Rail transit product maintenance optimization method and system and rail transit vehicle

Abstract

The invention discloses a rail transit product maintenance optimization method and system and a rail transit vehicle, which effectively establish association between factors such as a reliability index, a preventive maintenance interval, unit labor hour cost of maintenance personnel, material price and the like and a maintenance optimization target, and obtain an optimal to-be-optimized parameter list and an optimization target thereof. Therefore, the optimal maintenance scheme is obtained, the maintenance cost is reduced, the availability and reliability of the rail transit product are greatly ensured, and the service life of the rail transit product is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a rail transit product maintenance optimization method, system and rail transit vehicle. Background Art

[0002] The maintenance economy of rail transit products consists of many items. Since the human and material resources available for various tasks are limited, it is necessary to determine the items most worthy of optimization before conducting a maintenance economy optimization analysis. However, under the existing technology, technicians are often at a loss when faced with a large number of maintenance items. They can only rely on experience to determine one or several items for optimization, resulting in the work not achieving the best results. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a rail transit product maintenance optimization method, system and rail transit vehicle in view of the deficiencies in the existing technology, so as to reduce the maintenance cost of rail transit products and improve the availability and service life of rail transit products.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a rail transit product maintenance optimization method, comprising the following steps:

[0005] Build the optimization model:

[0006]

[0007] in,

[0008] Δ i =[(LCC0-LCC i ) / LCC0]×100%;

[0009] LCC0=f PM_hr (x1,…,x i ,…,x n )+f PM_mat (x1,…,x i ,…,x n )+f CM_hr (x1,…,x i ,…,x n )

[0010] +f CM_mat (x1,…,x i ,…,x n );

[0011] LCC i =f PM_hr (x1,…,x i +α i xi ,…,x n )+f PM_mat (x1,…,x i +α i x i ,…,x n )+f CM_hr (x1,…,

[0012] x i +α i x i ,…,x n )+f CM_mat (x1,…,x i +α i x i ,…,x n );

[0013] LCC0 is the initial result of the maintenance calculation model, αi is the percentage of change of the i-th parameter, LCC i is the result of maintenance calculation after the i-th parameter changes, f PM_hr (x1,…,x i ,…,x n ) represents the labor cost of preventive maintenance, f PM_mat (x1,…,x i ,…,x n ) represents the preventive maintenance material cost, f CM_hr (x1,…,x i ,…,x n ) represents the labor cost of corrective repairs, f CM_mat (x1,…,x i ,…,x n ) represents the cost of corrective maintenance materials, A% represents the reduction setting value of the maintenance calculation model, x i represents the i-th optimizable parameter, i = 1, 2, ..., j; j = 1, 2, ..., n; n is the number of optimizable parameters;

[0014] Using the optimization model, the parameters to be optimized are determined as: x1, x2, ..., x j ;

[0015] The target values ​​of the parameters to be optimized are: x1+α1x1, x2+α2x2,…,x j +α i x i .

[0016] In the present invention, the target value of the optimized parameter is substituted into the maintenance calculation model to obtain the best maintenance strategy.

[0017] The optimizable parameters include reliability index, preventive maintenance interval, maintenance man-hours, unit man-hour cost, and material price of each node in the product configuration tree.

[0018] The reliability indicators of each node in the product configuration tree include reliability, mean mileage between failures, and failure rate per million kilometers.

[0019] Failure rate per million kilometersλ i The calculation formula is:

[0020] As an inventive concept, the present invention also provides a rail transit product maintenance optimization system, comprising:

[0021] Model building unit, used to build the following optimization models:

[0022]

[0023] in,

[0024] Δ i =[(LCC0-LCC i ) / LCC0]×100%;

[0025] LCC0=f PM_hr (x1,…,x i ,…,x n )+f PM_mat (x1,…,x i ,…,x n )+f CM_hr (x1,…,x i ,…,x n )

[0026] +f CM_mat (x1,…,x i ,…,x n );

[0027] LCC i =f PM_hr (x1,…,x i +α i x i ,…,x n )+f PM_mat (x1,…,x i +α i x i ,…,x n )+f CM_hr (x1,…,

[0028] x i +α i x i ,…,xn )+f CM_mat (x1,…,x i +α i x i ,…,x n );

[0029] LCC0 is the initial result of the maintenance calculation model, αi is the percentage of change of the i-th parameter, LCC i is the result of maintenance calculation after the i-th parameter changes, f PM_hr (x1,…,x i ,…,x n ) represents the labor cost of preventive maintenance, f PM_mat (x1,…,x i ,…,x n ) represents the preventive maintenance material cost, f CM_hr (x1,…,x i ,…,x n ) represents the labor cost of corrective repairs, f CM_mat (x1,…,x i ,…,x n ) represents the cost of corrective maintenance materials, A% represents the reduction setting value of the maintenance calculation model, x i represents the i-th optimizable parameter, i = 1, 2, ..., j; j = 1, 2, ..., n; n is the number of optimizable parameters;

[0030] A parameter determination unit is used to determine the parameters to be optimized using the optimization model; the parameters to be optimized are: x1, x2, ..., x j ;

[0031] The parameter optimization unit is used to obtain the target value of the optimized parameter; the target values ​​of the optimized parameter are: x1+α1x1, x2+α2x2,…, x j +α i x i .

[0032] As an inventive concept, the present invention also provides a rail transit product maintenance optimization system, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the steps of the above method.

[0033] As an inventive concept, the present invention also provides a rail transit vehicle, which adopts the above-mentioned rail transit product maintenance optimization system.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: the application of the present invention can effectively establish the relationship between factors such as reliability indicators, preventive maintenance intervals, unit labor costs of maintenance personnel, material prices and maintenance optimization goals, and obtain the optimal list of parameters to be optimized and their optimization goals, thereby obtaining the optimal maintenance plan, while reducing maintenance costs, greatly ensuring the availability and reliability of rail transit products, and improving the service life of rail transit products. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] This embodiment provides a method for optimizing the economic efficiency of rail transit product maintenance based on sensitivity analysis, which mainly includes the following steps:

[0038] 1) Determine the product configuration tree

[0039] The computational analysis of the mathematical model constructed in the embodiment of the present invention requires determining the variation range of various factors, and the carrier of the specific numerical values ​​of the quantitative analysis results of various factors is the product configuration tree.

[0040] The configuration tree refers to a basic structural model that can manage and display the names of the components of rail transit equipment and their related information, the subordinate relationships between the components, and the installation locations of the components, so as to carry various types of data and perform statistical analysis according to the different dimensions contained in the configuration, for example:

[0041] Table 1 Configuration tree example

[0042]

[0043] 2) Determine the reliability index and preventive maintenance process of each node in the configuration tree

[0044] In addition to the maintenance work (CM maintenance) carried out after the failure occurs, the main maintenance work used is the PM maintenance work carried out regularly. The definitions of the two types of maintenance work are as follows:

[0045] CM: Corrective Maintenance refers to the maintenance performed to restore the product to a state where it can perform the specified functions after fault identification.

[0046] PM: Preventive Maintenance refers to maintenance performed regularly or according to predetermined criteria in order to prevent functional degradation and reduce the probability of failure; that is, preventive maintenance also includes condition-based maintenance and planned maintenance.

[0047] After the configuration tree is determined, it is necessary to determine the reliability index of each node and the corresponding preventive maintenance schedule. In the rail transit industry, the failure rate is usually measured by FPMK, that is, failure per million kilometers, and the calculation formula is:

[0048]

[0049] If it is an existing product, it can be evaluated through actual failure data.

[0050] If it is a new product, you can refer to the data obtained from the evaluation of similar products, or you can obtain it through experiments.

[0051] For example, the failure rates of various components are evaluated as follows:

[0052] Table 2 Component reliability index

[0053]

[0054]

[0055] The preventive maintenance process mainly includes three elements: maintenance task type, maintenance task interval, and maintenance task substitution relationship. In the field of rail transit, the maintenance task interval is usually in two dimensions: time interval and mileage interval. The first one to arrive will prevail. The maintenance content included in the high-level maintenance process will also cover the maintenance content of the low-level. This needs special attention to avoid double calculation of the cost of a certain maintenance process. The following is an example of a preventive maintenance process:

[0056] Table 3 Preventive maintenance schedule

[0057] Repair Course Time interval Substitution relationship First level maintenance 3 months Secondary maintenance Secondary maintenance 6 months Level 3 maintenance Level 3 maintenance 1 year Level 4 maintenance Level 4 maintenance 3 years Level 5 maintenance Level 5 maintenance 6 years Six-level maintenance Six-level maintenance 12 years

[0058] 3) Perform maintenance task analysis on repair and preventive maintenance tasks to determine the single maintenance hours and maintenance material list

[0059] For each node in the configuration, perform maintenance task analysis (MTA) on its repair and preventive maintenance tasks. The main steps include but are not limited to:

[0060] a Maintenance task input: Analyze the maintenance tasks required for each node of the product configuration tree. For example, different maintenance tasks should be input for different failure modes, and different maintenance tasks should also be input for different preventive maintenance procedures.

[0061] b. Maintenance task merging: Among the maintenance tasks entered in the previous step, many are repeated, and many need to be merged into a single task in actual maintenance work; therefore, the maintenance tasks need to be merged according to the actual situation.

[0062] cMaintenance task analysis: Analyze the merged maintenance tasks one by one according to the specific maintenance process. The analysis content includes but is not limited to the resource requirements such as equipment, facilities, tools, personnel skills, number of personnel, working hours, spare parts, consumables, etc. required to complete the maintenance tasks.

[0063] d MTA output: By analyzing the maintenance tasks, the single maintenance man-hours and maintenance material lists (including but not limited to material numbers and quantities) corresponding to the reliability indicators of each node are output, as well as the single maintenance man-hours and maintenance material lists (including but not limited to material numbers and quantities) corresponding to each preventive maintenance process.

[0064] When maintenance work needs to be completed by multiple people, and there are differences in the unit labor hour costs corresponding to different professional skills or different levels of personnel, it is necessary to list the single maintenance hours corresponding to each type of personnel separately to facilitate the subsequent accurate calculation and analysis of labor hour costs.

[0065] For example, by analyzing the single labor hours and repair materials for repair and maintenance, the following table is shown:

[0066] Table 4 Component single repair and maintenance data

[0067]

[0068] The analysis of the single working hours and maintenance materials for preventive maintenance is shown in the following table:

[0069] Table 5 Single preventive maintenance data

[0070]

[0071] 4) Determine the allowable adjustment range for maintenance optimization

[0072] In the mathematical model constructed by the method of this embodiment, the adjustable range of some parameters relative to the current value is limited, so constraints need to be made in advance. These parameters include but are not limited to reliability indicators, preventive maintenance intervals, unit labor hours of maintenance personnel, and material prices.

[0073] a. Determine the feasible range of variation of each node reliability index

[0074] The reliability index of each node is an important parameter for calculating the CM maintenance cost of the node. Different nodes have different reliability indexes and different adjustable ranges, which need to be confirmed. In this method, the reliability index is FPMK. The FPMK of the whole product (system) is composed of the FPMK of each node of the configuration tree, that is, all nodes of the whole product can be constructed into a series model as follows:

[0075]

[0076] Series Model

[0077] For a unified description, the FPMK of the following whole machine (system) is set to λ s , the corresponding calculation formula is:

[0078] in:

[0079] λ i ——Failure rate of each component that makes up the basic reliability model

[0080] n——Number of components that make up the basic reliability model

[0081] If there are multiple identical components, combined into the same block diagram, the above formula can be rewritten as

[0082] in:

[0083] λ i ——Failure rates of various components that make up the basic reliability model

[0084] m i ——The number of various components that make up the basic reliability model

[0085] n——Types of components that make up the basic reliability model

[0086] So we need to confirm that each λ i The adjustable range α i , combined with the configuration tree, the following table is formed:

[0087] Table 6 Examples of adjustable ranges of reliability indicators

[0088]

[0089] b. Determine the feasible range of variation of preventive maintenance intervals

[0090] For the preventive maintenance interval, two dimensions can be used. One is to define the preventive maintenance interval of each node and the feasible range of variation based on the configuration tree. The other is to directly set the feasible range of variation based on the preventive maintenance process interval. Compared with the first solution, this dimension implies which configuration tree nodes need to be maintained under each preventive maintenance process.

[0091] In tabular form, the two dimensions are as follows:

[0092] Table 7 Based on the configuration tree dimensions

[0093]

[0094] Table 8 Based on the dimensions of preventive maintenance process

[0095] Repair Course interval <![CDATA[Adjustable range α i > First level maintenance 3 months Secondary maintenance 6 months Level 3 maintenance 1 year Level 4 maintenance 3 years Level 5 maintenance 6 years Six-level maintenance 12 years

[0096] In this example, it is assumed that the repair interval will not be adjusted and optimized for the time being.

[0097] c. Determine the unit labor cost of each type of personnel and its feasible range of variation

[0098] During the MTA (Maintenance Task Analysis), the working hours of different types of personnel in each maintenance task have been analyzed. It is necessary to determine the current value of hourly labor costs and the feasible range of changes.

[0099] In this example, it is assumed that the hourly rate per employee is the same, that is, regardless of the employee type:

[0100] Table 9 Unit labor time cost and variable range of each type of personnel

[0101]

[0102] d. Determine the price of each material and its feasible range of variation

[0103] When repairing a component, different repair methods may be used according to the different failure modes of the component. Sometimes the component is replaced as a whole, sometimes it only needs to be adjusted mechanically (almost no material cost), and sometimes it needs to be disassembled and lubricated (incurring some material costs). The resulting material combination is complex. Therefore, the material costs corresponding to different failure modes in the MTA analysis conclusion and the proportion of various failure modes can be used to calculate the weighted average. The total material costs generated by the failure repair of the component in the past 12 months can also be divided by the number of repairs to obtain an average value as the material price of the component for a single failure repair. After analyzing the average material cost of each repair and maintenance of each node in the configuration tree to obtain the current value, the variable range of each indicator can be discussed with the purchasing department.

[0104] Preventive maintenance is similar to this. In this example, only the adjustment and optimization of preventive maintenance hours and costs are considered:

[0105] Table 10 Man-hours, material prices and adjustable range for preventive maintenance

[0106]

[0107] 5) Establish maintenance economic calculation model:

[0108] The economic model is an optimized LCC (Life Cycle Cost) model. LCC (Life Cycle Cost) refers to the sum of all costs consumed in the development, production, use, maintenance and retirement of equipment during its life cycle.

[0109] Taking into account the guiding role of the analysis model in economic analysis and the difficulty of cost collection, an LCC model that is more suitable for rail transit products is designed, that is, the optimized economic model only analyzes the costs of the operation and maintenance stages. Among them, the costs of the operation and maintenance stages are divided into two categories: PM costs and CM costs, and each type of cost is divided into two categories: labor costs and material costs.

[0110] According to the above model, combined with the configuration tree of the analysis object system, the calculation formula can be established as follows:

[0111]

[0112] LCC i =(PM i +CM i )×m i

[0113] PM i =PM_m i +PM_h i

[0114] CM i =CM_m i +CM_h i

[0115]

[0116]

[0117]

[0118] CM_m i =MCM_m i ×λ i ÷106 ×L×365×t×v

[0119] CM_h i =MCM_h i ×λ i ÷10 6 ×L×365×t×v

[0120] in:

[0121] LCC s ——LCC of the whole machine (system);

[0122] LCC i - the LCC of the i-th component of the system;

[0123] n——type of components of the system;

[0124] PM i ——PM maintenance cost of the i-th component of the system;

[0125] CM i ——CM maintenance cost of the i-th component of the system;

[0126] m i ——the number of the i-th component of the system;

[0127] PM_m i ——Material cost in PM maintenance cost of the i-th component of the system;

[0128] PM_m ij ——The material cost of the i-th component of the system in the j-th PM repair process;

[0129] C j ——The number of times the jth PM maintenance process of the system occurs during the life cycle of the train to which it belongs;

[0130] L——Full life cycle of the train to which the system belongs (years);

[0131] D j ——the interval between the jth PM maintenance of the system (years);

[0132] R j ——The number of times the jth PM maintenance process of the system is replaced by other maintenance processes during the whole life cycle;

[0133] PM_h i ——Labor cost in PM maintenance cost of the i-th component of the system;

[0134] PM_h ij ——Labor cost of the i-th component of the system in the j-th PM repair process;

[0135] CM_m i ——Material cost in the CM maintenance cost of the i-th component of the system;

[0136] MCM_m i ——The average CM material cost per time for the i-th component of the system;

[0137] λ i ——the failure rate of the i-th component of the system;

[0138] t——the average daily operating time of the trains belonging to the system at the owner's location (hours);

[0139] v – the average travel speed of the trains belonging to the system at the owner’s location (km / h);

[0140] CM_h i ——Labor cost in the CM maintenance cost of the i-th component of the system.

[0141] MCM_h i ——The average CM labor cost per time for the i-th component of the system.

[0142] For PM_h in the above formula ij and MCM_h i Further analysis shows that:

[0143]

[0144] Where:

[0145] PM_h ijk - working hours of the kth type of employee in the jth repair process for the i-th type of component;

[0146] MCM_h ik — the working hours of the kth type of employee in each CM maintenance of the i-th type of component;

[0147] C_h k ——The working cost of one hour of the kth employee.

[0148] 6) Conduct sensitivity analysis

[0149] The essence of sensitivity analysis is to analyze the degree of influence of changes in factors that cause changes in things on the results of changes in things.

[0150] The embodiment of the present invention adopts single factor sensitivity analysis. Single factor sensitivity analysis means that during analysis, the analysis object is the impact of the change of each factor on the overall output, that is, when analyzing the factor, it is assumed that other factors remain unchanged.

[0151] According to the maintenance economic calculation model, the parameters x can be optimized i The feasible range of variation (obtained from step 4, expressed as a percentage α i ,) calculate the variation range of the corresponding maintenance economy calculation model output LCC one by one. The specific calculation formula is:

[0152] LCC0=f PM_hr (x1,…,x i ,…,x n )+f PM_mat (x1,…,x i ,…,x n )+

[0153] f CM_hr (x1,…,x i ,…,x n )+f CM_mat (x1,…,x i ,…,x n )

[0154] LCC i =f PM_hr (x1,…,x i +α i x i ,…,x n )+f PM_mat (x1,…,x i +α i x i ,…,x n )+

[0155] f CM_hr (x1,…,x i +α i x i ,…,x n )+f CM_mat (x1,…,x i +α i x i ,…,x n )

[0156] Δ i =[(LCC0-LCC i ) / LCC0]×100%

[0157] Where:

[0158] LCC0 is the initial result of the maintenance economy calculation model;

[0159] α i is the percentage of change of the i-th parameter;

[0160] LCCi is the result of maintenance economic calculation after the i-th parameter changes;

[0161] Δ i is the sensitivity of the i-th parameter.

[0162] The parameters with adjustable ranges set in step 4 are denoted as x1, x2, …, x n , and put it into the above formula to calculate, the results are shown in the following table:

[0163] Table 11 Example of sensitivity analysis results

[0164]

[0165]

[0166]

[0167] 7) Form a list of maintenance economic optimization projects. Arrange the sensitivity results obtained in step 6 in descending order and re-record them as Δ j , then:

[0168] Δ1≥Δ2≥……≥Δ j ≥……≥Δ n

[0169] The j value that can meet the optimization goal is obtained by the following formula:

[0170] min(j)

[0171] stΣ1 j (Δ j )≥A%

[0172] The list of parameters to be optimized that can achieve the maintenance economy optimization goal is:

[0173] x 1 ,x 2 ,…,x j

[0174] The target value of the corresponding parameter optimization is:

[0175] x1+α1x1,x2+α2x2,…,x j +α i x i

[0176] In this example, it is assumed that the maintenance economy optimization goal is: LCC reduction is not less than 5%. After sorting and calculating according to the above steps, the parameters to be optimized and the optimization target values ​​are as follows:

[0177] Table 12 Parameters to be optimized and optimization target values

[0178]

[0179] After optimization, the LCC can be reduced by 5.03%, meeting the requirements of the maintenance economy optimization target.

[0180] Example 2

[0181] Embodiment 2 of the present invention provides a rail transit product maintenance optimization system corresponding to the above-mentioned embodiment 1, including:

[0182] Model building unit, used to build the following optimization models:

[0183]

[0184] in,

[0185] Δ i =[(LCC0-LCC i ) / LCC0]×100%;

[0186] LCC0=f PM_hr (x1,…,x i ,…,x n )+f PM_mat (x1,…,x i ,…,x n )+f CM_hr (x1,…,x i ,…,x n )

[0187] +f CM_mat (x1,…,x i ,…,x n );

[0188] LCC i =f PM_hr (x1,…,x i +α i x i ,…,x n )+f PM_mat (x1,…,x i +α i x i ,…,x n )+f CM_hr (x1,…,

[0189] x i +α i x i ,…,x n )+f CM_mat (x1,…,x i +α i xi ,…,x n );

[0190] LCC0 is the initial result of the maintenance calculation model, αi is the percentage of change of the i-th parameter, LCC i is the result of maintenance calculation after the i-th parameter changes, f PM_hr (x1,…,x i ,…,x n ) represents the labor cost of preventive maintenance, f PM_mat (x1,…,x i ,…,x n ) represents the preventive maintenance material cost, f CM_hr (x1,…,x i ,…,x n ) represents the labor cost of corrective repairs, f CM_mat (x1,…,x i ,…,x n ) represents the cost of corrective maintenance materials, A% represents the reduction setting value of the maintenance calculation model, x i represents the i-th optimizable parameter, i = 1, 2, ..., j; j = 1, 2, ..., n; n is the number of optimizable parameters;

[0191] A parameter determination unit is used to determine the parameters to be optimized using the optimization model; the parameters to be optimized are: x1, x2, ..., x j ;

[0192] The parameter optimization unit is used to obtain the target value of the optimized parameter; the target values ​​of the optimized parameter are: x1+α1x1, x2+α2x2,…, x j +α i x i .

[0193] Example 3

[0194] Embodiment 3 of the present invention provides a rail transit vehicle corresponding to the above-mentioned embodiment 1, which adopts the optimization system of the above-mentioned embodiment 2.

[0195] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0196] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A rail transit product maintenance optimization method, characterized in that: The following steps are involved: Build the optimization model: in, Δ i =[(LCC0—LCC i ) / LCC0]×100%; LCC0=f PM_hr (x1,…,x i ,…,x n )+f PM_mat (x1,…,x i ,…,x n )+f CM_hr (x1,…,x i ,…,x n ) +f CM_mat (x1,…,x i ,…,x n ); LCC i =f PM_hr (x1,…,x i +a i x i ,…,x n )+f PM_mat (x1,…,x i +a i x i ,…,x n )+f CM_hr (x1,…, x i +α i x i ,…,x n )+f CM_mat (x1,…,x i +α i x i ,…,x n ); LCC0 is the initial result of the maintenance calculation model, αi is the percentage of change of the i-th parameter, LCC i is the result of maintenance calculation after the i-th parameter changes, f PM_hr (x1,…,x i ,…,x n ) represents the labor cost of preventive maintenance, f PM_mat (x1,…,x i ,…,x n ) represents the cost of preventive maintenance materials, f CM_hr (x1,…,x i ,…,x n ) represents the labor cost of corrective repairs, f CM_mat (x1,…,x i ,…,x n ) represents the cost of corrective maintenance materials, A% represents the reduction setting value of the maintenance calculation model, x i represents the i-th optimizable parameter, i = 1, 2, ..., j; j = 1, 2, ..., n; n is the number of optimizable parameters; Using the optimization model, the parameters to be optimized are determined as: x1, x2, ..., x j ; The target values ​​of the parameters to be optimized are: x1+α1x1, x2+α2x2,…,x j +α i x i .

2. The rail transit product maintenance optimization method according to claim 1, characterized in that: The optimizable parameters include reliability index, preventive maintenance interval, maintenance man-hours, unit man-hour cost, and material price of each node in the product configuration tree.

3. The rail transit product maintenance optimization method according to claim 2, characterized in that: The reliability indicators of each node in the product configuration tree include reliability, mean mileage between failures, and failure rate per million kilometers.

4. The rail transit product maintenance optimization method according to claim 3, characterized in that: Failure rate per million kilometersλ i The calculation formula is:

5. A rail transit product maintenance optimization system, characterized in that: include: Model building unit, used to build the following optimization models: in, Δ i =[(LCC0—LCC i ) / LCC0]×100%; LCC0=f PM_hr (x1,…,x i ,…,x n )+f PM_mat (x1,…,x i ,…,x n )+f CM_hr (x1,…,x i ,…,x n ) +f CM_mat (x1,…,x i ,…,x n ); LCC i =f PM_hr (x1,…,x i +a i x i ,…,x n )+f PM_mat (x1,…,x i +a i x i ,…,x n )+f CM_hr (x1,…, x i +α i x i ,…,x n )+f CM_mat (x1,…,x i +α i x i ,…,x n ); LCC0 is the initial result of the maintenance calculation model, αi is the percentage of change of the i-th parameter, LCC i is the result of maintenance calculation after the i-th parameter changes, f PM_hr (x1,…,x i ,…,x n ) represents the labor cost of preventive maintenance, f PM_mat (x1,…,x i ,…,x n ) represents the cost of preventive maintenance materials, f CM_hr (x1,…,x i ,…,x n ) represents the labor cost of corrective repairs, f CM_mat (x1,…,x i ,…,x n ) represents the cost of corrective maintenance materials, A% represents the reduction setting value of the maintenance calculation model, x i represents the i-th optimizable parameter, i = 1, 2, ..., j; j = 1, 2, ..., n; n is the number of optimizable parameters; A parameter determination unit is used to determine the parameters to be optimized using the optimization model; the parameters to be optimized are: x1, x2, ..., x j ; The parameter optimization unit is used to obtain the target value of the optimized parameter; the target values ​​of the optimized parameter are: x1+α1x1, x2+α2x2,…, x j +α i x i .

6. The rail transit product maintenance optimization system according to claim 5, characterized in that: The optimizable parameters include reliability index, preventive maintenance interval, maintenance man-hours, unit man-hour cost, and material price of each node in the product configuration tree.

7. The rail transit product maintenance optimization system according to claim 5, characterized in that: The reliability indicators of each node in the product configuration tree include reliability, mean mileage between failures, and failure rate per million kilometers.

8. The rail transit product maintenance optimization system according to claim 5, characterized in that: Failure rate per million kilometersλ i The calculation formula is:

9. A rail transit product maintenance optimization system, comprising a memory, a processor, and a computer program stored in the memory; characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A rail transit vehicle, characterized in that: It adopts the rail transit product maintenance optimization system described in any one of claims 5 to 8 above.

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