Subway station large passenger flow risk management and control method and system

Abstract

The embodiment of the invention provides a subway station large passenger flow risk management and control method and system, and the method comprises the steps: dividing a key region according to a station structure, obtaining the risk level of the key region through employing a passenger flow data-based risk threshold dividing method, and calculating the risk level of a station; taking the passenger flow management and control comprehensive cost and the station coupling passenger flow risk as optimization targets, and enabling the risk level of the station to meet a preset risk level; and carrying out optimization solution on the optimization target, selecting an optimal solution according to different scenes, and taking the optimal solution as a station large passenger flow risk management and control strategy. According to the method, the large passenger flow risk in the station is prevented by generating the dynamic management and control strategy of the passenger flow risk.

Description

Technical Field

[0001] This document relates to the technical field of large passenger flow risk control in stations, and in particular to a method and system for large passenger flow risk control in subway stations. Background Art

[0002] With the acceleration of my country's urbanization process and population growth, the passenger flow in the subway is also increasing, and there will be peak passenger flow during the morning and evening commuting hours and holidays. A large number of passengers gathered inside the subway station at the same time will form a high passenger flow risk, and passenger flow accidents often cause casualties and property casualties. This puts higher requirements on the normalized large passenger flow control inside the subway. How to control the passenger flow risk within a relatively safe range at a lower cost is a problem that needs to be solved through scientific theoretical calculations.

[0003] The subway system is the backbone of the urban transportation system. As the key nodes of the subway system, subway stations are responsible for transporting passengers, transferring and connecting to other important tasks. Therefore, it is necessary to reasonably control the passenger flow inside the station to prevent traffic paralysis caused by passenger flow accidents at the station nodes. As for the passenger flow control inside the station, the number of personnel and handling time are currently arranged mainly based on the experience of the staff, without reasonable calculation, which will lead to resource redundancy or uncontrolled passenger flow. Summary of the invention

[0004] The present invention provides a method and system for controlling large passenger flow risks in a subway station, which comprehensively considers the station's coupled passenger flow risks and the comprehensive cost of control, generates a dynamic control strategy for passenger flow risks, and prevents large passenger flow risks in the station.

[0005] The present invention provides a method for controlling the risk of large passenger flow in a subway station, comprising:

[0006] S1. Divide the key areas according to the station structure, use the risk threshold division method based on passenger flow data to obtain the risk level of the key areas, and calculate the risk level of the station;

[0007] S2. The optimization goal is to minimize the comprehensive cost of passenger flow control and ensure that the risk of station-coupled passenger flow is within the safety threshold, so that the risk level of the station meets the preset risk level;

[0008] S3. Optimize and solve the optimization target, select the optimal solution according to different scenarios, and use the optimal solution as the risk management strategy for large passenger flow at the station.

[0009] The present invention provides a subway station large passenger flow risk management and control system, comprising:

[0010] The risk level acquisition module is used to divide the key areas according to the station structure, obtain the risk level of the key areas by using the risk threshold division method based on passenger flow data, and calculate the risk level of the station;

[0011] The objective function module is used to minimize the comprehensive cost of passenger flow control and ensure that the risk of station-coupled passenger flow is within the safety threshold as the optimization goal, so that the risk level of the station meets the preset risk level;

[0012] The solution module is used to optimize and solve the optimization target, select the optimal solution according to different scenarios, and use the optimal solution as the risk management strategy for large passenger flow at the station.

[0013] According to the embodiment of the present invention, the passenger flow risk of the subway station is calculated by dividing the station into key areas and calculating the congestion degree of the key areas respectively, and finally calculating the weight of each key area in the passenger flow risk. The passenger flow risk is controlled at a preset level by using a calculation method of two optimization objectives, namely, the station coupled passenger flow risk and the comprehensive cost of passenger flow control. This application fully considers the station coupled passenger flow risk and the comprehensive cost of control, and minimizes the phenomenon of resource redundancy or passenger flow out-of-control. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 This is a flow chart of a method for controlling large passenger flow risks in a subway station according to an embodiment of the present invention;

[0016] Figure 2 The key area and sub-area division of subway station A in an embodiment of the present invention;

[0017] Figure 3 A cloud diagram showing the weight of the platform area in the passenger flow risk calculation according to an embodiment of the present invention;

[0018] Figure 4 A cloud diagram showing the weight of the channel area in the passenger flow risk calculation according to an embodiment of the present invention;

[0019] Figure 5 A cloud diagram showing the weight of the staircase area in the passenger flow risk calculation according to an embodiment of the present invention;

[0020] Figure 6A cloud diagram showing the weight of the escalator area in the passenger flow risk calculation according to an embodiment of the present invention;

[0021] Figure 7 This is a schematic diagram of an optimization situation considering the risk of station system coupling passenger flow according to an embodiment of the present invention;

[0022] Figure 8 This is a schematic diagram of the optimization of a station platform considering the passenger flow risk of emergencies in key areas according to an embodiment of the present invention;

[0023] Fig. 9 This is a schematic diagram of the optimization of station passages considering the passenger flow risk of emergencies in key areas according to an embodiment of the present invention;

[0024] Fig.10 This is a schematic diagram of the optimization of station stairs considering the risk of passenger flow in critical areas due to emergencies according to an embodiment of the present invention;

[0025] Fig.11 This is a schematic diagram of the optimization of station escalators considering the risk of sudden passenger flow in key areas according to an embodiment of the present invention;

[0026] Fig.12 Schematic diagram of a subway station large passenger flow risk management and control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0028] Method Embodiment

[0029] According to an embodiment of the present invention, a method for risk control of large passenger flow in a subway station is provided. Figure 1 This is a flow chart of a method for controlling the risk of large passenger flow in a subway station according to an embodiment of the present invention. Figure 1 As shown, the subway station large passenger flow risk control method of the embodiment of the present invention specifically includes:

[0030] S1. Divide the key areas according to the station structure, use the risk threshold division method based on passenger flow data to obtain the risk level of the key areas, and calculate the risk level of the station;

[0031] S1 specifically includes:

[0032] S11, dividing the subway station into N key areas;

[0033] In the embodiment of the present invention, the subway station is divided into four key areas A = {A p ,A c ,A s ,A e}, where A p Represents the platform area, A c represents the channel area, A s Represents the staircase area, A e Represents the escalator area.

[0034] S12. Using the degree of congestion to characterize the risk level of the key area;

[0035] In the four key areas divided by S11, the passenger flow congestion is used to characterize the risk level. The calculation method of the passenger flow congestion degree c(t) at time t is:

[0036] c(t)=N(t) / S eff ,

[0037] Where N(t) is the number of people gathered in the area at time t, S eff The effective area of ​​the area is set according to the design specifications or industry standards of the subway industry. The congestion threshold is divided into four levels L = {L1, L2, L3, L4}, where L1 is the lowest risk level and L4 is the highest risk level.

[0038] S13, setting a crowding degree threshold for each key area and a crowding degree threshold for a station, and then dividing the crowding degree of each key area, and obtaining the crowding degree of each key area;

[0039] A cloud model based on historical passenger flow data is used to generate passenger flow risk level thresholds for station systems.

[0040]

[0041]

[0042] They represent the morning and evening peak passenger flow density index of sub-area j of key area a on the kth day, max{c a (t)} represents the maximum risk threshold of key area a. y 、E n and H e denote the mean, entropy and super entropy in the cloud model, respectively. represents the comprehensive passenger flow density index of sub-area j of key area a on the kth day, m represents the passenger flow data of m days in total, and f represents the number of sub-areas in the key area.

[0043] S14. Obtain the congestion level of the station according to the congestion level of the key area, and obtain the risk level of the station according to the congestion level of the station and the congestion level threshold of the station.

[0044] Use the following formula to calculate the weight of each key area in the passenger flow risk:

[0045]

[0046] It represents the average importance of the platform area calculated by the cloud model. represents the average importance of the channel area calculated by the cloud model, represents the average importance of the staircase area calculated by the cloud model, represents the average importance of the escalator area calculated by the cloud model, represents the average importance of key area a;

[0047] Finally, the following formula is used to calculate the passenger flow risk threshold division of the subway station:

[0048]

[0049] S2. The optimization goal is to minimize the comprehensive cost of passenger flow control and ensure that the risk of station-coupled passenger flow is within the safety threshold, so that the risk level of the station meets the preset risk level;

[0050] The station coupled passenger flow risk reaching the safety threshold in the embodiment of the present invention is explained based on the ALARP principle, that is, when the passenger flow risk is reduced to within the L1 range, the optimization demand of the passenger flow risk can be met.

[0051] The comprehensive cost objective function of passenger flow control is:

[0052]

[0053] in, represents the control time used in sub-area j of critical area type a, represents the number of security personnel in sub-area j of key area a. It should be noted that the two variables and need to satisfy certain constraints to reflect the actual passenger flow control situation. Their constraint relationship is as follows:

[0054]

[0055] This indicates that the value of the control time varies depending on the presence or absence of security personnel. , it means that no time cost of personnel management is incurred in this area, so The value of must also be 0. , it means that additional security personnel have been added to the area to manage traffic, and it takes time to control it, so The value must be greater than 0.

[0056] The station coupled passenger flow risk is a dynamic calculation of the comprehensive risk of the station system based on the passenger flow changes in key areas. The passenger flow in the station can be regarded as a continuous combination of passengers passing through various areas. Therefore, the dynamic changes of passenger flow in the station can be considered based on the changes in passenger flow lines.

[0057] The calculation method of the objective function c′(t+Δt) of the station coupled passenger flow risk is:

[0058]

[0059]

[0060] is the number of passengers in the ith sub-area of ​​the station flow line b during period t, is the initial number of passengers in the ith sub-area at time t, is the number of passengers moving from sub-area i-1 to sub-area i, represents the number of passengers from the i-th zone to the next zone, l is the number of flow lines in the station, and s represents the number of sub-zones.

[0061] S3. Optimize and solve the optimization target, select the optimal solution according to different scenarios, and use the optimal solution as the risk management strategy for large passenger flow at the station.

[0062] Furthermore, the embodiment of the present invention selects a multi-objective optimization algorithm based on NSGA-Ⅱ to optimize and solve the two objectives f1 and f2 at the same time. The solution result shows that the passenger flow coupling risk and the comprehensive control cost are inversely proportional in the two-dimensional plane diagram. According to the actual control of passenger flow risk, two optimal solution selection principles are given:

[0063] Principle 1, the principle of minimum comprehensive cost: select the leftmost solution where the passenger flow risk is within the safety threshold. The advantage of this solution is the lowest comprehensive cost. The disadvantage is that although the passenger flow risk has reached below the safety threshold, the risk is likely to escalate to level 2 again.

[0064] Principle 2, the optimal principle of station-coupled passenger flow risk: In this case, the comprehensive cost is appropriately increased to ensure that the passenger flow risk is at a low level within the safety threshold. The advantage of this solution is that it reduces the possibility of risk escalation again, but it will increase the control time or the number of security personnel. When the passenger risk difference between two adjacent solutions is less than 0.1 and the comprehensive cost difference is greater than 50, it is considered that too much cost does not need to be consumed to reduce a very small risk value, so the left solution of the two adjacent solutions is selected as the optimal solution.

[0065] The specific operation steps of the embodiment of the present invention are as follows:

[0066] The station structure of subway station A and the passenger flow on a certain day are selected for analysis. The key areas and sub-areas in the station are divided as follows: Figure 2 As shown in Table 1, the passenger flow risk thresholds for key areas are defined according to industry policy documents and regulations.

[0067] Table 1 Passenger flow risk thresholds for key areas

[0068]

[0069] We further selected the passenger flow conditions of a certain ten days and calculated the passenger flow risk threshold of the station system based on passenger flow, such as Figure 3 , Figure 4 , Figure 5 and Figure 6 The obtained station coupling passenger flow risk threshold range is shown in Table 2:

[0070] Table 2 Classification of risk threshold ranges for station coupled passenger flow

[0071]

[0072] The actual passenger flow distribution of a station on a certain day is selected for multi-objective optimization solution, and two calculation backgrounds are set respectively:

[0073] Background 1: The station has a normal large passenger flow. At this time, the risk of station system coupling passenger flow is considered. The solution is as follows: Figure 7 As shown;

[0074] Background 2: Considering the passenger flow gathering caused by emergencies in a local area, the solution is as follows Figure 8-Figure 11 shown.

[0075] After solving and analyzing, it is found that for background 1, when a large passenger flow occurs in the station on a regular basis, the optimal solution can be selected based on the optimal solution selection principle 1, because this can ensure that the current passenger flow risk remains within a safe range. With the arrival of vehicles and passengers entering the station, the passenger flow risk increases again. At this time, the optimization solution is performed again. The cycle can keep the large passenger flow risk within a safe range and reduce the cost of passenger flow control as much as possible. The increase in risk is a normal phenomenon during peak hours. Figure 7 The optimal solution shown is to spend 99.5 units of comprehensive cost, which can make the congestion level of the subway station system reach 1.56333 people / square meter. Reduce the coupling risk of the station system to the safety threshold. In addition, a security guard should be stationed at Platform 2, Escalator 8, Escalator 4, Escalator 7 and Channel 5 to maintain passenger flow. Maintain 3, 4.2, 4.4, 4.2, and 4.1 minutes respectively. Table 3 shows the variable values ​​of the comprehensive cost objective function of passenger flow control. This case considers the scenario of normal large passenger flow during the morning peak. First of all, it should be clear that crowd congestion is a normal phenomenon in key areas. Maintaining the normalized large passenger flow at a safer risk level is a more important decision-making principle in this scenario. Therefore, we do not need to use too much cost to keep the risk at a lower level of the safety threshold, so the solution obtained by adopting the principle of comprehensive cost minimization is the optimal solution.

[0076] Table 3 shows the variable values ​​of the comprehensive cost objective function of passenger flow control

[0077]

[0078] In view of the situation in Background 2, when an emergency occurs in a key area and causes an increase in passenger flow risk, it is very likely to cause casualties and property losses. Therefore, it is necessary to reduce the risk as much as possible and prevent the risk from rising again. If necessary, some control costs can be sacrificed. At this time, the optimal solution can be selected based on the optimal solution selection principle 2.

[0079] According to the embodiment of the present invention, the passenger flow risk of the subway station is calculated by dividing the station into key areas and calculating the congestion degree of the key areas respectively, and finally calculating the weight of each key area in the passenger flow risk. The passenger flow risk is controlled at a preset level by using a calculation method of two optimization objectives, namely, the station coupled passenger flow risk and the comprehensive cost of passenger flow control. This application fully considers the station coupled passenger flow risk and the comprehensive cost of control, and minimizes the phenomenon of resource redundancy or passenger flow out-of-control.

[0080] System Example

[0081] According to an embodiment of the present invention, a subway station large passenger flow risk management system is provided. Fig.12Schematic diagram of a subway station large passenger flow risk management system according to an embodiment of the present invention. Fig.12 As shown, the subway station large passenger flow risk management and control system of the embodiment of the present invention specifically includes:

[0082] The risk level acquisition module 121 is used to divide the key areas according to the station structure, obtain the risk level of the key areas by using the risk threshold division method based on passenger flow data, and calculate the risk level of the station; the risk level acquisition module is specifically used to:

[0083] The subway station is divided into four key areas A = {A p ,A c ,A s ,A e}, where A p Represents the platform area, A c represents the channel area, A s Represents the staircase area, A e Represents the escalator area. The passenger flow congestion level is used to characterize the risk level in the four key areas. The passenger flow congestion level c(t) at time t is calculated as c(t) = N(t) / S eff , where N(t) is the number of people gathered in the area at time t, S eff is the effective area of ​​the area. According to the design specifications or industry standards of the subway industry, the congestion threshold of the key area is divided into four levels L = {L1, L2, L3, L4}, where L1 is the lowest risk level and L4 is the highest risk level.

[0084] A cloud model based on historical passenger flow data is used to generate passenger flow risk level thresholds for station systems.

[0085]

[0086] They represent the morning and evening peak passenger flow density index of sub-area j of key area a on the kth day, max{c a (t)} represents the maximum risk threshold of key area a. y 、E n and H e denote the mean, entropy and super entropy in the cloud model, respectively. represents the comprehensive passenger flow density index of sub-area j of key area a on the kth day, m represents the passenger flow data of m days in total, and f represents the number of sub-areas in the key area.

[0087] The following formula is used to calculate the weight of each key area in the passenger flow risk:

[0088]

[0089] It represents the average importance of the platform area calculated by the cloud model. represents the average importance of the channel area calculated by the cloud model, represents the average importance of the staircase area calculated by the cloud model, represents the average importance of the escalator area calculated by the cloud model, represents the average importance of key area a;

[0090] Finally, the passenger flow risk threshold division of the subway station is calculated by the following formula:

[0091] c′(t)=θ p ·c(S p )+θ c ·c(S c )+θ s ·c(S s )+θ e ·c(S e ),

[0092] Objective function module 122, used to minimize the comprehensive cost of passenger flow control and ensure that the risk of station coupled passenger flow is within a safety threshold as optimization goals, so that the risk level of the station meets the preset risk level;

[0093] The passenger flow risk reaching the safety threshold is explained based on the ALARP principle, that is, when the passenger flow risk is reduced to within the L1 range, the optimization needs of the passenger flow risk can be met.

[0094] The comprehensive cost objective function of passenger flow control is:

[0095]

[0096] It is important to note that the two variables and need to satisfy certain constraints to reflect the actual passenger flow control situation. Their constraint relationship is as follows:

[0097]

[0098] This indicates that the value of the control time varies depending on the presence or absence of security personnel. , it means that no time cost of personnel management is incurred in this area, so The value of must also be 0. , it means that additional security personnel have been added to the area to manage traffic, and it takes time to control it, so The value must be greater than 0.

[0099] The coupling risk is a dynamic calculation of the comprehensive risk of the station system based on the passenger flow changes in key areas. The passenger flow in the station can be regarded as a continuous combination of passengers passing through various areas, so the dynamic changes of passenger flow in the station can be considered based on the changes in passenger flow lines.

[0100] The calculation method of the coupling risk c′(t+Δt) of the station system is:

[0101]

[0102] is the number of passengers in the ith sub-area of ​​the station flow line b during period t, is the initial number of passengers in the ith sub-area at time t, is the number of passengers moving from sub-area i-1 to sub-area i, represents the number of passengers from the i-th partition to the next partition.

[0103] The solution module 123 is used to optimize and solve the optimization target, select the optimal solution according to different scenarios, and use the optimal solution as the risk management strategy for large passenger flow at the station.

[0104] The solution module 123 is specifically used to select a multi-objective optimization algorithm based on NSGA-Ⅱ to optimize and solve the two objectives f1 and f2 at the same time. The solution result shows that the passenger flow coupling risk and the comprehensive control cost are inversely proportional to each other in the two-dimensional plane diagram. According to the actual control of passenger flow risk, two optimal solution selection principles are given:

[0105] Principle 1, the principle of minimum comprehensive cost: select the leftmost solution where the passenger flow risk is within the safety threshold. The advantage of this solution is the lowest comprehensive cost. The disadvantage is that although the passenger flow risk has reached below the safety threshold, the risk is likely to escalate to level 2 again.

[0106] Principle 2, the optimal principle of station-coupled passenger flow risk: In this case, the comprehensive cost is appropriately increased to ensure that the passenger flow risk is at a low level within the safety threshold. The advantage of this solution is that it reduces the possibility of risk escalation again, but it will increase the control time or the number of security personnel. When the passenger risk difference between two adjacent solutions is less than 0.1 and the comprehensive cost difference is greater than 50, it is considered that too much cost does not need to be consumed to reduce a very small risk value, so the left solution of the two adjacent solutions is selected as the optimal solution.

[0107] According to the embodiment of the present invention, the passenger flow risk of the subway station is calculated by dividing the station into key areas and calculating the congestion degree of the key areas respectively, and finally calculating the weight of each key area in the passenger flow risk. The passenger flow risk is controlled at a preset level by using a calculation method of two optimization objectives, namely, the station coupled passenger flow risk and the comprehensive cost of passenger flow control. This application fully considers the station coupled passenger flow risk and the comprehensive cost of control, and minimizes the phenomenon of resource redundancy or passenger flow out-of-control.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for risk control of large passenger flow in a subway station, characterized in that: include: S1. Divide the key areas according to the station structure, use the risk threshold division method based on passenger flow data to obtain the risk level of the key areas, and calculate the risk level of the station; S2. The optimization goal is to minimize the comprehensive cost of passenger flow control and ensure that the risk of station-coupled passenger flow is within the safety threshold, so that the risk level of the station meets the preset risk level; S3. Optimize and solve the optimization target, select the optimal solution according to different scenarios, and use the optimal solution as the risk management strategy for large passenger flow at the station.

2. The method according to claim 1, characterized in that The S1 specifically includes: S11, dividing the subway station into N key areas; S12. Using the degree of congestion to characterize the risk level of the key area; S13, setting a crowding degree threshold of each key area and a crowding degree threshold of a station, and then dividing the crowding degree of each key area, and obtaining the crowding degree of each key area; S14. Obtain the congestion level of the station according to the congestion level of the key area, and obtain the risk level of the station according to the congestion level of the station and the congestion level threshold of the station.

3. The method according to claim 2, characterized in that The S12 specifically includes: The passenger flow congestion degree c(t) at time t is obtained by formula 1: c(t)=N(t) / S eff Formula 1; Where N(t) is the number of people gathered in the area at time t, S eff is the effective area of ​​the region.

4. The method according to claim 3, characterized in that S13 specifically includes: The crowding threshold of the station system is generated by using the cloud model based on historical passenger flow data through formula 2; in, They represent the morning and evening peak passenger flow density index of sub-area j of key area a on the kth day, max{c a (t)} represents the maximum risk threshold of key area a, represents the comprehensive passenger flow density index of sub-area j of key area a on the kth day; The mean value E of the cloud model is obtained by formula 3 y , the entropy E of the cloud model is obtained by formula 4 n , the super entropy H of the cloud model is obtained by formula 5 e ; m represents the passenger flow statistics for a total of m days, and f represents the number of sub-areas in the key area.

5. The method according to claim 3, characterized in that: The step of obtaining the risk level of the station according to the risk level of the key area specifically includes: Use Formula 6 to obtain the weight θ of each key area in the passenger flow risk a ; It represents the average importance of the platform area calculated by the cloud model. represents the average importance of the channel area calculated by the cloud model, represents the average importance of the staircase area calculated by the cloud model, represents the average importance of the escalator area calculated by the cloud model, represents the average importance of the key area a, and the congestion level of the station c′(t) is obtained by formula 7; c′(t) = θ p ·c(S p ) + θ c ·c(S c ) + θ s ·c(S s ) + θ e ·c(S e ) Formula 7; The risk level of the station is determined according to the congestion level c′(t) of the station and the congestion level threshold of the station.

6. The method according to claim 1, characterized in that The S2 specifically includes: The objective function f1 of the comprehensive cost of passenger flow control is obtained through formula 8: in, represents the control time used in sub-area j of critical area type a, represents the number of safety officers used in sub-area j of critical area a, and The constraint relationship is: The calculation method of the objective function c′(t+Δt) of the station coupled passenger flow risk is: is the number of passengers in the ith sub-area of ​​the station flow line b during period t, obtained by formula 11; in, is the initial number of passengers in the ith sub-area at time t, is the number of passengers moving from sub-area i-1 to sub-area i, represents the number of passengers from the i-th zone to the next zone, l is the number of flow lines in the station, and s represents the number of sub-zones.

7. The method according to claim 5, characterized in that The S3 specifically includes: A multi-objective solution algorithm is used to simultaneously optimize and solve the two objectives f1 and f2, and the optimal solution is selected based on the principle of minimum comprehensive cost and optimal risk of station coupled passenger flow.

8. The method according to claim 6, characterized in that The principle of minimum comprehensive cost is to select the leftmost solution with passenger flow risk within the safety threshold.

9. The method according to claim 6, characterized in that The optimal principle of station-coupled passenger flow risk is to ensure the leftmost solution of passenger flow risk within the safety threshold range by increasing the comprehensive cost.

10. A subway station passenger flow risk management system, characterized in that: include: The risk level acquisition module is used to divide the key areas according to the station structure, obtain the risk level of the key areas by using the risk threshold division method based on passenger flow data, and calculate the risk level of the station; The objective function module is used to minimize the comprehensive cost of passenger flow control and ensure that the risk of station-coupled passenger flow is within the safety threshold as the optimization goal, so that the risk level of the station meets the preset risk level; The solution module is used to optimize and solve the optimization target, select the optimal solution according to different scenarios, and use the optimal solution as the risk management strategy for large passenger flow at the station.

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