traffic organization adjustment strategy includes: Combine the station passenger flow information with the feedback from dispatchers, station attendants and station attendants to determine whether additional trains are needed. If necessary, analyze the time it takes for the spare cars to arrive and the number of passengers transported based on the available spare cars on the line that day, and evaluate and draw up a plan for the running interval and number of trains.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the subway station large passenger flow emergency command linkage method as described in any one of claims 1-11 are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the subway station large passenger flow emergency command linkage method as described in any one of claims 1-11 are implemented.

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