High-speed railway motor train unit application situation deduction method and system

Abstract

The invention provides a high-speed railway motor train unit application situation deduction method and system, and relates to the technical field of railway engineering.The method comprises the steps that a connection network of a to-be-deduced motor train unit is constructed based on a high-speed railway motor train unit connection rule and train number information of all trains in a working diagram; constructing a motor train unit application situation deduction model based on the continuous network, wherein the motor train unit application situation deduction model comprises a target function and constraint conditions; and deducing the motor train unit application situation deduction model based on an interference event and the continuing network, and solving by combining a rolling time domain algorithm and a heuristic method based on a field strategy to obtain an application adjustment scheme of the to-be-deduced motor train unit. According to the method, the problem that the actual situation deviates from the plan due to the fact that the application of the high-speed railway motor train unit is easily interfered by uncertain factors is solved, and the problems that the current situation and the future trend are difficult to accurately grasp and intelligent scheduling is difficult to support are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of railway engineering, in particular to a high-speed railway EMU operation situation deduction method and system. BACKGROUND

[0002] With the continuous increase of high-speed railway network coverage, the continuous improvement of road network complexity and the steady growth of EMU ownership, China's high-speed railway passenger transport has entered a new development stage, and high-speed railway passenger transport organization has ushered in a new requirement for quality improvement and efficiency increase. High-speed railway dispatching command also faces new challenges in the direction of intelligent development. High-speed railway EMU operation is an important part of passenger transport organization, but also a key factor restricting the efficiency of passenger transport. It is easy to be disturbed by uncertain factors, so that the actual operation of the EMU deviates from the original EMU operation plan, and then affects the quality of transport task completion. How to correctly grasp the current situation and future development trend of EMU operation, provide decision support for intelligent dispatching of EMUs, and ensure a good and stable EMU operation situation has become a key problem to be solved at present. SUMMARY

[0003] The purpose of the present application is to provide a high-speed railway EMU operation situation deduction method and system to improve the above problems. In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0004] In a first aspect, the present application provides a high-speed railway EMU operation situation deduction method, comprising:

[0005] Based on the connection rules of high-speed railway EMUs and the train information in the diagram, a connection network of the EMU to be deduced is constructed;

[0006] Based on the connection network, an EMU operation situation deduction model is constructed, which includes a target function and a constraint condition;

[0007] Based on the interference event and the connection network, the EMU operation situation deduction model is deduced, and a rolling time domain algorithm and a heuristic method based on field strategy are used to solve, to obtain an operation adjustment scheme of the EMU to be deduced.

[0008] In a second aspect, the present application also provides a high-speed railway EMU operation situation deduction system, comprising:

[0009] A first construction module is used to construct a connection network of the EMU to be deduced based on the connection rules of high-speed railway EMUs and the train information in the diagram;

[0010] A second construction module is used to construct an EMU operation situation deduction model based on the connection network, which includes a target function and a constraint condition;

[0011] The deduction module is used for deducing a model of the EMU operation situation based on the interference event and the EMU connection network, combining a rolling time domain algorithm and a heuristic method based on field strategy to obtain an operation adjustment scheme of the EMU to be deduced.

[0012] The application has the advantages that: the application uses EMU connection rules to construct the EMU operation process as an EMU connection network, constructs an integer linear programming model of the EMU operation situation deduction to obtain an operation adjustment scheme of the EMU, and further obtains the EMU operation related data at a future time. On this basis, a hybrid solving framework combining a rolling time domain algorithm and a heuristic method is designed, the rolling update of the situation deduction is realized through the rolling time domain mechanism, the model solving efficiency is improved by combining the field strategy experience rules, the field scheduling experience is converted into heuristic rules and embedded into the algorithm framework to make the deduction result closer to the field actual situation, and the dynamic update of the situation deduction result is realized through the mechanism of mixed driving of time and event.

[0013] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the present application. The objects and other advantages of the application can be realized and attained by means of the instrumentalities particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0015] Figure 1 The figure is a flowchart of the EMU operation situation deduction method of the high-speed railway described in the embodiments of the present application.

[0016] Figure 2 The figure is a schematic diagram of the connection network structure described in the embodiments of the present application.

[0017] Figure 3 The figure is a schematic diagram of the EMU operation situation representation system of the high-speed railway described in the embodiments of the present application.

[0018] Figure 4 The figure is a situation curve deduction result graph in the embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to make the purposes, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters refer to like items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are merely used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0021] Embodiment 1

[0022] The embodiment provides a high-speed railway motor train unit operation situation deduction method.

[0023] It should be noted that the motor train unit operation plan is a comprehensive plan of motor train unit operation and maintenance, which mainly arranges specific tasks of motor train units in the operation process according to a given train diagram, under the premise of meeting corresponding motor train unit maintenance regulations, that is, makes specific arrangements for each motor train unit at what time, what station, which train, and where and when to complete what maintenance tasks. It clearly defines the space-time attributes of various operations such as motor train unit operation and maintenance, and establishes a high degree of unity between various operations.

[0024] Motor train unit operation adjustment refers to that, after an interference occurs, a motor train dispatcher carries out corresponding adjustment on the operation tasks of motor train units based on the original motor train unit operation plan according to an adjusted train diagram (adjustment diagram), which can also be understood as combining each train task in the adjusted train diagram into an operation circuit and assigning a motor train unit to undertake, while ensuring the rationality of the operation circuit and as far as possible ensuring the execution of the adjustment diagram. The motor train unit operation plan needs to be adjusted mainly because the interference has a great impact on train operation, and according to the adjusted train diagram, the original motor train unit operation plan cannot meet the needs of motor train unit turnover in the new train diagram, so the operation circuit of the motor train unit needs to be changed. And when adjusting the motor train unit operation, the deviation degree between the motor train unit operation adjustment plan and the original motor train unit operation plan should be as small as possible.

[0025] Meanwhile, the EMU operation situation can be defined as a macro description of the current state and future development trend of the EMU operation in a certain space-time range. The EMU operation situation can reflect whether each link and each operation activity of the EMU operation at the current time is normally carried out according to the relevant plan arrangement. If the EMU operation actual work task is executed according to the established plan, the EMU operation situation is good. When the system internal and external factors interfere with the execution of the relevant plan of the EMU operation, the EMU operation deviates from the ideal state, that is, an adverse situation is generated. When the adverse situation is generated, the EMU operation needs to be adjusted to some extent to restore the ideal state.

[0026] Referring to Figure 1 , the method comprises steps S1, S2 and S3.

[0027] Step S1: based on the EMU connection rules and the train information in the train diagram, a connection network of the EMU to be deduced is constructed;

[0028] In step S1, the construction of the connection network of the EMU to be deduced comprises:

[0029] Step S11: defining the connection rules of the EMU, the connection rules comprising minimum connection time standards, car type / consist matching rules and maintenance connection rules;

[0030] Step S12: obtaining the train diagram of the EMU to be deduced, the train diagram comprising train basic attributes and train execution states;

[0031] In this step, the train basic attributes comprise train number, time, station, mileage, car type, etc., and the train execution states comprise normal operation, cancellation, etc.

[0032] Step S13: defining the node set of the connection network of the EMU to be deduced based on the connection rules and the train diagram, the node set comprising a virtual starting point, a set of starting nodes, a set of train nodes, a set of terminal nodes and a virtual terminal point, and each node in the node set being assigned an eight-tuple attribute;

[0033] In this step, based on the EMU connection rules and the train information in the train diagram, the connection network G k = (V k ,A k ) of the EMU k is constructed, and the connection network as shown in Table 2 is obtained, wherein V k represents the node set of the EMU k, and A k represents the arc set of the EMU k.

[0034] In order to construct the single-source single-sink EMU connection network, a virtual starting point and a virtual terminal point Origin node set Origin station or EMU depot of EMU k; train node set Train task; origin node set Destination station or EMU depot of EMU k.

[0035] For each node i∈V k An octet attribute is introduced:

[0036]

[0037] Wherein, r i , l i , h i and n i represent the origin station, destination station, departure time, arrival time, operation time, operation mileage, corresponding train and required number of EMU units of node i respectively.

[0038] All attributes of each train node are valued according to actual conditions; all attribute values of virtual starting point and virtual terminal are 0; origin node and take the same value, and are valued according to actual station, and also take the same value, and are valued according to actual departure time, and the rest of the attribute values are 0; the attribute values of terminal node are referred to the and take the same value, and are valued according to actual station, and also take the same value, and are valued according to actual arrival time, and the rest of the attribute values are 0.

[0039] Step S14: Defining the arc set of the connection network based on the node set, wherein the arc set includes virtual arcs, departure arcs, connection arcs and arrival arcs, and each arc in the arc set is endowed with a binary attribute;

[0040] In this step, the arc set A k includes virtual arcs departure arcs connection arcs arrival arcs

[0041] Virtual arcs are used to connect and nodes Virtual arcs are used to connect nodes and represents that the motor train unit k is overhauled or parked at the station corresponding to i; a virtual arc to connect nodes and nodes At this time, the starting station and the terminal station of the motor train unit k are the same, that is, the motor train unit k is not enabled within the planning period.

[0042] departure arc to connect nodes and nodes represents that the motor train unit k departs from i and takes over j, that is, the departure station, if the motor train unit k needs to run empty from the initial parking place to a different place to continue the train, at this time, the empty running time needs to meet the continuation requirement.

[0043] continuation arc The role of the continuation arc is to connect two train nodes that meet the continuation condition and represents that the motor train unit k continues to take over i and j.

[0044] arrival arc to connect nodes and nodes represents that after taking over i, the motor train unit k returns to the station corresponding to node j, that is, the arrival station, to carry out overhaul work or parking.

[0045] For each arc (i, j) ∈ A k A binary tuple attribute (r i,j , l i,j ) is introduced, which respectively represents the use time and the use mileage of the motor train unit k from to . Among them, all the attribute values of the virtual arc are 0; when is equal to , the attribute value of the arc (i, j) ∈ A k is also 0, wherein i and j represent node indexes.

[0046] Step S15: based on the path integrity check and the constraint compliance check, the nodes and arcs of the continuation network are modified.

[0047] In this step, the network is modified through the path integrity check (to ensure that a complete motor train unit path exists) and the constraint compliance check (to ensure that the nodes / arcs meet the continuation rules), so as to eliminate invalid nodes / arcs and ensure that the network fits the actual use logic.

[0048] Step S2: based on the continuation network, a motor train unit use situation deduction model is constructed, the motor train unit use situation deduction model includes a target function and a constraint condition;

[0049] In step S2, the EMU operation situation deduction model is constructed based on the connection network, and the method comprises the following steps:

[0050] In step S21, a deduction boundary of the EMU operation situation deduction model is set based on an actual operation scene of the EMU to be deduced.

[0051] In this step, the deduction boundary is a model assumption of the EMU operation situation deduction model, and specifically comprises the following aspects:

[0052] Before the disturbance occurs, each EMU operates according to the original plan; after the disturbance occurs, the train operated by the EMU is operated according to the given adjustment diagram, and no consideration is given to the train operated by the EMU in the next section;

[0053] The EMU adopts a non-fixed section operation mode, and after the disturbance occurs, empty running connection is allowed in some sections. If the daily inventory of each EMU depot or station does not meet the plan demand of the next day, the EMU position can be adjusted by empty running back.

[0054] No consideration is given to the reconnection / dissociation operation of the EMU during the completion of the daily transportation task.

[0055] Only the first-level repair of the EMU is considered.

[0056] The EMU is not allowed to operate a train with a fixed number greater than its total passenger carrying capacity. In the selection of marshalling form, the difference in fixed number between different EMUs is not considered, and only the single or heavy connection marshalling form is determined according to the train demand.

[0057] In the EMU operation situation deduction model, the hot standby EMU is directly included in the set of available EMUs, and no distinction is made between the EMUs used in the original plan and the hot standby EMUs. In the input data and adjustment scheme, the EMUs used in the original plan and the hot standby EMUs can be distinguished by the number of EMUs.

[0058] In step S22, the model parameters of the EMU operation situation deduction model are set based on the connection network.

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

[0060] Table 1 Model parameter description table

[0061]

[0062]

[0063]

[0064] It can be understood that i, j, p and q all represent the index of the node, so i, j, p and q can be replaced with each other, but attention should be paid to the set to which the node belongs. For example, hj Not described in Table 1, but known from this j denotes the corresponding train of node j, j∈V k ; to determine whether the motor train unit k executes the arc (j, p)∈A k , if executed as 1, not executed as 0.

[0065] At the same time, as a virtual starting point and so on are specific nodes, for example, when , then this node i is actually a virtual starting point Therefore, among the above parameters, the specific node can be substituted into the index of the node, for example to determine whether the motor train unit k executes the arc if executed as 1, not executed as 0. to determine whether the motor train unit k executes the arc if executed as 1, not executed as 0; n t denotes the number of motor train units required by the train t.

[0066] And the train set,

[0067] Step S23: In order to minimize the negative impact of serious interference on motor train unit operation, set the target item weight in combination with the scheduling priority, construct a target function, and the target items of the target function include the least number of train cancellations, the smallest motor train unit operation offset, the smallest total motor train unit operation time, the smallest total motor train unit operation mileage, and the smallest motor train depot and station inventory deviation.

[0068] It can be understood that, since the motor train unit operation situation deduction is closely related to the preparation of the motor train unit operation adjustment plan, an integer linear programming model of the motor train unit operation situation deduction is constructed to obtain the motor train unit operation adjustment scheme under the condition of serious interference, and then the key problem of calculating the motor train unit operation related data under the condition of serious interference is solved. The key criterion for preparing the motor train unit operation adjustment plan is to shorten the time required for the train to resume normal operation order as much as possible under the premise of ensuring transportation safety, and to reduce the deviation degree of the actual motor train unit operation from the original plan. Therefore, the sub-targets of the motor train unit operation situation deduction model are related to the deviation degree of the original motor train unit operation plan and the deviation degree of the original motor train unit operation plan. The target function minZ constructed by five target items is expressed as follows:

[0069]

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

[0071] Step S24: setting constraints, including virtual node constraints, outbound and inbound station constraints, EMU connection constraints, EMU unit quantity and car type balance constraints, EMU storage site capacity constraints, EMU maintenance constraints and train demand constraints.

[0072] In this step, regarding the virtual node constraints, the number of outbound arcs of each EMU at the virtual starting point should be equal to 1, and the number of inbound arcs at the virtual terminal point should also be equal to 1, to ensure that each EMU has a clear operation path from the virtual starting point to the virtual terminal point in its connection network, so the virtual node constraints are:

[0073]

[0074] Regarding the outbound and inbound station constraints, the number of outbound arcs of each EMU at the starting node should be equal to 1, i.e. the EMU departs from the starting node at the beginning of the day's operation, to ensure that each EMU departs from the station / EMU depot; the number of inbound arcs at the terminal node should also be equal to 1, i.e. EMU k returns to the terminal node after completing its train task, to ensure that the EMU returns to the depot. Therefore, the outbound and inbound station constraints are:

[0075]

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

[0077] The flow balance constraint is:

[0078]

[0079] The uniqueness constraint is:

[0080]

[0081] In the connection time constraint, when connecting at the same station, the connection time should satisfy:

[0082]

[0083] In the connection time constraint, when connecting at different stations, the connection time should satisfy:

[0084]

[0085] EMU operation offset judgment is to determine whether each EMU k connects to tasks i and j (corresponding to train nodes p and q in the connection network according to the original plan) according to the original EMU operation plan, and the following conditions are given:

[0086]

[0087] EMU unit quantity and car type balance constraints are:

[0088]

[0089] Since the number of EMUs stored in each station cannot exceed the storage capacity of each station, the EMU storage site capacity constraint is set, which is specifically:

[0090]

[0091] Regarding the EMU maintenance constraint, the cumulative operation time and operation mileage requirements of EMU k∈E are:

[0092]

[0093] At the same time, the train arrangement of each EMU type and the number of EMU units contained therein should meet the train demand, and the same train task can only be undertaken by one type of EMU, so the train demand constraint is:

[0094]

[0095] Step S3: Based on the interference event and the connection network, the EMU operation situation deduction model is deduced, combined with the rolling time domain algorithm and the heuristic method based on the field strategy to solve, and the operation adjustment scheme of the to-be-deduced EMU is obtained.

[0096] It should be noted that the interference event in this step is a serious interference, which is different from the very serious interference. In this case, there is no need to prepare a temporary train operation scheme, and the subsequent plan can be adjusted reasonably to deal with the adverse effects of interference on the transportation order. At this time, due to the influence of serious interference on part of the previous train, the EMU carrying the previous train task cannot continue to undertake the subsequent train task when the delay time exceeds a certain limit.

[0097] In order to suppress the spread of train delay, control the influence range of interference time, and ensure the reliability and stability of transportation service, the original EMU operation plan (abbreviated as original operation plan or original plan) needs to be adjusted according to the adjustment diagram to obtain the operation adjustment scheme (adjusted EMU operation plan), wherein changing the connection relationship, EMU empty running allocation / return, enabling hot standby EMU, canceling the train, etc. are common dispatching strategies in operation adjustment.

[0098] In step S3, the rolling time domain algorithm and the heuristic method based on the field strategy are combined to solve, and the operation adjustment scheme of the to-be-deduced EMU is obtained, including:

[0099] Step S31: Obtain the parameter information of the to-be-deduced EMU, and the parameter information includes the original operation diagram, the original operation plan and the interference event information;

[0100] In this step, first, initialization is performed, that is, the first time of deduction start time, the entire deduction period length and the first time of deduction window length are set, and the deduction time step Δt is set.

[0101] Step S32: interference event information detection is performed according to the parameter information, and a detection result is obtained;

[0102] Step S33: the detection result is used to solve the EMU operation situation deduction model, the operation adjustment scheme of the to-be-deduced EMU is generated based on the heuristic method of the field strategy, and the parameter information is updated;

[0103] The step S33 includes:

[0104] Step S331: if the detection result is that no interference occurs or no new interference information is obtained, the relevant data of the EMU operation at all to-be-deduced time points in the remaining deduction period is obtained;

[0105] In this step, the relevant data is obtained, and then the step S334 is directly entered.

[0106] Step S332: if the detection result is that interference occurs or the interference event duration is updated, the deduction start time is reset, and the remaining deduction period is calculated according to the reset deduction start time;

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

[0108] According to the reset deduction start time, the window length is adjusted accordingly, that is, the remaining deduction period of the a-th deduction is calculated, Wherein, t a represents the remaining deduction period of the a-th deduction, that is, t a = t a - t 0 total t represents the deduction period length, t a represents the a-th deduction start time, t 0 represents the first time of deduction start time.

[0109] Step S333: based on the heuristic method of the field strategy, the EMU operation situation deduction model is deduced and solved in the remaining deduction period, the operation adjustment scheme is generated, and the relevant data of the EMU operation at all to-be-deduced time points in the remaining deduction period is obtained;

[0110] Step S334: the parameter information is updated according to the relevant data.

[0111] Step S34: the deduction start time is updated;

[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 of fusing the rolling horizon algorithm and the heuristic method not only enables the system to obtain feedback information in time, make real-time response, and dynamically adjust the window coverage range to adapt to the gradual shortening of the deduction period, but also generates a motor train unit operation adjustment scheme based on on-site strategy as needed, facilitates the calculation of motor train unit operation related data in the case of serious interference, and further calculates the motor train unit operation situation representation index value and the motor train unit operation situation level, thereby improving the motor train unit operation situation deduction efficiency and practicability.

[0133] Further, after obtaining the operation adjustment scheme of the motor train unit to be deduced, the motor train unit operation situation representation index is calculated based on the obtained motor train unit operation related data, and the situation level prediction is completed by linking the motor train unit operation situation evaluation model, thereby realizing the prediction of the motor train unit operation situation level at any time within the deduction period.

[0134] Specifically, the motor train unit operation situation is divided into operation state sub-situation, maintenance state sub-situation, depot parking state sub-situation and standby state sub-situation by comprehensively considering the motor train unit operation situation influencing factors and taking the state of a single motor train unit as the starting point, and the motor train unit operation situation representation index is selected and quantified based on this, and the high-speed railway motor train unit operation situation representation system as shown in Table 3 is obtained.

[0135] Regarding the operation state sub-situation, the train arrival punctuality rate, the train cancellation rate, the motor train unit connection reliability, the motor train unit operation task deviation degree and the motor train unit operation quantity deviation degree are selected as the representation indexes of the motor train unit operation state sub-situation.

[0136] The train arrival punctuality rate R1 is:

[0137]

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

[0139] The train cancellation rate R2 is:

[0140]

[0141] In the formula, n v represents the number of train cancellations at T' time.

[0142] The motor train unit connection reliability R3 is:

[0143]

[0144] In the formula, N rN (T') represents the actual number of EMUs at time T', c k N (T') represents the actual number of EMUs at time T', c

[0145] The EMU operation task deviation R4 is:

[0146]

[0147] In the formula, N d N (T') represents the actual number of EMUs at time T', c c N (T') represents the actual number of EMUs at time T', c

[0148] The EMU operation quantity deviation R5 is:

[0149]

[0150] In the formula, n p N (T') represents the actual number of EMUs at time T', c

[0151] Secondly, the EMU maintenance according to the maintenance plan is beneficial to the normal execution of the next stage of EMU operation plan, and the EMU maintenance task deviation is taken as a representation index of the EMU maintenance state sub-situation.

[0152] The EMU maintenance task deviation describes the actual execution of the maintenance plan. The EMU maintenance task degree can be quantitatively represented by the ratio of the number of EMUs actually maintained without a plan to the number of EMUs required to be maintained according to the maintenance plan, and therefore the representation index of the maintenance state sub-situation is the EMU maintenance task deviation R6:

[0153]

[0154] In the formula, N (T') represents the actual number of EMUs at time T', c N (T') represents the actual number of EMUs at time T', c

[0155] Under ideal conditions, the EMUs maintained according to the plan should all be maintained, and the EMUs maintained without a plan should not be maintained, i.e. the value of the index should be 0; if a single EMU is affected by a sudden situation or random disturbance, the maintenance thereof cannot be performed according to the original plan, and therefore the value of the representation index increases. The smaller the index value of the EMU maintenance task deviation, the better the execution of the maintenance plan and the smoother the maintenance operation.

[0156] Meanwhile, the types of EMUs parked at each EMU depot or station with storage capacity and the number of EMUs of various types parked at each EMU depot or station need to meet the requirements of the EMU operation plan, so as to ensure the smooth implementation of the EMU operation plan. In this paper, the EMU inventory deviation degree is selected as the representation index of the depot and storage state sub-situation.

[0157] The EMU inventory deviation degree is used to describe the deviation of the EMU inventory of each EMU depot or station with storage capacity in the regional road network. When quantifying this representation index, the types of EMUs need to be considered, because when the total number of inventories of an EMU depot or station meets the demand, it does not mean that the number of EMUs of a certain type also meets the demand. The difference between the actual number and the planned number of EMUs of each type parked at each station can be used to quantitatively represent the EMU inventory deviation degree, and therefore, the representation index of the depot and storage state sub-situation is the EMU inventory deviation degree R7:

[0158]

[0159] In the formula, represents the number of EMUs of type e parked at station s at time T', represents the number of EMUs of type e parked at station s at time T', N d represents the total number of EMUs at each station at time T', m1 represents the number of stations, and m2 represents the number of types.

[0160] Finally, when a backup EMU is enabled, the hot standby EMU is considered first. The hot standby EMU enable rate is selected as the representation index of the standby state sub-situation of the EMU.

[0161] It is assumed that when the EMU allocation scheme is developed, the number of hot standby EMUs has been reasonably planned, so that the number of hot standby EMUs is within a reasonable range, that is, it can meet the scheduling requirements in emergency situations and avoid waste of resources due to excessive configuration. The hot standby EMU enable rate can reflect the enablement of the current hot standby EMU, and the ratio of the number of enabled hot standby EMUs to the number of configured hot standby EMUs can be used to quantify the index, that is, the representation index of the standby state sub-situation is the hot standby EMU enable rate R8:

[0162]

[0163] In the formula, represents the number of hot standby EMUs actually enabled at time T', represents the number of hot standby EMUs planned to be configured at time T'.

[0164] Therefore, R1 to R8 are used as the representation indexes of the EMU operation situation (simply referred to as representation indexes).

[0165] The obtained operation adjustment scheme of the to-be-derived motor train unit further comprises:

[0166] Step S4: constructing a motor train unit operation situation assessment model;

[0167] In this step, an initial index matrix is constructed through 8 motor train unit operation situation characterization indexes, and the initial index matrix has been normalized.

[0168] Step S41: based on the initial index matrix, a first weight coefficient of each characterization index is calculated based on an entropy weight method. And based on the initial index matrix, a comparative intensity and a conflict of each characterization index are calculated through a CRITIC method, and a second weight coefficient of each characterization index is calculated through the comparative intensity and the conflict.

[0169] Step S42: based on a minimum discrimination information principle, a combined weight model is constructed through 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 indexes, w a′ represents a combined weight of the a'th characterization index, represents the first weight coefficient of the a'th characterization index, represents the second weight coefficient of the a'th characterization index.

[0172] Step S43: the combined weight model is solved through a Lagrange function, and a combined weight of each characterization index is obtained:

[0173]

[0174] Step S44: constructing a motor train unit operation situation assessment model based on the combined weight-GRA-TOPSIS.

[0175] Specifically, step S44 comprises:

[0176] Step S441: constructing a weighted normalized matrix.

[0177] Suppose there are multiple to-be-evaluated objects, the multiple to-be-evaluated objects correspond to operation adjustment schemes at different time points, and each to-be-evaluated object includes corresponding motor train unit operation situation characterization indexes (i.e., corresponding 8 characterization index values). The multiple to-be-evaluated objects and the corresponding motor train unit operation situation characterization indexes are sequentially used to form an initial evaluation matrix.

[0178] The initial evaluation matrix is normalized to obtain a normalized matrix. The normalized matrix is multiplied by the combined weight to obtain a weighted normalized matrix.

[0179] Step S442: Calculate the positive ideal solution and the negative ideal solution of the weighted standardized matrix. Calculate the distance of each to-be-evaluated object to the positive ideal solution and the negative ideal solution. Calculate the grey correlation coefficient matrix of each to-be-evaluated object with the positive ideal solution and the negative ideal solution respectively.

[0180] Step S443: Calculate the grey correlation degree of each to-be-evaluated object with the positive ideal solution and the negative ideal solution respectively based on the grey correlation coefficient matrix. Normalize the distance and the grey correlation degree respectively to obtain the normalized distance and the normalized grey correlation degree.

[0181] Step S444: Weightedly fuse the normalized distance and the normalized grey correlation degree to obtain the first closeness and the second closeness. The first closeness is the closeness of each to-be-evaluated object to the positive ideal solution, and the second closeness is the closeness of each to-be-evaluated object to the negative ideal solution.

[0182] Step S445: Calculate the relative closeness by the first closeness and the second closeness.

[0183] Specifically, the relative closeness is:

[0184]

[0185] In the formula, denotes the relative closeness of the b'to-be-evaluated object, denotes the first closeness of the b'to-be-evaluated object, denotes the second closeness of the b'to-be-evaluated object.

[0186] Step S446: Construct a situation grade division table based on the relative closeness.

[0187] Divide the situation grades from high to low into levels I to V, level I is excellent, level II is better, level III is general, level IV is worse, and level V is poor. Divide the relative closeness by the equal division principle, and the corresponding relationship between the situation grade and the calculation result of the relative closeness is shown in Table 5.

[0188] Table 2 Situation grade division table

[0189]

[0190] Among them, level I means excellent, that is, the actual EMU operation is basically consistent with the original plan, each operation link is orderly carried out, and the whole is coordinated, and basically no scheduling adjustment is needed. The actual EMU operation is the plan after the operation adjustment scheme is used.

[0191] Grade II represents better, that is, there are individual adverse factors affecting the EMU operation, the actual EMU operation deviates from the original plan to a lower degree, each operation link is carried out smoothly, and a simple local dispatching adjustment can make the EMU operation return to normal.

[0192] Grade III represents general, that is, there are partial adverse factors, the actual EMU operation deviates from the original plan to a certain degree, and certain dispatching adjustment measures need to be taken in time to prevent the EMU operation situation from further deteriorating.

[0193] Grade IV represents poor, that is, there are more adverse factors affecting the EMU operation, the actual EMU operation deviates from the original plan to a greater degree, and the development of part of the operation links is greatly affected, which needs to be paid attention to and corresponding dispatching adjustment measures need to be taken immediately.

[0194] Grade V represents severe, that is, the EMU operation system is seriously disturbed, the actual EMU operation deviates from the original plan to a serious degree, and multiple operation links are seriously affected and cannot be carried out normally, so that large-scale and multi-aspect dispatching adjustment needs to be taken immediately, and a long time is needed to make the EMU operation return to normal state and complete each operation according to the established plan.

[0195] Step S5: calculating the EMU operation situation representation index of the operation adjustment scheme;

[0196] In this step, R1 to R8 of the operation adjustment scheme at different prediction times are calculated.

[0197] Step S6: inputting the EMU operation situation representation index into the EMU operation situation evaluation model for evaluation to obtain the situation grade of the operation adjustment scheme.

[0198] The application selects corresponding representation indexes and quantifies them mathematically in combination with the characteristics of the EMU operation system, and constructs an EMU operation situation representation system including operation state, maintenance state, depot parking state and standby state sub-situations. In the aspect of EMU operation situation evaluation, the complementarity of each weight determination method and the limitation of single evaluation method are comprehensively considered, an EMU operation situation evaluation model based on combined weight-GRA-TOPSIS is constructed, and the situation grade and representation index grade are divided.

[0199] R1 to R8 of the operation adjustment scheme at different prediction times obtained by deduction and prediction are input into the EMU operation situation evaluation model for calculation and evaluation, so that the prediction of the situation grade at any time within the deduction period can be realized.

[0200] Example 2:

[0201] In this embodiment, the train involved is the train in which the EMU is assigned to run in a certain area in the deduction period, and the corresponding train data can be obtained from the train operation adjustment diagram, including train number, starting station, terminal station, starting time, terminal time, operation time, operation mileage, train type, and train fixed number of personnel.

[0202] The EMU operation route is represented by the feasible path from the virtual starting point to the virtual terminal point composed of nodes and arcs in the connection network. Each node needs to specify the starting station, the terminal station, the starting time, the terminal time, and the operation time. Each arc needs to specify the operation time, the operation mileage, and the serial number of the two nodes connected by the arc. The corresponding data information can be obtained from the original EMU operation plan.

[0203] The model-related parameter configuration includes: the minimum connection time standard is 15 minutes, the first-level maintenance time standard is 2880 minutes, the first-level maintenance mileage standard is 5500 km, and the weight of each target item of the objective function.

[0204] In this embodiment, due to a certain sudden serious disturbance, a certain section of high-speed railway is completely interrupted. It is assumed that the duration of the disturbance is uncertain when the serious disturbance occurs, and the specific duration is updated as time goes on. By adjusting the start time and duration of the serious disturbance, a serious disturbance scenario is constructed.

[0205] The specific setting of the serious disturbance scenario in this embodiment is the start time 9:00 and the duration: {9:00, 9:00-10:00}, {10:00, 9:00-11:00}. That is, at the start time 9:00 of the serious disturbance scenario, it is judged according to the obtained disturbance information that the disturbance will last for 60 minutes. At 10:00, the disturbance information is updated, and at this time the disturbance has not ended, and it is expected to continue for 60 minutes, i.e. the duration of this serious disturbance is 9:00-11:00, and at 11:00 the disturbance ends and the section capacity is restored.

[0206] Before solving the EMU operation situation deduction model, the related parameter settings of the rolling time domain algorithm are described. In the rolling time domain algorithm, the reasonable value of the deduction time step can be determined based on a large amount of experimental data. The deduction time step is taken as 1h, the start time of the first round of deduction is taken as 6:00, and the length of the entire deduction period is taken as 24h, so the window length of the first round of deduction should be taken as 24h.

[0207] In this scenario, a total of 24 rounds of situation deduction are performed, of which the number of times triggered by time driving is 22, and the number of times triggered by event driving is 2.

[0208] No interference event occurs when the first round to the third round of deduction is carried out, and the original motor train unit operation plan is executed. When the fourth round of deduction is carried out, the deduction start time is 9:00, the deduction window length is 21h, the model solving result is shown in Table 3, and the model solving time is 4.5s.

[0209] Table 3 Model solving result table of the fourth round of deduction

[0210]

[0211] When the fifth round of deduction is carried out, the deduction start time is 10:00, the deduction window length is 20h, the model solving result is shown in Table 4, and the model solving time is 5.9s.

[0212] Table 4 Model solving result table of the fifth round of deduction

[0213]

[0214] In 2h intervals, 8:00, 10:00, …, 4:00, 6:00 are selected in turn, and the situation level of high-speed railway motor train unit operation at the 12 time points in each serious interference scenario is calculated according to the situation assessment calculation process.

[0215] Since no interference event occurs when the first round to the third round of deduction is carried out in this scenario, the deduction result is that the motor train unit is actually operated according to the original motor train unit operation plan, and the motor train unit operation situation level of each to-be-deduced time point obtained by the first round to the third round of deduction is all level I.

[0216] The situation representation index value calculation result obtained by the fourth round of deduction is shown in the table.

[0217] Table 5 Situation representation index value result table of the fourth round of deduction

[0218] Time [R1] [R2] [R3] [R4] [R5] [R6] [R7] [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 situation level calculation result is shown in Table 6.

[0220] Table 6 Situation level calculation result table of the fourth round of deduction

[0221]

[0222]

[0223] After the multiple rounds of deduction, the situation level after each round of deduction is not the same, as shown in Figure 4 It can be seen that after multiple rounds of deduction update, the situation curve is also updated, and the updated situation level at different time points can be directly observed.

[0224] Example 3:

[0225] The embodiment provides a high-speed railway motor train unit operation situation deduction system, and the system comprises:

[0226] A first construction module is used for constructing a connection network of a motor train unit to be deduced based on high-speed railway motor train unit connection rules and train information in a train diagram;

[0227] A second construction module is used for constructing a motor train unit operation situation deduction model based on the connection network, wherein the motor train unit operation situation deduction model comprises a target function and a constraint condition;

[0228] A deduction module is used for deducing the motor train unit operation situation deduction model based on an interference event and the connection network, and solving the motor train unit operation situation deduction model by combining a rolling time domain algorithm and a heuristic method based on a field strategy to obtain an operation adjustment scheme of the motor train unit to be deduced.

[0229] The first construction module comprises:

[0230] A first definition unit is used for defining connection rules of the high-speed railway motor train unit, and the connection rules comprise minimum connection time standards, car type / consist matching rules and maintenance connection rules;

[0231] A first acquisition unit is used for acquiring a train diagram of the motor train unit to be deduced, and the train diagram comprises train basic attributes and train execution states;

[0232] A second definition unit is used for defining a node set of the connection network of the motor train unit to be deduced based on the connection rules and the train diagram, wherein the node set comprises a virtual starting point, a set of starting nodes, a set of train nodes, a set of terminal nodes and a virtual terminal point, and each node in the node set is endowed with an eight-tuple attribute;

[0233] A third definition unit is used for defining an arc set of the connection network based on the node set, wherein the arc set comprises a virtual arc, a departure arc, a connection arc and a parking arc, and each arc in the arc set is endowed with a two-tuple attribute;

[0234] A correction unit is used for correcting the nodes and the arcs of the connection network based on path integrity checking and constraint compliance checking.

[0235] The deduction module comprises:

[0236] A second acquisition unit is used for acquiring parameter information of the motor train unit to be deduced, and the parameter information comprises an original train diagram, an original operation plan and interference event information;

[0237] A detection unit is used for detecting the interference event information according to the parameter information to obtain a detection result;

[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 high-speed railway EMU operation situation deduction method, characterized in that, The application relates to a high-speed railway train operation adjustment method. The method comprises the following steps: Based on the high-speed train connection rules and the train information in the train diagram, a connection network of the train to be deduced is constructed; Based on the connection network, a train operation situation deduction model is constructed, which comprises a target function and a constraint condition; 2. The high-speed railway EMU operation situation deduction method according to claim 1, characterized in that Based on the interference event and the connection network, the train operation situation deduction model is deduced, and a rolling time domain algorithm and a heuristic method based on a field strategy are combined to solve, so that an operation adjustment scheme of the train to be deduced is obtained. The construction of the connection network of the train to be deduced comprises the following steps: The connection rules of the high-speed train are defined, and the connection rules comprise a minimum connection time standard, a train type / formation matching rule and a maintenance connection rule; The train diagram of the train to be deduced is obtained, and the train diagram comprises train basic attributes and train execution states; Based on the connection rules and the train diagram, a node set of the connection network of the train to be deduced is defined, the node set comprises a virtual starting point, a starting node set, a train node set, a terminal node set and a virtual terminal point, and each node in the node set is endowed with an eight-tuple attribute; Based on the node set, an arc set of the connection network is defined, the arc set comprises a virtual arc, a departure arc, a connection arc and a parking arc, and each arc in the arc set is endowed with a two-tuple attribute; 3. The high-speed railway EMU operation situation deduction method according to claim 1, characterized in that Based on path integrity checking and constraint compliance checking, the nodes and arcs of the connection network are modified. The construction of the train operation situation deduction model based on the connection network comprises the following steps: Based on the actual operation scene of the train to be deduced, a deduction boundary of the train operation situation deduction model is set; Based on the connection network, model parameters of the train operation situation deduction model are set; In order to minimize the negative influence of serious interference on the train operation, a target function is constructed by combining a dispatching priority to set a target item weight, and the target item of the target function comprises the least number of train cancellations, the minimum train operation deviation, the minimum total train operation time, the minimum total train operation mileage and the minimum train depot and station inventory deviation; 4. The high-speed railway EMU operation situation deduction method according to claim 1, characterized in that Constraint conditions are set, and the constraint conditions comprise virtual node constraints, station constraints, train connection constraints, train unit quantity and train type balance constraints, train storage location capacity constraints, train maintenance constraints and train demand constraints. The solution of the train operation situation deduction model by combining the rolling time domain algorithm and the heuristic method based on the field strategy to obtain the operation adjustment scheme of the train to be deduced comprises the following steps: Parameter information of the train to be deduced is obtained, and the parameter information comprises an original train diagram, an original operation plan and interference event information; According to the parameter information, interference event information detection is performed to obtain a detection result; According to the detection result, the train operation situation deduction model is solved, the heuristic method based on the field strategy is used to generate the operation adjustment scheme of the train to be deduced, and the parameter information is updated; The deduction start time is updated; According to the updated deduction start time, a remaining deduction period is calculated, if the remaining deduction period is greater than zero, the interference event information detection is performed again according to the updated parameter information, the next deduction solving is performed, a new operation adjustment scheme is generated, and if the remaining deduction period is not greater than zero, the deduction is stopped.

5. The high-speed railway EMU operation situation deduction method according to claim 4, characterized in that , the model is solved according to the detection result, a heuristic method based on field strategy is used to generate an operation adjustment scheme of the EMU to be deduced, and parameter information is updated, including: If the detection result is that no interference occurs or no new interference information is obtained, relevant data of EMU operation at all to-be-deduced time points in the remaining deduction period is obtained; If the detection result is that interference occurs or the duration of the interference event is updated, the deduction start time is reset, and the remaining deduction period is calculated according to the reset deduction start time; The heuristic method based on field strategy is used to deduce and solve the EMU operation situation deduction model in the remaining deduction period, generate an operation adjustment scheme, and obtain relevant data of EMU operation at all to-be-deduced time points in the remaining deduction period; The parameter information is updated according to the relevant data.

6. The high-speed railway EMU operation situation deduction method according to claim 5, characterized in that The operation adjustment scheme is generated, including: A set of out-of-service trains, a first set of EMUs that take over train tasks when the interference occurs, train tasks being taken over by the EMUs, and a set of subsequent trains taken over by the EMUs, the set of subsequent trains being filtered through the set of out-of-service trains; For each EMU in the first set of EMUs, it is judged whether the subsequent task of the EMU can be normally connected due to the current interference through the train task being taken over by the EMU, and the EMU that cannot be normally connected is stored in a second set of EMUs, the subsequent task being a train task in the set of subsequent trains; It is judged whether the second set of EMUs is empty, if yes, the original operation plan is taken as the operation adjustment scheme, otherwise, for the subsequent task of each EMU in the second set of EMUs, resource matching and replacement are performed according to the priority; After resource matching and replacement are performed on all EMUs in the second set of EMUs, empty running and return are arranged for the EMUs that are parked outside and have not returned, and an operation adjustment scheme of the EMU to be deduced is generated.

7. The high-speed railway EMU operation situation deduction method according to claim 1, characterized in that After the operation adjustment scheme of the EMU to be deduced is obtained, the following steps are further included: An EMU operation situation evaluation model is constructed; An EMU operation situation representation index of the operation adjustment scheme is calculated; The EMU operation situation representation index is input into the EMU operation situation evaluation model for evaluation, and a situation level of the operation adjustment scheme is obtained.

8. A high-speed railway EMU operation situation deduction system, characterized in that, It includes: A first construction module is configured to construct a connection network of the EMU to be deduced based on a connection rule of the EMU and train information in a train diagram; A second construction module is configured to construct an EMU operation situation deduction model based on the connection network, the EMU operation situation deduction model including an objective function and a constraint condition; A deduction module is configured to deduce the EMU operation situation deduction model based on an interference event and the connection network, and solve the model by combining a rolling time domain algorithm and a heuristic method based on field strategy to obtain an operation adjustment scheme of the EMU to be deduced.

9. The high-speed railway EMU operation situation deduction system according to claim 7, characterized in that, The first construction module includes: A first definition unit is configured to define a connection rule of the EMU, the connection rule including a minimum connection time standard, a car type / consist matching rule, and a maintenance connection rule; A first acquisition unit is configured to acquire a train diagram of the EMU to be deduced, the train diagram including train basic attributes and train execution states; A second defining unit is configured to define a node set of the connection network of the to-be-derived motor train unit based on the connection rule and the running graph, the node set including a virtual start point, a set of departure nodes, a set of train nodes, a set of arrival nodes and a virtual end point, each node in the node set being assigned an eight-tuple attribute; A third defining unit is configured to define an arc set of the connection network based on the node set, the arc set including a virtual arc, a departure arc, a connection arc and an arrival arc, each arc in the arc set being assigned a two-tuple attribute; A correcting unit is configured to correct the nodes and arcs of the connection network based on the path integrity check and the constraint compliance check.

10. The high-speed railway EMU operation situation deduction system according to claim 7, characterized in that, The deriving module comprises: A second obtaining unit is configured to obtain parameter information of the to-be-derived motor train unit, the parameter information including an original running graph, an original operation plan and disturbance event information; A detecting unit is configured to detect the disturbance event information according to the parameter information to obtain a detection result; A solving unit is configured to solve the motor train unit operation situation deriving model according to the detection result, generate an operation adjustment scheme of the to-be-derived motor train unit based on a heuristic method of a field strategy, and update the parameter information; An updating unit is configured to update a deriving start time; A judging unit is configured to calculate a remaining deriving time period according to the updated deriving start time, if the remaining deriving time period is greater than zero, re-detect the disturbance event information according to the updated parameter information, perform next deriving solution, and generate a new operation adjustment scheme, and if the remaining deriving time period is not greater than zero, stop the derivation.

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