A multi-scenario and multi-strategy train operation control method and system under severe interference

Abstract

The present disclosure provides a multi-scenario, multi-strategy train operation control method and system under severe interference, relating to the field of rail transit technology. The method includes obtaining initial operation data of high-speed railway trains within the operation control range; predicting the development trend of severe environmental interference, estimating the type of impact of severe environmental interference on the interval's passing capacity and the duration of the interference; establishing a train operation adjustment model under multiple interference scenarios under the influence of the severe environment on the train's interval's passing capacity, with minimizing the degree of train delays and minimizing the weighted sum of train cancellation and delay penalties as optimization goals, creating constraints, and solving the train operation adjustment model using a two-stage algorithm. The train operation control plan before and after the interference ends are obtained, respectively, to obtain a final train operation control global map. The present disclosure can quickly provide a near-optimal train operation control adjustment map.

Description

Technical Field

[0001] The present disclosure relates to the field of rail transit technology, and in particular to a multi-scenario and multi-strategy train operation control method under severe interference. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] As people's travel needs increase, considering factors such as distance, comfort, and safety, high-speed rail has gradually become people's first choice for long-distance travel.

[0004] To promote high-quality railway development and improve railway service, the safety, stability, and speed of high-speed railways have become key priorities. However, the high-speed railway network is vast, transport organization is complex, and the cross-line area and operating environment are highly variable. Therefore, trains are highly susceptible to various disruptions during operation. These disruptions can be broadly categorized into three types: equipment, environmental, and human factors. Depending on the severity of the disruption, emergencies can be categorized as minor or major. Minor disruptions are those that can be restored through minor adjustments to the train timetable, such as arranging trains to run on time, adjusting train arrival and departure sequences, and shortening train stops. Major disruptions are those that, in addition to minor disruption adjustments, require significant adjustments to the timetable, such as suspending trains or reversing trains. When a train experiences a minor disruption, dispatchers can quickly and rationally formulate a transport plan based on real-time information to ensure a rapid and orderly restoration of train operations. However, when trains experience severe disruptions, the high-speed railway dispatching system cannot implement real-time adjustments. For example, when a railway section is impacted by external factors such as strong winds, heavy rain, or snow, train operations can be disrupted. The impact of environmental factors on train operation adjustments differs from that caused by fixed equipment failures. The impact of rain, snow, wind, and other environmental factors on train operations often develops over a period of time. When the rain or snow is mild, trains can continue to operate normally. However, when the impact reaches a certain level, it will affect normal operation. Different levels of speed limit strategies are required for different levels of disruption. In severe cases, trains may even be prohibited from traveling along certain sections. Dispatching based on staff experience is highly subjective. This approach of dispatchers relying on experience has significant limitations, and the resulting train operation adjustment plans fail to meet multiple requirements, such as passenger satisfaction and train operation.

[0005] There are some technologies for adjusting train operations under minor and major interference, but they still have the following defects: (1) Most of the methods proposed so far are for adjusting train operations under minor interference, and the adjustment methods generally involve changing train arrival and departure times, shortening train stop times, etc., which cannot quickly restore train operations under major interference. (2) Most of the research on adjusting train operations under major interference focuses on adjusting train operations when the section's passing capacity is completely lost due to equipment failures, etc., without considering the major interference caused by the harsh external environment. (3) The problem of adjusting train operations under major interference has high timeliness requirements for the solution time of the adjustment plan. Some existing technical solutions use commercial solvers for solution, which has low solution efficiency. Summary of the Invention

[0006] In order to solve the above problems, the present disclosure proposes a multi-scenario and multi-strategy train operation control method and system under severe interference. Taking into account the severity of the interference situation, a train scheduling optimization model is constructed to minimize delay time and the number of canceled trains. Train operation is adjusted by various measures such as speed limit operation, train cancellation, changing train operation sequence, and changing train arrival and departure times. The method and system are solved through a two-stage algorithm to obtain a train operation scheduling plan under harsh environments, thereby improving the efficiency of high-speed railway train operation control adjustment and improving train operation safety.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] A multi-scenario and multi-strategy train operation control method under severe interference, comprising:

[0009] Obtaining initial operation data of high-speed railway trains within the operation control range;

[0010] Predict the development trend of severe environmental conditions based on real-time or historical weather data, and estimate the type of impact severe environmental disturbances will have on the area's throughput capacity and the duration of the disturbances;

[0011] A train operation adjustment model is established under multiple interference scenarios where harsh environments affect the train section passing capacity. The optimization objectives are to minimize the degree of train delays and minimize the weighted sum of train cancellation and delay penalties. Constraints are created, and a two-stage algorithm is used to solve the train operation adjustment model. The train operation control schemes before and after the end of the interference are obtained respectively, and the final global train operation control diagram is obtained.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions:

[0013] A multi-scenario and multi-strategy train operation control system under severe interference, including:

[0014] A data acquisition module is used to obtain the initial operation data of high-speed railway trains within the operation control range;

[0015] Interference classification module, used to predict the development trend of severe environment based on real-time or historical weather data, estimate the type of impact of severe environmental interference on the section's passing capacity and the duration of the interference;

[0016] The operation control solution module is used to establish a train operation adjustment model under multiple interference scenarios where harsh environments affect the train section's passing capacity. The optimization objectives are to minimize the degree of train delays and minimize the weighted sum of train cancellation and delay penalties. Constraints are created and a two-stage algorithm is used to solve the train operation adjustment model. The train operation control schemes before and after the end of the interference are obtained respectively, to obtain the final global train operation control diagram.

[0017] According to some embodiments, the present disclosure adopts the following technical solutions:

[0018] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the multi-scenario and multi-strategy train operation control method under severe interference is implemented.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the multi-scenario and multi-strategy train operation control method under severe interference.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] This disclosure abstracts the elements and common strategies involved in the operation of high-speed trains into a mathematical model and linearizes it. Unlike manual experience adjustment, the train operation adjustment plan obtained by this method is more reasonable and scientific.

[0023] The present disclosure not only focuses on the scenario where the train section passing capacity fails, but also divides the impact of the external environment on the section passing capacity into three situations: the section passing capacity only decreases, the section passing capacity only fails, and the section passing capacity first decreases to failure and then recovers within a certain period of time. On the basis of adjusting the train arrival and departure times and the train operation sequence, multiple strategies such as train cancellation and speed limit operation are introduced. The present invention has higher adaptability.

[0024] This disclosure also includes an efficient two-stage solution algorithm. In severe interference situations, the exact solution obtained using the solver takes a long time to be solved. In severe interference situations, it is more important to quickly provide a near-optimal adjustment map than a global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0026] Figure 1 This is a schematic diagram of train operation adjustment when the section throughput capacity decreases in an embodiment of the present disclosure;

[0027] Figure 2 A schematic diagram of train operation adjustment in the case of section throughput failure in an embodiment of the present disclosure;

[0028] Figure 3 Schematic diagram of train operation adjustment in a case where the section throughput capacity first decreases to failure and then recovers in an embodiment of the present disclosure;

[0029] Figure 4 This is a flow chart of a method for adjusting train operation control under severe interference conditions in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0032] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0033] Example 1

[0034] In one embodiment of the present disclosure, a multi-scenario and multi-strategy train operation control method under severe interference is provided, comprising the following steps:

[0035] Step 1: Obtain the initial operation data of high-speed railway trains within the operation control range;

[0036] Step 2: Predict the development trend of severe environmental conditions based on real-time or historical weather data, and estimate the type of impact of severe environmental interference on the section's throughput capacity and the duration of the interference;

[0037] Step 3: Establish a train operation adjustment model for multiple interference scenarios where harsh environments affect the train's section passing capacity. Minimize the degree of train delays and minimize the weighted sum of train cancellation and delay penalties as the optimization objectives. Create constraints and solve the train operation adjustment model using a two-stage algorithm. Obtain the train operation control plan before and after the end of the interference, and obtain the final global train operation control diagram.

[0038] As an embodiment, a specific implementation process of a multi-scenario and multi-strategy train operation control method under severe interference is as follows:

[0039] Step 1: Collect relevant data of high-speed railway trains within the scope of operation adjustment: the train's scheduled arrival and departure times at each station, planned stop plans, and safe operation time intervals;

[0040] Step 2: Predict the development trend of severe environmental conditions, including strong winds, heavy rain, and snowstorms, based on real-time or historical information. Estimate the type of impact of severe interference on the section's throughput capacity and the duration of the interference.

[0041] Severe environmental interference can impact section capacity in three ways: a mere decrease in section capacity, a mere failure, and a period of decline followed by recovery. A train operation adjustment model is established for each of these three scenarios, employing various measures to adjust train operations, including speed restrictions, train cancellations, changes in train sequence, and changes in train arrival and departure times. Speed restrictions require the configuration of a speed limit factor.

[0042] Step 3: Establish a train operation adjustment model based on the impact of harsh environments on the train section's passing capacity;

[0043] Specifically, the conditions for constructing the train operation adjustment model under multiple interference scenarios include:

[0044] 1) There are sufficient lines to and from the origin and destination stations;

[0045] 2) Considering the passenger evacuation problem during mid-line suspension, the cancelled trains in the model refer to the cancellations at the originating station;

[0046] 3) The train can stop at any station along the route;

[0047] 4) Trains that have not passed through the interference section before the section through-capacity failure occurs return to the previous station and wait for the section through-capacity to be restored;

[0048] 5) Up and down trains do not interfere with each other during operation.

[0049] The present disclosure considers the impact of severe environments such as strong winds, heavy rain, and heavy snow on the section throughput capacity and divides it into three categories: the section throughput capacity only decreases, the section throughput capacity only fails, and the section throughput capacity first decreases to failure and then recovers within a certain period of time. In these three cases, the train arrival and departure time constraints, train speed limit constraints, and prohibition of passage constraints during the period of failure of throughput capacity are considered. With the train operation sequence, arrival and departure time, and whether to cancel the train as decision variables, a train scheduling optimization model is constructed to minimize the delay time and the number of canceled trains. Various measures such as speed limit operation, train cancellation, change of train operation sequence, and change of train arrival and departure time are adopted to adjust the train operation, and the solution is obtained through a two-stage algorithm.

[0050] A train operation adjustment model is established under multiple interference scenarios where harsh environments affect train section throughput. The optimization objectives are to minimize train delays and minimize the weighted sum of train cancellation and delay penalties. Constraints are created and a two-stage algorithm is used to solve the train operation adjustment model, specifically:

[0051] The objective function to be optimized with the goal of minimizing the degree of train delays and minimizing the weighted sum of train cancellations and delay penalties is:

[0052]

[0053] Where K is the set of trains; S is the set of railway line stations; i is the station subscript; k, k' are the train subscripts; is the actual arrival time of train k at station i after the timetable adjustment; is the planned arrival time of subsequent train k at station i; is the actual departure time of train k at station i after the timetable adjustment; is the scheduled departure time of subsequent train k at station i; is a binary variable, indicating that train k leaves from station i * Is it cancelled? Train k is at the departure station i. * The value is 1 when the train is canceled, otherwise it is 0; μ1 and μ2 represent the penalty value for the train deviating from the planned operation diagram and the penalty value for the canceled train, respectively.

[0054] The created constraints include:

[0055] (1) The train will not be earlier than the planned operation diagram constraint

[0056] The adjusted departure and arrival times of trains at various stations should not be earlier than the departure and arrival times in the train schedule.

[0057]

[0058]

[0059] Where: M is a sufficiently large positive number, here it is 1440.

[0060] (2) Minimum train stop time constraint

[0061] Normally operating trains must ensure a minimum stop time at stations with planned stops. That is:

[0062]

[0063] Where: x ki is a binary variable, indicating whether train k stops at station i or not. When the stop plan is adjusted, the value is 1 if train k stops at station i, otherwise it is 0; is the shortest time required for train k to stop at station i; k is the departure station of train k; D k It is the terminal station for train k.

[0064] (3) Train stop plan constraints

[0065] In order to ensure smooth boarding and alighting of passengers and necessary technical operations, the train must stop at the stations where it is scheduled to stop in the original stop plan.

[0066]

[0067] Where: u ki Indicates whether train k stops at station i in the planned stop plan. The value is 1 if the planned stop is made, and 0 otherwise.

[0068] (4) Restrictions on trains being prohibited from passing through sections

[0069] In [t 4 ,t 5 During the period of ], the section through-capacity fails and trains are prohibited from passing through, so departure operations cannot be performed at this station. The train waiting at the first station i′ affected by the failure of the section through-capacity is not allowed to depart to the section (i', i'+1). That is:

[0070]

[0071] Where: t 4 is the starting time of failure of the section passing capacity; t 5is the end time of failure of the section passing capability; [t 4 ,t 5 ] is the time period during which the section’s passing capacity fails.

[0072] When a section prohibits trains from passing through, the train must stop at the station ahead of the section where the train's ability to pass is invalid and wait until the interference ends.

[0073]

[0074]

[0075]

[0076]

[0077] in: is a binary variable; It takes the value 1 when train k leaves station i before the interference ends, and takes the value 0 otherwise; It takes the value 1 when train k arrives at station i before the interference ends, otherwise it takes the value 0; It means that the value is 1 when train k arrives at station i+1 before the interference starts, and 0 otherwise.

[0078] When a train stops and waits at a station, it needs to occupy a station line. In order to ensure that the number of trains in the station before the prohibited train passage section is less than the station capacity, it is necessary to establish an interference capacity constraint for the station ahead. That is:

[0079]

[0080] Where: represents the number of trains that arrive at station i before the interference ends. represents the number of trains that leave station i before the interference ends. represents the number of trains that stay at station i before the interference ends.

[0081] (5) Constraints related to the reduction of section capacity

[0082] In [t 1 ,t 2 During the period of ], the section's capacity decreases and the train needs to reduce its speed. That is:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] in: o k It is a binary variable used to assist in deciding whether a train is affected by the reduction in section capacity; represents the shortest running time of the interval (i,i+1); θ is the speed reduction factor.

[0090] (6) Train tracking interval and overtaking constraints

[0091] To avoid collisions between trains, adjacent trains must meet spacing constraints when running at stations or sections.

[0092]

[0093]

[0094]

[0095]

[0096] Among them: H a 、H d are the minimum safe time intervals between two adjacent trains arriving at and leaving the same station; kk'i is a binary variable representing the order in which train k and train k' depart from station i and arrive at station i+1. It takes the value 1 if train k departs from station i and arrives at station i+1 before train k', and 0 otherwise. These constraints ensure that trains can only overtake each other at stations and are prohibited from overtaking each other within a section. That is, the order in which two adjacent trains depart from station i is the order in which they arrive at the next station i+1.

[0097] (7) Station capacity and departure / arrival time constraints

[0098] When a train arrives or leaves a station, it must occupy a station line. The station needs to reserve a station line for the train to occupy in advance. Therefore, a train capacity constraint is required to ensure that there is at least one idle line for the train to use when it passes through the station. That is:

[0099]

[0100]

[0101]

[0102] Where: kk'iis a binary variable, which takes the value of 1 if the arrival time of train k at station i is earlier than the arrival time of train k' at the same station, otherwise it takes the value of 0; kk'i is a binary variable, which takes the value 1 if the departure time of train k at station i is earlier than the arrival time of train k' at the station, otherwise it takes the value 0; C i For station i capacity; represents the number of trains that have arrived at station i before train k' arrives at the station; represents the number of all trains that departed from station i before train k' arrived at the station i.

[0103] represents the number of trains that stop at station i when train k' arrives at station i.

[0104] In order to ensure the safe operation of trains and smooth entry and exit of the station, the departure and arrival time intervals of two adjacent trains need to be met. That is:

[0105]

[0106] Where: H ad Indicates the minimum arrival and departure interval of trains.

[0107] (8) Interval running time constraints

[0108] To ensure the safety of train operation, it is necessary to meet the interval operation time constraints. That is:

[0109]

[0110]

[0111] Where: Represents the shortest running time of the interval (i,i+1); represents the longest running time of the interval (i,i+1); Δt q Represents the additional time for the train to depart after stopping at the station; Δt t Represents the additional time required for the train to brake before stopping at a station.

[0112] Step 4: Determine the degree of impact of the adverse environment on the through-pass capacity of the interval: if it only causes the through-pass capacity of the interval to decrease, remove the constraint group (4) and solve the model; if it only causes the through-pass capacity of the interval to fail, remove the constraint group (5) and solve the model; if the through-pass capacity of the interval first decreases to failure and then recovers within a certain period of time, directly solve the above model.

[0113] Step 5: Based on the solution of the above model, control and adjust the train operation diagram.

[0114] The model established by this invention is an NP-hard problem, making it difficult to find an optimal solution in a short period of time for large-scale problems. The train operation adjustment algorithm of this embodiment employs a two-stage algorithm to solve the problem. To speed up the solution, the original problem is decomposed into two stages. The first stage only adjusts trains departing from the originating station before the end of the interference, ignoring the station capacity constraints after the interference ends. This optimizes the original problem and obtains a train operation adjustment plan before the interference ends. The second stage fixes the train operation adjustment plan of the first stage, considers the capacity constraints after the interference ends, and solves the train operation diagram after the interference ends, thereby obtaining a global operation diagram.

[0115] Schematic diagram of operation adjustment in three cases, Figure 1 In the case of reduced interval capacity, the speed reduction is adopted. Figure 2 、 Figure 3 In addition to reducing the speed, it is also necessary to stop and wait for the section's passing capacity to be restored, or even cancel the train plan.

[0116] Example 2

[0117] In one embodiment of the present disclosure, a multi-scenario and multi-strategy train operation control system under severe interference is provided, including:

[0118] A data acquisition module is used to obtain the initial operation data of high-speed railway trains within the operation control range;

[0119] Interference classification module, used to predict the development trend of severe environment based on real-time or historical weather data, estimate the type of impact of severe environmental interference on the section's passing capacity and the duration of the interference;

[0120] The operation control solution module is used to establish a train operation adjustment model under multiple interference scenarios where harsh environments affect the train section's passing capacity. The optimization objectives are to minimize the degree of train delays and minimize the weighted sum of train cancellation and delay penalties. Constraints are created and a two-stage algorithm is used to solve the train operation adjustment model. The train operation control schemes before and after the end of the interference are obtained respectively, to obtain the final global train operation control diagram.

[0121] Example 3

[0122] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the multi-scenario and multi-strategy train operation control method under severe interference is implemented.

[0123] Example 4

[0124] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the multi-scenario and multi-strategy train operation control method under severe interference.

[0125] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A multi-scenario and multi-strategy train operation control method under severe interference, characterized in that: include: Obtaining initial operation data of high-speed railway trains within the operation control range; Predict the development trend of severe environmental conditions based on real-time or historical weather data, and estimate the type of impact severe environmental disturbances will have on the area's throughput capacity and the duration of the disturbances; A train operation adjustment model is established for multiple interference scenarios where harsh environments affect train section throughput. The optimization objectives are to minimize train delays and minimize the weighted sum of train cancellation and delay penalties. Constraints are created and a two-stage algorithm is used to solve the train operation adjustment model. The train operation control plans before and after the interference are obtained, respectively, to obtain the final global train operation control diagram. The created constraints include: train no earlier than the planned operation diagram constraint, train minimum stop time constraint, train stop plan constraint, section train prohibition related constraints, section through capacity reduction related constraints, train tracking interval and overtaking constraints, station capacity and departure time constraints, and section operation time constraints; The train operation adjustment model is solved using a two-stage algorithm. The two-stage algorithm is as follows: in the first stage, only trains departing from the originating station before the end of the interference are adjusted, and the station capacity constraints after the end of the interference are ignored. The original problem is optimized to obtain a train operation adjustment plan before the end of the interference; In the second stage, the train operation adjustment plan of the first stage is fixed, the capacity constraint after the interference ends is considered, and the train operation diagram after the interference ends is solved to obtain the global operation diagram.

2. The multi-scenario and multi-strategy train operation control method under severe interference according to claim 1, characterized in that: The initial train operation data includes: the train's planned arrival and departure times at each station, planned stop plans, and safe operation time intervals.

3. The multi-scenario and multi-strategy train operation control method under severe interference according to claim 1, characterized in that: The conditions for constructing a train operation adjustment model under multiple interference scenarios include: a sufficient number of departure and arrival lines from the originating and terminal stations; considering the passenger evacuation problem during mid-line stoppages, the canceled trains in the model refer to the cancellation of the originating station; trains can stop at all stations along the way; trains that have not passed through the interference section before the section's through-capacity failure occurs return to the previous station to wait for the section's through-capacity to be restored; and uplink and downlink trains do not interfere with each other during operation.

4. The multi-scenario and multi-strategy train operation control method under severe interference according to claim 1, characterized in that: The objective function to be optimized with the goal of minimizing the degree of train delays and minimizing the weighted sum of train cancellations and delay penalties is: in, Assemble for the train; Assemble for railway line stations; bid for the station; Submit bid for the train; After the operation diagram is adjusted, the train At the station The actual arrival time; For subsequent trains At the station The planned arrival time; After the operation diagram is adjusted, the train At the station The actual time of departure; For subsequent trains At the station The planned departure time; is a binary variable, representing the train At the departure station Is the train cancelled? At the departure station The value is 1 when canceled, otherwise it is 0; They represent the penalty value for train deviation from the planned operation diagram and the penalty value for train cancellation respectively.

5. The multi-scenario and multi-strategy train operation control method under severe interference according to claim 1, characterized in that: The types of impacts of severe environmental interference on section capacity include: section capacity only decreases, section capacity only fails, and section capacity first decreases to failure and then recovers within a certain period of time. A train operation adjustment model is established under these three types of situations, and a variety of measures such as speed limit operation, train cancellation, change of train operation sequence, and change of train arrival and departure times are adopted to adjust train operation.

6. A multi-scenario multi-strategy train operation control system under severe interference using the multi-scenario multi-strategy train operation control method under severe interference as claimed in claim 1, characterized in that: include: A data acquisition module is used to obtain the initial operation data of high-speed railway trains within the operation control range; Interference classification module, used to predict the development trend of severe environment based on real-time or historical weather data, estimate the type of impact of severe environmental interference on the section's passing capacity and the duration of the interference; The operation control solution module is used to establish a train operation adjustment model under multiple interference scenarios where harsh environments affect the train section's passing capacity. The optimization objectives are to minimize the degree of train delays and minimize the weighted sum of train cancellation and delay penalties. Constraints are created and a two-stage algorithm is used to solve the train operation adjustment model. The train operation control schemes before and after the end of the interference are obtained respectively, to obtain the final global train operation control diagram.

7. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the multi-scenario and multi-strategy train operation control method under severe interference as described in any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement a multi-scenario and multi-strategy train operation control method under severe interference as described in any one of claims 1 to 5.

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