Method and device for determining rail transit emergency scheduling scheme and electronic equipment

Abstract

The invention discloses a rail transit emergency scheduling scheme determination method and device and electronic equipment. The method comprises the steps that graph theory modeling is conducted on a target rail transit system, a directed graph is obtained, nodes in the directed graph represent stations, and edges of the directed graph represent traffic lines between the stations; when the transport capacity failure occurs, the failure propagation result of the transport capacity failure is determined according to the directed graph, and the failure propagation result is used for determining the influence range of the transport capacity failure; and updating the transport capacity failure area according to the failure propagation result, and determining an emergency scheduling scheme corresponding to the transport capacity failure area. According to the method and the device, the technical problem that accurate emergency scheduling cannot be implemented due to the fact that the failure propagation range is not accurately determined when the rail transit has the transport capacity failure in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rail transit, in particular to a rail transit emergency scheduling scheme determination method and device and electronic equipment. BACKGROUND

[0002] Rail transit is a typical complex system, and its network transportation capacity and safe operation level not only depends on the physical conditions, but also depends on the scientificity and systematicness of management, organization and collaborative control during operation, which is the key to whether a city or even a regional rail transit can maximize its value, and is the core of the construction of urban group, metropolitan area and cross-provincial rail transit emergency command system. However, the related art is not accurate enough in determining the propagation range of the failure when the rail transit fails, which leads to the inability to implement accurate emergency scheduling.

[0003] In view of the above problems, no effective solution has been proposed so far. SUMMARY

[0004] The embodiments of the present application provide a rail transit emergency scheduling scheme determination method and device and electronic equipment to at least solve the technical problem that the related art is not accurate enough in determining the propagation range of the failure when the rail transit fails, which leads to the inability to implement accurate emergency scheduling.

[0005] According to an aspect of an embodiment of the present application, a rail transit emergency scheduling scheme determination method is provided, including: performing graph theory modeling on a target rail transit system to obtain a directed graph, wherein the nodes in the directed graph represent stations, and the edges of the directed graph represent traffic lines between stations; when a failure occurs, determining a failure propagation result of the failure according to the directed graph, wherein the failure propagation result is used to determine the influence range of the failure; updating the failure area according to the failure propagation result, and determining an emergency scheduling scheme corresponding to the failure area.

[0006] In some embodiments of the present application, performing graph theory modeling on a target rail transit system to obtain a directed graph includes: determining network parameters corresponding to the network structure of the target rail transit system, wherein the network parameters include the number of lines in the network structure, the number of stations of each line; for each line, splitting each station into a single node according to the uplink and downlink direction to obtain a first number of nodes corresponding to the number of stations of each line; under the condition that the nodes remain unchanged, splitting each traffic line connecting the nodes into two single edges representing different directions to obtain a second number of edges corresponding to the number of lines; and determining the directed graph according to the first number of nodes and the second number of edges.

[0007] In some embodiments of the present application, further comprising: determining a number of origin-destination pairs corresponding to each line at any one time point, wherein the number of origin-destination pairs is used to represent the number of all possible combinations of any two nodes in the line node as the origin and the destination; determining a number of failure space points of the target rail transit system, wherein the failure space points include the traffic line sections and stations where the traffic capacity failure occurs; determining the origin-destination pair combinations affected by the traffic capacity failure of each failure space point; determining a number of failure origins corresponding to the number of failure space points according to the operation information of the target rail transit system, the failure origins reflecting different starting points due to the traffic capacity failure at any time.

[0008] In some embodiments of the present application, determining the directed graph according to the first number of nodes and the second number of edges comprises: accumulating the first number of nodes of all lines to obtain a number of composite nodes; obtaining a number of transfer nodes of the network structure of the target rail transit system from the network parameters, wherein the number of transfer nodes includes a number of two-line transfer nodes and a number of three-line transfer nodes; determining a target number of nodes of the directed graph according to the number of composite nodes and the number of transfer nodes; and determining the directed graph according to the target number of nodes and the second number of edges.

[0009] In some embodiments of the present application, when the traffic capacity failure occurs, determining the failure propagation result of the traffic capacity failure according to the directed graph comprises: determining the operation characteristics of the target traffic line where the traffic capacity failure occurs, wherein the operation characteristics are used to describe whether the target traffic line has a turnaround capability; in the case that the operation characteristics indicate that the target traffic line has the turnaround capability, determining to adopt a short cross-line operation mode, wherein the short cross-line operation mode is that the train runs back and forth in a sub-traffic line of the target traffic line; determining a short cross-line section corresponding to the sub-traffic line from the directed graph, wherein the short cross-line section contains the stations and the traffic lines through which the train runs; and determining the failure propagation result corresponding to the length of the short cross-line section.

[0010] In some embodiments of the present application, when the traffic capacity failure occurs, determining the failure propagation result of the traffic capacity failure according to the directed graph comprises: obtaining historical operation data of the target rail transit system, and determining the traffic demand corresponding to all historical path sets in the directed graph according to the historical operation data; determining all traffic capacity failure scenarios corresponding to the directed graph, and determining the shortest path set under each traffic capacity failure scenario, wherein the shortest path set includes a set of paths with the shortest travel time between a given starting point and a destination; determining a target path set containing the starting point of the traffic capacity failure from the shortest path set; iteratively matching the target path set with the historical path set to obtain a third node affected by the traffic capacity failure; and determining the failure propagation result according to the third node.

[0011] In some embodiments of the present application, the updating of the transport capacity failure area according to the failure propagation result comprises: obtaining the inbound and outbound traffic data of the third node; determining the dynamic load degree of the third node according to the inbound and outbound traffic data, wherein the dynamic load degree is used to quantitatively represent the real-time load condition of the third node; and updating the transport capacity failure area according to the dynamic load degree.

[0012] According to another aspect of the embodiments of the present application, a device for determining an emergency scheduling scheme of rail transit is also provided, which comprises: a modeling module, configured to model a target rail transit system by graph theory to obtain a directed graph, wherein a node in the directed graph represents a station and an edge of the directed graph represents a traffic line between stations; a determining module, configured to determine a failure propagation result of transport capacity failure according to the directed graph when the transport capacity failure occurs, wherein the failure propagation result is used to determine an influence range of the transport capacity failure; and an updating module, configured to update a transport capacity failure area according to the failure propagation result and determine an emergency scheduling scheme corresponding to the transport capacity failure area.

[0013] According to still another aspect of the embodiments of the present application, an electronic device is also provided, which comprises: a memory and a processor, the memory is configured to store program instructions; the processor is connected with the memory and is configured to execute the above-mentioned method for determining an emergency scheduling scheme of rail transit.

[0014] According to still another aspect of the embodiments of the present application, a non-volatile storage medium is also provided, which comprises a stored computer program, wherein a device in which the non-volatile storage medium is located executes the above-mentioned method for determining an emergency scheduling scheme of rail transit by running the computer program.

[0015] According to still another aspect of the embodiments of the present application, a computer program product is also provided, which comprises computer instructions, the computer instructions are executed by a processor to implement the above-mentioned method for determining an emergency scheduling scheme of rail transit.

[0016] In the embodiments of the present application, the rail transit system is modeled by graph theory, when the transport capacity failure occurs, the influence range of the transport capacity failure is determined according to the directed graph, and the transport capacity failure area and the emergency scheduling scheme corresponding to the transport capacity failure area are updated according to the influence range, so as to achieve the purpose of accurately evaluating the influence range of the transport capacity failure and quickly determining the emergency scheduling scheme, thereby realizing the technical effect of improving the response speed and scheduling efficiency of the rail transit system in the face of sudden transport capacity failure, and further solving the technical problem that the related art cannot accurately determine the failure propagation range when the rail transit system has transport capacity failure, which leads to the failure of accurate emergency scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0018] Figure 1 Fig. 1 is a hardware structure block diagram of a computer terminal of a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0019] Figure 2 Fig. 2 is a flow chart of a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0020] Figure 3 Fig. 3 is a schematic diagram of an emergency operation scheme of an axial line according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0021] Figure 4 Fig. 4 is a schematic diagram of a basic mode of an axial failure unreachable path according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0022] Figure 5 Fig. 5 is a schematic diagram of a network passenger flow influence quantity calculation parameter of an axial failure according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0023] Figure 6 Fig. 6 is a schematic diagram of an initial propagation boundary and unreachable node search calibration according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0024] Figure 7 Fig. 7 is a schematic diagram of a rapid spread period propagation boundary and unreachable node search calibration according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0025] Figure 8 Fig. 8 is a schematic diagram of an influence maximization propagation boundary and unreachable node search calibration according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0026] Figure 9 Fig. 9 is a schematic diagram of a propagation boundary and global node search a according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0027] Figure 10 Fig. 10 is a schematic diagram of a propagation boundary and global node search b according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0028] Figure 11 Fig. 11 is a schematic diagram of a propagation boundary and global node search c according to a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the application;

[0029] Figure 12 FIG. 2 is a schematic diagram of a propagation boundary and global node search of a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the present application;

[0030] Figure 13 FIG. 3 is a schematic diagram of a propagation boundary and global node search of a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the present application;

[0031] Figure 14 FIG. 4 is a schematic diagram of a propagation boundary and global node search of a determination method of an emergency dispatch scheme of rail transit according to an embodiment of the present application;

[0032] Figure 15 FIG. 5 is a structural schematic diagram of a determination device of an emergency dispatch scheme of rail transit according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0034] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0035] With the rapid development of urban rail transit systems, the safety and emergency dispatch of subway networks has become an important problem in system operation. In particular, when there is a failure of transport capacity, how to quickly identify the affected area and effectively respond to the emergency has become a key to improving the resilience and recovery ability of the rail transit system. For the handling of transport capacity failure events in the rail transit network, the related technologies mainly have the following problems:

[0036] (1) Failure propagation characteristics analysis is insufficient, and related technologies often fail to fully consider the dynamic propagation characteristics of the capacity failure event in the complex rail transit network, for example, ignoring the complex propagation mode under the influence of the propagation speed, direction, and factors such as the transfer node and line intersection of the failure event;

[0037] (2) The failure propagation range calculation is not accurate, and related technologies often rely on static model analysis when calculating the failure propagation range, which is difficult to adjust and reflect the actual situation of dynamic changes in real time, so that the decision basis of emergency scheduling may be biased, leading to resource mismatch and unable to effectively cover or control the actual affected area;

[0038] (3) The efficiency of emergency scheduling is low, and related technologies often have a long response time when dealing with capacity failure, and the scheduling decision is not fast enough, which is difficult to take effective action in the first time to alleviate the impact, leading to low efficiency of the coordination mechanism of emergency scheduling, and there is often delay or conflict in the information transmission and resource allocation between operating units.

[0039] To solve the above technical problems, the embodiments of the present application provide corresponding solutions, which are described in detail below.

[0040] The rail transit emergency scheduling scheme determination method embodiment provided by the embodiments of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing the rail transit emergency scheduling scheme determination method is shown. As Figure 1 shown, the computer terminal 10 can include one or more (shown in the figure as 102a, 102b, …, 102n) processors (the processor can include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication function connected through a wired and / or wireless network. In addition, it can also include a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the I / O interface), a network interface, a BUS bus. Those skilled in the art can understand, Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can also include more or fewer components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0041] It should be noted that the one or more processors and / or other data processing circuitry described above can be referred to herein generally as "data processing circuitry." The data processing circuitry can be embodied in whole or in part as software, hardware, firmware, or any combination thereof. Furthermore, the data processing circuitry can be a single standalone processing module, or it can be incorporated in whole or in part within any one of the other elements of the computer terminal 10. As referred to in the embodiments of the present application, the data processing circuitry functions as a processor to control, for example, the selection of the variable resistance terminal path in connection with the interface.

[0042] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage means corresponding to the determination method of rail transit emergency dispatching scheme in the embodiments of the present application. The processor executes the software programs and modules stored in the memory 104, thereby performing various functional applications and data processing, i.e., implementing the determination method of rail transit emergency dispatching scheme described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0043] The transmission module 106 is configured to receive or send data via a network. Specific examples of the above-mentioned network can include a wireless network provided by a communication service provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC) which can be connected to other network devices through a base station so as to be able to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (Radio Frequency, RF) module configured to communicate with the Internet in a wireless manner.

[0044] The display can be, for example, a touch screen type liquid crystal display (LCD) which can enable a user to interact with the user interface of the computer terminal 10.

[0045] It should be noted that in some alternative embodiments, the above-mentioned Figure 1 The computer terminal shown can include hardware elements (including circuitry), software elements (including computer code stored on a computer readable medium), or a combination of both hardware and software elements. It should be noted that in some embodiments, the functions of the computer terminal described above can be implemented as a plurality of separate modules, or as a plurality of separate hardware components. Figure 1 is merely one example of a particular implementation, and is intended to illustrate the types of components that can be present in the computer terminal described above.

[0046] Under the above operating environment, the embodiment of the present application provides a method for determining an emergency dispatching scheme of rail transit, and it should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0047] Figure 2 is a flowchart of a method for determining an emergency dispatching scheme of rail transit according to an embodiment of the present application, as shown in the figure, the method comprises the following steps: Figure 2

[0048] In step S202, the target rail transit system is modeled by graph theory to obtain a directed graph, wherein the nodes in the directed graph represent stations, and the edges of the directed graph represent the traffic routes between stations.

[0049] In step S202, the process of graph theory modeling is to abstract the physical structure and operating characteristics of the rail transit system into nodes and edges in graph theory, so as to facilitate analysis and calculation using mathematical methods, for example, by calculating the shortest path between nodes, the effective capacity of nodes, and the efficiency of information propagation, to analyze the propagation characteristics of the influence under the failure of transport capacity. The directed graph is a graph theory model in which the edges have directions, indicating the path from one node to another node, used to represent the directional connection between stations and simulate the running direction of trains and the directional propagation of failure events.

[0050] In some embodiments of the present application, each station in the rail transit network can be abstracted as a node in the directed graph, and the actual traffic path between each two stations is regarded as a directed edge, and the direction represents the running direction of the train, such as uplink or downlink. It should be noted that the transfer station can be processed as a multi-node connection to more accurately describe the path selection and passenger flow diversion in the transfer process.

[0051] Considering that the lines in the rail transit network usually support bidirectional operation, in some embodiments of the present application, each bidirectional line can be split into two independent directed edges, respectively representing the uplink and downlink directions, so that even if a transport capacity failure occurs at a certain point on a line, the system can accurately calculate the propagation and influence range of the failure in a single direction, without mistakenly considering the influence as also propagating in the other direction.

[0052] ​In order to more accurately simulate the actual running direction of the train, the target rail transit system can be modeled by graph theory in the following manner: determining network parameters corresponding to the network structure of the target rail transit system, wherein the network parameters include the number of lines in the network structure, the number of stations of each line; for each line, splitting each station into a unidirectional node according to the uplink and downlink directions to obtain a first number of nodes corresponding to the number of stations of each line; splitting each traffic line connecting the nodes into two unidirectional edges representing different directions while keeping the nodes unchanged to obtain a second number of edges corresponding to the number of lines; and determining a directed graph according to the first number of nodes and the second number of edges.

[0053] The directed graph is determined according to the first number of nodes and the second number of edges: the first number of nodes of all lines is accumulated to obtain a composite node number; the number of transfer nodes of the network structure of the target rail transit system is obtained from the network parameters, wherein the number of transfer nodes includes the number of two-line transfer nodes and the number of three-line transfer nodes; the target node number of the directed graph is determined according to the composite node number and the number of transfer nodes; and the directed graph is determined according to the target node number and the second number of edges.

[0054] Specifically, the method for modeling the rail transit network structure by graph theory includes:

[0055] For example, a rail network includes N lines, any line has K stations, and the number of transfer nodes is C (assuming that the number of single-line nodes is C single-line, and the number of common-line transfer nodes is C common-line, then C=C single-line+C common-line). The overall network constitutes a graph G={V,E}. Generally, when a network is modeled by graph theory, an edge is defined as a unidirectional edge. If it is a bidirectional edge, it needs to be split. Generally, the points are fixed and the edges are divided into two unidirectional edges. In the real world, the space edge of the traffic network including expressways, highways, and rails is actually a bidirectional edge. The impact of the failure of the transport capacity is generally propagated along a clear uniaxial directed edge. When determining the dynamic range of the spread space and searching the boundary, all the uplink and downlink intervals and the stations belonging to the uniaxial lines of the rail network need to be split into unidirectional nodes for graph theory modeling.

[0056] For a normal urban rail transit network, the number of station nodes after splitting any line according to the uplink and downlink directions is 2K, and the number of uniaxial line intervals is 2(K-1). The number of composite nodes V=∑2K without splitting is removed by removing the repeated nodes formed by the transfer nodes, and then V=∑[2K-C common-line]. After further splitting according to the uniaxial directed edge, E=∑[2(K-1)]. A typical urban rail network constitutes a graph G{V,E}=G(∑2K-C common-line,N*(K-1)*2). Assuming that it only includes two-line transfer nodes C2-line and three-line transfer nodes C3-line, C=C2-line+C3-line, i.e.:

[0057] (Formula 1)

[0058] (Formula 2)

[0059] In some embodiments of the present application, the following steps can also be performed: determining the number of origin-destination pairs corresponding to each line at any one time point, wherein the number of origin-destination pairs is used to represent the number of all possible combinations of any two nodes in the line node as the origin and the destination; determining the number of failure space points of the target rail transit system, wherein the failure space points include the traffic line sections and stations where the traffic capacity failure occurs; determining the combination of origin-destination pairs affected by the traffic capacity failure of each failure space point; and determining the number of failure origins corresponding to the number of failure space points according to the operation information of the target rail transit system, wherein the failure origins reflect different starting points due to the traffic capacity failure at any time.

[0060] The number of origin-destination pairs, also known as the number of OD (Origin-Destination) flow pairs, refers to the number of origin-destination pairs that can be formed between any two nodes (stations) in each line, reflecting the complexity of the OD flow (i.e., the flow of passengers from the departure station to the destination station) of the line.

[0061] The failure space point refers to the location in the rail transit system where the traffic capacity failure can occur, including specific traffic line sections and stations, and is used to quantitatively analyze the influence range of the failure event. When a failure space point has a traffic capacity failure, the influence set of all possible origin-destination pairs by this event is the combination of origin-destination pairs. The failure origin refers to the new origin generated due to the traffic capacity failure, i.e., the initial position affected by the failure event.

[0062] In some embodiments of the present application, according to the number of stations K of each line, after considering the uplink and downlink directions of each station, the number of all possible origin-destination pairs is calculated; according to the network structure and operation data of the target rail transit system, all possible points where the traffic capacity failure can occur are identified, including line sections and stations, to obtain the number of failure space points F S ; for each failure space point, list all possible combinations of origin-destination pairs that can be affected, for example, use the connectivity algorithm in graph theory, such as depth-first search (DFS) or breadth-first search (BFS), to determine which OD pairs cannot be reached or are delayed starting from the failure point; and combine real-time operation information such as train timetable, traffic density, passenger demand, etc., to dynamically determine the failure origins generated due to the traffic capacity failure at any time.

[0063] Specifically, any split individual node at any point in time constitutes an OD (origin-destination) flow pair number (2K-C2 line-C3 line-1), and the spatial point F of the rail transit network failure S =N*(K-1)*2, then the corresponding OD flow pair number N*(K-1)*2*(K*2-C2 line-C3 line-1) is formed; assuming that the OD flow time step of the rail network is 5 minutes and the operation time is T hours (i.e., operation information), the number of failure starting points formed by any space-time combination is F S-T , which is the basic data form number of diffusion spread and boundary search, that is:

[0064] (Formula 3)

[0065] (Formula 4)

[0066] Step S204, when the operation capacity failure occurs, determining the failure propagation result of the operation capacity failure according to the directed graph, wherein the failure propagation result is used to determine the influence range of the operation capacity failure.

[0067] In the above step S204, the operation capacity failure refers to the decline or complete loss of the transportation capacity of a line or a station in the rail transit system due to various factors, thereby affecting normal operation and passenger service.

[0068] The failure propagation result refers to the range and degree of the influence of the operation capacity failure calculated by the directed graph model when the operation capacity failure occurs. The influence range is the influence range caused by the operation capacity failure event on the whole or part of the rail transit system, including but not limited to the affected line section, station and surrounding area. It should be noted that the failure propagation result is not limited to direct physical influence, but also covers indirect operation adjustment, passenger flow change and other secondary effects.

[0069] In some embodiments of the present application, the directed graph can be traversed based on a depth-first search algorithm from the starting point of the operation capacity failure to explore all possible failure propagation paths. This process records all reachable nodes and edges on the path, thereby determining which line sections and stations will be directly affected. It should be noted that a breadth-first search algorithm can also be used to start from the occurrence point of the operation capacity failure and spread to the surrounding area. The node set that can be reached by the failure influence in each time step is calculated. Through this step-by-step expansion search method, the influence range of the operation capacity failure in different time periods can be accurately calculated.

[0070] In order to solve the problem of how to maximize the use of remaining capacity in the case of local failure, when the capacity failure occurs, the failure propagation result of the capacity failure can be determined in the following manner: determining the operation characteristics of the target traffic line where the capacity failure occurs, wherein the operation characteristics are used to describe whether the target traffic line has a turnaround capability; in the case where the operation characteristics indicate that the target traffic line has a turnaround capability, determining to adopt a short cross-line operation mode, wherein the short cross-line operation mode is that the train runs back and forth in a sub-traffic line of the target traffic line; determining a short cross-line section corresponding to the sub-traffic line from the directed graph, wherein the short cross-line section contains the stations and traffic lines passed by the train in operation; determining a failure propagation result corresponding to the length of the short cross-line section.

[0071] In some embodiments of the present application, by querying the operation file or real-time system of the line, it is confirmed whether the line is configured with a turnaround line and signal control equipment, and whether the turnaround operation can still be performed in the case of capacity failure; if the target line has a turnaround capability, the system will automatically adjust the train scheduling to run back and forth in the sub-line section with less impact; by using graph theory modeling, by analyzing the nodes and edges in the directed graph, it is determined which line sections (i.e. the combination of stations and lines) are not affected by the capacity failure and are suitable for short cross-line operation; by calculating the length and number of nodes of the short cross-line section, combined with the train running speed and transfer efficiency, the impact speed, edge and limit of the capacity failure under the short cross-line operation mode are estimated.

[0072] Specifically, the method for failure propagation and boundary calculation includes: when the line capacity fails, if the line adopts a short cross-line operation mode, the failure impact propagation speed, the edge and the limit of the impact under any operation scheme are different, if the starting line of the capacity failure has a turnaround capability, and the short cross-line section is M, then F S =M*N*2(K-1)*(2K-C 2线 -C 3线 -1), for example, taking a line in a city as an example, the number of short cross-line sections M=9 (reference Figure 3 ), then the failure propagation form F=1,571,400 starting from the line.

[0073] When the transport capacity failure occurs, the failure propagation result of the transport capacity failure can also be determined through the following steps: obtaining historical operation data of the target rail transit system, and determining the traffic demand corresponding to all historical path sets in the directed graph according to the historical operation data; determining all transport capacity failure scenarios corresponding to the directed graph, and determining the shortest path set under each transport capacity failure scenario, wherein the shortest path set includes a path set with the shortest travel time between a given starting point and a terminal point; determining a target path set containing the starting point of the transport capacity failure from the shortest path set; iteratively matching the target path set with the historical path set to obtain a third node affected by the transport capacity failure; and determining the failure propagation result according to the third node.

[0074] The historical operation data refers to the operation records of the rail transit system collected in the past period of time, including train running time, passenger flow, station utilization rate and other information, which are used to predict passenger flow patterns under normal and abnormal conditions. The traffic demand refers to the passenger demand from one station to another within a certain time window, which is obtained by analyzing the historical operation data.

[0075] The transport capacity failure scenario is a variety of situations that theoretically may occur, resulting in a decrease or loss of transport capacity of a station or line section. The shortest path set refers to the fastest path set from any starting point to the terminal point when the transport capacity failure occurs, which is found by a graph theory algorithm (such as Dijkstra algorithm). These paths will be used to guide emergency scheduling and passenger guidance.

[0076] The third node refers to the node determined to be affected by the transport capacity failure in the transport capacity failure analysis, including directly affected nodes and indirectly affected nodes due to failure propagation. In some embodiments of the present application, the target path set under each scenario can be iteratively matched with the historical path set to find those paths that are frequently used in historical operation but cannot normally pass due to the transport capacity failure. Any node appearing on these paths will be marked as a third node, i.e. a node affected by the transport capacity failure.

[0077] In step S206, the transport capacity failure area is updated according to the failure propagation result, and an emergency scheduling scheme corresponding to the transport capacity failure area is determined.

[0078] In the above step S206, the emergency scheduling scheme includes a set of strategies generated by the system to cope with the transport capacity failure, guide passenger travel and adjust the train operation plan after the transport capacity failure area is determined.

[0079] In some embodiments of the present application, the system updates the transport capacity failure area according to the nodes and line sections affected by the transport capacity failure determined in the failure propagation result, marks all failed stations and sections, and the surrounding area that may need to adjust the service; based on the updated transport capacity failure area, the system generates multiple emergency scheduling schemes through intelligent algorithms (such as genetic algorithm, simulated annealing algorithm, etc.), including adjusting train departure interval, enabling standby trains, arranging temporary transfer points, etc.; further, the optimal scheme can also be selected according to the cost-benefit analysis, passenger satisfaction prediction and other standards.

[0080] In some embodiments of the present application, the transport capacity failure area can be updated by: obtaining the inbound and outbound traffic data of the third node; determining the dynamic load degree of the third node according to the inbound and outbound traffic data, wherein the dynamic load degree is used to quantitatively represent the real-time load condition of the third node; updating the transport capacity failure area according to the dynamic load degree.

[0081] For example, the real-time data of passengers entering and leaving the station can be collected through the sensor network (such as gates, cameras) integrated in the station, and these data are aggregated and transmitted to the central processing system for analysis; the system compares the real-time collected inbound and outbound traffic data with the theoretical carrying capacity of the station, and calculates the dynamic load degree, for example, if the theoretical carrying capacity of a station is to handle 1000 passengers every 5 minutes, and the inbound and outbound traffic reaches 1200 passengers in a certain time window, the dynamic load degree in that time will exceed 100%, indicating that the station may be in an overload state; when the dynamic load degree of the third node exceeds a predetermined threshold (such as 70% or higher), the system adds the node to the transport capacity failure area. By continuously monitoring and updating the dynamic load degree of each node, the transport capacity failure area can be dynamically adjusted to identify the diffusion trend of the failure.

[0082] It should be noted that after the occurrence of transport capacity failure in any section, the diffusion and spread of passenger unreachable paths and the affected boundary are propagated from the node and section where the transport capacity decreases to the stations and lines far from the space position. In the constructed urban rail transit network diagram, the prominent feature is the staggered propagation between the shaft regions, the rapid directional spread in the urban region, the transfer of the node to the orbital shaft to the small range of orbital network in the satellite city, and the continued spread and dissipation.

[0083] That is, when transport capacity failure occurs in any section of the rail transit system (such as train failure, power interruption, signal system problem, etc.), the impact of this event is not limited to the directly failed section, but will quickly spread along the nodes and edges connected to the section in the network diagram, affecting the operation efficiency of the entire network and the travel plan of passengers. This process in the urban rail transit network shows a complex and directional spread phenomenon, which can be further divided into the following key points:

[0084] (1) Interlaced propagation between shafts: Shaft refers to the main line shaft in the rail transit system, which constitutes the backbone of the network. When the capacity failure occurs on a certain line shaft, its impact will spread to other line shafts through the transfer nodes connected to it, forming an interlaced propagation path. This phenomenon reflects the dependence and mutual influence between the lines within the network.

[0085] (2) Rapid directional spread within the urban area: Urban area usually refers to the city center area, which is the place with the highest demand for capacity and the most concentrated lines. Once a capacity failure occurs, the impact will rapidly spread in a directional manner due to the concentration of lines and transfer nodes, and the impact range will rapidly expand, causing a chain reaction on the transportation of the city center.

[0086] (3) Small-scale rail network in satellite city: Satellite city refers to areas far from the city center, and the rail network in these areas is relatively small and independent. When the impact of capacity failure spreads to the transfer nodes in the city center, this impact will be diverted to the rail network in the satellite city through the node, and continue to spread in a smaller range until the impact dissipates. This reflects the process of failure impact transfer between networks of different scales.

[0087] Figure 4 The basic mode of shaft line failure inaccessibility path is to fix the freedom of subway network physical form, that is, to set all stations not to exit operation under the condition of capacity failure, to allow train operation network to fix an emergency routing scheme under the condition of maintaining basic conditions such as locomotive, power, and signal, and to completely release the freedom of network passenger flow for propagation calculation and information coordination efficiency verification. Figure 4 In the figure, the shaft line (or line section) where the capacity failure occurs is marked in red, and the normal operation stations and sections are in blue. It describes how to adapt to this sudden situation through the emergency routing scheme after the shaft line failure, while keeping as many stations open and information unblocked as possible, and allowing passenger flow to flow freely in the open network. The purpose of this processing method is to minimize the impact of capacity failure on passenger travel, while testing the effectiveness of the information coordination mechanism in responding to emergencies to optimize future emergency response strategies and improve the resilience of rail transit systems.

[0088] Figure 5 is a schematic diagram of the calculation parameters of the impact of passenger flow on the shaft line failure network according to the determination method of the rail transit emergency dispatching scheme according to the embodiments of the present application, in which Figure 5 the meanings of the parameters are as follows:

[0089] : the flow entering the nth node station and going up;

[0090] : the flow entering the nth node station and going down;

[0091] : the uplink traffic from the nth node station;

[0092] : the downlink traffic from the nth node station;

[0093] : the uplink one-way axial traffic from the nth node to the nth+1 node;

[0094] : the downlink one-way axial traffic from the nth+1 node to the nth node;

[0095] : the total uplink traffic from the nth node station;

[0096] : the total downlink traffic from the nth node station;

[0097] Pnode: the number of passengers affected by the capacity failure between the nth node and the nth+1 node;

[0098] Pinterval: the number of passengers affected by the capacity failure between the nth interval and the nth+1 interval (including the number of passengers in the interval during the period);

[0099] Supplementary parameters:

[0100] : the uplink axial traffic per unit time when the capacity and the traffic are normally matched;

[0101] : the downlink axial traffic per unit time when the capacity and the traffic are normally matched;

[0102] C: the effective capacity of a node (interval), used for calculating the dynamic load degree of the node;

[0103] C B : the dynamic load degree of a node (interval).

[0104] The above parameters satisfy the following relationships:

[0105] (Formula 5)

[0106] (Formula 6)

[0107] (Formula 7)

[0108] (Formula 8)

[0109] In some embodiments of the present application, as Figures 6 to 8As shown, it describes the failure propagation boundary and the global node search of unreachable paths, wherein, Figure 6 The propagation boundary of the initial stage of failure impact in the rail network is shown, as well as the search calibration of the global nodes of unreachable paths. The legend and color coding here reflect the load status of different stations, and how the impact of the transport capacity failure spreads to the stations in the network over time (from 17:00 to 18:30). In the figure, red represents high dynamic load (more than 70%), orange represents higher dynamic load (more than 50%), green represents directly affected internal stations, and blue represents directly affected peripheral stations. Figure 7 The propagation boundary of the rapid spread stage of failure impact and the search calibration of the global nodes of unreachable paths are shown. This figure highlights the spread of transport capacity failure in the system and emphasizes that in this stage, not only the directly affected areas, but also the indirectly affected areas begin to appear. Figure 8 The propagation boundary of the maximum failure impact and the search results of the global nodes of unreachable paths are shown. In this stage, the geographical spatial range of failure impact reaches the maximum value, almost touching every corner of the network.

[0110] Specifically, the failure impact propagation calculation and information coordination control steps include:

[0111] Step 1: Based on the entire theoretical feasible OD path set of the rail network, first determine the path flow distribution table for the target date and time period according to the most similar principle in terms of workdays, non-workdays, weather, transportation plans, etc. in historical dates, calculate the directed path set R and its demand vector D. According to the 5-minute step, and assuming the total failure duration Tf, the number of distribution tables S is: S=N*(K-1)*2*(K*2-C2 line-C3 line-1)*Tf / 5.

[0112] Step 2: List the shortest path set P corresponding to all failure scenarios (number FS), including the normal train operation density in the corresponding time period of the historical similar day corresponding to the failure period, the station stop and interval travel time of each node, and list the point table of each node information device.

[0113] Step 3: Considering the influence of vehicle depot return and departure, combined with transportation organization control and passenger safety factors, the transport capacity failure is calculated in both directions from the incident point, and then propagates in both directions to the associated lines after passing through transfer stations. According to this principle, filter the paths in P that contain the failure starting node and all nodes, and iteratively match with R. When P is less than R, the corresponding O station is the node within the failure impact back-propagation range, and the boundary nodes and global nodes of each target period propagation are obtained by summarizing one by one, i.e. I boundary and I global.

[0114] Step 4: ∑( ) is the amount of progress, ∑( ) for the amount of non-out, with the node theory bearing capacity comparison, the dynamic load degree is calculated.

[0115] Step 5: Compare I boundary and I global with each node information device point table respectively, automatically trigger the propagation of boundary node information terminal, realize the dispersion of individual dynamic cooperative triggering linkage.

[0116] Through the above steps S202 to S206, by adopting the graph theory modeling of the rail transit system, when the transport capacity failure occurs, the influence range of the transport capacity failure is determined according to the directed graph, and the transport capacity failure area and the emergency dispatching scheme corresponding to the transport capacity failure area are updated according to the influence range, so as to accurately evaluate the influence range of the transport capacity failure, quickly determine the emergency dispatching scheme, so as to realize the technical effect of improving the response speed and scheduling efficiency of the rail transit system in the face of sudden transport capacity failure, and further solve the technical problems that the related technology cannot accurately determine the failure propagation range when the rail transit system fails, resulting in the inability to implement accurate emergency scheduling.

[0117] In some embodiments of the present application, assuming that in the rail transit network of a city and its surrounding cities, a certain subway line has a transport capacity failure, causing part of the area of the line to be unable to operate normally, the failure first occurs at station A and quickly affects adjacent stations B and C. According to the failure propagation model, the influence range propagates from the starting station A to other stations (such as B, C, etc.), and finally affects part of the entire network. The determination method of the rail transit emergency dispatching scheme can include the following steps:

[0118] S1: First, the rail transit network is modeled by graph theory to form a directed graph G, where each node represents a station and the edge represents the traffic path between stations. In this model, all station nodes and lines are split into single-direction edges to accurately simulate the propagation process of the failure. For example, the uplink and downlink directions of a certain rail line are split into two single-direction paths (as shown in the figure, a bidirectional line is split into two single-direction edges), which helps better describe the directed propagation of the failure and the calculation of the influence range.

[0119] S2: When the failure event occurs, based on the graph theory model, the influence boundary of the failure is determined by calculating the failure propagation of the nodes, for example, in this case, if the transport capacity of the subway line fails, the failure starts from station A and spreads rapidly through adjacent stations B, C, etc. Through boundary calculation, the system can accurately identify which stations are affected and which interval passengers cannot reach the target station in time.

[0120] S3: After the scope of failure propagation is determined, the system will use information sharing and collaborative control technology to dynamically update the failure impact area. For example, when the failure of station A affects stations B and C, the system will update the affected area in real time and issue warnings to the affected stations through information terminals, prompting station staff and passengers of possible congestion. These messages can be conveyed not only through the station broadcast system but also through mobile applications to provide real-time travel recommendations to passengers.

[0121] S4: Based on the results of failure propagation, the system will automatically calculate and optimize emergency dispatching schemes. For example, based on the current passenger flow at stations B and C, the system will adjust the train departure interval in real time, dispatch additional standby trains, or guide passengers to avoid congested stations through temporary transfer schemes. For example, if the passenger flow at station B starts to increase, the system can instruct passengers to divert the passenger flow through other lines or stations, or adjust the train dispatching density to avoid excessive congestion.

[0122] It should be noted that the system can also calculate the load of each station in real time (e.g., comparing the passenger flow in and out of the station with the carrying capacity of the station), and dynamically update the dispatching scheme based on these data. For example, if the load of station C is close to or exceeds the maximum carrying capacity, the system will automatically issue instructions to limit passenger access to the site and guide them to other less busy sites or transfer lines.

[0123] To facilitate understanding of the above process, a specific example is explained below.

[0124] To verify the universality of the algorithm, several sets of 5-minute granularity in and out of the station and path simulation data of the entire network were randomly generated to test the efficiency of the entire process of failure propagation search and collaborative emergency calculation.

[0125] According to formulas (3) and (4), the city and its surrounding satellite cities in a certain year have 24 lines of urban rail transit network, a total of 428 stations, and 64 transfer stations (including 3 three-line transfer stations) as the object, then F S =832; the F S-T =658,944 corresponding to any 5-minute period; if the operation duration is 17 hours, then the total F S-T (T=204)=134,424,576, i.e. according to the pre-screening and storage technology, more than 1.3*108 OD data tables need to be prepared. In the specific simulation verification, the failure duration is set to 60 minutes, relying on the Teradata data warehouse, F S-T(T=12) = 7,907,328 I-class OD table for fast calculation and derivation. The results show that the I-boundary and I-global propagation search speed is between 27-120s, which can quickly define the maximum impact space range of the operational failure at a specific moment, and the dynamic boundary search speed can meet the needs of situation early prediction, transfer node passenger transport organization and passenger information release; all affected station and interval information terminal matching speed is 2-4s, which can greatly improve the level of urban group rail network failure coordination emergency and information interaction release (reference Figures 9-14 ). At the same time, after I-global search, according to formula (5)-formula (8), the calculation and dynamic update of station node capacity bearing in the dynamic affected range with 5min step can be realized, and travel guidance information can be released and prompt to take passenger transport organization adjustment measures in advance.

[0126] Figures 9 to 14 For the propagation boundary and global node search schematic diagram, Figure 9 The rail transit network state just after the operational failure occurs (initial failure time, denoted as FT0) is captured. At this time point, the direct impact of the failure is limited to the initially affected stations and sections, but the emergency response mechanism has been activated. It is worth noting that Figure 9 In the figure, different colors are used to mark the dynamic load degree of the stations ( Figures 10-14 Similarly), red dots represent a load degree exceeding 70%, orange dots represent a load degree exceeding 50%, green dots represent directly affected internal stations, and blue dots represent directly affected outer stations. In Figure 10 At the time point of FT0+10min, the impact of the operational failure begins to expand, affecting more stations and sections, Figure 10 The dynamic load degree of each station is updated, showing how the failure impact spreads around the network through nodes and transfer points in a short time. At this time, information sharing and collaborative control technology begins to play a role between affected stations, trying to alleviate the pressure by adjusting train scheduling and passenger flow guidance strategies. In Figure 11 As the failure event continues to develop to FT0+20min, the affected range further expands, and the dynamic load degree of multiple key nodes in the network significantly increases. In Figure 12 By FT0+30min, the impact of the operational failure has reached a certain scale, and the dynamic load degree of multiple stations exceeds the normal range, and the emergency scheduling plan enters the full implementation stage. Figure 12 The system shows how to take measures to control passenger flow based on the previously calculated failure propagation range, such as adjusting the departure interval, increasing the standby train service, or arranging temporary transfers to alleviate the pressure on stations. In Figure 13In the FT0+40min, it can be seen that the system is trying to gradually restore normal service to the affected stations and sections by coordination and scheduling, although the impact of the failure is still there, but the emergency measures begin to show results, and the dynamic load of the stations begins to show signs of decline, especially in the areas where effective passenger flow diversion strategies have been taken. Figure 14 In the FT0+60min, most of the stations and sections affected by the failure of the transport capacity have begun to recover, or have achieved a controllable state through emergency scheduling, Figure 14 The system shows how to dynamically adjust the strategy to finally reduce the dynamic load of the station to an acceptable level and gradually restore normal operation in the long-term impact of the failure.

[0127] Figure 15 Figure 1 is a structural diagram of a determination device of an emergency scheduling scheme of a rail transit according to an embodiment of the present application, as shown in the figure, the device comprises: Figure 15

[0128] The modeling module 1502 is configured to perform graph theory modeling on the target rail transit system to obtain a directed graph, wherein the nodes in the directed graph represent stations, and the edges of the directed graph represent traffic lines between stations.

[0129] The determination module 1504 is configured to determine a failure propagation result of the failure of transport capacity according to the directed graph when the failure of transport capacity occurs, wherein the failure propagation result is used to determine the impact range of the failure of transport capacity.

[0130] The updating module 1506 is configured to update the failure area of transport capacity according to the failure propagation result, and determine an emergency scheduling scheme corresponding to the failure area of transport capacity.

[0131] It should be noted that, Figure 15 The determination device of the emergency scheduling scheme of the rail transit shown in the figure is used to execute the determination method of the emergency scheduling scheme of the rail transit shown in the figure, so Figure 2 The related explanations and descriptions in the determination method of the emergency scheduling scheme of the rail transit in Figure 2 The determination device of the emergency scheduling scheme of the rail transit shown in the figure is used to execute the determination method of the emergency scheduling scheme of the rail transit shown in the figure, so Figure 15 The related explanations and descriptions in the determination method of the emergency scheduling scheme of the rail transit in

[0132] The electronic device comprises a memory and a processor, wherein the memory is used to store program instructions; the processor is connected with the memory and is used to execute the steps of the determination method of the emergency scheduling scheme of the rail transit in the embodiments of the present application.

[0133] ​For example, the processor performs the following functions by executing program instructions stored in the memory: performing graph theory modeling on a target rail transit system to obtain a directed graph, wherein a node in the directed graph represents a station, and an edge of the directed graph represents a traffic line between stations; when a traffic capacity failure occurs, determining a failure propagation result of the traffic capacity failure according to the directed graph, wherein the failure propagation result is used to determine an influence range of the traffic capacity failure; and updating a traffic capacity failure area according to the failure propagation result, and determining an emergency dispatching scheme corresponding to the traffic capacity failure area.

[0134] The embodiment of the present application further provides a nonvolatile storage medium, which comprises a stored computer program, wherein a device in which the nonvolatile storage medium is located performs the steps of the method for determining an emergency dispatching scheme of a rail transit system in various embodiments of the present application by running the computer program.

[0135] The embodiment of the present application further provides a computer program product, which comprises computer instructions, and the computer instructions are executed by a processor to implement the steps of the method for determining an emergency dispatching scheme of a rail transit system in various embodiments of the present application.

[0136] The embodiment of the present application further provides a computer program, which is executed by a processor to implement the steps of the method for determining an emergency dispatching scheme of a rail transit system in various embodiments of the present application.

[0137] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0138] In the above-mentioned embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0139] In the several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the unit embodiment described above is only illustrative, and for example, the division of units can be a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0140] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0141] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0142] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0143] The above is only the preferred embodiment of the present application. It should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method for determining an emergency dispatching scheme of rail transit, characterized in that, The method comprises: performing graph theory modeling on a target rail transit system to obtain a directed graph, wherein a node in the directed graph represents a station, and an edge of the directed graph represents a traffic line between the stations; when a traffic capacity failure occurs, determining a failure propagation result of the traffic capacity failure according to the directed graph, wherein the failure propagation result is used to determine an influence range of the traffic capacity failure; updating a traffic capacity failure area according to the failure propagation result, and determining an emergency dispatching scheme corresponding to the traffic capacity failure area.

2. The method of claim 1, wherein, The method comprises: performing graph theory modeling on a target rail transit system to obtain a directed graph, comprising: determining network parameters corresponding to a network structure of the target rail transit system, wherein the network parameters include a number of lines in the network structure, and a number of stations for each line; for each line, splitting each station into a single-direction node according to an uplink direction or a downlink direction to obtain a first number of nodes corresponding to the number of stations of the line; without changing the nodes, splitting each traffic line connecting the nodes into two single-direction edges representing different directions to obtain a second number of edges corresponding to the number of lines; 3. The method of claim 2, wherein, determining the directed graph according to the first number of nodes and the second number of edges. The method further comprises: determining a number of origin-destination pairs corresponding to each line at an arbitrary time point, wherein the number of origin-destination pairs represents a number of all possible combinations of any two nodes in a line node as an origin and a destination; determining a number of failure space points of the target rail transit system, wherein the failure space points include traffic line sections and stations where a traffic capacity failure occurs; determining a number of combinations of origin-destination pairs affected by a traffic capacity failure of each failure space point; 4. The method of claim 2, wherein, determining a number of failure origins corresponding to the number of failure space points according to operation information of the target rail transit system, wherein the failure origins reflect different starting points due to a traffic capacity failure at an arbitrary time. Determining the directed graph according to the first number of nodes and the second number of edges comprises: accumulating the first number of nodes of all lines to obtain a number of composite nodes; obtaining a number of transfer nodes of the network structure of the target rail transit system from the network parameters, wherein the number of transfer nodes includes a number of two-line transfer nodes and a number of three-line transfer nodes; determining a target number of nodes of the directed graph according to the number of composite nodes and the number of transfer nodes; 5. The method of claim 1, wherein, determining the directed graph according to the target number of nodes and the second number of edges. When a traffic capacity failure occurs, determining a failure propagation result of the traffic capacity failure according to the directed graph comprises: determining an operation characteristic of a target traffic line where the traffic capacity failure occurs, wherein the operation characteristic is used to describe whether the target traffic line has a turnaround capability; in a case where the operation characteristic indicates that the target traffic line has a turnaround capability, determining to adopt a short cross-line operation mode, wherein the short cross-line operation mode is that a train runs back and forth in a sub-traffic line of the target traffic line. determine a short cross-line section corresponding to the sub-traffic line from the directed graph, wherein the short cross-line section contains stations and traffic lines through which a train runs; determine the failure propagation result corresponding to the length of the short cross-line section.

6. The method of claim 1, wherein, When a transport capacity failure occurs, determine the failure propagation result of the transport capacity failure according to the directed graph, including: obtain historical operation data of the target rail transit system, and determine the traffic demand corresponding to all historical path sets in the directed graph respectively according to the historical operation data; determine all transport capacity failure scenarios corresponding to the directed graph, and determine the shortest path set under each transport capacity failure scenario, wherein the shortest path set includes a set of paths with the shortest travel time between a given starting point and an ending point; determine a target path set containing the starting point of the transport capacity failure from the shortest path set; iteratively match the target path set with the historical path set to obtain a third node affected by the transport capacity failure; determine the failure propagation result according to the third node.

7. The method of claim 6, wherein, update the transport capacity failure area according to the failure propagation result, including: obtain the in-and-out station traffic data of the third node; determine the dynamic load degree of the third node according to the in-and-out station traffic data, wherein the dynamic load degree is used to quantitatively represent the real-time load condition of the third node; update the transport capacity failure area according to the dynamic load degree.

8. A device for determining an emergency dispatching scheme of rail transit, characterized in that, including: a modeling module configured to model a target rail transit system by graph theory to obtain a directed graph, wherein nodes in the directed graph represent stations, and edges of the directed graph represent traffic lines between the stations; a determining module configured to determine a failure propagation result of a transport capacity failure according to the directed graph when the transport capacity failure occurs, wherein the failure propagation result is used to determine an influence range of the transport capacity failure; an updating module configured to update a transport capacity failure area according to the failure propagation result, and determine an emergency dispatching scheme corresponding to the transport capacity failure area.

9. An electronic device, comprising: including: a memory and a processor, the memory is configured to store program instructions; the processor is connected with the memory and is configured to execute a determination method of a rail transit emergency dispatching scheme according to any one of claims 1 to 7.

10. A non-volatile storage medium, comprising: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the determination method of the rail transit emergency dispatching scheme according to any one of claims 1 to 7 by running the computer program.

11. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the determination method of the rail transit emergency dispatching scheme according to any one of claims 1 to 7.

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