Compilation method, device and product for flexibly marshalling train working diagram in transition time period

Abstract

The invention discloses a compilation method, device and product for a flexible marshalling working diagram in a transition period, and relates to the technical field of train operation organization and control, and the method comprises the steps: obtaining train operation line topological structure information, time parameter information, peak period parameter information and flat peak period parameter information, and obtaining a flexible marshalling working diagram in a transition period based on the train operation information; according to the method, a transition-time-period-oriented flexible marshalling train working diagram drawing model is constructed, the flexible marshalling train working diagram drawing model comprises a constraint condition set and a target function, the target function takes the uniform departure interval of each train number as a target, and the flexible marshalling train working diagram drawing model is solved by utilizing a genetic algorithm; and a flexible marshalling train working diagram for the transition time period is obtained. By constructing the flexible marshalling train working diagram drawing model, automatic drawing of the train working diagram in the transition period in the mechanical flexible marshalling mode is achieved, and the working diagram drawing efficiency and the travel experience of passengers are improved.

Description

Technical Field

[0001] The present application relates to the technical field of train operation organization and control, and in particular to a method, device and product for compiling a train operation diagram for flexible formation during a transition period. Background Art

[0002] In recent years, urban rail transit has developed rapidly and played an indispensable role in meeting residents' travel needs and alleviating urban traffic congestion. Due to the diversity of urban regional planning and passenger travel needs, the temporal and spatial distribution of passenger flow on some urban rail lines connecting urban areas and suburbs is extremely uneven, and the traditional fixed-formation operation mode is difficult to adapt to complex passenger flow needs.

[0003] The mechanical flexible marshaling mode is a new operating mode of urban rail transit. It can flexibly marshal or unmarshall according to the passenger flow characteristics of different time periods and sections, so as to adapt to different forms of passenger flow distribution. According to the passenger flow characteristics of most current urban rail systems, there is a situation of "supply exceeding demand" of transportation capacity during peak hours, and all large-scale trains need to be operated to meet passenger flow demand. During off-peak hours, passenger flow demand is generally low, and all small-scale trains can be operated to reduce capacity waste and increase service frequency. Therefore, under the mechanical flexible marshaling mode, the compilation of operation diagrams for peak and off-peak periods can refer to the compilation method under the fixed marshaling mode, and the difficulty is mainly concentrated in the transition period between high and off-peak hours.

[0004] The operation diagram preparation process and decision-making process in the transition period between high and flat peaks are more complicated than those in peak and flat peak periods. It is impossible to refer to the preparation method under the fixed marshaling mode, which increases the difficulty of train operation diagram preparation. Therefore, realizing the automatic drawing of train operation diagrams in transition periods under the mechanical flexible marshaling mode has become a key research direction at present. Summary of the invention

[0005] The purpose of this application is to provide a method, equipment and product for compiling a flexible train operation diagram for a transition period, which can realize the automatic drawing of a flexible train operation diagram for a transition period, thereby improving the efficiency of diagram compilation and the travel experience of passengers.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for compiling a transition period flexible marshaling train operation diagram, comprising:

[0008] Acquire the line topology information, time parameter information and time period parameter information of the train operation; the time period parameter information includes peak period parameter information and non-peak period parameter information;

[0009] Based on the line topology information, the time parameter information and the time period parameter information, a flexible train operation diagram laying model for the transition period is constructed; the flexible train operation diagram laying model includes a constraint condition set and an objective function, and the objective function is a function with the goal of uniform departure intervals of each train;

[0010] The flexible train operation diagram drawing model is solved by using a genetic algorithm to obtain a flexible train operation diagram for a transition period.

[0011] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for compiling a flexible train operation diagram for a transition period.

[0012] In a third aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for compiling a flexible train operation diagram for a transition period.

[0013] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0014] The present application provides a method, device and product for compiling a flexible train operation diagram for a transition period, the method comprising obtaining line topology information, time parameter information, peak period parameter information and off-peak period parameter information of the train operation; based on the line topology information, time parameter information, peak period parameter information and off-peak period parameter information, constructing a flexible train operation diagram laying model for the transition period; wherein the flexible train operation diagram laying model comprises a set of constraints and an objective function, the objective function is to achieve a uniform departure interval for each train; the flexible train operation diagram laying model is solved by using a genetic algorithm to obtain a train operation diagram for the transition period. The present application establishes an automatic train operation diagram laying model by deeply analyzing the line topology, time parameters and characteristics of peak and off-peak periods, combined with a genetic algorithm, which realizes the automatic laying of the transition period train operation diagram under the mechanical flexible marshaling mode, improves the efficiency of the flexible marshaling operation diagram compilation, thereby reducing manual intervention, reducing the workload of the map makers, and effectively improving the travel experience of passengers, providing strong support for the intelligent development of urban rail transit. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is an application environment diagram of a method for compiling a flexible train operation diagram for a transition period in one embodiment of the present application;

[0017] Figure 2 A schematic flow chart of a method for compiling a flexible train operation diagram for a transition period provided in one embodiment of the present application;

[0018] Figure 3 A schematic diagram of line topology information required for the compilation of a flexible train operation diagram provided in an embodiment of the present application;

[0019] Figure 4 This is a schematic diagram of the operation during the transition period from peak to flat peak under the mechanical flexible marshaling mode;

[0020] Figure 5 This is a schematic diagram of the operation during the transition period from flat peak to peak under the mechanical flexible marshaling mode;

[0021] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] In the mechanical flexible marshaling mode, the automatic drawing of the operation diagram during the transition period between high and flat peaks is a key difficulty. Taking the transition from peak to flat peak as an example, the task of compiling the operation diagram during this transition period requires reasonable decision-making on the return train from many trains running online, and ensuring that the intervals between each train are uniform. In this process, the transition from dense to sparse train departure intervals must be reasonably decided, otherwise it may lead to uneven train intervals, and even cause congestion in front of the reversing station, thus affecting the travel experience of passengers. In the mechanical flexible marshaling mode, in addition to the above difficulties, it is necessary to further consider the number of vehicles and marshaling methods used during high and flat peak periods, determine whether the train needs to be marshaled or disassembled, and meet the constraints such as marshaling and disassembling time, and the occupation of space-time resources of the reversing track. The above problems make the compilation and decision-making of the flexible marshaling train operation diagram during the transition period more complicated, and increase the difficulty of compilation. Therefore, the present application proposes a method, equipment and product for compiling a flexible marshaling train operation diagram during the transition period, so as to realize the automatic drawing of the train operation diagram during the transition period under the mechanical flexible marshaling mode.

[0024] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The train operation diagram compilation method for transition period provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the acquired train operation information to the server 104. After the server 104 receives the line topology information, time parameter information and time period parameter information of the train operation, the server 104 constructs a flexible marshaling train operation diagram laying model for the transition period based on the above train operation information, and solves the flexible marshaling train operation diagram laying model using a genetic algorithm to obtain a flexible marshaling train operation diagram for the transition period. The server 104 can feedback the obtained flexible marshaling train operation diagram for the transition period to the terminal 102. In addition, in some embodiments, the method for compiling a flexible marshaling train operation diagram for the transition period can also be implemented separately by the server 104 or the terminal 102, such as the terminal 102 can directly process the acquired train operation information, or the server 104 can obtain the train operation information from the data storage system and process the train operation information.

[0026] The terminal 102 may be, but is not limited to, various desktop computers and IoT devices, and the server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0027] In an exemplary embodiment, Figure 2 As shown, a method for compiling a transition period flexible marshaling train operation diagram is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 203. Among them:

[0028] Step 201, obtaining line topology information, time parameter information and time period parameter information of train operation; wherein the time period parameter information includes peak period parameter information and non-peak period parameter information.

[0029] The specific implementation process of step 201 includes:

[0030] According to the actual situation of the urban rail system, the line topology information, time parameter information, peak period parameter information and non-peak period parameter information required for the compilation of the train operation diagram in the mechanical flexible marshaling mode are configured, such as Figure 3 As shown in the figure, the set of all station platforms on the line is I = {1,2,…,|I|,|I|+1,...,2|I|}, where |I| represents the number of platforms in the upward direction; the shortest stop time of platform i in the set I is and the longest stop time Define platform 1 to platform |I| as the upward direction, and platform |I|+1 to platform 2|I| as the downward direction, and stipulate the shortest interval running time from platform i to platform i+1 and maximum running time Configure a platform set I connected to the return track turn , the station set connected to the depot I depot ; Define the shortest tracking interval of a train at platform i The shortest train return time u min ; Configure the set of all trains in the transition period K = {1, 2, ..., |K|}, which can be further divided into the peak period train set K high K low ; It is stipulated that train marshaling / disassembling operations are only carried out in the reversing track area at the end of the line, and the shortest train marshaling operation time is recorded as t cp , the shortest decompilation operation time is t ucp ; Configure the number of large trains N to run during peak hours high, the number of small-scale trains running during off-peak hours N low , peak-hour departure interval H high , departure interval during off-peak hours H low .

[0031] Step 202, based on the line topology information, time parameter information and time period parameter information, construct a flexible train operation diagram layout model for the transition period; wherein the flexible train operation diagram layout model includes a constraint set and an objective function, the constraint set includes the transition period train schedule constraints, the vehicle bottom turnover constraints and the train assembly and disassembly process constraints in the reversing track area, and the objective function is a function with the goal of uniform departure intervals for each train.

[0032] The specific implementation process of step 202 includes:

[0033] Step 221, constructing the transition period train schedule constraints, including stop time constraints, interval running time constraints, tracking interval constraints, peak period departure interval constraints and non-peak period departure interval constraints, as follows:

[0034] In order to facilitate passengers to board and alight, the threshold of the stop time needs to be set as follows:

[0035]

[0036] The above formula indicates that the stop time of the urban rail train at the platform must meet the maximum and minimum constraints, symbol d k,i represents the departure time of train number k at platform i, symbol a k,i represents the arrival time of train number k at platform i.

[0037]

[0038] The running time of the train in the section must also be limited to a certain range. The above formula indicates that the running time of the urban rail train in the section must meet the maximum and minimum constraints. The symbol d k,i-1 Indicates the departure time of train number k at platform i-1.

[0039] In order to ensure operational safety, a tracking interval constraint should be introduced to limit the interval between the departure time of the previous train and the arrival time of the next train during the transition period, namely:

[0040]

[0041] Among them, the symbol d k-1,i It represents the departure time of train number k-1 at platform i.

[0042] The problem solved by this application is the compilation of train operation diagrams during the transition period. Therefore, in order to meet the departure intervals during peak and off-peak periods, the following constraints are defined:

[0043]

[0044] Among them, the symbol a k-1,i represents the arrival time of train number k-1 at platform i.

[0045] Step 2022, construct the vehicle bottom turnover constraints in the mechanical flexible marshaling mode, as follows:

[0046] After executing a trip, if the train wants to continue running, it needs to turn around and change ends before it can execute the reverse trip. The duration of this process must be greater than the shortest turnaround time, which is expressed as:

[0047]

[0048] Among them, x k,l,i is a 0-1 variable, x k,l,i =1 means that the train enters the turnaround terminal configured at platform i after completing train number k and executes the reverse train number l; symbol η(i) represents the reverse platform of platform i, a l,η(i) represents the time when train number l arrives at platform η(i); M in the formula represents a sufficiently large positive number; I turn Represents the set of platforms connected to the switchback track.

[0049] During the transition period between peak and off-peak hours, trains need to leave or return to the depot. The number of trains entering and leaving the depot is related to the number of cars during peak and off-peak hours. In order to meet the needs of the transition period between peak and off-peak hours, the following constraints are defined:

[0050]

[0051] The symbol c in the above formula k and b k indicates whether the train number k returns to the depot or leaves the depot. The formula calculates the number of trains entering and leaving the depot during the transition period from peak to off-peak and from off-peak to peak. The symbol p=0 indicates the transition from off-peak to peak, and p=1 indicates the transition from peak to off-peak. N high N is the number of large trains running during peak hours. low Indicates the number of small-scale trains running during off-peak hours.

[0052] Step 223, constructing the process constraints of train marshaling and unmarshaling in the reversing track area in the mechanical flexible marshaling mode, as follows:

[0053] First, define the binary variable sequence dc kTo indicate whether train k is mechanically coupled with train k+1. Specifically, if train k is mechanically coupled with train k+1, then dc k = 1. On the contrary, if train number k and train number k+1 are two independently operated small trains, then dc k =0.

[0054] In this embodiment, it is assumed that trains in peak hours are run in large formations, and trains in off-peak hours are run in small formations. Therefore, the following constraints can be obtained:

[0055]

[0056] Next, according to the meaning of the binary variable sequence and the train operation characteristics of mechanical flexible formation, the train departure interval constraint is reconstructed as shown in the following formula:

[0057]

[0058] The above formula indicates that if train number k and train number k+1 are in mechanical marshaling, their arrival and departure times at platform i should be the same.

[0059] Finally, the process of trains dynamically occupying track resources in the mechanical flexible marshaling mode is micro-modeled. In the mechanical flexible marshaling mode, in addition to normal reversing operations, trains also need to perform marshaling and unmarshaling related operations on the reversing track. This process involves the conversion of turnout states and conflicts between various routes, and requires the introduction of a large number of decision variables and logical judgment constraints, as follows:

[0060] a. After the train departs from the terminal station, it enters the reversing track and waits for the end change. After the train changes the end on the reversing track, it drives out of the reversing track and arrives at the opposite starting platform. During this period, the constraints of the end change and the shortest running time must be met, as shown in the following formula:

[0061]

[0062] In the above formula, a k,0 represents the time when train number k arrives at the starting platform in the upward direction, d k,2|I| Indicates the time when the train departs from the platform of the terminal station in the down direction; α k,j represents the time when train k arrives at turnaround track j, β k,j represents the time when train number k departs from turnaround track j, t run Indicates the travel time of the train on the reversing track, t turn Represents the time for changing ends on the reversing track; the above formula constrains the train's running time on the reversing track and the train's end-changing time.

[0063] b. The logical constraints for the time of train formation and disbanding on the reversing track are as follows:

[0064]

[0065] The above formula indicates that if train numbers k and k-1 are in a disassembly state before turning around and in a marshaling state after turning around, it means that train numbers k and k-1 have undergone mechanical marshaling operations on the turning track, and the two trains must meet the marshaling operation time constraint; conversely, if train numbers k and k-1 are in a marshaling state before turning around and in a disassembly state after turning around, it means that the above train numbers have undergone a disassembly operation on the turning track, and the two trains must meet the disassembly operation time constraint.

[0066] Since there may be conflicts in the use of routes and line resources when trains enter and exit the reversing track, we need to constrain potential train conflicts. In this embodiment, taking the single reversing track after the station as an example, the time constraints for the front and rear trains entering the reversing track are as follows:

[0067]

[0068] In the above formula, x k-1,l,i Indicates whether train k-1 will continue to run train l after turning back at platform i; t set Indicates the time when the route is cleared and unlocked, the switch is completed, and the route is established and locked; dc l To indicate whether train number l is in a mechanical coupling state with train number l+1. The above formula indicates that only when the current train leaves the reversing track can the following train enter the reversing track, unless the two trains are in a mechanical coupling state. For the case of multiple reversing tracks after the station, the above formula can be used as an example for derivation.

[0069] Step 2024, this embodiment constructs an objective function with the goal of uniform departure intervals for each train. According to the time when each train arrives at the station and departs from the station, a variance calculation formula is constructed, and variance minimization is used as the objective function, thereby achieving the goal of uniform departure intervals.

[0070] When compiling the train operation diagram, the urban rail operating company tries to make the train departure intervals as uniform as possible and constructs the objective function as follows:

[0071]

[0072] Among them, σ 2 represents the uniformity of the departure interval, |K| represents the total number of trains in the transition period, |I| represents the total number of platforms, and d k,i represents the departure time of train number k at platform i, d k-1,i represents the departure time of train number k-1 at platform i, μ represents the average departure interval of each train number, and the calculation formula is:

[0073]

[0074] Step 203, using a genetic algorithm to solve the flexible train operation diagram layout model to obtain a train operation diagram for the transition period.

[0075] Furthermore, the genetic algorithm is used to solve the flexible train operation diagram layout model to obtain the train operation diagram for the transition period, which specifically includes:

[0076] Step 2031, convert the nonlinear constraints and objective functions in the flexible marshaling train operation diagram layout model into linear forms to obtain a linear flexible marshaling train operation diagram layout model; the nonlinear constraints are the train tracking interval constraints, train marshaling time constraints and train disassembly time constraints in the constraint condition set.

[0077] First, convert the quadratic term in the objective function into absolute value form as follows:

[0078]

[0079] This absolute value expression can have the same effect as the quadratic expression.

[0080] Further define the auxiliary variable ξ k,i Substitute the absolute value symbol and add auxiliary variables as follows:

[0081]

[0082] Among them, ξ k,i ≥d k,i -d k-1,i -μ,ξ k,i ≥μ+d k-1,i -d k,i .

[0083] Next, it is noted that the train departure interval constraint in the mechanical flexible marshaling mode in step 2021 is a nonlinear constraint form of a quadratic product. Therefore, the process of linearizing the constraint using the artificial variable large M is as follows:

[0084]

[0085] Finally, the time constraints on train formation / disbanding operations in the model are linearized as follows:

[0086]

[0087]

[0088] At this point, the model constructed in this application has been transformed into a mixed integer linear programming model, namely, a linear flexible train operation diagram drawing model.

[0089] Step 2032, using the genetic algorithm to solve the linear flexible train operation diagram layout model, and output the decision information of the train at each platform; wherein the decision information includes departure time, arrival time, marshaling or disassembling status and vehicle bottom turnover, and the vehicle bottom turnover refers to the connection relationship between upstream and downstream trains and the decision on whether the train enters and exits the vehicle depot.

[0090] Furthermore, the genetic algorithm is used to solve the linear flexible train operation diagram layout model and output the decision information of the train at each platform, including:

[0091] The 0-1 binary variable encoding method is used to encode the car bottom turnover decision variables (i.e., whether the car number k returns to the depot or leaves the depot) c of each car number in the linear flexible train operation diagram drawing model. k and b k They respectively represent that gene encoding is performed, and an initial population including a plurality of gene encoding strings is created.

[0092] Each gene coding string in the initial population is subjected to crossover, mutation and selection operations in turn to obtain the decision information of the train at each platform. Specifically, the crossover operation is to randomly select two gene coding strings and then exchange parts of them; the mutation operation is to randomly change the value of a certain position in a gene coding string, thereby generating a new gene coding string and forming a new population; the selection operation is to input the gene coding string in the new population into the linear flexible marshaling train operation diagram layout model, and use the CPLEX solver to solve the linear flexible marshaling train operation diagram layout model to obtain the objective function value of each gene coding string, and then calculate the fitness of each gene coding string based on the objective function value, and then use the roulette wheel selection method to select the gene coding string with high fitness based on the fitness to enter the next stage, and repeat the above process in a cycle, that is, to perform crossover, mutation and selection operations on each gene coding string in turn until the preset number of iterations is met, and the final gene coding string is used as the decision information.

[0093] The calculation formula of the roulette selection method is as follows:

[0094]

[0095] In the above formula, P(x i ) represents individual x i The probability of being selected, N represents the size of the population, f(x i ) is the individual fitness calculated according to the objective function.

[0096] Step 2033, drawing a train operation diagram for the transition period based on the decision information.

[0097] Taking the departure interval of 160s during peak hours and 300s during off-peak hours as an example, the number of trains used during peak hours is 48 small-scale trains, and the number of trains used during off-peak hours is 15 small-scale trains. Figure 4 The transition period operation diagram of Beijing Metro Line 19 under the mechanical flexible marshaling mode is displayed.

[0098] From a macro perspective, before the morning peak ebbs, there are 48 small-sized trains on the line. All trains run at the peak departure interval of 160s. During the peak period, all trains are large-sized trains, which provides sufficient capacity for the line and guarantees passenger service. As time goes by, some trains during the morning peak begin to return to the depot, thereby reducing capacity and saving operating costs. In this process, it can be seen that a total of 33 small-sized trains return to the depot, which meets the constraint of the number of trains used. The depot of Line 19 is located at the terminal station in the downward direction. It can be seen that after the large-sized trains in the downward direction arrive at the reversing track, some trains choose to disassemble into two small-sized trains on the reversing track (indicated by thin lines in the figure), one of which returns to the depot and the other continues to run. Some large-sized trains also return directly to the depot after reversing on the reversing track. The arrival and departure intervals of trains during the entire transition period are relatively uniform.

[0099] Furthermore, we provide the operation diagram layout effect of the transition from flat peak to peak peak in the mechanical flexible grouping mode, such as Figure 5 As shown. During the daytime off-peak period, it can be seen that all trains run at an off-peak departure interval of 300 seconds. At the same time, all trains run in small formations, which can save operating costs while meeting passenger flow demand. When the time comes to the evening peak, the trains start to leave the depot, realizing the transition from off-peak to peak. Figure 5 It can be seen that some small trains form large trains at the turnaround track with small trains that have just left the depot. Some trains are directly dispatched from the depot in the form of large trains, and in this process, the departure interval is continuously shortened to reach the peak capacity, thus meeting the passenger flow demand during peak hours.

[0100] By implementing the above-mentioned steps 201 to 203, the present application has established a comprehensive train operation diagram automatic drawing model through in-depth analysis of the topological structure, time parameters and peak and off-peak periods of train operation, combined with modern optimization algorithms. The model realizes the automatic drawing of the train operation diagram during the transition period, improves the efficiency of operation diagram compilation, thereby reducing manual intervention, reducing the workload of map compilers, and effectively improving passengers' travel experience, providing strong support for the intelligent development of urban rail transit.

[0101] The present application also provides an application scenario, which applies the above-mentioned flexible marshaling train operation diagram compilation method. Specifically: the flexible marshaling train operation diagram compilation method provided in this embodiment can be applied to the flexible marshaling train operation diagram compilation scenario during the transition period between high and flat peaks. The train operation diagram compilation scenario during the transition period between high and flat peaks includes a flexible marshaling train operation diagram compilation link and a flexible marshaling train operation diagram display link; wherein, the flexible marshaling train operation diagram compilation link is used to automatically lay out the train operation diagram during the transition period between high and flat peaks, and the flexible marshaling train operation diagram display link is used to display the flexible marshaling train operation diagram during the transition period; the train operation diagram compilation method provided in this embodiment belongs to the train operation diagram compilation link.

[0102] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store processing data of train operation information. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for compiling a flexible train operation diagram for a transition period is implemented.

[0103] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0104] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0105] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0106] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0107] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0108] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for compiling a transition period flexible marshaling train operation diagram, characterized in that: The method for compiling the transition period flexible marshaling train operation diagram comprises: Acquire the line topology information, time parameter information and time period parameter information of the train operation; the time period parameter information includes peak period parameter information and non-peak period parameter information; Based on the line topology information, the time parameter information and the time period parameter information, a flexible train operation diagram laying model for the transition period is constructed; the flexible train operation diagram laying model includes a constraint condition set and an objective function, and the objective function is a function with the goal of uniform departure intervals of each train; The flexible train operation diagram drawing model is solved by using a genetic algorithm to obtain a flexible train operation diagram for a transition period.

2. The method for compiling a transition period flexible marshaling train operation diagram according to claim 1, characterized in that: The line topology information includes all platform sets on the line, platform sets connected to the reentry track, station sets connected to the depot, and locations where trains can be marshaled and unmarshaled; The time parameter information includes the shortest stop time, the longest stop time, the shortest running time between platforms in the upward direction, the longest running time between platforms in the upward direction, the shortest running time between platforms in the downward direction, the longest running time between platforms in the downward direction, the shortest train tracking interval, the shortest train return time, the shortest train marshaling operation time and the shortest train disassembly operation time; The time period parameter information includes the number of large-scale trains running during peak hours, the departure intervals during peak hours, the number of small-scale trains running during off-peak hours, and the departure intervals during off-peak hours.

3. The method for compiling a transition period flexible marshaling train operation diagram according to claim 1, characterized in that: The set of constraint conditions includes the train schedule constraint during the transition period, the vehicle bottom turnover constraint, and the process constraint of train marshaling and unmarshaling in the reversing track area; The transition period train schedule constraints include platform stop time constraints, running time constraints, tracking interval constraints, peak period departure interval constraints and off-peak period departure interval constraints; The vehicle bottom turnover constraints include the turnaround time constraint and the number of trains entering and leaving the vehicle depot constraint; The process constraints of train marshaling and disassembling in the reversing track area include train tracking interval constraints, train marshaling time constraints, train disassembling time constraints and train occupying reversing track time constraints in the mechanical flexible marshaling mode.

4. The method for compiling a transition period flexible marshaling train operation diagram according to claim 1, characterized in that: The calculation formula of the objective function is: Among them, σ 2 represents the uniformity of the departure interval, |K| represents the total number of trains in the transition period, |I| represents the total number of platforms, μ represents the average departure interval of each train, and d k,i represents the departure time of train number k at platform i, d k-1,i It represents the departure time of train number k-1 at platform i.

5. The method for compiling a transition period flexible marshaling train operation diagram according to claim 1, characterized in that: The flexible marshaling train operation diagram drawing model is solved by using a genetic algorithm to obtain a flexible marshaling train operation diagram for the transition period, which specifically includes: The nonlinear constraints and objective functions in the flexible marshaling train operation diagram laying model are converted into linear forms to obtain a linear flexible marshaling train operation diagram laying model; the nonlinear constraints are the train tracking interval constraints, the train marshaling time constraints and the train disassembly time constraints in the constraint condition set; The linear flexible marshaling train operation diagram drawing model is solved by using the genetic algorithm to output the decision information of the train at each platform; wherein the decision information includes the departure time, arrival time, marshaling or unmarshaling status and vehicle bottom turnover, and the vehicle bottom turnover refers to the connection relationship between the up and down trains and the decision on whether the train enters and exits the vehicle depot; Based on the decision information, a flexible train operation diagram for the transition period is drawn.

6. The method for compiling a transition period flexible marshaling train operation diagram according to claim 5, characterized in that: The nonlinear constraints and objective functions in the flexible marshaling train operation diagram laying model are converted into linear forms to obtain a linear flexible marshaling train operation diagram laying model, which specifically includes: Converting the quadratic term in the objective function into an absolute value form, determining an auxiliary variable, and using the auxiliary variable to replace the quadratic term in absolute value form to obtain a linear objective function; Obtaining a positive variable, and using the positive variable to transform the nonlinear constraint into a linear form, to obtain a set of linear constraint conditions; According to the linear objective function and the set of linear constraints, a linear flexible marshaling train operation diagram laying model is constructed.

7. The method for compiling a transition period flexible marshaling train operation diagram according to claim 5, characterized in that: The genetic algorithm is used to solve the linear flexible marshaling train operation diagram drawing model, and the decision information of the train at each platform is output, which specifically includes: Genetically encoding the vehicle bottom turnover decision variables of each train number in the linear flexible marshaling train operation diagram drawing model by using a 0-1 binary variable encoding method to obtain an initial population; the initial population includes multiple gene encoding strings; A crossover operation, a mutation operation and a selection operation are sequentially performed on each gene coding string in the initial population to obtain the decision information.

8. The method for compiling a transition period flexible marshaling train operation diagram according to claim 7, characterized in that: In the selection operation, the CPLEX solver is used to solve the linear flexible train operation diagram drawing model to obtain the objective function value of each gene coding string, and the fitness of each gene coding string is calculated based on the objective function value. Based on the fitness, the roulette selection method is used to determine the decision information.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for compiling a transition period flexible train operation diagram as described in any one of claims 1-8.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements a method for compiling a transition period flexible train operation diagram as described in any one of claims 1-8.

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