Subway frame overhaul planning data processing method and device, medium and electronic equipment

Abstract

Embodiments of the present application provide a subway frame overhaul planning data processing method, device, medium and electronic equipment. The method comprises: based on the decision reference data associated with the train frame overhaul planning of the whole subway network, determining the first planning data including the train frame overhaul planning data of each month within the next 5 years through a first mixed integer programming model; based on the decision reference data and the first planning data, determining the second planning data including the train frame overhaul planning data of each date within the next 1 year through a second mixed integer programming model; based on the decision reference data, the historical first planning data and the historical second planning data determined in the last year, determining the third planning data including the train frame overhaul planning data of each date remaining in the current year through a third mixed integer programming model. The technical scheme of the embodiments of the present application can improve the accuracy of subway frame overhaul planning data processing.

Description

Technical Field

[0001] This application relates to the field of computer and subway maintenance planning data processing technology, and more specifically, to a subway overhaul planning data processing method, device, medium and electronic equipment. Background Technology

[0002] Throughout the entire lifecycle of urban rail transit trains, train operation and maintenance are crucial. To ensure the smooth, efficient, and orderly operation of subway trains, overhauls are an important component of regular train maintenance. These overhauls typically involve comprehensive and in-depth disassembly and repair of trains that have been in operation for 5 / 10 years (i.e., the target operating time) or have accumulated 600,000 / 1.2 million kilometers (i.e., the target operating mileage). After an overhaul, the train's appearance, interior, and overall performance can approach the standards of a new train, allowing it to be put back into service. With the further development of the large-scale subway network, the workload of subway train overhauls is increasing year by year, resulting in a large number of trains requiring overhaul maintenance and a heavy workload. When formulating overhaul maintenance plans for the entire subway network (all subway lines), it is essential to fully utilize resources and ensure orderly subway operation and maintenance. In the long-term, medium-term, and short-term overhaul maintenance plans for subways, the long-term plan is a 5-year plan, the medium-term plan is an annual overhaul plan, and the short-term plan is a monthly overhaul plan.

[0003] Currently, long-term, medium-term, and short-term plans for subway train overhauls are manually formulated by planners based on subway maintenance procedures and their own professional experience. Planners develop plans based on factors such as train operating hours, operating mileage, the maximum number of trains that can be impounded on the subway line, the contractor's maximum number of impounded trains, the contractor's monthly new work starts, and overhaul milestones. However, manual planning has inherent limitations and biases. The resulting plans cannot guarantee the full utilization of existing resources and the achievement of optimal results, leading to resource waste, uneven task allocation, and unreasonable situations. This affects the accuracy of subway overhaul planning data processing, ultimately impacting train availability, on-time delivery of overhaul projects, and the stability of subway operations.

[0004] Therefore, improving the accuracy of subway overhaul planning data processing is an urgent technical problem to be solved. Summary of the Invention

[0005] The embodiments of this application provide a method, apparatus, computer program product or computer program, computer-readable medium and electronic device for processing subway overhaul planning data, which can at least improve the accuracy of subway overhaul planning data processing to a certain extent.

[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0007] According to one aspect of the embodiments of this application, a method for processing subway train overhaul planning data is provided. The method includes: acquiring decision reference data associated with the overhaul planning of train frames in the entire subway network; determining first planning data based on the decision reference data using a pre-constructed first mixed integer programming model, wherein the first planning data includes train frame overhaul planning data for each month within the next 5 years; determining second planning data based on the decision reference data and the first planning data using a pre-constructed second mixed integer programming model, wherein the second planning data includes train frame overhaul planning data for each date within the next year; acquiring the first planning data and the second planning data determined in the previous year, respectively as historical first planning data and historical second planning data, and determining third planning data based on the decision reference data, the historical first planning data, and the historical second planning data using a pre-constructed third mixed integer programming model, wherein the second planning data includes train frame overhaul planning data for each remaining date within the current year.

[0008] In some embodiments of this application, based on the foregoing scheme, the objective function of the first mixed-integer programming model includes the following formulas 1 and 2:

[0009] Formula 1:

[0010]

[0011] Formula 2:

[0012]

[0013] In this context, Formula 1 has a higher priority than Formula 2; O represents the set of overhaul work packages; T1 represents the set of months over the next 5 years; M i,o X represents the target month for the overhaul work package o of the maintenance rack of the i-th train; i,o,t This indicates whether the i-th train will begin a major overhaul of the maintenance rack in month t within the next 5 years, with a value of 0 or 1; β is a penalty coefficient, which will be applied if the actual month of maintenance differs significantly from the target month; TY represents the set of all years within the next 5 years; YD ty It represents the absolute value of the difference between the number of train frame overhauls and repairs in two consecutive years, ty and ty+1, for any subway line; α is the penalty coefficient.

[0014] In some embodiments of this application, based on the foregoing scheme, the constraint functions of the first mixed-integer programming model include the following formulas 3, 4, and 5:

[0015] Formula 3:

[0016]

[0017] Formula 4:

[0018]

[0019] Formula 5:

[0020]

[0021] Where O represents the set of overhaul work packages; T1 represents the set of months over the next 5 years; X i,o,t Indicates whether the i-th train will begin a major overhaul on the maintenance rack in month t within the next 5 years, with a value of 0 or 1; MU i,o This represents the upper limit of operating time / operating kilometers for the overhaul work package o of the i-th train maintenance rack, with a time granularity of months; MD i,o Let represent the lower limit of the operating time / operating mileage for the overhaul work package o of the i-th train maintenance rack, with a time granularity of months; I represents the set of subway trains; T ty Y represents the set of months in year ty; ty This indicates the total number of train frame overhauls and repairs in year ty.

[0022] In some embodiments of this application, based on the foregoing scheme, the objective function of the second mixed-integer programming model includes the following formulas 6 and 7:

[0023] Formula 6:

[0024]

[0025] Formula 7:

[0026]

[0027] In this context, Formula 6 has a higher priority than Formula 7; O represents the set of overhaul work packages; T2 represents the set of dates within the next year; N i,o Y represents the target date for the maintenance overhaul package o of the i-th train within the next year; i,o,t This indicates whether the i-th train will begin its major overhaul work on the maintenance rack on date t within the next year, with a value of 0 or 1; δ is a penalty coefficient, which will be applied if the actual start date of maintenance differs significantly from the target date; TM1 represents the set of all months within the next year, YD tm γ represents the absolute value of the difference between the number of train frame overhauls and repairs in two consecutive months, tm month and tm+1 month, for any subway line; γ is the penalty coefficient.

[0028] In some embodiments of this application, based on the foregoing scheme, the constraint functions of the second mixed-integer programming model include the following formulas 8 and 9:

[0029] Formula 8:

[0030]

[0031] Formula 9:

[0032]

[0033] Where O represents the set of overhaul work packages; T2 represents the set of dates within the next year; Y i,o,t Indicates whether the i-th train will begin its major overhaul work on the maintenance rack on date t within the next year, with a value of 0 or 1; MUD i,o This represents the upper limit of operating time / operating kilometers for the overhaul work package o of the i-th train maintenance rack, with a time granularity of days; MDD i,o This represents the lower limit of the operating time / operating kilometers for the overhaul work package o of the i-th train maintenance frame, with a time granularity of days.

[0034] In some embodiments of this application, based on the foregoing scheme, the objective function of the third mixed-integer programming model includes the following formulas 10 and 11:

[0035] Formula 10:

[0036]

[0037] Formula 11:

[0038]

[0039] Where O represents the set of overhaul work packages; T3 represents the set of dates within the current year; S i,o Z represents the target date for the overhaul work package o of the maintenance frame for the i-th train within the current year; i,o,t Indicates whether the i-th train starts its overhaul work package o on date t within the current year, with a value of 0 or 1; μ is a penalty coefficient, which will be applied if the actual start date of maintenance differs significantly from the target date; TM2 represents the set of all months within the current year, YD tm θ represents the absolute value of the difference between the number of train frame overhauls and repairs in two consecutive months, tm month and tm+1 month, for any subway line; θ is the penalty coefficient.

[0040] In some embodiments of this application, based on the foregoing scheme, the constraint functions of the third mixed-integer programming model include the following formulas 12 and 13:

[0041] Formula 12:

[0042]

[0043] Formula 13:

[0044]

[0045] Where O represents the set of overhaul work packages; T3 represents the set of dates within the current year; Z i,o,t Indicates whether the i-th train will begin its overhaul work on the maintenance rack on date t within the current year, with a value of 0 or 1; MUD i,o This represents the upper limit of operating time / operating kilometers for the overhaul work package o of the i-th train maintenance rack, with a time granularity of days; MDD i,o This represents the lower limit of the operating time / operating kilometers for the overhaul work package o of the i-th train maintenance frame, with a time granularity of days.

[0046] According to one aspect of the embodiments of this application, a subway train overhaul planning data processing apparatus is provided. The apparatus includes: an acquisition unit for acquiring decision reference data associated with the overhaul planning of train frames in the entire subway network; a first determination unit for determining first planning data based on the decision reference data using a pre-constructed first mixed integer programming model, wherein the first planning data includes train frame overhaul planning data for each month within the next 5 years; a second determination unit for determining second planning data based on the decision reference data and the first planning data using a pre-constructed second mixed integer programming model, wherein the second planning data includes train frame overhaul planning data for each date within the next 1 year; and a third determination unit for acquiring the first planning data and second planning data determined in the previous year, respectively serving as historical first planning data and historical second planning data, and determining third planning data based on the decision reference data, the historical first planning data, and the historical second planning data using a pre-constructed third mixed integer programming model, wherein the second planning data includes train frame overhaul planning data for each remaining date within the current year.

[0047] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0048] According to one aspect of the embodiments of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the above embodiments.

[0049] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method described in the above embodiments.

[0050] This application employs a global optimization approach, establishing a planning decision-making scheme encompassing long-term, medium-term, and short-term planning. Based on a mixed-integer programming model, a multi-stage planning method is used to sequentially formulate and execute the overhaul plan. Specifically, the 5-year overhaul plan uses monthly granularity and spans 5 years; the annual overhaul plan uses day granularity and spans 1 year; and the monthly overhaul plan uses day granularity and spans less than 1 year. The planning time span and granularity decrease from large to small, and the number of factors considered increases from few to many, ultimately resulting in a maintenance plan accurate to the day—determining when the train enters and exits the overhaul. This application effectively reduces the complexity of the planning solution and improves the accuracy and speed of data extraction.

[0051] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0053] Figure 1 A flowchart of a subway overhaul planning data processing method according to an embodiment of this application is shown;

[0054] Figure 2 A block diagram of a subway overhaul planning data processing apparatus according to an embodiment of this application is shown;

[0055] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0056] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0057] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0058] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0059] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0060] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0061] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0062] Before elaborating on this plan, we will first briefly explain the technical concepts related to the subway overhaul plan.

[0063] The entire network refers to a multi-level subway system (such as Line 1, Line 2, etc.) designed according to requirements for military preparedness, transportation, and urban planning.

[0064] Overhaul: Overhaul is an important part of the regular maintenance of trains. It usually involves a comprehensive and in-depth disassembly and repair of trains that have been in operation for 5 or 10 years (target operating time) or have an operating mileage of 600,000 or 1.2 million kilometers (target operating mileage).

[0065] Overhaul work packages: Overhaul work packages are divided into first overhaul, first major overhaul, second overhaul, second major overhaul, and third overhaul. The duration of an overhaul work package can be 30 days, 45 days, 90 days, or 120 days.

[0066] Cross-year scaffolding overhaul: This refers to a scaffolding overhaul whose start and end dates are not in the same year. For example, a scaffolding overhaul starts at the end of this year and is completed at the beginning of next year.

[0067] Number of major overhauls started: refers to the number of trains that have started major overhauls in the current year.

[0068] Number of trains that have completed major overhauls in the current year: This refers to the number of trains that have completed major overhauls in the current year.

[0069] Contractor: refers to the construction party that performs the major overhaul project.

[0070] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0071] In this application, the overhaul planning includes long-term, medium-term, and short-term plans. In the long-term, medium-term, and short-term overhaul and maintenance plans for subway trains, the long-term plan is a 5-year plan, the medium-term plan is an annual plan, and the short-term plan is a monthly plan. The long-term plan will determine the number of overhauls and maintenance tasks for the next 5 years (excluding the current year). The long-term overhaul plan for subway trains primarily considers the upper and lower limits of train operating time and operating mileage, scheduling trains within reasonable maintenance periods to ensure maintenance is performed as close as possible to the target operating time (i.e., the time when the train's operating time reaches the target operating time) / target operating mileage. The medium-term plan will determine the overhaul and maintenance tasks for the next year (excluding the current year). The short-term plan will determine the overhaul and maintenance tasks for the next month. In formulating medium- and short-term plans for major overhauls of subway trains, the main considerations are the upper and lower limits of train operating time and operating mileage, scheduling trains for maintenance at reasonable times, and ensuring that trains are maintained within the target operating time / target operating mileage as much as possible. At the same time, it is necessary to consider the conditions and restrictions on the number of trains impounded by the contractor and the number of trains impounded on the subway line, the monthly conditions and restrictions on the number of new projects started by the contractor for the corresponding model, and whether the trains span across years.

[0072] Figure 1 A flowchart illustrating a subway overhaul planning data processing method according to an embodiment of this application is shown. This method can be executed by a device with computational processing capabilities. (Refer to...) Figure 1 As shown, the data processing method for the subway overhaul planning includes at least steps 110 to 140, which are detailed below:

[0073] In step 110, decision reference data related to the overhaul planning of train frames in the entire subway network is obtained.

[0074] In this application, the decision-making reference data related to the overhaul planning of train frames in the entire metro network includes at least the following points:

[0075] 1. Record data of each train in the last maintenance frame overhaul work package.

[0076] 2. Maintenance base data (vehicle impoundment data at the maintenance base).

[0077] 3. Train data (basic train data, train type, train number).

[0078] 4. Overhaul work package data (work package basic data, train type, work package name, work package number).

[0079] 5. Overhaul milestone data (acceptance, signaling, application for trial operation, trial operation, application for launch).

[0080] 6. Train status data (provides the daily mileage of the train).

[0081] Continue to refer to Figure 1 In step 120, based on the decision reference data, a first planning data is determined by a pre-constructed first mixed integer programming model. The first planning data includes train frame overhaul planning data for each month within the next 5 years (excluding the current year).

[0082] In this application, the first planning data includes which trains and which overhaul work packages need to be carried out in which month within the next 5 years.

[0083] In this application, the time granularity of the first mixed-integer programming model is monthly. The first mixed-integer programming model includes the following model objective:

[0084] Objective 1: To ensure that trains are maintained within the target operating time / target operating mileage as much as possible. In this application, the operating mileage of the train can be converted into operating time. For example, the target operating time for the first major overhaul of a certain train model is the time when the operating time reaches 5 years / the target operating mileage is 625,000 kilometers. If maintenance is carried out in advance, train resources will be wasted, as parts will need to be repaired before they reach the required operating time / operating mileage. If maintenance is delayed, train malfunctions will occur, resulting in a loss of train safety. The objective function of the model includes the following formula (1):

[0085]

[0086] Where O represents the set of overhaul work packages; T1 represents the set of months over the next 5 years; M i,o This indicates the target month for the major overhaul of the maintenance frame of train i over the next 5 years; X i,o,t Indicates whether the i-th train will begin a major overhaul of the maintenance frame in month t within the next 5 years, with a value of 0 or 1. i,o,t =1 indicates that the i-th train will begin its major overhaul work on the maintenance frame in month t within the next 5 years, X i,o,t =0 indicates that the i-th train will not start its overhaul work package o in month t within the next 5 years; β is a penalty coefficient, which will be applied if the actual month of maintenance starts differs greatly from the target month.

[0087] Objective 2: To ensure a balanced number of train overhauls and repairs each year for the next 5 years, i.e., to keep the number of trains undergoing major overhauls and repairs as similar as possible each year for the next 5 years, in order to reduce the pressure on maintenance delivery. The objective function of the model includes the following formula (2):

[0088]

[0089] Where TY represents the collection of all years over the next 5 years, and YD ty Let represent the absolute value of the difference between the number of major overhauls and repairs of train frames in two consecutive years ty and ty+1 for any subway line. The objective function of the model is to minimize the absolute value of the difference between the number of major overhauls and repairs of train frames in each consecutive two years, and α is the penalty coefficient.

[0090] In this application, objective one has a higher priority than objective two.

[0091] In this application, the first mixed-integer programming model includes the following model constraints:

[0092] Constraint 1: The upper limit of operating time / operating mileage must be met. The operating time / operating mileage of the train during maintenance cannot exceed the upper limit of operating time / operating mileage. For example, the first overhaul of a certain model must be carried out before the operating time reaches 6 years or before the target operating mileage is 725,000 kilometers. If the upper limit is exceeded, the safety of the train cannot be guaranteed. For time and mileage, the mileage is replaced with the corresponding time, and the minimum value of the two times is taken as the upper limit. The constraint function of the model includes the following formula (3):

[0093]

[0094] Where O represents the set of overhaul work packages; T1 represents the set of months over the next 5 years; X i,o,t Indicates whether the i-th train will begin a major overhaul of the maintenance frame in month t within the next 5 years, with a value of 0 or 1. i,o,t =1 indicates that the i-th train will begin its major overhaul work on the maintenance frame in month t within the next 5 years, X i,o,t =0 indicates that the i-th train will not begin its major overhaul work on the maintenance frame in month t within the next 5 years; MU i,o This represents the upper limit of operating time / operating kilometers for the overhaul work package o of the i-th train maintenance rack, with a time granularity of months.

[0095] Constraint 2: The lower limit constraint of operating time / operating mileage must be met. The mileage already traveled during train maintenance cannot be less than the lower limit constraint of operating time / operating mileage. For example, the first overhaul of a certain train model must be carried out after 4.5 years of operation or more than 575,000 kilometers of target operating mileage. If it is less than the lower limit, the train parts will be wasted. For time and mileage, the mileage is replaced with the corresponding time, and the minimum of the two times is taken as the lower limit. The constraint function of the model includes the following formula (4):

[0096]

[0097] Where O represents the set of overhaul work packages; T1 represents the set of months over the next 5 years; X i,o,t Indicates whether the i-th train will begin a major overhaul of the maintenance frame in month t within the next 5 years, with a value of 0 or 1. i,o,t =1 indicates that the i-th train will begin its major overhaul work on the maintenance frame in month t within the next 5 years, X i,o,t =0 indicates that the i-th train will not begin its major overhaul work on the maintenance frame in month t within the next 5 years; MD i,o This represents the lower limit of the operating time / operating kilometers for the overhaul work package o of the i-th train maintenance frame, with a time granularity of months.

[0098] Constraint 3: The annual maintenance quantity constraint must be met, and the annual train maintenance quantity must be statistically calculated. The constraint functions of the model include the following formula (5):

[0099]

[0100] Among them, Y ty I represents the total number of train frame overhauls in year ty; I represents the set of subway trains; O represents the set of frame overhaul work packages; T ty X represents the set of months in year ty; i,o,t Indicates whether the i-th train will begin a major overhaul of the maintenance frame in month t within the next 5 years, with a value of 0 or 1. i,o,t =1 indicates that the i-th train will begin its major overhaul work on the maintenance frame in month t within the next 5 years, X i,o,t =0 indicates that the i-th train will not begin its overhaul work on the maintenance frame in month t within the next 5 years.

[0101] The first mixed-integer programming model uses whether each train's overhaul work package will be maintained in each month of the next 5 years as the main decision variable, and finds the optimal solution. The optimal solution corresponds to the monthly granular maintenance plan that maximizes the achievement of the planning objective while meeting the overhaul planning constraints, through the first mixed-integer programming model.

[0102] Continue to refer to Figure 1 In step 130, based on the decision reference data and the first planning data, a second planning data is determined by a pre-constructed second mixed integer programming model. The second planning data includes train frame overhaul planning data for each date within the next year.

[0103] In this application, the number of maintenance work packages to be started and completed for each train type and line each year for the next 5 years is determined by the first planning data. Based on the decision reference data and the number of maintenance work packages to be started and completed for each train type and line each year for the next 5 years, the second planning data is determined by a pre-constructed second mixed integer programming model. The second planning data includes train frame maintenance planning data for each date in the next year (excluding the current year), that is, determining which train frame maintenance work packages need to be started on which dates in the next year.

[0104] In this application, the time granularity of the second mixed-integer programming model is days. The second mixed-integer programming model includes the following model objectives:

[0105] Objective 1: To ensure that trains are maintained as close as possible to the target operating time / target operating mileage. For example, the target operating time for the first major overhaul of a certain train model is 5 years of operation / 625,000 kilometers. If maintenance is carried out prematurely, train resources will be wasted, as parts will need to be repaired before reaching the required operating time / mileage. If maintenance is delayed, train malfunctions will occur, compromising train safety. The objective function of the model includes the following formula (6):

[0106]

[0107] Where O represents the set of overhaul work packages; T2 represents the set of dates within the next year; N i,o Y represents the target date for the maintenance overhaul package o of the i-th train within the next year; i,o,t Indicates whether the i-th train will begin its overhaul work on a maintenance rack on date t within the next year, with a value of 0 or 1. i,o,t =1 indicates that the i-th train will begin its major overhaul work on the maintenance frame on date t within the next year, Y i,o,t =0 indicates that the i-th train will not begin its overhaul work on the maintenance rack on date t within the next year; δ is a penalty coefficient, which will be applied if the actual start date of maintenance differs significantly from the target date.

[0108] Objective 2: To ensure a balanced number of trains undergoing major overhauls and repairs each month for the next year, i.e., to keep the number of trains undergoing major overhauls and repairs as similar as possible each month for the next year, in order to reduce the pressure on maintenance delivery. The objective function of the model includes the following formula (7):

[0109]

[0110] Where TM1 represents the set of all months in the next year, and YD tm Let γ represent the absolute value of the difference between the number of train frame overhauls and repairs in two consecutive months tm and tm+1 for any subway line. The objective function of the model is to minimize the absolute value of the difference between the number of train frame overhauls and repairs in each consecutive month, and γ is the penalty coefficient.

[0111] In this application, objective one has a higher priority than objective two.

[0112] In this application, the second mixed-integer programming model includes the following model constraints:

[0113] Constraint 1: The upper limit of operating time / operating mileage must be met. The operating time / operating mileage of the train during maintenance cannot exceed the upper limit of operating time / operating mileage. For example, the first overhaul of a certain model must be carried out before the operating time reaches 6 years or the target operating mileage is below 725,000 kilometers. If the upper limit is exceeded, the safety of the train cannot be guaranteed. For time and mileage, the mileage is replaced with the corresponding time, and the minimum value of the two times is taken as the upper limit. The constraint function of the model includes the following formula (8):

[0114]

[0115] Where O represents the set of overhaul work packages; T2 represents the set of dates within the next year; Y i,o,t Indicates whether the i-th train will begin its overhaul work on a maintenance rack on date t within the next year, with a value of 0 or 1. i,o,t =1 indicates that the i-th train will begin its major overhaul work on the maintenance frame on date t within the next year, Y i,o,t =0 indicates that the i-th train will not begin its overhaul work on the maintenance rack on date t within the next year; MUD i,o This represents the upper limit of the operating time / operating kilometers for the overhaul work package o of the i-th train maintenance frame, with a time granularity of days.

[0116] Constraint 2: The lower limit constraint of operating time / operating mileage must be met. The mileage already traveled during train maintenance cannot be less than the lower limit constraint of operating time / operating mileage. For example, the first overhaul of a certain train model must be carried out after 4.5 years of operation or more than 575,000 kilometers of target operating mileage. If it is less than the lower limit, the train parts will be wasted. For time and mileage, the mileage is replaced with the corresponding time, and the minimum of the two times is taken as the lower limit. The constraint function of the model includes the following formula (9):

[0117]

[0118] Where O represents the set of overhaul work packages; T2 represents the set of dates within the next year; Y i,o,t Indicates whether the i-th train will begin its overhaul work on a maintenance rack on date t within the next year, with a value of 0 or 1. i,o,t =1 indicates that the i-th train will begin its major overhaul work on the maintenance frame on date t within the next year, Y i,o,t =0 indicates that the i-th train will not begin its overhaul work on the maintenance rack on date t within the next year; MDD i,o This represents the lower limit of the operating time / operating kilometers for the overhaul work package o of the i-th train maintenance frame, with a time granularity of days.

[0119] In this application, the second mixed-integer programming model also needs to meet the daily train impoundment requirements for subway lines, supporting parameter configuration. For example, it can be configured that the daily impoundment number for major overhauls is less than or equal to the default impoundment number for the subway line, or it can be configured that the daily impoundment number for major overhauls is less than or equal to the impoundment number within a specified time interval. It also needs to meet the contractor's daily impoundment requirements, supporting parameter configuration. For example, it can be configured that the total daily impoundment number of subway line trains maintained by the contractor is less than or equal to the contractor's default daily impoundment number requirement, or it can be configured that the daily impoundment number for major overhauls is less than or equal to the contractor's daily impoundment number requirement within a specified time interval. Furthermore, it needs to meet the monthly new train start-up requirements for subway lines, supporting parameter configuration. For example, it can be specified that the maximum monthly new start-up number for each train type for each subway line is 2 trains. Finally, it needs to meet the annual maintenance quantity constraints for subway lines. The annual start-up and completion numbers for each train type for each subway line must be met. For example, if subway line A starts maintenance on 12 trains this year, then the annual plan must arrange for the completion of 12 trains this year.

[0120] In this application, the second mixed-integer programming model uses the date (in days) within the next year for the start of the overhaul work package for each train as the main decision variable, and finds the optimal solution. The optimal solution corresponds to the day-granular maintenance plan that maximizes the achievement of the planning target within the next year while meeting the overhaul planning constraints, through the second mixed-integer programming model.

[0121] Continue to refer to Figure 1 In step 140, the first planning data and the second planning data determined in the previous year are obtained and used as historical first planning data and historical second planning data, respectively. Based on the decision reference data, the historical first planning data and the historical second planning data, the third planning data is determined by a pre-constructed third mixed integer programming model. The second planning data includes the train frame overhaul planning data for each remaining date in the current year.

[0122] In this application, based on the decision reference data, the number of maintenance work packages started and completed for each line and each type of train in the next five years determined in the previous year, the second planning data determined in the previous year, and the number of maintenance work packages started and completed for each line and each type of train in the next five years determined in the previous year, are used to determine the third planning data through a pre-constructed third mixed integer programming model. The third planning data includes the train frame maintenance planning data for each remaining date in the current year, that is, determining which trains and which frame maintenance work packages need to start maintenance on which date in the current year.

[0123] In this application, the time granularity of the third mixed-integer programming model is days. The third mixed-integer programming model includes the following model objectives:

[0124] Objective 1: To ensure that trains are maintained as close as possible to the target operating time / target operating mileage. For example, the target operating time for the first major overhaul of a certain train model is 5 years of operation / 625,000 kilometers. If maintenance is carried out prematurely, train resources will be wasted, as parts will need to be repaired before reaching the required operating time / mileage. If maintenance is delayed, train malfunctions will occur, compromising train safety. The objective function of the model includes the following formula (10):

[0125]

[0126] Where O represents the set of overhaul work packages; T3 represents the set of dates within the current year; S i,o Z represents the target date for the overhaul work package o of the maintenance frame for the i-th train within the current year; i,o,t Indicates whether the i-th train will begin its overhaul work on the maintenance rack on date t within the current year, with a value of 0 or 1. i,o,t =1 indicates that the i-th train begins its overhaul work on the maintenance rack on date t within the current year, Z i,o,t =0 indicates that the i-th train does not start its overhaul work package o on date t within the current year; μ is a penalty coefficient, which will be applied if the actual start date of maintenance differs greatly from the target date.

[0127] Objective 2: To ensure a balanced number of train overhauls and repairs each month within the current year, i.e., to keep the number of trains undergoing overhauls and repairs as similar as possible each month within the current year, in order to reduce maintenance delivery pressure. The objective function of the model includes the following formula (11):

[0128]

[0129] Where TM2 represents the set of all months in the current year, and YD tm Let θ represent the absolute value of the difference between the number of train frame overhauls and repairs in two consecutive months tm and tm+1 for any subway line. The objective function of the model is to minimize the absolute value of the difference between the number of train frame overhauls and repairs in each consecutive month, and θ is the penalty coefficient.

[0130] In this application, objective one has a higher priority than objective two.

[0131] In this application, the third mixed-integer programming model includes the following model constraints:

[0132] Constraint 1: The upper limit of operating time / operating mileage must be met. The operating time / operating mileage of the train during maintenance cannot exceed the upper limit of operating time / operating mileage. For example, the first overhaul of a certain model must be carried out before the operating time reaches 6 years or the target operating mileage is below 725,000 kilometers. If the upper limit is exceeded, the safety of the train cannot be guaranteed. For time and mileage, the mileage is replaced with the corresponding time, and the minimum value of the two times is taken as the upper limit. The constraint function of the model includes the following formula (12):

[0133]

[0134] Where O represents the set of overhaul work packages; T3 represents the set of dates within the current year; Z i,o,t Indicates whether the i-th train will begin its overhaul work on the maintenance rack on date t within the current year, with a value of 0 or 1. i,o,t =1 indicates that the i-th train begins its overhaul work on the maintenance rack on date t within the current year, Z i,o,t =0 indicates that the i-th train does not start its overhaul work on the maintenance rack on date t within the current year; MUD i,o This represents the upper limit of the operating time / operating kilometers for the overhaul work package o of the i-th train maintenance frame, with a time granularity of days.

[0135] Constraint 2: The lower limit constraint of operating time / operating mileage must be met. The mileage already traveled during train maintenance cannot be less than the lower limit constraint of operating time / operating mileage. For example, the first overhaul of a certain train model must be carried out after 4.5 years of operation or more than 575,000 kilometers of target operating mileage. If it is less than the lower limit, the train parts will be wasted. For time and mileage, the mileage is replaced with the corresponding time, and the minimum of the two times is taken as the lower limit. The constraint function of the model includes the following formula (13):

[0136]

[0137] Where O represents the set of overhaul work packages; T3 represents the set of dates within the current year; Z i,o,t Indicates whether the i-th train will begin its overhaul work on the maintenance rack on date t within the current year, with a value of 0 or 1. i,o,t =1 indicates that the i-th train begins its overhaul work on the maintenance rack on date t within the current year, Z i,o,t =0 indicates that the i-th train does not start its overhaul work on the maintenance rack on date t within the current year; MDD i,o This represents the lower limit of the operating time / operating kilometers for the overhaul work package o of the i-th train maintenance frame, with a time granularity of days.

[0138] In this application, the third mixed-integer programming model also satisfies the daily train impoundment requirement for subway lines, supporting parameter configuration. For example, it can be configured that the daily impoundment number for major overhauls is less than or equal to the default impoundment number for the subway line, or that the daily impoundment number for major overhauls is less than or equal to the impoundment number within a specified time interval. It also satisfies the contractor's daily impoundment requirement, supporting parameter configuration. For example, it can be configured that the total daily impoundment number of subway line trains maintained by the contractor is less than or equal to the contractor's default daily impoundment requirement, or that the daily impoundment number for major overhauls is less than or equal to the contractor's daily impoundment number within a specified time interval. Furthermore, it satisfies the monthly new train start-up requirement for subway lines, supporting parameter configuration. For example, it can specify a maximum of 2 new trains start-ups per month for each train type on each subway line. Finally, it satisfies the annual maintenance quantity constraint for subway lines. The number of trains started and completed for each subway line and each train type must be met annually. The annual major overhaul plan differs from the monthly major overhaul plan; if a monthly major overhaul plan is formulated in January, the entire year's plan needs to be calculated. If a major overhaul plan is to be formulated in March, it is necessary to read the actual major overhaul data for January and February to calculate the plan from March to the end of the year.

[0139] In this application, the third mixed-integer programming model uses the date (in days) within the current year when the overhaul work package for each train begins maintenance as the main decision variable, and finds the optimal solution. The optimal solution corresponds to the maintenance plan with a day-level granularity that maximizes the achievement of the planning target within the current year while meeting the overhaul planning constraints, through the third mixed-integer programming model.

[0140] This application employs a global optimization approach, establishing a planning decision-making scheme encompassing long-term, medium-term, and short-term planning. Based on a mixed-integer programming model, a multi-stage planning method is used to sequentially formulate and execute the overhaul plan. Specifically, the 5-year overhaul plan uses monthly granularity and spans 5 years; the annual overhaul plan uses day granularity and spans 1 year; and the monthly overhaul plan uses day granularity and spans less than 1 year. The planning time span and granularity decrease from large to small, and the number of factors considered increases from few to many, ultimately resulting in a maintenance plan accurate to the day—determining when the train enters and exits the overhaul. This application effectively reduces the complexity of the planning solution and improves the accuracy and speed of data extraction.

[0141] This application addresses the problems caused by current manual planning: low accuracy of planning data processing, long planning time, low planning flexibility, poor planning effect, and low resource utilization.

[0142] The following describes an embodiment of the apparatus described in this application, which can be used to execute the subway overhaul planning data processing method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the subway overhaul planning data processing method described above in this application.

[0143] Figure 2 A block diagram of a subway overhaul planning data processing apparatus according to an embodiment of this application is shown.

[0144] Reference Figure 2 As shown, a subway overhaul planning data processing device 200 according to an embodiment of this application includes: an acquisition unit 201, a first determination unit 202, a second determination unit 203 and a third determination unit 204.

[0145] The system includes: an acquisition unit 201 for acquiring decision reference data related to the train frame overhaul planning of the entire metro network; a first determination unit 202 for determining first planning data based on the decision reference data using a pre-constructed first mixed-integer programming model, wherein the first planning data includes train frame overhaul planning data for each month within the next 5 years; a second determination unit 203 for determining second planning data based on the decision reference data and the first planning data using a pre-constructed second mixed-integer programming model, wherein the second planning data includes train frame overhaul planning data for each date within the next year; and a third determination unit 204 for acquiring the first and second planning data determined in the previous year as historical first and historical second planning data, respectively, and determining third planning data based on the decision reference data, the historical first planning data, and the historical second planning data using a pre-constructed third mixed-integer programming model, wherein the second planning data includes train frame overhaul planning data for each remaining date within the current year.

[0146] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0147] It should be noted that, Figure 3 The computer system 300 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0148] like Figure 3As shown, the computer system 300 includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage portion 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0149] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0150] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs various functions defined in the system of this application.

[0151] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0153] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0154] In another aspect, this application also provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0155] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0156] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0157] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0158] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0159] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for processing subway overhaul planning data, characterized in that, The method includes: Obtain decision-making reference data related to the overhaul planning of train frames in the entire subway network; Based on the decision reference data, the first planning data is determined by a pre-constructed first mixed integer programming model. The first planning data includes train frame overhaul planning data for each month in the next 5 years. Based on the decision reference data and the first planning data, the second planning data is determined by a pre-constructed second mixed integer programming model. The second planning data includes train frame overhaul planning data for each date within the next year. The first and second planning data determined in the previous year are obtained and used as historical first planning data and historical second planning data, respectively. Based on the decision reference data, the historical first planning data and the historical second planning data, the third planning data is determined by a pre-constructed third mixed integer programming model. The second planning data includes the train frame overhaul planning data for each remaining date in the current year. The objective function of the first mixed-integer programming model includes the following formulas 1 and 2: Formula 1: Formula 2: Among them, Formula 1 has a higher priority than Formula 2; This refers to a collection of overhaul work packages for the frame; This represents the set of months over the next 5 years; Indicates the first Train maintenance frame overhaul work package Target month; Indicates the first Will the train be available in the next 5 years? Started with the overhaul work kit for the repair rack The value can be 0 or 1; As a penalty factor, if the actual month of maintenance starts differs significantly from the target month, Punishment will be imposed; It represents the set of all years over the next 5 years; Indicates any two consecutive years between subway lines nianhe +Absolute value of the difference in the number of train frame overhauls and repairs in 1 year; This is the penalty coefficient; The constraint functions of the first mixed-integer programming model include the following formulas 3, 4, and 5: Formula 3: Formula 4: Formula 5: in, This refers to a collection of overhaul work packages for the frame; This represents the set of months over the next 5 years; Indicates the first Will the train be available in the next 5 years? Started with the overhaul work kit for the repair rack The value can be 0 or 1; Indicates the first Train maintenance frame overhaul work package The upper limit of operating time / operating kilometers, with a time granularity of months; Indicates the first Train maintenance frame overhaul work package The lower limit of operating time / operating mileage, with a time granularity of months; Indicates a collection of subway trains; Indicates the first A collection of months in a given year; Indicates the first Total number of train frame overhauls and repairs performed annually; The objective function of the second mixed-integer programming model includes the following formulas 6 and 7: Formula 6: Formula 7: Among them, Formula 6 has a higher priority than Formula 7; This refers to a collection of overhaul work packages for the frame; A set of dates within the next year; Indicates the first Overhaul work package for the train maintenance frame within the next year Target date; Indicates the first Whether the train will be on a date within the next year Started with the overhaul work kit for the repair rack The value can be 0 or 1; As a penalty factor, if the actual start date of maintenance differs significantly from the target date, Punishment will be imposed; This represents the set of all months within the next year. Indicates two consecutive months for any subway line Yuehe +Absolute value of the difference in the number of train frame overhauls and repairs in January; This is the penalty coefficient; The constraint functions of the second mixed-integer programming model include the following formulas 8 and 9: Formula 8: Formula 9: in, This refers to a collection of overhaul work packages for the frame; A set of dates within the next year; Indicates the first Whether the train will be on a date within the next year Started with the overhaul work kit for the repair rack The value can be 0 or 1; Indicates the first Train maintenance frame overhaul work package The upper limit of operating time / operating kilometers, with a time granularity of days; Indicates the first Train maintenance frame overhaul work package The lower limit of operating time / operating mileage, with a time granularity of days; The objective function of the third mixed-integer programming model includes the following formulas 10 and 11: Formula 10: Formula 11: in, This refers to a collection of overhaul work packages for the frame; A set of dates within the current year; Indicates the first Overhaul work package for the maintenance frame of the train within the current year Target date; Indicates the first Are the trains within the current year's dates? Started with the overhaul work kit for the repair rack The value can be 0 or 1; As a penalty factor, if the actual start date of maintenance differs significantly from the target date, Punishment will be imposed; This represents the set of all months within the current year. Indicates two consecutive months for any subway line Yuehe +Absolute value of the difference in the number of train frame overhauls and repairs in January; This is the penalty coefficient; The constraint functions of the third mixed-integer programming model include the following formulas 12 and 13: Formula 12: Formula 13: in, This refers to a collection of overhaul work packages for the frame; A set of dates within the current year; Indicates the first Are the trains within the current year's dates? Started with the overhaul work kit for the repair rack The value can be 0 or 1; Indicates the first Train maintenance frame overhaul work package The upper limit of operating time / operating kilometers, with a time granularity of days; Indicates the first Train maintenance frame overhaul work package The lower limit of operating time / operating mileage, with a time granularity of days.

2. A data processing device for subway overhaul planning, characterized in that, The device includes: The acquisition unit is used to acquire decision-making reference data related to the overhaul planning of train frames in the entire subway network; The first determining unit is used to determine the first planning data based on the decision reference data by using a pre-constructed first mixed integer programming model. The first planning data includes train frame overhaul planning data for each month in the next 5 years. The second determining unit is used to determine the second planning data based on the decision reference data and the first planning data by using a pre-constructed second mixed integer programming model. The second planning data includes train frame overhaul planning data for each date within the next year. The third determining unit is used to obtain the first planning data and the second planning data determined in the previous year, which are respectively used as historical first planning data and historical second planning data. Based on the decision reference data, the historical first planning data and the historical second planning data, the third planning data is determined by a pre-constructed third mixed integer programming model. The second planning data includes the train frame overhaul planning data for each remaining date in the current year. The objective function of the first mixed-integer programming model includes the following formulas 1 and 2: Formula 1: Formula 2: Among them, Formula 1 has a higher priority than Formula 2; This refers to a collection of overhaul work packages for the frame; This represents the set of months over the next 5 years; Indicates the first Train maintenance frame overhaul work package Target month; Indicates the first Will the train be available in the next 5 years? Started with the overhaul work kit for the repair rack The value can be 0 or 1; As a penalty factor, if the actual month of maintenance starts differs significantly from the target month, Punishment will be imposed; It represents the set of all years over the next 5 years; Indicates any two consecutive years between subway lines nianhe +Absolute value of the difference in the number of train frame overhauls and repairs in 1 year; This is the penalty coefficient; The constraint functions of the first mixed-integer programming model include the following formulas 3, 4, and 5: Formula 3: Formula 4: Formula 5: in, This refers to a collection of overhaul work packages for the frame; This represents the set of months over the next 5 years; Indicates the first Will the train be available in the next 5 years? Started with the overhaul work kit for the repair rack The value can be 0 or 1; Indicates the first Train maintenance frame overhaul work package The upper limit of operating time / operating kilometers, with a time granularity of months; Indicates the first Train maintenance frame overhaul work package The lower limit of operating time / operating mileage, with a time granularity of months; Indicates a collection of subway trains; Indicates the first A collection of months in a given year; Indicates the first Total number of train frame overhauls and repairs performed annually; The objective function of the second mixed-integer programming model includes the following formulas 6 and 7: Formula 6: Formula 7: Among them, Formula 6 has a higher priority than Formula 7; This refers to a collection of overhaul work packages for the frame; A set of dates within the next year; Indicates the first Overhaul work package for the train maintenance frame within the next year Target date; Indicates the first Whether the train will be on a date within the next year Started with the overhaul work kit for the repair rack The value can be 0 or 1; As a penalty factor, if the actual start date of maintenance differs significantly from the target date, Punishment will be imposed; This represents the set of all months within the next year. Indicates two consecutive months for any subway line Yuehe +Absolute value of the difference in the number of train frame overhauls and repairs in January; This is the penalty coefficient; The constraint functions of the second mixed-integer programming model include the following formulas 8 and 9: Formula 8: Formula 9: in, This refers to a collection of overhaul work packages for the frame; A set of dates within the next year; Indicates the first Whether the train will be on a date within the next year Started with the overhaul work kit for the repair rack The value can be 0 or 1; Indicates the first Train maintenance frame overhaul work package The upper limit of operating time / operating kilometers, with a time granularity of days; Indicates the first Train maintenance frame overhaul work package The lower limit of operating time / operating mileage, with a time granularity of days; The objective function of the third mixed-integer programming model includes the following formulas 10 and 11: Formula 10: Formula 11: in, This refers to a collection of overhaul work packages for the frame; A set of dates within the current year; Indicates the first Overhaul work package for the maintenance frame of the train within the current year Target date; Indicates the first Are the trains within the current year's dates? Started with the overhaul work kit for the repair rack The value can be 0 or 1; As a penalty factor, if the actual start date of maintenance differs significantly from the target date, Punishment will be imposed; This represents the set of all months within the current year. Indicates two consecutive months for any subway line Yuehe +Absolute value of the difference in the number of train frame overhauls and repairs in January; This is the penalty coefficient; The constraint functions of the third mixed-integer programming model include the following formulas 12 and 13: Formula 12: Formula 13: in, This refers to a collection of overhaul work packages for the frame; A set of dates within the current year; Indicates the first Are the trains within the current year's dates? Started with the overhaul work kit for the repair rack The value can be 0 or 1; Indicates the first Train maintenance frame overhaul work package The upper limit of operating time / operating kilometers, with a time granularity of days; Indicates the first Train maintenance frame overhaul work package The lower limit of operating time / operating mileage, with a time granularity of days.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations described in claim 1.

4. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operation performed by the method as described in claim 1.

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