A high-speed railway motor train unit operation situation deduction method and system

Abstract

The application provides a high-speed railway motor train unit operation situation deduction method and system, relates to the field of railway engineering technology, and comprises the following steps: based on high-speed railway motor train unit connection rules and train information in a train diagram, a connection network of a motor train unit to be deduced is constructed; a motor train unit operation situation deduction model is constructed based on the connection network, the motor train unit operation situation deduction model comprises a target function and a constraint condition; the motor train unit operation situation deduction model is deduced based on an interference event and the connection network, a rolling time domain algorithm and a heuristic method based on a field strategy are combined to solve, and an operation adjustment scheme of the motor train unit to be deduced is obtained. The application solves the problem that the high-speed railway motor train unit operation is easily interfered by uncertain factors, leading to actual deviation from the plan, and solves the problems that the current situation, the future trend are difficult to accurately grasp and intelligent scheduling is difficult to support.

Description

Technical Field

[0001] This invention relates to the field of railway engineering technology, and more specifically, to a method and system for predicting the operational status of high-speed railway EMUs. Background Technology

[0002] With the continuous expansion of the high-speed railway network, the increasing complexity of the network, and the steady growth of the number of high-speed trains, my country's high-speed railway passenger transport has entered a new stage of development. High-speed railway passenger transport organization faces new requirements for improving quality and efficiency, and high-speed railway dispatching and command are also facing new challenges in developing towards intelligent systems. The operation of high-speed trains is an important component of passenger transport organization and a key factor restricting passenger transport efficiency. It is susceptible to interference from uncertain factors, causing actual train operation to deviate from the original operation plan, thus affecting the quality of transport task completion. How to correctly grasp the current status and future development trends of high-speed train operation, provide decision support for intelligent train dispatching, and ensure a good and stable train operation situation has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for predicting the operational status of high-speed railway EMUs, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for predicting the operational status of high-speed railway EMUs, including:

[0005] Based on the high-speed railway EMU connection rules and the train number information of each train in the timetable, a connection network of the EMU to be simulated is constructed.

[0006] Based on the aforementioned connection network, a simulation model of the operational status of high-speed trains is constructed. The simulation model of the operational status of high-speed trains includes an objective function and constraints.

[0007] Based on the interference events and the connection network, the operational situation simulation model of the EMU is simulated. The solution is obtained by combining the rolling time domain algorithm and the heuristic method based on the field strategy. The operational adjustment scheme of the EMU to be simulated is obtained.

[0008] Secondly, this application also provides a high-speed railway EMU operation status simulation system, including:

[0009] The first construction module is used to construct the connection network of the EMU to be simulated based on the connection rules of high-speed railway EMUs and the train number information of each train in the timetable.

[0010] The second construction module is used to construct a train operation situation simulation model based on the connection network. The train operation situation simulation model includes an objective function and constraints.

[0011] The simulation module is used to simulate the operational status of the EMU based on the interference events and the connection network. It combines the rolling time domain algorithm and the heuristic method based on the field strategy to solve the problem and obtain the operational adjustment scheme of the EMU to be simulated.

[0012] The beneficial effects of this invention are as follows: This invention utilizes EMU (Electric Multiple Unit) connection rules to construct the EMU operation process as an EMU connection network, and constructs an integer linear programming model for EMU operation situation projection to obtain EMU operation adjustment schemes, thereby obtaining relevant EMU operation data at a future time. Based on this, a hybrid solution framework combining a rolling time-domain algorithm and a heuristic method is designed. The rolling time-domain mechanism enables rolling updates of the situation projection, while combining on-site strategy experience rules improves the model's solution efficiency. Simultaneously, on-site scheduling experience is transformed into heuristic rules and embedded into the algorithm framework to make the projection results more closely reflect actual on-site conditions. The dynamic updating of the situation projection results is achieved through a time- and event-driven mechanism.

[0013] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the high-speed railway EMU operational situation simulation method described in this embodiment of the invention;

[0016] Figure 2 This is a schematic diagram of the connection network structure described in an embodiment of the present invention;

[0017] Figure 3 This is a schematic diagram of the operational status representation system for high-speed railway EMUs in an embodiment of the present invention;

[0018] Figure 4 This is a graph showing the result of the situation curve deduction in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Example 1:

[0022] This embodiment provides a method for predicting the operational status of high-speed railway EMUs.

[0023] It should be noted that the EMU operation plan is a comprehensive plan for the operation and maintenance of EMUs. Based on a given train timetable and under the premise of meeting corresponding EMU maintenance procedures, it arranges the specific tasks of EMUs during operation. Specifically, it specifies the time, station, train number, and maintenance tasks to be performed by each EMU. It clarifies the spatiotemporal attributes of various operations such as EMU operation and maintenance, and establishes a highly unified relationship between these operations.

[0024] EMU (Electric Multiple Unit) operation adjustment refers to the process by which EMU dispatchers, after an interference event, adjust the operation tasks of EMUs based on the original EMU operation plan and the revised train timetable (adjustment timetable). This can also be understood as combining the various train tasks in the revised timetable into operational routes and assigning EMUs to them, ensuring the rationality of the operational routes while maximizing the execution of the adjusted timetable. The EMU operation plan needs adjustment primarily because interference has a significant impact on train operation. According to the revised train timetable, the original EMU operation plan cannot meet the EMU turnaround needs of the new timetable, thus necessitating changes to EMU routes. Furthermore, when adjusting EMU operation, the deviation between the adjusted EMU operation plan and the original EMU operation plan should be minimized.

[0025] Meanwhile, the operational status of high-speed trains can be defined as a macroscopic description of the current state and future development trend of high-speed train operation within a certain time and space range. The operational status reflects whether each link and operation of high-speed train operation is proceeding normally according to the relevant plans. If the actual work tasks of high-speed train operation are executed according to the established plan, the operational status is good. When internal and external factors interfere with the execution of the relevant plans for high-speed train operation, it will cause the operation to deviate from the ideal state, i.e., an unfavorable status occurs. When an unfavorable status occurs, certain adjustments need to be made to the operation of high-speed trains to restore it to the ideal state.

[0026] See Figure 1 The figure shows that the method includes steps S1, S2 and S3.

[0027] Step S1: Based on the high-speed railway EMU connection rules and the train number information of each train in the timetable, construct the connection network of the EMU to be simulated;

[0028] In step S1, constructing the connection network of the trainset to be simulated includes:

[0029] Step S11: Define the connection rules for high-speed railway EMUs, including minimum connection time standards, train type / train formation matching rules, and maintenance connection rules;

[0030] Step S12: Obtain the train operation diagram of the train to be simulated, the operation diagram including the basic attributes of the train and the execution status of the train;

[0031] In this step, the basic attributes of the train include train number, time, station, mileage, and train type, while the train's execution status includes normal operation and cancellation.

[0032] Step S13: Define the node set of the connection network of the EMU to be simulated based on the connection rules and the operation diagram. The node set includes a virtual starting point, a starting node set, a train node set, a destination node set, and a virtual ending point. Each node in the node set is assigned an octet attribute.

[0033] In this step, the connection network G of EMU k is established based on the EMU connection rules and the train number information of each train in the timetable. k =(V k A k The network was constructed to obtain the successor network shown in Table 2, V. k Let A represent the set of nodes for train k. k Let represent the arc set of train set k.

[0034] To construct a single-source, single-input EMU connection network, a virtual starting point is introduced. and virtual endpoint Starting node set Used to represent the originating station or depot of EMU k; train node set Used to represent train missions; originating node set Used to indicate the final station or depot of the EMU (Electric Multiple Unit) k.

[0035] For each node i∈V k Introduce an octet property:

[0036]

[0037] in, r i l i h i and n i These represent the originating station, destination station, departure time, arrival time, operating time, operating mileage, corresponding train, and the number of EMU units required for node i, respectively.

[0038] All attributes of each train node are set to values ​​based on actual conditions; all attribute values ​​for the virtual start point and virtual end point are 0; the originating node's... and If the values ​​are the same, select the corresponding station based on the actual location. and Also take the same value, based on the actual departure time, and set the other attribute values ​​to 0; the destination node attribute values ​​refer to the originating node. and If the values ​​are the same, select the corresponding station based on the actual location. and The same value is also taken, based on the actual entry time, while the values ​​of other attributes are 0.

[0039] Step S14: Define the arc set of the continuation network based on the node set. The arc set includes virtual arcs, departure arcs, continuation arcs and termination arcs. Each arc in the arc set is assigned a binary attribute.

[0040] In this step, arc set A k Including virtual arcs Starting arc Continuing arc End of car arc

[0041] Virtual arc Used to connect and nodes Virtual arc Used to connect nodes and This indicates that the train set k is under maintenance or parked at the location corresponding to i; virtual arc Used to connect nodes and nodes At this time, the originating station and the destination station of the EMU K are the same, that is, the EMU K is not used during the planned time period.

[0042] Starting arc Used to connect nodes and nodes This indicates that the EMU k starts from i and takes on the role of j, i.e., it leaves the depot. If the EMU k needs to run empty from the initial parking location to a different location to connect with the train, the empty running time must meet the connection requirements.

[0043] Continuing arc Its function is to connect two train nodes that meet the connection conditions. and This indicates that train k takes over the roles of i and j.

[0044] End of car arc Used to connect nodes and nodes This indicates that after completing its task at node i, the train k will return to the station corresponding to node j, either to carry out maintenance work or to park.

[0045] For each arc (i,j)∈A k Introduce a binary attribute (r) i,j ,l i,j ), respectively representing the train set k from to The application time and application mileage. Among them, each virtual arc All attribute values ​​are 0; when and When they are equal, arc(i,j)∈A k The attribute value is also 0, where i and j both represent node indices.

[0046] Step S15: Based on path integrity verification and constraint compliance verification, correct the nodes and arcs of the connecting network.

[0047] In this step, the network is corrected by verifying path integrity (ensuring the existence of a complete EMU path) and constraint compliance (ensuring that nodes / arcs conform to the connection rules), which can eliminate invalid nodes / arcs and ensure that the network conforms to the actual application logic.

[0048] Step S2: Construct a train operation situation simulation model based on the connection network. The train operation situation simulation model includes an objective function and constraints.

[0049] In step S2, the construction of the EMU operation situation simulation model based on the connection network includes:

[0050] Step S21: Set the simulation boundaries of the EMU operation situation simulation model based on the actual operation scenario of the EMU to be simulated;

[0051] In this step, the model assumptions of the simulation boundary, i.e., the situation simulation model applied to the EMU, are as follows:

[0052] Before the interference occurred, each EMU operated as scheduled. After the interference occurred, all trains operated by this section operated according to the given adjustment schedule, and trains operated by other sections were not considered.

[0053] The EMU trains adopt a non-fixed section operation mode. After the interference occurs, empty running is allowed in some sections. If the daily inventory of each EMU depot or station does not meet the planned demand for the next day, the EMU position can be adjusted by empty running back.

[0054] The use of multiple-unit / disassembled EMU trains during the completion of the day's transportation tasks is not considered;

[0055] Only considering the first-level maintenance of high-speed trains;

[0056] EMU trains are not allowed to operate as trains with a capacity exceeding their total passenger capacity. In the selection of train formation, the differences in capacity between different EMU train models are not taken into account; the choice of single or double formation is determined solely based on train requirements.

[0057] In the EMU operational situation simulation model, the standby EMUs are directly included in the set of available EMUs, without distinguishing between the EMUs originally planned to be used and the standby EMUs. In the input data and adjustment plan, the EMU numbers can be used to distinguish between the EMUs originally planned to be used and the standby EMUs.

[0058] Step S22: Set the model parameters of the EMU operation situation simulation model based on the connection network;

[0059] In this step, the model parameters are explained as shown in Table 1-4.

[0060] Table 1. Model Parameter Description

[0061]

[0062]

[0063]

[0064] It's understandable that i, j, p, and q all represent node indices, so i, j, p, and q can be interchanged, but attention must be paid to the set to which the node belongs. For example, hj It is not shown in Table 1, but it can be inferred from this that h j This represents the train corresponding to node j, where j∈V k ; Used to determine whether train k executes arc (j,p)∈A k If executed, the value is 1; if not executed, the value is 0.

[0065] At the same time, such as virtual starting point "etc." refers to specific nodes, such as when In this case, node i is actually a virtual starting point. Therefore, specific nodes can be substituted into the node index in the above parameters, for example... Used to determine whether train k executes arc. If executed, the value is 1; if not executed, the value is 0. Used to determine whether train k executes arc. If executed, the value is 1; if not executed, the value is 0. t This indicates the number of trainset units required for train t.

[0066] And the train assembly,

[0067] Step S23: With the goal of minimizing the negative impact of severe interference on the operation of EMUs, the objective function is constructed by setting the weight of the objective items in combination with the scheduling priority. The objective items of the objective function include minimizing the number of train cancellations, minimizing the EMU operation offset, minimizing the total EMU operation time, minimizing the total EMU operation mileage, and minimizing the inventory deviation of the EMU depot and stations.

[0068] Understandably, since the simulation of EMU operational status is closely related to the formulation of EMU operational adjustment plans, an integer linear programming model for EMU operational status simulation is constructed to obtain EMU operational adjustment schemes under severe interference conditions, thereby solving the key problem of estimating relevant data on EMU operation under severe interference conditions. The key criterion for formulating EMU operational adjustment plans is to minimize the time required for trains to return to normal operation after interference occurs, while ensuring transportation safety, and to reduce the deviation between actual EMU operation and the original plan. Therefore, the sub-objectives of the EMU operational status simulation model are related to the degree of deviation of the original EMU operating state and the degree of deviation of the original EMU operational plan. The constructed objective function minZ consists of 5 objective terms, and its expression is as follows:

[0069]

[0070] In the formula, ω1, ω2, ω3, ω4 and ω5 are all non-negative weighting coefficients.

[0071] Step S24: Set constraints, including virtual node constraints, station entry and exit constraints, EMU connection constraints, EMU unit quantity and model balance constraints, EMU storage location capacity constraints, EMU maintenance constraints, and train demand constraints.

[0072] In this step, regarding virtual node constraints, the number of outgoing arcs at the virtual starting point and the number of incoming arcs at the virtual ending point for each trainset should be equal to 1, to ensure that each trainset has a clear operational path from the virtual starting point to the virtual ending point in its connection network. Therefore, the virtual node constraints are:

[0073]

[0074] Regarding station entry / exit constraints, the number of exit arcs for each EMU at the originating node should be equal to 1, meaning the EMU departs from the originating node at the start of the day's operation, thus ensuring that all EMUs depart from the station / depot; the number of entry arcs at the destination node should also be equal to 1, meaning that EMU k returns to the destination node after completing its train mission, thus ensuring the EMU's entry into the depot. Therefore, the station entry / exit constraints are:

[0075]

[0076] The constraints on EMU connection include flow balance constraints, uniqueness constraints, connection time constraints, and EMU application offset judgment.

[0077] The flow balance constraint is as follows:

[0078]

[0079] The uniqueness constraint is:

[0080]

[0081] In the connection time constraint, the connection time for connection at the same station should meet the following requirements:

[0082]

[0083] In the connection time constraint, the connection time for inter-station connection should meet the following requirements:

[0084]

[0085] The offset determination for high-speed train operation is to determine whether each high-speed train k continues to serve as i and j according to the original high-speed train operation plan (the corresponding train nodes in the connection network according to the original plan are p and q, respectively), given the following conditions:

[0086]

[0087] The constraints on the number of EMU units and the balance of train models are as follows:

[0088]

[0089] Since the number of EMU units stored at each station cannot exceed the station's storage capacity, a capacity constraint is set for EMU storage locations, specifically:

[0090]

[0091] Regarding the maintenance constraints for high-speed trains, specifically the cumulative operating time and mileage requirements for first-level maintenance of high-speed trains k∈E, the following are:

[0092]

[0093] Meanwhile, the type of trainsets assigned to each train and the number of trainset units they comprise should be sufficient to meet the train's needs, and the same train mission can only be undertaken by trainsets of one type. Therefore, the train demand constraints are:

[0094]

[0095] Step S3: Based on the interference events and the connection network, the operational situation simulation model of the EMU is simulated. The simulation is solved by combining the rolling time domain algorithm and the heuristic method based on the field strategy to obtain the operational adjustment scheme of the EMU to be simulated.

[0096] It should be noted that the interference event in this step is a severe interference. Unlike very severe interference, severe interference does not require the development of a temporary train operation plan. Reasonable adjustments to subsequent schedules are sufficient to address the adverse impact of the interference on transportation order. In this case, because some preceding trains are severely affected by the interference and cannot arrive at their destinations on time, if the delay exceeds a certain limit, the trains assigned to those preceding trains will no longer be able to continue serving as subsequent trains.

[0097] In order to suppress the spread of train delays, control the impact of interference time, and ensure the reliability and stability of transportation services, it is necessary to make corresponding adjustments to the original EMU operation plan (abbreviated as original operation plan or original plan) according to the adjustment schedule, so as to obtain the operation adjustment plan (adjusted EMU operation plan). Among them, changing the connection relationship, empty EMU transfer / return, activating hot standby EMUs, and canceling trains are common scheduling strategies used in operation adjustment.

[0098] In step S3, the method of combining the rolling time-domain algorithm and the heuristic method based on the on-site strategy to solve the problem yields the operation and adjustment scheme of the trainset to be simulated, including:

[0099] Step S31: Obtain the parameter information of the EMU to be simulated, including the original timetable, the original operation plan and interference event information;

[0100] In this step, initialization is performed first, which involves setting the start time of the first simulation, the length of the entire simulation cycle, and the window length of the first simulation, as well as setting the simulation time step Δt.

[0101] Step S32: Detect interference events based on parameter information and obtain the detection results;

[0102] Step S33: Solve the EMU operational situation simulation model based on the detection results, generate the operational adjustment plan for the EMU to be simulated based on the heuristic method of on-site strategy, and update the parameter information;

[0103] Step S33 includes:

[0104] Step S331: If the detection result is that no interference occurred or no new interference information was obtained, then obtain the relevant data of the operation of the EMU for all the time periods to be simulated within the remaining simulation period.

[0105] If the relevant data is obtained in this step, proceed directly to step S334.

[0106] Step S332: If the detection result indicates that interference has occurred or the duration of the interference event has been updated, then reset the simulation start time and calculate the remaining simulation period based on the reset simulation start time;

[0107] In this step, if interference occurs, the time of interference occurrence is determined and used as the start time of the reset simulation; if the duration of the interference event is updated, the time of the update of the interference duration information is determined and used as the start time of the reset simulation.

[0108] Based on the reset start time of the simulation, the window length is adjusted accordingly, i.e., the remaining simulation period is calculated. in, t represents the remaining time interval for the a-th simulation, i.e., t total Indicates the length of the deduction period. This indicates the start time of the a-th simulation. This indicates the start time of the first simulation.

[0109] Step S333: Based on the heuristic method of on-site strategy, the EMU operation situation simulation model is simulated and solved during the remaining simulation period to generate an operation adjustment plan and obtain relevant data on the operation of the EMU at all simulation times during the remaining simulation period.

[0110] Step S334: Update the parameter information based on the relevant data.

[0111] Step S34: Update the simulation start time;

[0112] In this step, the window length is actually dynamically adjusted, and the start time of the current simulation is adjusted according to the simulation time step. Specifically: This indicates the start time of the (a+1)th simulation.

[0113] Step S35: Calculate the remaining simulation period based on the updated simulation start time. If the remaining simulation period is greater than zero, re-detect the interference event information based on the updated parameter information, perform the next simulation solution, and generate a new application adjustment scheme. If the remaining simulation period is not greater than zero, stop the simulation.

[0114] In this step, according to The window length is dynamically adjusted, i.e., the remaining simulation period is calculated. This represents the remaining time period of the (a+1)th simulation.

[0115] Understandably, when the rolling time-domain algorithm detects an interference event or an update to the duration of the interference event, it immediately invokes a heuristic method based on on-site strategies to solve the model, quickly generating a train operation adjustment plan that conforms to the actual scheduling logic. During train operation, when faced with train delays or cancellations due to changes in the internal and external environment, to ensure the rapid restoration of transportation order, emergency scheduling work needs to be quickly carried out to adjust train operation, such as adjusting train connections and calling up backup resources. The heuristic method based on on-site strategies summarizes effective strategies in actual scheduling, transforming actual scheduling experience into executable rules. By replacing global search with rule-driven approaches, it significantly improves response speed while ensuring the feasibility of the solution. The steps of the heuristic method based on on-site strategies are shown in steps A1-A4.

[0116] In step S333, the generation and application of the adjustment scheme includes:

[0117] Step A1: Obtain the set of trains that have been suspended, the first set of EMUs that were responsible for the train tasks when the interference occurred, the train tasks that the EMUs were currently responsible for, and the set of subsequent trains that the EMUs were responsible for. The set of subsequent trains is filtered by the set of trains that have been suspended.

[0118] In this step, based on the adjusted schedule and the original operational plan, the set of trains to be suspended in the adjusted schedule, T, is determined. adjust The first EMU (Electric Multiple Unit) set K that performs the train mission when interference occurs. taking The train mission that the high-speed train K is currently undertaking. At the same time, the assembly of the subsequent trains operated by EMU K was determined. like There is a train t∈T adjust Then remove it from the set Remove from the middle and set Trains t in the list are sorted in non-decreasing order according to their departure time.

[0119] Step A2: For each EMU in the first EMU set, determine whether its subsequent task cannot be continued normally due to the current interference based on the train task currently being undertaken by the EMU, and store the EMUs that cannot be continued normally in the second EMU set. The subsequent task is the train task in the subsequent train set.

[0120] In this step, for k∈K taking To determine whether its continued role as the next train is affected, and thus to identify the set K of the second EMU trains whose role as the next train is affected. effected Then, sort the k in the set in a non-decreasing order according to the priority of the train tasks they undertake.

[0121] Step A3: Determine whether the second EMU set is empty. If so, use the original operation plan as the operation adjustment plan. Otherwise, for the subsequent tasks of each EMU in the second EMU set, resource matching and replacement are performed according to priority.

[0122] In this step, if K effected If it is not empty, proceed to step A4; if K effected If the result is empty, the algorithm ends, and the original application plan remains unchanged, which means the original application plan is used as the application adjustment plan.

[0123] Step A4: After performing resource matching and replacement on all EMUs in the second EMU set, arrange empty return trips for EMUs that are parked outside and not yet returned to their positions, and generate an operation adjustment plan for the EMUs to be simulated.

[0124] Step A4 includes:

[0125] Step A41: Based on the current set K effected The k-th element that is sorted as 1 in the middle corresponds to The destination station determines whether there is a non-idle EMU k′ that can serve as the successor to k, and whether k can serve as the successor to k′; if so, the connection between the two EMUs is changed, and k is removed from set K. effected Remove the middle part and proceed to step A46; otherwise, proceed to step A42.

[0126] Step A42: According to The system checks the originating station information of the train currently ranked 1 to determine if there is an available EMU k′ at that station that can take over the subsequent train task of train k. If so, k′ is assigned to the corresponding EMU k. At this point, k changes to a stop. The idle EMU trains at the terminal station will move k from set k. effectedRemove the middle part and proceed to step A46; otherwise, proceed to step A43.

[0127] Step A43: Determine if there is an available EMU k' that can take over the train task following k. If so, assign k' to the task corresponding to k. At this point, k changes to a stop. The idle high-speed trains arriving at the terminal station will move k from set K. effected Remove the middle part and proceed to step A46; otherwise, proceed to step A44.

[0128] Step A44: Determine if there is a hot-standby EMU k′ in the EMU set that can serve as the corresponding EMU k. If so, the standby EMU K′ will be activated. At this point, k changes to a stop. The idle EMUs at the terminal station, and k from set K effected Remove the middle part and proceed to step A46; otherwise, proceed to step A45.

[0129] Step A45: Set the set corresponding to train k The train with the highest order of 1 in the set is cancelled and removed from the set. Remove the middle part and add the train to the cancellation set corresponding to k. If the set is removed If not empty, proceed to step A42; otherwise, proceed to step A46.

[0130] Step A46: If K at this time effected If not empty, proceed to step A41; otherwise, arrange for the empty return of the EMU parked outside, output the operation adjustment plan, and stop the iteration.

[0131] Therefore, in order to meet the needs of dynamic simulation, based on the complexity of the problem and the structural characteristics of the model, an algorithm framework combining rolling time-domain algorithm and heuristic method was designed. This algorithm framework is based on fixed time step and updated interference event information. It covers all remaining time periods through rolling time-domain strategy to realize dynamic simulation of the EMU operation status. As needed, a heuristic method based on on-site strategy is used to solve the model during simulation, simulate on-site adjustment strategy to obtain future EMU operation adjustment scheme, so that the obtained adjustment scheme is closer to the actual on-site situation.

[0132] The solution framework that integrates rolling time-domain algorithms and heuristic methods not only enables the system to obtain feedback information in a timely manner and respond in real time, and dynamically adjust the window coverage to adapt to the gradual shortening of the simulation cycle, but also generates EMU operation adjustment schemes based on on-site strategies as needed. This facilitates the calculation of relevant data on EMU operation under severe interference conditions, thereby calculating the EMU operation status characterization index value and EMU operation status level, and improving the efficiency and practicality of EMU operation status simulation.

[0133] Furthermore, after obtaining the operational adjustment plan for the EMU to be simulated, the system also calculates the operational status characterization index of the EMU using the acquired EMU operational data, and uses the EMU operational status assessment model to complete the status level prediction, thereby enabling the prediction of the EMU operational status level at any time within the simulation period.

[0134] Specifically, taking into account the factors affecting the operational status of EMUs, and using the status of a single EMU as the starting point, the operational status of EMUs is divided into operational status sub-status, maintenance status sub-status, depot shutdown status sub-status, and standby status sub-status. Based on this, the operational status characterization indicators of EMUs are selected and quantified, resulting in the high-speed railway EMU operational status characterization system shown in Table 3.

[0135] Regarding the operational status sub-state, the following indicators were selected: train arrival punctuality rate, train cancellation rate, EMU connection reliability, EMU operational task deviation, and EMU operational quantity deviation.

[0136] The on-time arrival rate R1 of the train is:

[0137]

[0138] In the formula, β t This is a 0-1 variable, representing whether train t is late at time T′. It is 1 if it is not late and 0 if it is. r This represents the total number of trains running on the line at time T′.

[0139] The train cancellation rate R2 is:

[0140]

[0141] In the formula, n v This indicates the number of trains cancelled at time T′.

[0142] The reliability of EMU connection R3 is:

[0143]

[0144] In the formula, N rc represents the actual number of EMU trains in use at time T′. k This indicates the reliability of the connection of train set k at time T′.

[0145] The deviation R4 of the EMU operation is:

[0146]

[0147] In the formula, N d N represents the number of times at time T′ the train failed to perform its assigned task as originally planned and the number of times the train failed to execute its scheduled connection. c This represents the total number of application tasks executed at time T′.

[0148] The deviation R5 in the number of EMUs in use is:

[0149]

[0150] In the formula, n p This represents the planned number of EMUs to be used at time T′, where |·| indicates taking the absolute value.

[0151] Secondly, carrying out EMU maintenance according to the maintenance plan is conducive to the normal execution of the EMU operation plan in the next stage, and the deviation of the EMU maintenance task is used as a characterization indicator of the EMU maintenance status.

[0152] The deviation of EMU maintenance tasks describes the actual execution of the maintenance plan. The EMU maintenance task deviation can be quantified by the ratio of the number of EMUs that underwent unplanned maintenance but were actually maintained to the number of EMUs required to undergo maintenance as specified in the maintenance plan. Therefore, the characterization index of the maintenance status sub-state is the EMU maintenance task deviation R6:

[0153]

[0154] In the formula, This indicates the number of EMU trains that did not undergo maintenance as planned at time T′. This indicates the number of EMU trains that require maintenance according to the maintenance plan at time T′.

[0155] Under ideal conditions, all EMUs scheduled for maintenance should undergo maintenance, while those without scheduled maintenance should not; that is, the value of this indicator should be 0. However, if a single EMU is affected by unforeseen circumstances or random disturbances, its maintenance may not proceed as planned, thus increasing the value of this indicator. The smaller the value of the EMU maintenance task deviation indicator, the better the execution of the maintenance plan and the smoother the maintenance work.

[0156] Meanwhile, the types and quantities of EMUs parked at various depots or stations with storage capacity must meet the needs of the EMU operation plan to ensure its smooth execution. This paper selects the EMU inventory deviation as a characterizing indicator of the depot's parking status.

[0157] The EMU (Electric Multiple Unit) inventory deviation is used to describe the deviation of EMU inventory in various depots or stations with storage capacity within a regional railway network. When quantifying this indicator, it is necessary to consider different train types, because meeting the total inventory of a depot or station does not necessarily mean that the quantity of EMUs of a particular train type also meets the demand. The EMU inventory deviation can be quantified by the ratio of the difference between the actual and planned quantities of each train type parked at each station to the total EMU inventory at each station. Therefore, the indicator representing the depot's parking status is the EMU inventory deviation R7.

[0158]

[0159] In the formula, This indicates the number of EMU trains of type e actually parked at station s at time T′. N represents the number of EMU trains of type e that are planned to be parked at station s at time T′. d ′ represents the total number of EMUs in stock at each station at time T′, m1 represents the number of stations, and m2 represents the number of train models.

[0160] Finally, when activating standby EMUs, the use of hot standby EMUs should be considered first. The activation rate of hot standby EMUs is selected as a characterization index of the standby status sub-state of EMUs.

[0161] Assuming that the number of standby EMUs was reasonably planned during the EMU allocation scheme, ensuring it remains within a reasonable range—meeting dispatching needs in emergencies while avoiding resource waste due to over-allocation—the standby EMU utilization rate reflects the current status of standby EMUs. This indicator can be quantified by the ratio of the number of active standby EMUs to the total number of standby EMUs allocated. Therefore, the standby status sub-state indicator is the standby EMU utilization rate R8.

[0162]

[0163] In the formula, This indicates the number of standby EMUs actually activated at time T′. This indicates the planned number of standby EMU trains at time T′.

[0164] Therefore, R1 to R8 are used as indicators of the operational status of high-speed trains (referred to as indicators).

[0165] After obtaining the operational adjustment plan for the EMU to be simulated, the following is also included:

[0166] Step S4: Construct a high-speed train operation status assessment model;

[0167] In this step, an initial indicator matrix is ​​constructed using situational characterization indicators from eight EMU trains. The initial indicator matrix has been standardized.

[0168] Step S41: Based on the initial index matrix, calculate the first weight coefficient of each characterization index using the entropy weight method. Furthermore, based on the initial index matrix, calculate the contrast strength and conflict of each characterization index using the CRITIC method, and calculate the second weight coefficient of each characterization index based on the contrast strength and conflict.

[0169] Step S42: Based on the principle of minimum discriminative information, construct a combined weight model using the first weight coefficient and the second weight coefficient.

[0170]

[0171] In the formula, minF(w) a′ ) represents the combined weight model, A′ represents the number of characterization indicators, and w a′ This represents the combined weight of the a′-th representation index. This represents the first weight coefficient of the a′-th characterization index. This represents the second weighting coefficient of the a′-th characterization index.

[0172] Step S43: Solve the combined weight model using the Lagrangian function to obtain the combined weights of each representation index:

[0173]

[0174] Step S44: Construct a high-speed train operation status assessment model based on combined weights-GRA-TOPSIS.

[0175] Specifically, step S44 includes:

[0176] Step S441: Construct a weighted normalization matrix.

[0177] Assume there are multiple objects to be evaluated, each corresponding to a different operational adjustment plan at a different time, and each object includes a corresponding EMU operational status characterization index (i.e., eight characterization index values). An initial evaluation matrix is ​​constructed by sequentially evaluating the multiple objects and their corresponding EMU operational status characterization indices.

[0178] The initial evaluation matrix is ​​standardized to obtain a standardized matrix. The standardized matrix is ​​then multiplied by the combined weights to obtain a weighted standardized matrix.

[0179] Step S442: Calculate the positive and negative ideal solutions of the weighted standardized matrix. Calculate the distance from each object to be evaluated to the positive and negative ideal solutions. Calculate the grey relational coefficient matrix between each object to be evaluated and the positive and negative ideal solutions.

[0180] Step S443: Calculate the grey relational degree between each object to be evaluated and the positive and negative ideal solutions based on the grey relational coefficient matrix. Normalize the distance and grey relational degree to obtain normalized distance and normalized grey relational degree.

[0181] Step S444: Weighted fusion of normalized distance and normalized grey relational degree to obtain the first degree of proximity and the second degree of proximity. The first degree of proximity is the degree of proximity between each object to be evaluated and the positive ideal solution, and the second degree of proximity is the degree of proximity between each object to be evaluated and the negative ideal solution.

[0182] Step S445: Calculate the relative fitting progress based on the first and second proximity levels.

[0183] Specifically, the relative progress is as follows:

[0184]

[0185] In the formula, This represents the relative fitting progress of the b′-th object to be evaluated. This represents the first degree of proximity of the b′-th object to be evaluated. This indicates the second degree of proximity of the b′-th object to be evaluated.

[0186] Step S446: Construct a situation level classification table based on the relative progress.

[0187] The situation status is divided into levels I to V, from highest to lowest: Level I is excellent, Level II is relatively good, Level III is average, Level IV is poor, and Level V is severe. The relative proximity is assigned using an equal-division principle, and the correspondence between the situation status level and the calculated relative proximity is shown in Table 5.

[0188] Table 2 Situation Level Classification Table

[0189]

[0190] Level I indicates excellent, meaning that the actual operation of the EMU is basically consistent with the original plan, all operational processes are carried out in an orderly manner, and the overall coordination is good, requiring virtually no scheduling adjustments. This actual operation of the EMU is the plan after the operation adjustment scheme has been implemented.

[0191] Level II indicates a relatively good situation, meaning that there are some unfavorable factors affecting the operation of EMUs, the actual operation of EMUs deviates from the original plan to a low degree, the operation of each link is relatively smooth, and the operation of EMUs can be restored to normal by taking relatively simple local scheduling adjustments.

[0192] Level III indicates a general situation, meaning that there are some unfavorable factors, and the actual operation of the EMU deviates from the original plan to a certain extent. It is necessary to continue to monitor the situation and take timely scheduling and adjustment measures to prevent the EMU operation from deteriorating further.

[0193] Level IV indicates poor performance, meaning there are many unfavorable factors affecting the operation of EMUs, the actual operation of EMUs deviates significantly from the original plan, and the implementation of some operational processes is greatly affected. This requires attention and immediate implementation of corresponding scheduling and adjustment measures.

[0194] Level V indicates severe disruption, meaning that the operation of the EMU system has been seriously interfered with, the actual operation of the EMU has deviated significantly from the original plan, many operational processes have been severely affected and cannot be carried out normally, and it is necessary to make immediate and extensive scheduling adjustments. It will take a long time to restore the operation of the EMU to normal and complete all operations according to the established plan.

[0195] Step S5: Calculate the operational status indicators of the EMU using the adjustment scheme;

[0196] In this step, we calculate R1 to R8 for the application of the adjustment scheme at different prediction times.

[0197] Step S6: Input the EMU operational status characterization index into the EMU operational status assessment model for evaluation to obtain the status level of the operational adjustment plan.

[0198] This invention, taking into account the characteristics of the EMU (Electric Multiple Unit) operation system, selects corresponding characterization indicators and mathematically quantifies them, constructing an EMU operation status characterization system that includes sub-status states: operation status, maintenance status, depot shutdown status, and standby status. Regarding EMU operation status assessment, considering the complementarity of various weight determination methods and the limitations of single assessment methods, an EMU operation status assessment model based on combined weights-GRA-TOPSIS is constructed, and status levels and characterization indicator levels are classified.

[0199] By using the R1 to R8 input schemes of the different prediction times obtained through simulation and prediction, the situation level at any time within the simulation period can be predicted by using the EMU situation assessment model for calculation and evaluation.

[0200] Example 2:

[0201] In this embodiment, the trains involved are the trains assigned to a certain location within the simulation period. The corresponding train data can be obtained through the train operation adjustment diagram, including train number, originating station, destination station, departure time, arrival time, operating time, operating mileage, train type, train capacity, and other data information.

[0202] The operation route of the EMU is represented by the feasible path from the virtual starting point to the virtual ending point in the connection network, which consists of points and arcs. Each node needs to specify the originating station, the destination station, the originating time, the destination time, the operating time, etc. Each arc needs to specify the operating time, the operating mileage, the sequence number of the two nodes connected by the arc, etc. The required data information can be obtained from the original EMU operation plan.

[0203] The model-related parameter configurations include: a minimum connection time standard of 15 minutes, a first-level maintenance time standard of 2880 minutes, a first-level maintenance mileage standard of 5500 km, and the weights of each objective term in the objective function.

[0204] In this embodiment, a sudden and severe disturbance causes a complete disruption in a section of the high-speed railway. It is assumed that the duration of the disturbance is uncertain at the time of occurrence and is updated over time. A severe disturbance scenario is constructed by adjusting the start time and duration of the disturbance.

[0205] In this embodiment, the severe interference scenario is specifically set to start at 9:00 AM, with durations of {9:00, 9:00-10:00} and {10:00, 9:00-11:00}. That is, at the start time of 9:00 AM, based on the acquired interference information, it is determined that the interference is expected to last 60 minutes. At 10:00 AM, the interference information is updated, and the interference has not yet ended, and is expected to continue for another 60 minutes. Therefore, the duration of this severe interference is from 9:00 AM to 11:00 AM. The interference ends at 11:00 AM, and the passage capacity of the section is restored.

[0206] Before applying the situational simulation model to solve the problem for the high-speed train, the relevant parameter settings for the rolling time-domain algorithm are explained. In the rolling time-domain algorithm, the appropriate value for the simulation time step can be determined based on a large amount of experimental data. Taking the simulation time step as 1 hour, the start time of the first simulation round as 6:00, and the total simulation cycle length as 24 hours, the window length for the first simulation round should also be 24 hours.

[0207] In this scenario, a total of 24 rounds of situation simulation were conducted, of which 22 were time-driven and 2 were event-driven.

[0208] No interference events occurred during the first three rounds of simulation, and the original EMU operation plan was followed. During the fourth round of simulation, the simulation started at 9:00 AM, the simulation window was 21 hours long, the model solution results are shown in Table 3, and the model solution time was 4.5 seconds.

[0209] Table 3. Results of the fourth round of simulation model solution

[0210]

[0211] When conducting the 5th round of simulation, the simulation started at 10:00, the simulation window length was 20h, the model solution results are shown in Table 4, and the model solution time was 5.9s.

[0212] Table 4. Solution Results of the Fifth Round of Simulation Model

[0213]

[0214] Using a 2-hour time interval, 12 time points were selected sequentially: 8:00, 10:00, ..., 4:00, and 6:00. The situation level of high-speed train operation at these 12 time points under each severe interference scenario was calculated according to the situation assessment calculation process.

[0215] Since no interference events occurred during the first to third rounds of simulations in this scenario, the simulation results show that the actual operation of the EMU is carried out according to the original EMU operation plan, and the EMU operation status level at each simulation time obtained from the first to third rounds of simulations is Level I.

[0216] The calculation results of the situation characterization index values ​​obtained from the fourth round of simulation are shown in the table.

[0217] Table 5 Results of Situation Characterization Indicator Values ​​in the 4th Round of Simulation

[0218] time <![CDATA[R1]]> <![CDATA[R2]]> <![CDATA[R3]]> <![CDATA[R4]]> <![CDATA[R5]]> <![CDATA[R6]]> <![CDATA[R7]]> <![CDATA[R8]]> 10:00 0.911 0.000 0.936 0.000 0.000 0.000 0.000 0.000 12:00 0.937 0.000 0.942 0.000 0.000 0.000 0.000 0.000 14:00 0.953 0.000 0.962 0.037 0.019 0.000 0.000 0.333 16:00 1.000 0.044 1.000 0.076 0.000 0.000 0.111 0.333 18:00 1.000 0.042 1.000 0.078 0.000 0.000 0.100 0.333 20:00 1.000 0.058 1.000 0.085 0.000 0.000 0.071 0.333 22:00 1.000 0.047 1.000 0.093 0.000 0.000 0.034 0.333 0:00 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 2:00 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 4:00 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 6:00 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000

[0219] The results of the situation level calculation are shown in Table 6.

[0220] Table 6: Calculation Results of Situation Level in the 4th Round of Simulation

[0221]

[0222]

[0223] After multiple rounds of simulations, the situation level after each round is not the same, such as... Figure 4 The results of several rounds of simulations are shown. It can be seen that after multiple rounds of simulations and updates, the situation curve is also updated accordingly, and the situation level after the update at different times can be seen intuitively.

[0224] Example 3:

[0225] This embodiment provides a high-speed railway EMU operational status simulation system, the system comprising:

[0226] The first construction module is used to construct the connection network of the EMU to be simulated based on the connection rules of high-speed railway EMUs and the train number information of each train in the timetable.

[0227] The second construction module is used to construct a train operation situation simulation model based on the connection network. The train operation situation simulation model includes an objective function and constraints.

[0228] The simulation module is used to simulate the operational status of the EMU based on the interference events and the connection network. It combines the rolling time domain algorithm and the heuristic method based on the field strategy to solve the problem and obtain the operational adjustment scheme of the EMU to be simulated.

[0229] The first building module includes:

[0230] The first definition unit is used to define the connection rules of high-speed railway EMUs, the connection rules include minimum connection time standards, model / train formation matching rules and maintenance connection rules;

[0231] The first acquisition unit is used to acquire the operation diagram of the EMU to be simulated, the operation diagram including the basic attributes of the train and the execution status of the train;

[0232] The second definition unit is used to define the node set of the connection network of the EMU to be simulated based on the connection rules and the operation diagram. The node set includes a virtual starting point, a starting node set, a train node set, a destination node set, and a virtual ending point. Each node in the node set is assigned an octet attribute.

[0233] The third definition unit is used to define the arc set of the continuation network based on the node set. The arc set includes virtual arcs, departure arcs, continuation arcs and termination arcs. Each arc in the arc set is assigned a binary attribute.

[0234] The correction unit is used to correct the nodes and arcs of the connected network based on path integrity verification and constraint compliance verification.

[0235] The deduction module includes:

[0236] The second acquisition unit is used to acquire parameter information of the train set to be simulated, including the original timetable, the original operation plan and interference event information.

[0237] The detection unit is used to detect interference events based on parameter information and obtain detection results.

[0238] The solution unit is used to solve the EMU operational situation simulation model based on the detection results, generate the operational adjustment plan of the EMU to be simulated based on the heuristic method of on-site strategy, and update the parameter information.

[0239] The update unit is used to update the simulation start time;

[0240] The judgment unit is used to calculate the remaining simulation period based on the updated simulation start time. If the remaining simulation period is greater than zero, the interference event information is detected again based on the updated parameter information, and the next simulation is performed to generate a new application adjustment scheme. If the remaining simulation period is not greater than zero, the simulation is stopped.

[0241] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0242] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0243] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting the operational status of high-speed railway EMUs, characterized in that, include: Based on the high-speed railway EMU connection rules and the train number information of each train in the timetable, a connection network of the EMU to be simulated is constructed. Based on the aforementioned connection network, a simulation model of the operational status of high-speed trains is constructed. The simulation model of the operational status of high-speed trains includes an objective function and constraints. Based on the interference events and the connection network, the operational situation simulation model of the EMU is simulated. The solution is obtained by combining the rolling time domain algorithm and the heuristic method based on the field strategy. The operational adjustment scheme of the EMU to be simulated is obtained. The method of combining the rolling time-domain algorithm and the heuristic method based on on-site strategy to solve the problem yields the operation and adjustment scheme of the EMU to be simulated, including: Obtain parameter information of the EMU to be simulated, including the original timetable, the original operation plan and interference event information; Interference event information is detected based on parameter information, and the detection results are obtained; Based on the test results, the operational status simulation model of the EMU is solved, and an operational adjustment plan for the EMU to be simulated is generated based on the heuristic method of on-site strategy, and the parameter information is updated. Update the start time of the simulation; The remaining simulation period is calculated based on the updated simulation start time. If the remaining simulation period is greater than zero, the interference event information is re-detected based on the updated parameter information, and the next simulation is performed to generate a new application adjustment scheme. If the remaining simulation period is not greater than zero, the simulation is stopped.

2. The high-speed railway EMU operational situation simulation method according to claim 1, characterized in that... The construction of the connection network for the train sets to be simulated includes: Define the connection rules for high-speed railway EMUs, which include minimum connection time standards, train type / train formation matching rules, and maintenance connection rules; Obtain the train operation diagram to be simulated, which includes the train's basic attributes and train execution status; Based on the connection rules and operation diagram, the node set of the connection network of the EMU to be simulated is defined. The node set includes a virtual starting point, a departure node set, a train node set, a destination node set, and a virtual ending point. Each node in the node set is assigned an octet attribute. The arc set of the connection network is defined based on the node set. The arc set includes virtual arcs, departure arcs, connection arcs and arrival arcs. Each arc in the arc set is assigned a binary attribute. Based on path integrity verification and constraint compliance verification, the nodes and arcs of the connecting network are corrected.

3. The high-speed railway EMU operational situation simulation method according to claim 1, characterized in that... The construction of the EMU operation situation simulation model based on the connection network includes: The deduction boundaries of the EMU operation situation deduction model are set based on the actual operation scenarios of the EMU to be simulated; The model parameters of the EMU operation situation simulation model are set based on the connection network. With the goal of minimizing the negative impact of severe interference on the operation of EMUs, the objective function is constructed by setting the weight of the objective items in combination with the scheduling priority. The objective items of the objective function include minimizing the number of train cancellations, minimizing the EMU operation offset, minimizing the total EMU operation time, minimizing the total EMU operation mileage, and minimizing the inventory deviation of the EMU and the stations. The constraints include virtual node constraints, station entry and exit constraints, EMU connection constraints, EMU unit quantity and model balance constraints, EMU storage location capacity constraints, EMU maintenance constraints, and train demand constraints.

4. The high-speed railway EMU operational situation simulation method according to claim 1, characterized in that... The process involves solving the operational status simulation model of the high-speed train based on the detection results, generating an operational adjustment plan for the high-speed train to be simulated using a heuristic method based on on-site strategies, and updating parameter information, including: If the detection result is that no interference occurred or no new interference information was obtained, then relevant data on the operation of the EMU at all times to be simulated within the remaining simulation period are obtained. If the detection result indicates that interference has occurred or the duration of the interference event has been updated, the simulation start time is reset, and the remaining simulation period is calculated based on the reset simulation start time. Based on the heuristic method of on-site strategy, the EMU operation situation simulation model is simulated and solved during the remaining simulation period to generate an operation adjustment plan and obtain relevant data on the operation of the EMU at all simulation times during the remaining simulation period. Update the parameter information based on the relevant data.

5. The high-speed railway EMU operational situation simulation method according to claim 4, characterized in that... The generation and application of the adjustment scheme includes: The system obtains the set of trains that have been suspended, the first set of EMUs that were responsible for the train missions when the interference occurred, the train missions that the EMUs were currently responsible for, and the set of subsequent trains that the EMUs were responsible for. The set of subsequent trains is filtered by the set of trains that have been suspended. For each EMU in the first EMU set, it is determined whether its subsequent task cannot be continued normally due to the current interference based on the train task that the EMU is currently undertaking. EMUs that cannot be continued normally are stored in the second EMU set. The subsequent task is the train task in the subsequent train set. Determine if the second set of EMUs is empty. If it is, use the original operation plan as the operation adjustment plan. Otherwise, for the subsequent tasks of each EMU in the second set of EMUs, resource matching and replacement are performed according to priority. After resource matching and replacement of all EMUs in the second EMU set, empty runs are arranged for the EMUs that are parked outside and not returned to their positions, and an operational adjustment plan for the EMUs to be simulated is generated.

6. The high-speed railway EMU operational situation simulation method according to claim 1, characterized in that... After obtaining the operational adjustment plan for the trainset to be simulated, the process also includes: Construct a high-speed train operation status assessment model; Calculate the operational status indicators of the EMUs using the adjustment scheme; The operational status characterization indicators of the EMU are input into the operational status assessment model of the EMU for evaluation, and the status level of the operational adjustment plan is obtained.

7. A high-speed railway EMU operational situation simulation system, characterized in that, include: The first construction module is used to construct the connection network of the EMU to be simulated based on the connection rules of high-speed railway EMUs and the train number information of each train in the timetable. The second construction module is used to construct a train operation situation simulation model based on the connection network. The train operation situation simulation model includes an objective function and constraints. The simulation module is used to simulate the operational status of the EMU based on the interference events and the connection network. It combines the rolling time domain algorithm and the heuristic method based on the field strategy to solve the problem and obtain the operational adjustment scheme of the EMU to be simulated. The inference module includes: The second acquisition unit is used to acquire parameter information of the train set to be simulated, including the original timetable, the original operation plan and interference event information. The detection unit is used to detect interference events based on parameter information and obtain detection results. The solution unit is used to solve the EMU operational situation simulation model based on the detection results, generate the operational adjustment plan of the EMU to be simulated based on the heuristic method of on-site strategy, and update the parameter information. The update unit is used to update the simulation start time; The judgment unit is used to calculate the remaining simulation period based on the updated simulation start time. If the remaining simulation period is greater than zero, the interference event information is detected again based on the updated parameter information, and the next simulation is performed to generate a new application adjustment scheme. If the remaining simulation period is not greater than zero, the simulation is stopped.

8. The high-speed railway EMU operational situation simulation system according to claim 7, characterized in that, The first building module includes: The first definition unit is used to define the connection rules of high-speed railway EMUs, the connection rules include minimum connection time standards, model / train formation matching rules and maintenance connection rules; The first acquisition unit is used to acquire the operation diagram of the EMU to be simulated, the operation diagram including the basic attributes of the train and the execution status of the train; The second definition unit is used to define the node set of the connection network of the EMU to be simulated based on the connection rules and the operation diagram. The node set includes a virtual starting point, a starting node set, a train node set, a destination node set, and a virtual ending point. Each node in the node set is assigned an octet attribute. The third definition unit is used to define the arc set of the continuation network based on the node set. The arc set includes virtual arcs, departure arcs, continuation arcs and termination arcs. Each arc in the arc set is assigned a binary attribute. The correction unit is used to correct the nodes and arcs of the connected network based on path integrity verification and constraint compliance verification.

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