Railway dispatching decision resilience evaluation method and system based on simulation driving

Abstract

A railway dispatching decision resilience evaluation method based on simulation driving, comprising: constructing a system overall architecture; from the perspective of function implementation, constructing a system overall architecture containing data processing, simulation driving, resilience evaluation analysis and visualization display functions; constructing a data processing module and mechanism; standardizing and regularizing the dynamic and static data of the system, constructing a dispatching decision related data structure and a correlation mapping relationship between the data structures; constructing a simulation driving module and mechanism; based on railway signals and railway operation rules, constructing a simulation verification mechanism and verification method for dispatching decisions, inputting the dispatching decisions and outputting the verification results; constructing an evaluation analysis module and mechanism; constructing an evaluation model containing resilience evaluation indexes and calculation methods of the dispatching decisions, and realizing resilience evaluation of the input dispatching decisions; a man-machine interaction and visualization display module; using a visualized way to display the resilience evaluation results of the dispatching decisions.

Description

Technical Field

[0001] This invention relates to a method and system thereof, and more particularly to a simulation-driven method and system for evaluating the resilience of railway dispatching decisions, belonging to the field of railway communication and signaling. Background Technology

[0002] Dispatching decisions are crucial for ensuring the orderly and planned operation of trains in railway operations, especially under the influence of unforeseen events. Scientific and effective dispatching decisions play a decisive role in reducing train delays. However, at present, railway dispatching decisions are still mainly based on manual experience, and the scientific validity, effectiveness, and feasibility of dispatching decisions are often assessed by verifying the actual effects after implementation. The main indicators are usually the final degree of train delay and the completion rate of various construction orders according to plan. This final evaluation has a certain lag, and it is rare to conduct a scientific evaluation and assessment of dispatching decisions during the decision-making stage. Summary of the Invention

[0003] This invention addresses the evaluation problem of railway dispatching decisions by proposing a simulation-driven method and system for evaluating the resilience of railway dispatching decisions. By combining simulation-driven approaches with resilience evaluation, it rapidly verifies and evaluates dispatching decisions generated by dispatchers or the dispatching system, enabling a quick assessment of the scientific validity, effectiveness, and feasibility of dispatching decisions, thus assisting users in decision-making. The technical solution is as follows:

[0004] A simulation-driven method for evaluating the resilience of railway dispatching decisions, characterized by:

[0005] Step S1: Construct the overall system architecture: From the perspective of functional implementation, construct the overall system architecture that includes data processing, simulation-driven, resilience evaluation and analysis, and visualization functions;

[0006] Step S2: Constructing the data processing module and mechanism: standardizing and regularizing the dynamic and static data of the system, and constructing data structures related to scheduling decisions and the correlation mapping relationships between data structures;

[0007] Step S3: Constructing the simulation-driven module and mechanism: Based on railway signals and railway operation rules, construct a simulation verification mechanism and verification method for scheduling decisions, input scheduling decisions, and output verification results;

[0008] Step S4: Construct an evaluation and analysis module and mechanism: Construct an evaluation model that includes resilience evaluation indicators and calculation methods for scheduling decisions, and realize the resilience assessment of the input scheduling decisions;

[0009] Step S5 Human-computer interaction and visualization module: Display the resilience assessment results of scheduling decisions in a visual way.

[0010] This invention discloses a non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute the above-described method.

[0011] This invention also discloses a simulation-driven railway dispatching decision resilience evaluation system, characterized in that the system includes: a processor, a memory, a communication interface, and a bus; the processor, the memory, and the communication interface are connected through the bus and communicate with each other; the memory stores executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to execute the above method.

[0012] Beneficial effects

[0013] (1) To achieve an effective evaluation of the resilience of railway dispatching decisions;

[0014] (2) A flexible scheduling decision simulation verification mechanism is proposed, which realizes effective verification of scheduling decisions in the formulation stage;

[0015] (3) A scheduling decision resilience evaluation method that integrates train delay absorption capacity, timetable recovery capacity and emergency response capacity is proposed, which enhances the scientificity, effectiveness and feasibility of scheduling decisions. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the simulation-driven railway scheduling decision resilience evaluation method and system flow of the present invention.

[0017] Figure 2 This is a schematic diagram of the simulation-driven railway dispatching decision resilience evaluation system of the present invention;

[0018] Figure 3 This is a logic diagram of the data processing module of the present invention;

[0019] Figure 4 This is the logic diagram of the simulation driver module of the present invention;

[0020] Figure 5 This is the simulation timing-driven logic diagram for this invention. Detailed Implementation

[0021] This invention proposes a simulation-driven method for evaluating the resilience of railway dispatching decisions. A flowchart of the method is shown below. Figure 1 As shown.

[0022] In step S1, the overall structure of the system was constructed from the perspective of system function implementation, including the following four functional modules, and their functions and logical relationships between modules are as follows: Figure 2As shown.

[0023] (1) Data processing module: Based on the characteristics of the data required for scheduling decision and operation environment simulation, a corresponding data structure is constructed to realize the standardization and regularization of data related to scheduling decision and simulation environment. It includes dynamic and static data such as train operation plan, scheduling command, signal equipment and status, and constructs the association mapping relationship between different data structures, including the relationship between train and operation plan, train and scheduling command, train and signal equipment, operation plan and signal equipment, and scheduling command and signal equipment, forming the basic data support layer of the simulation and evaluation module. The specific implementation is shown in step S2.

[0024] (2) The simulation driving module is used to construct an internal rapid simulation environment to verify the effect of scheduling decisions. It includes a signal driving layer that realizes the changes in the status of signal equipment such as track occupancy and vacancy, signal openness and prohibition, route locking, occupancy and vacancy, and temporary speed limit status of the line; an operation rule layer that is used to construct train tracking rules, train arrival and departure time sampling rules, train conflict judgment rules, and specific station details and scheduling rules; and a simulation driving layer that drives the train to run according to the scheduling decisions in the virtual simulation environment. Finally, it forms the scheduling decision results such as train arrival and departure information and scheduling command execution status, which serve as the input of the resilience evaluation module. The specific implementation is shown in step S3.

[0025] (3) Resilience evaluation module: Combined with the output results of the simulation driving module, construct a resilience evaluation index and corresponding calculation method that includes late point absorption capacity, recovery capacity and rupture resistance capacity, and output the final evaluation results. See step S4 for specific implementation.

[0026] (4) Human-computer interaction and visualization module: The module displays the resilience evaluation results of scheduling decisions through visualization methods such as charts, and provides a human-computer interaction interface for adjusting the rule settings of the simulation driving module and the resilience evaluation module. See step S5 for the specific implementation.

[0027] In step S2, a specific data structure and the relationship between dynamic and static data are constructed to form a data processing module, the main structure of which is as follows: Figure 3 As shown, the specific implementation method is as follows:

[0028] (1) Signal equipment can be abstracted into a vector set containing features such as tracks, routes, block sections, and signals. Based on the topological relationship of railway lines, the connection relationship between signal equipment is constructed. Block sections are associated with the section signals they protect, section signals are associated with the block sections on both sides, block sections near stations are associated with the entry and exit signals, entry and exit signals are associated with the routes they protect, and routes are associated with the corresponding receiving and dispatching tracks. According to the definition rules of train operation direction in my country, the up and down attributes of train operation are added, and finally a topological relationship with directionality is formed between signal equipment to support changes in the environmental state of train operation.

[0029] (2) The scheduling decision can be divided into two parts: train operation plan and scheduling command. The train operation plan can be abstracted into a vector set containing the train identifier, current station, arrival and departure time, working track, station operation type and other plan features; and based on the train plan features, the relationship between the train plan and the signaling equipment is constructed, and the train plan is associated with the track and the route, which is used to uniquely determine the train's receiving and departure track and the corresponding route, and to support the path driving in the train travel simulation process;

[0030] (3) The dispatching commands include construction commands, speed limit commands and track closure commands, which can be uniformly abstracted into a vector set containing features such as dispatching command identifier, dispatching command type, command execution period (start time, end time), execution subject (construction personnel, train), and execution object (corresponding turnout, section and other signal equipment); based on the dispatching command features, the association between dispatching command objects and signal equipment is constructed to support the impact of dispatching commands on the operating environment and the resulting changes in operating status during the simulation process;

[0031] (4) Using signal equipment as the key feature link, trace back the relationship between it and train plan and dispatching command, construct the relationship between train plan and dispatching command, form the correspondence between dispatching command and train plan, and use it to support the impact of dispatching command on train operation status during simulation.

[0032] In step S3, based on the data processing layer constructed in step S2, the inherent internal logic of railway signals and train operation logic are regularized, and corresponding simulation advancement rules are set to form a train operation simulation mechanism under scheduling decisions. Corresponding simulation modules are then constructed, such as... Figure 4 As shown, the specific steps are as follows:

[0033] Step S3_1: First, construct the operation rules layer. The specific implementation method is as follows:

[0034] (1) Initialize the rule engine. First, load the data-structured adjustment rules and station details configurations to control the operation rules during the simulation process.

[0035] (2) Load train sampling rules, that is, determine the arrival and departure times of trains at fixed points (station arrival and departure lines) based on train speed values ​​and signal equipment status. For example, the sampling calculation method for trains operating at the station can be that the arrival point is when the train has completely entered the arrival and departure lines and its speed drops to 0, and the departure point is when the train occupies the departure route and the protective signal changes to a prohibition signal; the average value of the time when the train enters and leaves the arrival and departure lines can be obtained through the train sampling calculation method; the simulation module generates the actual operation diagram through the train sampling during the simulation process, and outputs it as part of the simulation results;

[0036] (3) Load the train tracking rules, that is, initialize the minimum running time of the train in the section, the minimum tracking interval of the train in the section, the minimum departure interval of the train, the minimum receiving interval of the train, and other operating parameters, which are used for the calculation of the coordinated operation of multiple trains in the simulation process.

[0037] (4) Load train conflict and handling rules. Since there are multiple adjustment methods under train conflict, the system provides mature scheduling adjustment methods such as First-Come-First-Served (FCFS), First-Schedule-First-Served (FSFS), priority order, and heuristic algorithm through modularization. These methods can be selected through human-computer interaction and used to resolve train conflicts in the simulation process.

[0038] Step S3_2: Construct a signal-driven layer based on the operation rule layer. The specific implementation method is as follows:

[0039] (1) Construct signal control rules. Based on the signal equipment status and the relationships between signal equipment defined in step 1, and combined with mature railway signal interlocking logic, including route locking logic, turnout switching logic, signal opening logic, and opposing route logic, determine whether the interlocking conditions are met through real-time monitoring and analysis of the signal equipment status and relationships. For example, when a signal is opened, it must be ensured that the turnout ahead is in the correct position, the track circuit is free, and there are no opposing routes. Transform these interlocking conditions into specific logical expressions and control commands to realize mutual constraints and collaborative work between signal equipment.

[0040] (2) Constructing train operation calculation rules. Based on mature train dynamics equations, taking a single train as the object, and combining the operation plan, the full-journey speed curves of all trains are calculated at once, serving as the basic speed curves in the simulation process. During the simulation, when the operating environment of a section or station changes, resulting in restrictions on train operating speed (such as temporary speed limits), in order to improve calculation efficiency and timeliness, a speed curve splicing method is adopted, calculating only the speed curves within the affected section and station subset, thereby realizing rapid updates of train speed curves during the simulation process.

[0041] (3) Construct signal-train linkage rules. Based on the current position of the train, the status of the signal lights ahead, the route status, temporary speed limits, and other information, combined with the train operation calculation rules, calculate the train's running time in the next operating section in real time, as well as the signal status changes caused by the train's movement. Combined with the status of the signal equipment and the interlocking relationship, generate corresponding control commands, such as signal display switching and turnout conversion, to realize the linkage change of signal-train status.

[0042] (4) Construct scheduling command-signal linkage rules. Extract the time range of signal equipment status changes involved in the scheduling command. For example, the scheduling command specifies the blocking, temporary speed limit and other signal operations of a specific section within a certain time period. Combined with the signal equipment control rules, generate a specific signal equipment control command queue based on time and associate it with the corresponding signal equipment as a partial driving source of signal equipment status changes during the simulation.

[0043] Step S3_3: Construct the simulation driver layer. The specific implementation method is as follows:

[0044] (1) Construct a periodically triggered simulation clock: The simulation clock provides a time reference for the simulation process, which is triggered at a fixed period. Trigger and time advance signals are sent to the scheduling decision-making section and the signaling equipment section, respectively. The simulation clock uses a scaling mapping method based on the actual environment time, adjusting the virtual simulation time according to the actual environment time. The speed can be increased by multiples to achieve the purpose of ultra-fast simulation, such as... The value is 1 microsecond, which means that 1 microsecond corresponds to 1 second of actual time. Therefore, a 24-hour operation plan can be completed in 0.0864 seconds.

[0045] (2) Dispatch decision release events: After receiving the time trigger signal from the simulation clock, the dispatch decision part releases signal events and train events respectively. Signal events are transmitted to the signaling equipment, and train events are transmitted to the train, which are used to indicate relevant operations or status changes.

[0046] (3) Signal equipment response and update: After receiving the signal event issued by the dispatching decision, the signal equipment, in conjunction with the train's status information and the dispatching command information, calls the signal control driving rules, signal-train linkage rules, and signal-dispatch command linkage rules of the signal driving layer to update its own status. At the same time, the signal equipment receives the position and speed information fed back by the train and sends this information to the dispatching decision part to update the execution status of the dispatching decision, such as calculating the actual arrival and departure points of the train using the train point sampling rules.

[0047] (4) Train response and update: After receiving the train event issued by the dispatch decision, the train updates its own status according to the event content, combined with the train operation calculation rules, train tracking rules and signal-train linkage rules. It also calculates the conflict between trains based on the status of the signal equipment and the train position information. If there is a conflict, it determines the order of train operation based on the conflict handling rules and feeds back the position and speed information of each train to the signal equipment, providing real-time operation status data of the train for the signal equipment and dispatch decision.

[0048] (5) The simulation clock is continuously triggered, and the above steps are continuously repeated to realize the continuous simulation and dynamic update of the train operation status and signal equipment status based on the scheduling decision, and finally complete the entire execution process of the scheduling decision and output the simulation results.

[0049] In step S4, based on the simulation results of the scheduling decision output in step 3, the resilience evaluation index of the scheduling decision is calculated by combining the total train delay time, average delay time, number of delayed trains, and delay time of each station along the route. The specific implementation method is as follows:

[0050] (1) The resilience of scheduling decisions reflects their ability to resist and recover from disturbances, and to a certain extent, their ability to absorb train delays. Since making reasonable use of the redundant time reserved in the timetable is an important way to combat disturbances and absorb delays, the actual utilization rate of redundant time in the execution results of scheduling decisions can be used to reflect the ability of scheduling decisions to resist and recover from delays. Therefore, the redundant time absorption rate is defined. for:

[0051] ,

[0052] in, The total number of stations, For the first The station's delay time, For the first The redundancy time reserved in the station's timetable, This can measure the overall ability of the scheduling decision to absorb train delays. The closer the value is to 1, the more resilient the scheduling decision is.

[0053] (2) The resilience of the scheduling decision can also be assessed from the perspective of the final global delay of the train. That is, under the condition of adopting this decision, the ratio of the total delay time of the train at the terminal station to the planned running time and the redundancy time of the train. This ratio reflects the overall ability of the scheduling decision to absorb train delays. Therefore, the global delay level is defined. :

[0054] ,

[0055] in This represents the total delay time for the train. For the planned running time of the train, The total redundancy time of the running graph. , These are the weighting coefficients. The smaller the value, the better the resilience of the scheduling decision.

[0056] (3) The distribution of delay times of trains at each station can be statistically analyzed by the scheduling decision. For example, the standard deviation of the delay time at each station or the coefficient of variation (standard deviation / mean) can be used. If the uniformity of this value is higher, it indicates that the delay is better dispersed and the delay is easier to recover. Therefore, the uniformity of the delay distribution is defined. :

[0057] ,in The standard deviation of the delay times for each station. This represents the average delay time. The larger the value, the more evenly the delay time is distributed, and the stronger the resilience of the scheduling decision.

[0058] (4) The impact of scheduling decisions can be reflected by calculating the ratio of delayed trains to the total number of trains, and the proportion of affected trains can be defined. :

[0059] ,in The number of delayed trains, This represents the total number of trains. The larger the value, the fewer the number of delayed trains, and the stronger the resilience of the scheduling decision.

[0060] (5) Taking into account the above indicators, a comprehensive resilience evaluation index for scheduling decisions can be formed. :

[0061] ,

[0062] in As weights, normalize to the [0,1] interval. Therefore It can be used as a comprehensive evaluation index of the resilience of scheduling decisions; the larger the value, the better the resilience of the decision.

[0063] In step S5, different visualization methods are used as needed to display the train operation results and resilience evaluation results under the scheduling decision. For example, a timetable can be used to display the final train operation results, a heatmap can be used to display the delay areas, impact levels, and ranges, and a scatter plot can be used to display the distribution balance of delays. Mature human-computer interaction methods such as mouse clicks can be used to switch between different built-in simulation rules and evaluation rules.

[0064] This invention also discloses a simulation-driven railway dispatching decision resilience evaluation system, characterized in that the system includes: a processor, a memory, a communication interface, and a bus; the processor, the memory, and the communication interface are connected through the bus and communicate with each other; the memory stores executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to execute the above method.

[0065] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A simulation-driven method for evaluating the resilience of railway dispatching decisions, characterized by: step S1. Constructing the overall system architecture: From the perspective of functional implementation, construct the overall system architecture that includes data processing, simulation-driven, resilience evaluation and analysis, and visualization functions; Step S2: Constructing the data processing module and mechanism: Standardizing and regularizing the dynamic and static data of the system, constructing data structures related to scheduling decisions and the correlation mapping relationships between data structures, including the following: (1) Based on the topological relationship of railway lines, the connection relationship between signal equipment is constructed, and the block section is associated with the section signal it protects, the section signal is associated with the block sections on both sides, the block section of the station approaching section is associated with the entry and exit signal, the entry and exit signal is associated with the route it protects, and the route is associated with the corresponding receiving and dispatching track. According to the definition rules of train operation direction in my country, the up and down attributes of train operation are added, and finally a directional topological relationship between signal equipment is formed to support the changes in the environmental state of train operation. (2) The scheduling decision can be divided into two parts: train operation plan and scheduling command. Based on the characteristics of the train plan, the relationship between the train plan and the signal equipment is constructed, and the train plan is associated with the track and the route. This is used to uniquely determine the train's receiving and dispatching track and the corresponding route, and to support the path driving in the train travel simulation process. The scheduling command is uniformly abstracted into a vector set containing the scheduling command identifier, scheduling command type, command execution time period, execution subject, and execution object characteristics. Based on the characteristics of scheduling commands, the association between scheduling command objects and signal devices is constructed to support the impact of scheduling commands on the operating environment and the resulting changes in operating status during the simulation process; (3) Taking signal equipment as the key feature link, trace back the relationship between it and train plan and dispatching command, construct the relationship between train plan and dispatching command, and form the correspondence between dispatching command and train plan to support the impact of dispatching command on train operation status during simulation; Step S3: Constructing a simulation-driven module and mechanism: Based on railway signals and railway operation rules, construct a simulation verification mechanism and verification method for scheduling decisions, input scheduling decisions, and output verification results; Step S4: Construct an evaluation and analysis module and mechanism: Construct an evaluation model that includes resilience evaluation indicators and calculation methods for scheduling decisions, so as to realize the resilience assessment of the input scheduling decisions; Step S5, Human-Computer Interaction and Visualization Module: The resilience assessment results of scheduling decisions are displayed in a visual manner.

2. The simulation-driven railway scheduling decision resilience evaluation method according to claim 1, characterized in that, Step 1 includes the following: The overall architecture includes the following four functional modules: (1) Data processing module: Based on the characteristics of the data required for scheduling decision and operation environment simulation, construct the corresponding data structure to realize the standardization and regularization of data related to scheduling decision and simulation environment, and construct the association mapping relationship between different data structures to form the basic data support layer of simulation and evaluation module; (2) The simulation driving module is used to construct an internal rapid simulation environment to verify the effect of scheduling decisions; the simulation driving layer is used to drive the train to run in the virtual simulation environment according to the scheduling decisions, and finally form the train early and late arrival information, scheduling command execution status and scheduling decision results as input to the resilience evaluation module; (3) Resilience evaluation module: Based on the output results of the simulation-driven module, construct resilience evaluation indicators and corresponding calculation methods that include late point absorption capacity, recovery capacity and rupture resistance, and output the final evaluation results; (4) Human-computer interaction and visualization module: The module displays the resilience evaluation results of scheduling decisions in a visual manner and provides a human-computer interaction interface for adjusting the rule settings of the simulation driving module and the resilience evaluation module.

3. The simulation-driven railway scheduling decision resilience evaluation method according to claim 1, characterized in that step 3 includes the following: Step S3_1: First, construct the operation rule layer; Step S3_2: Construct a signal-driven layer based on the operation rule layer; Step S3_3: Construct the simulation driving layer.

4. The simulation-driven railway scheduling decision resilience evaluation method according to claim 3, characterized in that, Step S3_1 includes the following: (1) Initialize the rule engine. First, load the data-structured adjustment rules and station details configurations to control the operation rules during the simulation process. (2) Load the train sampling rules, that is, determine the arrival and departure times of the train at the station based on the train speed value and the status of the signal equipment on the arrival and departure lines; if there is a train operating at the station, the sampling calculation method is that the arrival point is when the train completely enters the arrival and departure lines and the speed drops to 0, and the departure point is when the train occupies the departure route and the protective signal changes to a prohibition signal; the average value of the time when the train enters the arrival and departure lines and leaves the arrival and departure lines can be obtained through the train sampling calculation method. The simulation module generates an actual running diagram by collecting train data points during the simulation process, which is then output as part of the simulation results. (3) Load the train tracking rules, that is, initialize the minimum running time of the train in the section, the minimum tracking interval of the train in the section, the minimum departure interval of the train, and the minimum receiving interval of the train, which are used for the calculation of the coordinated operation of multiple trains during the simulation process. (4) Load train conflict and handling rules. Since there are multiple adjustment methods under train conflict, the system provides a modular approach, including First-Come First-Served (FCFS), First-Schedule First-Served (FSFS), priority scheduling, and scheduling adjustment methods based on heuristic algorithms. These methods can be selected through human-computer interaction and used to resolve train conflicts during the simulation process.

5. The simulation-driven railway scheduling decision resilience evaluation method according to claim 3, characterized in that, Step S3_2 includes the following: (1) Construct signal control rules, based on the signal equipment status and the relationship between signal equipment defined in step 1, and combine the mature interlocking logic of railway signaling to monitor and analyze the signal equipment status and relationship in real time, and determine whether the interlocking conditions are met; (2) Constructing train operation calculation rules: Based on mature train dynamics equations, taking a single train as the object, and combining the operation plan, the full speed curve of all trains is calculated at once as the basic speed curve in the simulation process; during the simulation process, when the operating environment of the section or station changes, resulting in the train running speed being limited, in order to improve the calculation efficiency and timeliness, the speed curve splicing method is adopted, and only the speed curves in the affected section and station subset are calculated, so as to realize the rapid update of the train speed curve in the simulation process; (3) Construct signal-train linkage rules: Based on the current position of the train, the status of the signal lights ahead, the route status, and the temporary speed limit information, combined with the train operation calculation rules, calculate the train's running time in the next operating section in real time, as well as the signal status changes caused by the train's movement, and generate corresponding control commands based on the status of the signal equipment and the interlocking relationship. (4) Construct scheduling command-signal linkage rules: Extract the time range of signal device state changes involved in the scheduling command, combine with the signal device control rules, generate a specific signal device control command queue based on time, and associate it with the corresponding signal device as a partial driving source of signal device state changes during the simulation.

6. The simulation-driven railway scheduling decision resilience evaluation method according to claim 3, characterized in that, Step S3_3 includes the following: (1) Construct a periodically triggered simulation clock: The simulation clock provides a time reference for the simulation process, which is triggered at a fixed period. Trigger and time advance signals are sent to the scheduling decision-making section and the signaling equipment section respectively; the simulation clock adopts a scaling mapping method based on the actual environment time, adjusting the virtual simulation time according to the actual environment time. The simulation is accelerated in multiples of the given time to achieve ultra-fast simulation; where simu_cycle and T together represent the fixed period T. simu_cycle ; (2) Dispatch decision release events: After receiving the time trigger signal from the simulation clock, the dispatch decision part releases signal events and train events respectively; (3) Signal equipment response and update: After receiving the signal event issued by the dispatch decision, the signal equipment combines the train status information and the dispatch command information, and calls the signal control drive rules, signal-train linkage rules and signal-dispatch command linkage rules of the signal drive layer to update its own status; at the same time, the signal equipment receives the position and speed information fed back by the train, and sends this information to the dispatch decision part to update the execution status of the dispatch decision, such as the train point collection rule to calculate the actual arrival and departure points of the train; (4) Train response and update: After receiving the train event issued by the dispatch decision, the train updates its own status according to the event content, combined with the train operation calculation rules, train tracking rules and signal-train linkage rules. It also calculates the conflict between trains based on the status of the signal equipment and the train position information. If there is a conflict, it determines the order of train operation based on the conflict handling rules and feeds back the position and speed information of each train to the signal equipment, providing real-time operation status data of the train for the signal equipment and dispatch decision. (5) The simulation clock is continuously triggered, and the above steps are continuously repeated to realize the continuous simulation and dynamic update of the train operation status and signal equipment status based on the scheduling decision, and finally complete the entire execution process of the scheduling decision and output the simulation results.

7. The simulation-driven railway dispatching decision resilience evaluation method according to claim 3, characterized in that step S4 includes the following: (1) By evaluating the actual utilization rate of redundant time in the execution results of scheduling decisions, the resilience and recovery capability of scheduling decisions in the face of delays are reflected. Therefore, the redundant time absorption rate is defined. for: , in, The total number of stations, For the first The station's delay time, For the first The redundancy time reserved in the station's timetable, This can measure the overall ability of the scheduling decision to absorb train delays. The closer the value is to 1, the more resilient the scheduling decision is; (2) The resilience of scheduling decisions is assessed from the perspective of the final global delay level of trains. That is, under the condition of adopting this decision, the ratio of the total delay time of the train at the terminal station to the planned train running time and the redundancy time. This ratio reflects the overall ability of scheduling decisions to absorb train delays. Therefore, the global delay level is defined. : ， in, This represents the total delay time for the train. For the planned running time of the train, The total redundancy time of the running graph. , These are the weighting coefficients. The smaller the value, the better the resilience of the scheduling decision; (3) By statistically analyzing the distribution of delay times of trains at each station under this scheduling decision, such as using the standard deviation or coefficient of variation of delay times at each station, if the uniformity of the value is higher, it indicates that the delay is better dispersed and the delay is easier to recover. Therefore, the uniformity of the delay distribution is defined. : ,in The standard deviation of the delay times for each station. This represents the average delay time. The larger the value, the more evenly the delay time is distributed, and the stronger the resilience of the scheduling decision. (4) The impact range of the scheduling decision is reflected by calculating the ratio of the number of delayed trains to the total number of trains, and the proportion of affected trains is defined. : ,in The number of delayed trains, This represents the total number of trains. The larger the value, the fewer the number of delayed trains, and the stronger the resilience of the scheduling decision. (5) Taking into account the above indicators, a comprehensive resilience evaluation index for scheduling decisions is formed. : ， in As weights, normalize to the [0,1] interval. Therefore It can be used as a comprehensive evaluation index of the resilience of scheduling decisions; the larger the value, the better the resilience of the decision.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein the program, when executed, controls the device where the non-volatile storage medium is located to perform the method described in any one of claims 1 to 7.

9. A simulation-driven railway dispatching decision resilience evaluation system, characterized in that, The system includes: a processor, a memory, a communication interface, and a bus; the processor, the memory, and the communication interface are connected via the bus and communicate with each other; the memory stores executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to perform the method as described in any one of claims 1-7 above.

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