Autonomous adjustment method and system for train working diagram

Abstract

The invention discloses an autonomous adjustment method and system for a train working diagram, which defines a corresponding large passenger flow event according to an evacuation area, an evacuation route, a heavy passenger flow direction and a diversion coefficient in the heavy passenger flow direction, and ensures the accuracy and rationality of the large passenger flow event. Based on the ATS database and the corresponding lines, configuring the corresponding total train amount, the approved passenger capacity of each train and the maximum passenger capacity; effective operating vehicles are calculated according to the total number of the trains, the approved passenger capacity of each train and the maximum passenger capacity; the method comprises the following steps: matching corresponding passenger flow density and driving density based on effective operating vehicles and routes, defining predicted passenger flow according to the passenger flow density and the driving density, and generating a train working diagram according to the predicted passenger flow, large passenger flow evacuation starting time, evacuation ending time and the maximum necessary number of repaired trains on a large passenger flow occurrence day. And the autonomous adjustment of the train working diagram is triggered, so that the accuracy of the train working diagram and the autonomous adjustment of the train working diagram are ensured to be compatible with different scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of train operation diagrams, and in particular to an autonomous adjustment method and system for a train operation diagram. Background Art

[0002] With the development of science and technology, trains are gradually applied to people's lives and are regulated according to the instructions of the train operation diagram. The train operation diagram fully reflects the scheduling and status of the train. In the existing technology, the train operation diagram is produced based on the previous scheduling data, and the evacuation situation is not taken into account. It is impossible to achieve multi-dimensional control of the evacuation area, evacuation route, heavy passenger flow direction and diversion coefficient in the heavy passenger flow direction, which affects the accuracy of large passenger flow events. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides an autonomous adjustment method and system for a train operation diagram, which collects each train line and matches the corresponding large passenger flow model; defines the corresponding evacuation area and evacuation route based on each large passenger flow model; matches the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction based on external factors and each large passenger flow model; defines the corresponding large passenger flow event according to the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction, introduces multiple controls of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction, realizes the multi-dimensional control of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction, and ensures the accuracy and rationality of the large passenger flow event.

[0004] Furthermore, in the event of large passenger flow, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity are determined based on the ATS database and the corresponding line configuration; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity; based on the passenger flow density and traffic density corresponding to the effective operating vehicles and line matching, the predicted passenger flow is defined according to the passenger flow density and traffic density, and a train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered, thereby achieving an overall consideration of the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, ensuring the accuracy of the train operation diagram and the autonomous adjustment of the train operation diagram to be compatible with different scenarios.

[0005] The embodiment of the present invention provides an autonomous adjustment method for a train operation diagram, which is applied to the autonomous adjustment scenario of a train operation diagram;

[0006] The autonomous adjustment method of the train operation diagram comprises:

[0007] Collect data from each train line and match it with the corresponding large passenger flow model;

[0008] Define corresponding evacuation areas and evacuation routes based on each large passenger flow model;

[0009] Match the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction based on external factors and various large passenger flow models;

[0010] Define the corresponding large passenger flow events according to the evacuation area, evacuation intersection, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction;

[0011] In the event of a large passenger flow, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity corresponding to the ATS database and the corresponding line configuration; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity;

[0012] Based on the passenger flow density and traffic density corresponding to the effective operating vehicles and line matching, the passenger flow is predicted according to the passenger flow density and traffic density definitions. The train operation diagram is generated based on the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered.

[0013] Optionally, collecting each train line and matching the corresponding large passenger flow model includes:

[0014] Collect line layout diagram;

[0015] Define each train line based on the line layout diagram and the corresponding trains;

[0016] Collect every train line;

[0017] Collect historical data for each train line;

[0018] Based on the training of each train line and the corresponding time matching the corresponding large passenger flow model.

[0019] Optionally, the defining corresponding evacuation areas and evacuation routes based on each large passenger flow model includes:

[0020] Freeze the large passenger flow model;

[0021] Trigger detection based on large passenger flow model and corresponding routes;

[0022] Defining evacuation routes based on large passenger flow models and corresponding route detection;

[0023] Associate evacuation routes and evacuation control logic;

[0024] Define the corresponding evacuation areas and evacuation routes according to the evacuation path and evacuation control logic.

[0025] Optionally, the matching of the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction based on external factors and each large passenger flow model includes:

[0026] Freeze each large passenger flow model;

[0027] Associate external factors with various large passenger flow models. In this case, the external factors are holiday factors or weather factors.

[0028] Based on external factors and various large passenger flow models, the corresponding heavy passenger flow directions and the diversion coefficients in the heavy passenger flow directions are matched.

[0029] Optionally, the definition of a corresponding large passenger flow event according to the evacuation area, the evacuation intersection, the heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction includes:

[0030] Fixed evacuation area, evacuation intersection, heavy passenger flow direction and diversion coefficient in heavy passenger flow direction;

[0031] Multiple interactions are performed on evacuation areas, evacuation routes, heavy passenger flow directions, and diversion coefficients in heavy passenger flow directions;

[0032] Multiple combinations are formed according to the multiple interactions of evacuation areas, evacuation intersections, heavy passenger flow directions and diversion coefficients in heavy passenger flow directions;

[0033] Defining event characteristics based on the identification of each multiple combination;

[0034] Define corresponding large passenger flow events based on the characteristics of each event.

[0035] Optionally, in the event of a large passenger flow, based on the ATS database and the total number of trains corresponding to the corresponding line configuration, the approved passenger capacity of each train, and the maximum passenger capacity; calculating the effective operating vehicles according to the total number of trains, the approved passenger capacity of each train, and the maximum passenger capacity, includes:

[0036] Freeze large passenger flow events and monitor them in real time;

[0037] In the event of a large passenger flow, the ATS database and the corresponding line configuration are collected and associated with the ATS database and the corresponding line configuration.

[0038] Optionally, in the event of a large passenger flow, the total number of trains, the approved passenger capacity of each train, and the maximum passenger capacity corresponding to the ATS database and the corresponding line configuration; calculating the effective operating vehicles according to the total number of trains, the approved passenger capacity of each train, and the maximum passenger capacity, further includes:

[0039] The total number of trains, the approved passenger capacity and the maximum passenger capacity of each train based on the ATS database and the corresponding line configuration;

[0040] The total number of associated trains, the approved passenger capacity of each train and the maximum passenger capacity;

[0041] The effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity.

[0042] Optionally, the passenger flow density and vehicle density corresponding to the effective operating vehicles and line matching are defined according to the passenger flow density and vehicle density, a train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day of large passenger flow, and the autonomous adjustment of the train operation diagram is triggered, including:

[0043] Define effective operation of vehicles and routes;

[0044] Based on effective operation of vehicles and routes matching corresponding passenger flow density and vehicle density;

[0045] The passenger flow is predicted based on the passenger flow density and vehicle traffic density and defined according to the passenger flow density and vehicle traffic density.

[0046] Optionally, the matching of the passenger flow density and the vehicle density corresponding to the effective operating vehicles and the lines, defining the predicted passenger flow according to the passenger flow density and the vehicle density, generating a train operation diagram according to the predicted passenger flow, the start time of the large passenger flow evacuation, the end time of the evacuation, and the maximum number of necessary vehicle repairs on the day when the large passenger flow occurs, and triggering the autonomous adjustment of the train operation diagram, also includes:

[0047] Collect the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day of large passenger flow;

[0048] Generate a train operation diagram based on the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day of large passenger flow;

[0049] Trigger autonomous adjustments to the train diagram based on changing signals.

[0050] In addition, an embodiment of the present invention further provides an autonomous adjustment system for a train operation diagram, characterized in that the autonomous adjustment system for a train operation diagram is applied to the autonomous adjustment method for a train operation diagram as claimed in any one of claims 1 to 9, and the autonomous adjustment system for a train operation diagram comprises:

[0051] The collection module is used to collect data from each train line and match it with the corresponding large passenger flow model;

[0052] The evacuation module is used to define the corresponding evacuation areas and evacuation routes based on various large passenger flow models;

[0053] A diversion module is used to match the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction based on external factors and various large passenger flow models;

[0054] A large passenger flow event module is used to define corresponding large passenger flow events according to evacuation areas, evacuation routes, heavy passenger flow directions and diversion coefficients in heavy passenger flow directions;

[0055] A calculation module is used to calculate the effective operating vehicles according to the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity in the event of large passenger flow based on the ATS database and the corresponding line configuration;

[0056] The train operation diagram module is used to match the corresponding passenger flow density and vehicle traffic density based on the effective operating vehicles and lines, and to define the predicted passenger flow according to the passenger flow density and vehicle traffic density. It generates a train operation diagram based on the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, and triggers autonomous adjustment of the train operation diagram.

[0057] In an embodiment of the present invention, through the method in the embodiment of the present invention, each train line is collected and matched with the corresponding large passenger flow model; the corresponding evacuation area and evacuation route are defined based on each large passenger flow model; the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are matched based on external factors and each large passenger flow model; the corresponding large passenger flow event is defined according to the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction, and multiple controls of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are introduced, and the multi-dimensional control of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction is realized, thereby ensuring the accuracy and rationality of the large passenger flow event.

[0058] Furthermore, in the event of large passenger flow, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity are determined based on the ATS database and the corresponding line configuration; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity; based on the passenger flow density and traffic density corresponding to the effective operating vehicles and line matching, the predicted passenger flow is defined according to the passenger flow density and traffic density, and a train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered, thereby achieving an overall consideration of the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, ensuring the accuracy of the train operation diagram and the autonomous adjustment of the train operation diagram to be compatible with different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 is a schematic flow chart of an autonomous adjustment method of a train operation diagram in an embodiment of the present invention;

[0061] Figure 2 is a schematic flow chart of S11 in the autonomous adjustment method of the train operation diagram in the embodiment of the present invention;

[0062] Figure 3 is a schematic flow chart of S12 in the autonomous adjustment method of the train operation diagram in the embodiment of the present invention;

[0063] Figure 4 is a schematic flow chart of S13 in the autonomous adjustment method of the train operation diagram in the embodiment of the present invention;

[0064] Figure 5 is a schematic flow chart of S14 in the autonomous adjustment method of the train operation diagram in the embodiment of the present invention;

[0065] Figure 6 is a schematic flow chart of S15 in the autonomous adjustment method of the train operation diagram in the embodiment of the present invention;

[0066] Figure 7 is a schematic flow chart of S16 in the autonomous adjustment method of the train operation diagram in the embodiment of the present invention;

[0067] Figure 8 It is a schematic diagram of the structure of the autonomous adjustment system of the train operation diagram in an embodiment of the present invention;

[0068] Fig. 9 The figure is a hardware diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] See also Figures 1 to 9 , a method for autonomously adjusting a train operation diagram, applied to a scenario of autonomously adjusting a train operation diagram; the method for autonomously adjusting a train operation diagram comprises:

[0071] Step S11: collect data from each train line and match it with a corresponding large passenger flow model;

[0072] Step S12: defining corresponding evacuation areas and evacuation routes based on each large passenger flow model;

[0073] Step S13: defining corresponding evacuation areas and evacuation routes based on each large passenger flow model;

[0074] Step S14: defining corresponding large passenger flow events according to the evacuation area, evacuation intersection, heavy passenger flow direction and diversion coefficient in the heavy passenger flow direction;

[0075] Step S15: In a large passenger flow event, based on the ATS database and the corresponding line configuration, the total number of trains, the approved passenger capacity of each train, and the maximum passenger capacity; the effective operating vehicles are calculated according to the total number of trains, the approved passenger capacity of each train, and the maximum passenger capacity;

[0076] Step S16: Based on the passenger flow density and vehicle traffic density corresponding to the effective operating vehicles and line matching, the predicted passenger flow is defined according to the passenger flow density and vehicle traffic density, and a train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, and autonomous adjustment of the train operation diagram is triggered.

[0077] In an embodiment of the present invention, through the method in the embodiment of the present invention, each train line is collected and matched with the corresponding large passenger flow model; the corresponding evacuation area and evacuation route are defined based on each large passenger flow model; the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are matched based on external factors and each large passenger flow model; the corresponding large passenger flow event is defined according to the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction, and multiple controls of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are introduced, and the multi-dimensional control of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction is realized, thereby ensuring the accuracy and rationality of the large passenger flow event.

[0078] Furthermore, in the event of large passenger flow, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity are determined based on the ATS database and the corresponding line configuration; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity; based on the passenger flow density and traffic density corresponding to the effective operating vehicles and line matching, the predicted passenger flow is defined according to the passenger flow density and traffic density, and a train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered, thereby achieving an overall consideration of the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, ensuring the accuracy of the train operation diagram and the autonomous adjustment of the train operation diagram to be compatible with different scenarios.

[0079] refer to Figure 2 , in step S11, each train line is collected and matched with the corresponding large passenger flow model;

[0080] In the specific implementation process of the present invention, the specific steps may be:

[0081] S111: Collecting line layout diagram;

[0082] S112: defining each train line based on the line layout diagram and the corresponding trains;

[0083] S113: collect data for each train line;

[0084] S114: Collecting past data for each train line

[0085] S115: Matching the corresponding large passenger flow model based on the training of each train line and the corresponding time.

[0086] In an embodiment of the present application, a line layout diagram is collected and introduced, and overall management and control are performed on the line layout diagram, so that each train line is defined based on the line layout diagram and the corresponding train, and the line layout diagram and the corresponding train are compatible, thereby realizing multiple interactions of the line layout diagram and the corresponding trains, and ensuring the accuracy of each train line.

[0087] Therefore, data is collected from each train line; past data of each train line is collected; the corresponding large passenger flow model is matched based on the training of each train line and the corresponding time, which is compatible with the overall consideration of the training of each train line and the corresponding time, and the multi-dimensional control of the training of each train line and the corresponding time is realized, ensuring the accuracy of the large passenger flow model. At this time, each train line matches the corresponding large passenger flow model.

[0088] refer to Figure 3, in step S12, corresponding evacuation areas and evacuation routes are defined based on each large passenger flow model;

[0089] In the specific implementation process of the present invention, the specific steps may be:

[0090] S121: Fixed-frame large passenger flow model;

[0091] S122: trigger detection based on a large passenger flow model and corresponding lines;

[0092] S123: defining an evacuation route according to a large passenger flow model and detection of corresponding routes;

[0093] S124: Associating evacuation paths and evacuation control logic;

[0094] S125: Define corresponding evacuation areas and evacuation routes according to the evacuation path and evacuation control logic.

[0095] In an embodiment of the present application, a large passenger flow model is frozen, and control is performed on the large passenger flow model. At the same time, detection is triggered based on the large passenger flow model and the corresponding lines. The evacuation path is defined according to the detection of the large passenger flow model and the corresponding lines, thereby realizing the detection of the large passenger flow model and the corresponding lines and ensuring the accuracy of the evacuation path.

[0096] At the same time, the evacuation path and evacuation control logic are associated; the corresponding evacuation areas and evacuation routes are defined according to the evacuation path and evacuation control logic, which realizes the overall control of the evacuation path and evacuation control logic, realizes the multi-dimensional control of the evacuation path and evacuation control logic, and ensures the accuracy of the evacuation area and evacuation route.

[0097] refer to Figure 4 In step S13, the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are matched based on external factors and each large passenger flow model;

[0098] In the specific implementation process of the present invention, the specific steps may be:

[0099] S131: freeze each large passenger flow model;

[0100] S132: Associating external factors with various large passenger flow models. At this time, the external factors are holiday factors or weather factors.

[0101] S133: Matching corresponding heavy passenger flow directions and diversion coefficients in the heavy passenger flow directions based on external factors and various large passenger flow models.

[0102] In an embodiment of the present application, each large passenger flow model is frozen and further controlled. At the same time, external factors and each large passenger flow model are associated. At this time, the external factors are holiday factors or weather factors. Based on the external factors and each large passenger flow model, the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are matched, and the heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are introduced, thereby realizing further control of the heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction.

[0103] refer to Figure 5 , S14: defining the corresponding large passenger flow event according to the evacuation area, the evacuation intersection, the heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction;

[0104] In the specific implementation process of the present invention, the specific steps may be:

[0105] S141: Fixed evacuation area, evacuation intersection, heavy passenger flow direction and diversion coefficient in heavy passenger flow direction;

[0106] S142: multiple interactions are performed on evacuation areas, evacuation routes, heavy passenger flow directions and diversion coefficients in heavy passenger flow directions;

[0107] S143: forming multiple combinations according to multiple interactions of evacuation areas, evacuation intersections, heavy passenger flow directions, and diversion coefficients in the heavy passenger flow directions;

[0108] S144: defining event features based on the identification of each multiple combination;

[0109] S145: Define corresponding large passenger flow events based on the characteristics of each event.

[0110] In an embodiment of the present application, each train line is collected and matched with a corresponding large passenger flow model; the corresponding evacuation areas and evacuation routes are defined based on each large passenger flow model; the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are matched based on external factors and each large passenger flow model; the corresponding large passenger flow events are defined according to the evacuation areas, evacuation routes, heavy passenger flow directions and the diversion coefficient in the heavy passenger flow direction, and multiple controls of the evacuation areas, evacuation routes, heavy passenger flow directions and the diversion coefficient in the heavy passenger flow direction are introduced, and multi-dimensional control of the evacuation areas, evacuation routes, heavy passenger flow directions and the diversion coefficient in the heavy passenger flow direction is realized, thereby ensuring the accuracy and rationality of large passenger flow events.

[0111] At this time, the diversion coefficients in the evacuation area, evacuation intersections, heavy passenger flow directions and heavy passenger flow directions are fixed, and the diversion coefficients in the evacuation area, evacuation intersections, heavy passenger flow directions and heavy passenger flow directions are introduced to carry out overall control of the diversion coefficients in the evacuation area, evacuation intersections, heavy passenger flow directions and heavy passenger flow directions.

[0112] Furthermore, multiple interactions are performed on the diversion coefficients in the evacuation area, evacuation intersections, heavy passenger flow directions and heavy passenger flow directions; multiple combinations are formed according to the multiple interactions of the diversion coefficients in the evacuation area, evacuation intersections, heavy passenger flow directions and heavy passenger flow directions, and multiple combinations are introduced to achieve overall control of the diversion coefficients in the evacuation area, evacuation intersections, heavy passenger flow directions and heavy passenger flow directions, thereby defining event characteristics based on the identification of each multiple combination; corresponding large passenger flow events are defined based on each event characteristic, thereby ensuring the accuracy of large passenger flow events.

[0113] Therefore, according to the definitions of large passenger flow events corresponding to evacuation areas, evacuation routes, heavy passenger flow directions and diversion coefficients in heavy passenger flow directions, multiple controls of evacuation areas, evacuation routes, heavy passenger flow directions and diversion coefficients in heavy passenger flow directions are introduced, realizing multi-dimensional control of diversion coefficients in evacuation areas, evacuation routes, heavy passenger flow directions and heavy passenger flow directions, ensuring the accuracy and rationality of large passenger flow events.

[0114] refer to Figure 6 , S15: In a large passenger flow event, based on the ATS database and the corresponding line configuration corresponding to the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity; the effective operating vehicles are calculated according to the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity;

[0115] In the specific implementation process of the present invention, the specific steps may be:

[0116] S151: freeze large passenger flow events and monitor them in real time;

[0117] S152: In a large passenger flow event, collecting the ATS database and the corresponding route configuration, and associating the ATS database and the corresponding route configuration;

[0118] S153: The total number of trains, the approved passenger capacity and the maximum passenger capacity of each train corresponding to the ATS database and the corresponding line configuration;

[0119] S154: The total number of associated trains, the approved passenger capacity of each train and the maximum passenger capacity;

[0120] S155: Calculate the effective operating vehicles based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity.

[0121] In the embodiments of the present application, a large passenger flow event is frozen and monitored in real time, thereby achieving real-time monitoring of the large passenger flow event and further controlling the real-time monitoring.

[0122] Therefore, in large passenger flow events, the ATS database and the corresponding line configuration are collected and associated; based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity corresponding to the ATS database and the corresponding line configuration, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity are introduced.

[0123] Furthermore, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity are associated; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity, which is compatible with the overall consideration of the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity, and realizes multi-dimensional control of the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity, ensuring the calculation accuracy of the effective operating vehicles.

[0124] refer to Figure 7 , S16: Based on the passenger flow density and vehicle density corresponding to the effective operating vehicles and line matching, the predicted passenger flow is defined according to the passenger flow density and vehicle density, and the train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day of large passenger flow, and the autonomous adjustment of the train operation diagram is triggered;

[0125] In the specific implementation process of the present invention, the specific steps may be:

[0126] S161: Fixed effective operation of vehicles and routes;

[0127] S162: Matching corresponding passenger flow density and vehicle density based on effective operating vehicles and routes;

[0128] S163: predicting passenger flow according to passenger flow density and vehicle density;

[0129] S164: Collect the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs;

[0130] S165: Generate a train operation diagram based on the predicted passenger flow, the start time of the large passenger flow evacuation, the end time of the evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs;

[0131] S166: Triggering autonomous adjustment of the train operation diagram based on the change signal.

[0132] During the specific implementation of the present invention, in large passenger flow events, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity corresponding to the ATS database and the corresponding line configuration are used; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity; based on the passenger flow density and traffic density corresponding to the effective operating vehicles and line matching, the predicted passenger flow is defined according to the passenger flow density and traffic density, and a train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered, thereby achieving an overall consideration of the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, ensuring the accuracy of the train operation diagram and the autonomous adjustment of the train operation diagram to be compatible with different scenarios.

[0133] At this time, freeze the effective operating vehicles and routes; match the corresponding passenger flow density and vehicle density based on the effective operating vehicles and routes; predict the passenger flow based on the passenger flow density and vehicle density, and define the passenger flow density and vehicle density. It is compatible with the passenger flow density and vehicle density, and realizes multi-dimensional control of the passenger flow density and vehicle density, ensuring the accuracy of the predicted passenger flow.

[0134] Therefore, the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when large passenger flow occurs are collected; the train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when large passenger flow occurs; and the autonomous adjustment of the train operation diagram is triggered based on the change signal.

[0135] At the same time, the train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered, realizing the overall consideration of the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, ensuring the accuracy of the train operation diagram and the autonomous adjustment of the train operation diagram to be compatible with different scenarios.

[0136] In another embodiment of the present invention, the purpose of the present invention is to solve the defects of the above-mentioned prior art and provide a method for automatically adjusting the timetable according to the predictable large passenger flow, the direction of heavy passenger flow and the ratio of heavy passenger flow, so as to replace the automatic adjustment by the dispatcher. The method includes the definition of evacuation models with different ratios of passenger flow in both directions and the calculation of operation diagrams.

[0137] I. Evacuation routes, heavy passenger flow directions and heavy passenger flow ratios

[0138] A large passenger flow model is defined for each route. Each large passenger flow model needs to define the evacuation area, evacuation intersection, heavy passenger flow direction and diversion coefficient in the heavy passenger flow direction.

[0139] The evacuation area refers to the area with large passenger flow.

[0140] The main evacuation direction is the operating direction in which the main large passenger flow leaves the station where the passenger flow occurs.

[0141] The line needs to determine the corresponding evacuation area for each large passenger flow according to its spatial characteristics, and determine the temporary routes used for the evacuation of large passenger flows based on the evacuation areas.

[0142] The terminal stations at both ends of the temporary route are the turning stations at the boundary of the evacuation area. If the boundary station is not a turning station, it is the nearest turning station outside the evacuation area.

[0143] Each evacuation route also needs to define the heavy passenger flow direction and heavy passenger flow ratio based on the location of the high-passenger flow station in the evacuation area and historical data.

[0144] Next, we take the route shown in Figure (1) as an example to illustrate that each route can predict large passenger flow events.

[0145] And how to define large passenger flow evacuation areas and routes for different types of events.

[0146] The line has 23 stations. The operation direction from station 1 to station 23 is the upward direction, and the operation direction from station 23 to station 1 is the downward direction. Stations 1, 5, 9, 17, 19, 21 and 23 are turnaround stations for large and small routes. All stations from station 9 to 17 are located in the city center; stations 7 and 8 are near the Expo Center; the stadium is near station 21. Station 5 is a hub station, station 17 is in a key business district, and there are large tourist attractions near station 20.

[0147] Stations with large passenger flow for exhibitions and cultural and sports events are those near the exhibition center and stadium.

[0148] The evacuation area is from the busiest stations to the border stations of the city center.

[0149] The evacuation direction is from stations with large passenger flow to the city center.

[0150] Establish an evacuation area of ​​the Expo Center from Station 7 to 17 and an evacuation intersection from Station 5 to 17, with the heavy passenger flow direction being upward and the diversion coefficient being 1. Establish an evacuation area and evacuation intersection of the Stadium from Station 21 to Station 9, with the heavy passenger flow direction being downward and the heavy passenger flow ratio being 1.

[0151] During holidays, a large number of passengers will accumulate at hub stations, key business districts, and stations near tourist attractions. The large passenger flow evacuation area includes stations in various hub stations, key business districts, and tourist attractions. The evacuation direction is from these passenger flow distribution stations to the city center. Define an evacuation area for the line that includes all hub stations, stations near key business districts, stations near tourist attractions, and stations in the city center. If there are passenger flow distribution stations on both sides of the city center, the evacuation direction is in both directions.

[0152] The following figure shows the holiday evacuation area with large passenger flow. If there is only a passenger flow distribution station on the city center side, the direction of this area is from the passenger flow distribution station to the city center. For short holidays and National Day, a holiday evacuation area and route from station 5 to station 20 are established. The evacuation direction of the large passenger flow station 5 is upward, and the evacuation direction of the large passenger flow stations 17 and 20 is downward. The downward direction is the heavy passenger flow direction, and the heavy passenger flow ratio is 2 / 3.

[0153] After the Spring Festival, when people return to their hometowns to visit relatives, a large passenger flow will flow from the hub station to the city center. For the large passenger flow after the Spring Festival, an evacuation area and routes are defined from station 5 to station 17. The direction of heavy passenger flow is upward, and the heavy passenger flow ratio is 1.

[0154] In bad weather, passenger flow at stations generally increases. The heavy passenger flow evacuation route is the normal commuter route. Both directions are evacuation directions, and the passenger flow is balanced. In this case, the upward direction is designated as the heavy passenger flow direction, and the heavy passenger flow ratio is 1 / 2.

[0155] 2. Run the graph calculation

[0156] 1) Passenger capacity calculation Configure the total number of line trains, the approved passenger capacity of each train, and the maximum passenger capacity in the ATS database. rated and the maximum passenger capacity after overloading S max Calculated as follows:

[0157] S rated = K rated × B (1)

[0158] S max = (K rated + K over ) × B (2)

[0159] In formula (1) and (2), K rated is the approved passenger capacity of each car; K over is the overload tolerance of each car; B is the train composition.

[0160] 2) Effective operation vehicle calculation

[0161] Secondly, according to the vehicle situation of the current line, the effective operating vehicles are calculated as:

[0162] C atrain = C ttrain – C mtain (3)

[0163] In formula (3), C ttrain is the total number of trains on the line; C mtain The number of vehicles repaired daily after adjusting the maintenance plan for periods of high passenger flow.

[0164] 3) Determination of passenger flow density

[0165] Predicted passenger flow density D predicted It is the predicted number of passengers entering, leaving and staying at the station per hour. It can be predicted by the passenger flow prediction system or the user based on the passenger flow density of the same large passenger flow event in history and the passenger flow data provided by the event organizer. predicted If the station's maximum density is greater than the station's designed maximum density, the station's designed maximum density is used as the predicted passenger flow density to ensure that the operating interval after adding cars in the heavy passenger flow direction cannot be less than the minimum operating interval designed for this line. In fact, in this case, the station will definitely regulate the passenger flow organization to make the actual passenger flow density lower than the station's designed maximum density.

[0166] 4) Calculation of vehicle density

[0167] Finally, calculate the number of trains C required in the direction of heavy passenger flow pritrain and operating interval H pri , the number of trains required for light passenger flow evacuation direction C sectrain and operating interval H sec .

[0168] Based on the maximum passenger capacity of the train, calculate the number of evacuation trains C in the direction of heavy passenger flow pritrain and operating interval H pri The formula is as follows:

[0169] C pritrain = (D predicted × X1) / S max (4)

[0170] H pri =60 minutes / C pritrain (5)

[0171] In formula (4), X1 is the heavy passenger flow ratio.

[0172] The calculation formula for the number of available trains and operating intervals in the light passenger flow evacuation direction is as follows:

[0173] C sectrain = (D predicted × (1-X1)) / Smax + C secnortrain(6)

[0174] In formula (6), C secnortrain is the number of trains normally used in the direction of light passenger flow. pritrain +C sectrain ) If it is greater than the number of trains available on the entire line C atrain , then recalculate the number of trains in the light passenger flow direction according to the following formula:

[0175] C sectrain = C atrain - C pritrain (7)

[0176] H sec = 60 minutes / C sectrain (8)

[0177] Calculate according to the maximum passenger capacity of the train (C pritrain +C sectrain ) If it is less than the number of trains available on the entire line C atrain , indicating that there are still available trains. In order to ensure the comfort of passengers as much as possible, the number of evacuation trains that can be added is recalculated based on the rated passenger capacity of the train. added :

[0178] C added = (D predicted / S rated ) –C pritrain (9)

[0179] If we can increase the number of evacuation trains C added If the number of available trains is greater than the remaining available trains, all remaining available trains are added as evacuation trains to the direction of heavy passenger flow. added is the number of remaining available trains, otherwise only increase C added Then recalculate the operating interval H in the direction of heavy passenger flow pri :

[0180] H pri =60 minutes / (C pritrain +C added ). (10)

[0181] 5) Determination of evacuation period

[0182] Define the evacuation start time T1 and end time T2 for each evacuation area. For example, the start time of the evacuation of large passenger flow at an exhibition is defined as the closing time, and the duration is 1 hour. The start time of the evacuation of large passenger flow at cultural and sports events is defined as the end time, and the duration is 1 hour. The start time of the evacuation of large passenger flow on holidays is defined as 8 am to 4 pm. The evacuation time of large passenger flow in severe weather can be positioned as 8 am to 6 pm. These parameters can be determined based on the large passenger flow forecast data of this route.

[0183] 6) Determination of the opening ratio

[0184] If the evacuation route is different from the normal operation route, it is necessary to determine the ratio of evacuation trains and normal trains departing from stations with large passenger flow T3. If large passenger flow occurs during peak hours in the morning and evening, in order to reduce the impact on commuting passengers, evacuation trains and normal trains can be deployed from stations with large passenger flow at a ratio of 1:1. If large passenger flow occurs during non-peak hours, in order to evacuate the large passenger flow as quickly as possible, the ratio of evacuation trains and normal trains can be determined according to the ratio of sudden passenger flow to normal passenger flow.

[0185] 7) Generate a run diagram

[0186] After predicting that a large passenger flow will occur and obtaining the forecast information, select the corresponding large passenger flow event on the ATS interface.

[0187] Input the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, and generate an operation diagram according to the above algorithm.

[0188] The algorithm assumes that all spare trains can be parked in advance on temporary parking tracks or depot transfer tracks near stations with large passenger flows.

[0189] Therefore, when carrying out sudden large passenger flow evacuation, driving organization is difficult, driving intervals are close, the impact is wide, and there are many concerns. If the driving plan is not adjusted correctly and quickly, and the large passenger flow is not evacuated in a timely and effective manner, it may cause passenger unrest and cause stampede accidents. The invention provides a method for automatically calculating the operation diagram based on a two-way large passenger flow evacuation model, which can cover the one-way large passenger flow evacuation of large events, and the two-way large passenger flow evacuation scenarios during holidays and bad weather. It replaces the manual adjustment of the dispatcher, is timely, reliable, efficient and intelligent, and avoids the loss of the best driving adjustment opportunity caused by manual operation, the reduction of system operation capacity, and even the risk of causing passenger stampede.

[0190] In an embodiment of the present invention, through the method in the embodiment of the present invention, each train line is collected and matched with the corresponding large passenger flow model; the corresponding evacuation area and evacuation route are defined based on each large passenger flow model; the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are matched based on external factors and each large passenger flow model; the corresponding large passenger flow event is defined according to the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction, and multiple controls of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction are introduced, and the multi-dimensional control of the evacuation area, evacuation route, heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction is realized, thereby ensuring the accuracy and rationality of the large passenger flow event.

[0191] Furthermore, in the event of large passenger flow, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity are determined based on the ATS database and the corresponding line configuration; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity; based on the passenger flow density and traffic density corresponding to the effective operating vehicles and line matching, the predicted passenger flow is defined according to the passenger flow density and traffic density, and a train operation diagram is generated according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered, thereby achieving an overall consideration of the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when the large passenger flow occurs, ensuring the accuracy of the train operation diagram and the autonomous adjustment of the train operation diagram to be compatible with different scenarios.

[0192] See also Figure 8 , Figure 8 It is a schematic diagram of the structural composition of the autonomous adjustment system of the train operation diagram in an embodiment of the present invention.

[0193] like Figure 8 As shown, a system for autonomously adjusting a train operation diagram, the system for autonomously adjusting a train operation diagram comprises:

[0194] The collection module 21 is used to collect data from each train line and match it with a corresponding large passenger flow model;

[0195] An evacuation module 22, used to define corresponding evacuation areas and evacuation routes based on each large passenger flow model;

[0196] A flow distribution module 23, used for matching the corresponding heavy passenger flow direction and the flow distribution coefficient in the heavy passenger flow direction based on external factors and each large passenger flow model;

[0197] A large passenger flow event module 24 is used to define a corresponding large passenger flow event according to an evacuation area, an evacuation route, a heavy passenger flow direction and a diversion coefficient in the heavy passenger flow direction;

[0198] A calculation module 25 is used to calculate the effective operating vehicles according to the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity in the event of a large passenger flow based on the ATS database and the corresponding line configuration;

[0199] The train operation diagram module 26 is used to match the passenger flow density and vehicle traffic density corresponding to the effective operating vehicles and lines, define the predicted passenger flow according to the passenger flow density and vehicle traffic density, generate a train operation diagram according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, and trigger autonomous adjustment of the train operation diagram.

[0200] See also Fig. 9 , refer to the following Fig. 9 The electronic device 40 according to this embodiment of the present invention will be described. Fig. 9 The electronic device 40 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0201] like Fig. 9 As shown, the electronic device 40 is in the form of a general computing device. The components of the electronic device 40 may include but are not limited to: at least one processing unit 41, at least one storage unit 42, and a bus 43 connecting different system components (including the storage unit 42 and the processing unit 41).

[0202] The storage unit stores program codes, which can be executed by the processing unit 41, so that the processing unit 41 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0203] The storage unit 42 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0204] The storage unit 42 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0205] Bus 43 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0206] The electronic device 40 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 40, and / or any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 44. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 45. Fig. 9 As shown, the network adapter 45 communicates with other modules of the electronic device 40 via the bus 43. It should be understood that although Fig. 9 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup planning systems.

[0207] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0208] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. In addition, it stores computer program instructions, and when the computer program instructions are executed by a computer, the computer executes the above method.

[0209] In addition, the above describes in detail the autonomous adjustment method and system of the train operation diagram provided in the embodiments of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for autonomously adjusting a train operation diagram, characterized in that: Applied to the scenario of autonomous adjustment of train operation diagram; The autonomous adjustment method of the train operation diagram comprises: Collect data from each train line and match it with the corresponding large passenger flow model; Define corresponding evacuation areas and evacuation routes based on each large passenger flow model; Match the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction based on external factors and various large passenger flow models; Define the corresponding large passenger flow events according to the evacuation area, evacuation intersection, heavy passenger flow direction and diversion coefficient in the heavy passenger flow direction; In the event of a large passenger flow, the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity corresponding to the ATS database and the corresponding line configuration; the effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity; Based on the passenger flow density and traffic density corresponding to the effective operating vehicles and line matching, the passenger flow is predicted according to the passenger flow density and traffic density definitions. The train operation diagram is generated based on the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day when large passenger flow occurs, and the autonomous adjustment of the train operation diagram is triggered.

2. The autonomous adjustment method of the train operation diagram according to claim 1, characterized in that: The collecting of each train line and matching the corresponding large passenger flow model includes: Collect line layout diagram; Define each train line based on the line layout diagram and the corresponding trains; Collect every train line; Collect historical data for each train line; Based on the training of each train line and the corresponding time matching the corresponding large passenger flow model.

3. The autonomous adjustment method of the train operation diagram according to claim 1, characterized in that: The corresponding evacuation areas and evacuation routes are defined based on each large passenger flow model, including: Freeze the large passenger flow model; Trigger detection based on large passenger flow model and corresponding routes; Defining evacuation routes based on large passenger flow models and corresponding route detection; Associate evacuation routes and evacuation control logic; Define the corresponding evacuation areas and evacuation routes according to the evacuation path and evacuation control logic.

4. The autonomous adjustment method of the train operation diagram according to claim 3, characterized in that: The matching of the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction based on external factors and each large passenger flow model includes: Freeze each large passenger flow model; Associate external factors with various large passenger flow models. In this case, the external factors are holiday factors or weather factors. Based on external factors and various large passenger flow models, the corresponding heavy passenger flow directions and the diversion coefficients in the heavy passenger flow directions are matched.

5. The autonomous adjustment method of the train operation diagram according to claim 4, characterized in that: The corresponding large passenger flow events are defined according to the evacuation area, the evacuation intersection, the heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction, including: Fixed evacuation area, evacuation intersection, heavy passenger flow direction and diversion coefficient in heavy passenger flow direction; Multiple interactions are performed on evacuation areas, evacuation routes, heavy passenger flow directions, and diversion coefficients in heavy passenger flow directions; Multiple combinations are formed according to the multiple interactions of evacuation areas, evacuation intersections, heavy passenger flow directions and diversion coefficients in heavy passenger flow directions; Defining event characteristics based on the identification of each multiple combination; Define corresponding large passenger flow events based on the characteristics of each event.

6. The autonomous adjustment method of the train operation diagram according to claim 5, characterized in that: In the event of a large passenger flow, the total number of trains, the approved passenger capacity and the maximum passenger capacity of each train are configured based on the ATS database and the corresponding line; The effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity, including: Freeze large passenger flow events and monitor them in real time; In the event of a large passenger flow, the ATS database and the corresponding line configuration are collected and associated with the ATS database and the corresponding line configuration.

7. The autonomous adjustment method of the train operation diagram according to claim 6, characterized in that: In the event of a large passenger flow, the total number of trains, the approved passenger capacity and the maximum passenger capacity of each train are configured based on the ATS database and the corresponding line; The effective operating vehicle is calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity, and also includes: The total number of trains, the approved passenger capacity and the maximum passenger capacity of each train based on the ATS database and the corresponding line configuration; The total number of associated trains, the approved passenger capacity of each train and the maximum passenger capacity; The effective operating vehicles are calculated based on the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity.

8. The autonomous adjustment method of the train operation diagram according to claim 7, characterized in that: The method is based on matching the passenger flow density and vehicle density corresponding to the effective operating vehicles and lines, defining the predicted passenger flow according to the passenger flow density and vehicle density, generating a train operation diagram according to the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, and triggering the autonomous adjustment of the train operation diagram, including: Define effective operation of vehicles and routes; Based on effective operation of vehicles and routes matching corresponding passenger flow density and vehicle density; The passenger flow is predicted based on the passenger flow density and vehicle traffic density and defined according to the passenger flow density and vehicle traffic density.

9. The autonomous adjustment method of the train operation diagram according to claim 8, characterized in that: The method of matching the passenger flow density and the vehicle density corresponding to the effective operating vehicles and the lines, defining the predicted passenger flow according to the passenger flow density and the vehicle density, generating a train operation diagram according to the predicted passenger flow, the start time of the large passenger flow evacuation, the end time of the evacuation, and the maximum number of necessary vehicle repairs on the day when the large passenger flow occurs, and triggering the autonomous adjustment of the train operation diagram, also includes: Collect the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day of large passenger flow; Generate a train operation diagram based on the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary repairs on the day of large passenger flow; Trigger autonomous adjustments to the train diagram based on changing signals.

10. An autonomous adjustment system for a train operation diagram, characterized in that: The autonomous adjustment system of the train operation diagram is applied to the autonomous adjustment method of the train operation diagram according to any one of claims 1 to 9, and the autonomous adjustment system of the train operation diagram comprises: The collection module is used to collect data from each train line and match it with the corresponding large passenger flow model; The evacuation module is used to define the corresponding evacuation areas and evacuation routes based on various large passenger flow models; A diversion module is used to match the corresponding heavy passenger flow direction and the diversion coefficient in the heavy passenger flow direction based on external factors and various large passenger flow models; A large passenger flow event module is used to define corresponding large passenger flow events according to evacuation areas, evacuation routes, heavy passenger flow directions and diversion coefficients in heavy passenger flow directions; A calculation module is used to calculate the effective operating vehicles according to the total number of trains, the approved passenger capacity of each train and the maximum passenger capacity in the event of large passenger flow based on the ATS database and the corresponding line configuration; The train operation diagram module is used to match the corresponding passenger flow density and vehicle traffic density based on the effective operating vehicles and lines, and to define the predicted passenger flow according to the passenger flow density and vehicle traffic density. It generates a train operation diagram based on the predicted passenger flow, the start time of large passenger flow evacuation, the end time of evacuation, and the maximum number of necessary vehicle repairs on the day when large passenger flow occurs, and triggers autonomous adjustment of the train operation diagram.

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