A method, system, device and medium for managing spare parts of complex equipment

Abstract

The present application discloses a method, system, device and medium for managing spare parts of complex equipment, and relates to the field of equipment spare parts management. The method comprises: obtaining the values ​​of multiple evaluation indicators of any component in the complex equipment, and determining the comprehensive evaluation value of the component by using a fuzzy comprehensive evaluation method and an evidence reasoning algorithm according to the values ​​of the multiple evaluation indicators of the component; determining the variety of spare parts according to the comprehensive evaluation values ​​of each component in the complex equipment; determining the inventory of spare parts by using a fuzzy reasoning method according to the failure rate and replacement times of spare parts; and managing the spare parts of the complex equipment based on the variety of spare parts and the inventory of spare parts. The present application can improve the accuracy of spare parts variety and spare parts inventory, thereby ensuring the combat capability of complex equipment.

Description

Technical Field

[0001] The present application relates to the field of equipment spare parts management, and in particular to a spare parts management method, system, equipment and medium for complex equipment based on fuzzy comprehensive evaluation method and evidence reasoning. Background Art

[0002] Spare parts management is an important foundation for the launch support, maintenance and other tasks of complex equipment. Only by rationally configuring spare parts can the spare parts demand for complex equipment be met to the maximum extent under the conditions of reliability and economy. The supply of spare parts is a key link in equipment support work, which directly affects the timely repair of faults and the extension of equipment life during storage. If the variety and quantity of equipment cannot be determined scientifically and reasonably, it will have a very large impact on the combat readiness of the equipment and have a direct impact on the support and combat capabilities of the equipment.

[0003] In the complex and ever-changing working environment, the support task is arduous. The reserve quantity and reserve method of spare parts for complex equipment, the quantity of support during launch training, and the quantity of carrying during war directly affect the combat effectiveness of the troops. Therefore, the spare parts management of complex equipment is of great significance. Summary of the invention

[0004] The purpose of this application is to provide a spare parts management method, system, equipment and medium for complex equipment, which can improve the accuracy of spare parts variety and spare parts inventory, thereby ensuring the combat capability of complex equipment.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for managing spare parts of complex equipment, comprising:

[0007] For any component in the complex equipment, the values ​​of multiple evaluation indicators of the component are obtained, and according to the values ​​of the multiple evaluation indicators of the component, a fuzzy comprehensive evaluation method and an evidence reasoning algorithm are used to determine the comprehensive evaluation value of the component;

[0008] Determine the types of spare parts based on the comprehensive evaluation values ​​of each component in complex equipment;

[0009] According to the failure rate and replacement times of spare parts, the fuzzy reasoning method is used to determine the inventory of spare parts;

[0010] The spare parts of the complex equipment are managed based on the types of the spare parts and the inventory of the spare parts.

[0011] In a second aspect, the present application provides a spare parts management system for complex equipment, including:

[0012] A component evaluation module is used to obtain the values ​​of multiple evaluation indicators of any component in the complex equipment, and determine the comprehensive evaluation value of the component by using a fuzzy comprehensive evaluation method and an evidence reasoning algorithm according to the values ​​of the multiple evaluation indicators of the component;

[0013] The spare parts variety determination module is used to determine the variety of spare parts based on the comprehensive evaluation values ​​of each component in the complex equipment;

[0014] The spare parts inventory determination module is used to determine the spare parts inventory based on the failure rate and replacement times of spare parts using fuzzy reasoning method;

[0015] The spare parts management module is used to manage the spare parts of the complex equipment based on the types of the spare parts and the inventory of the spare parts.

[0016] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned spare parts management method for complex equipment.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned spare parts management method for complex equipment is implemented.

[0018] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0019] The present application provides a spare parts management method, system, equipment and medium for complex equipment. The types of spare parts are determined by a fuzzy comprehensive evaluation method and an evidence reasoning algorithm. Evidence reasoning, as an uncertain information processing method, can effectively integrate multi-source information to effectively determine the types of spare parts. Then, a fuzzy reasoning method is used to determine the inventory of spare parts, which can improve the accuracy of spare parts types and spare parts inventory, thereby ensuring the combat capability of complex equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is an application environment diagram of a spare parts management method for complex equipment in one embodiment of the present application.

[0022] Figure 2A flowchart of a method for managing spare parts for complex equipment provided in one embodiment of the present application.

[0023] Figure 3 A schematic diagram of functional modules of a spare parts management system for complex equipment provided in one embodiment of the present application.

[0024] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0027] The complex equipment spare parts management method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send the values ​​of multiple evaluation indicators of each component in the complex equipment to the server 104. After the server 104 receives the values ​​of multiple evaluation indicators of each component in the complex equipment, it uses a fuzzy comprehensive evaluation method and an evidence reasoning algorithm to determine the variety of spare parts, and uses a fuzzy reasoning method to determine the inventory of spare parts. The server 104 can feedback the variety and inventory of the obtained spare parts to the terminal 102. In addition, in some embodiments, the spare parts management method for complex equipment can also be implemented separately by the server 104 or the terminal 102.

[0028] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0029] In an exemplary embodiment, Figure 2As shown, a method for managing spare parts of complex equipment is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 204.

[0030] Step 201, for any component in the complex equipment, obtain the values ​​of multiple evaluation indicators of the component, and determine the comprehensive evaluation value of the component by using a fuzzy comprehensive evaluation method and an evidence reasoning algorithm based on the values ​​of the multiple evaluation indicators of the component.

[0031] In a specific example, five evaluation levels are given according to the spare parts effect, namely, "poor", "poor", "medium", "average", and "good", which are recorded as , that is, the identification framework .remember The quantitative values ​​corresponding to the fuzzy semantic values ​​of the evaluation level are {1, 2, 3, 4, 5}.

[0032] According to literature research and actual needs, the main evaluation indicators affecting spare parts variety include: criticality, consumability, availability, replaceability and economy.

[0033] Furthermore, in step 201, based on the values ​​of the multiple evaluation indicators of the component, a fuzzy comprehensive evaluation method and an evidence reasoning algorithm are used to determine the comprehensive evaluation value of the component, which specifically includes the following steps (21) to (25).

[0034] Step (21), according to the values ​​of the multiple evaluation indicators of the component, a scaling method is used to determine the relative importance of the evaluation indicators to obtain a judgment matrix.

[0035] In this application, the relative weights of the evaluation indicators are determined based on the expert experience and decision maker's preferences, thereby providing a judgment matrix. Due to the large number of indicators that affect the variety of spare parts, it is difficult to give a reasonable scoring scheme, and the relative importance judgment between two indicators is more reasonable. For this reason, a scale is introduced, as shown in Table 1.

[0036] Table 1 Scale correspondence table

[0037]

[0038] The introduction of the scale makes it easier to give the element values ​​of the judgment matrix. By comparing the relative importance of the five evaluation indicators, a 5×5 order judgment matrix can be formed: .

[0039] in, Ris the judgment matrix, For the i The evaluation index is relative to j The importance of the evaluation indicators, ; , .

[0040] Step (22), determining the weight of each evaluation index according to the judgment matrix.

[0041] In order to obtain the relative importance of the evaluation indicators, the arithmetic average method is used to obtain the indicator weight, that is, the following formula is used to determine the i The weight of the evaluation index.

[0042] .

[0043] in, For the i The weight of the evaluation index, For the i The evaluation index is relative to j The importance of the evaluation indicators, M is the number of evaluation indicators.

[0044] Step (23), obtaining the evaluation level of each evaluation indicator from multiple experts.

[0045] In step (24), based on the evaluation levels of each evaluation indicator and the weight of each evaluation indicator by multiple experts, an evidential reasoning algorithm is used to determine the confidence distribution of each evaluation indicator and the residual confidence level pointing to the global ignorance of the evaluation framework.

[0046] The confidence distribution includes the confidence level pointed to each evaluation level and the expected utility of each evaluation level.

[0047] In a specific example, experts give qualitative and quantitative descriptions of an evaluation indicator based on industry knowledge and historical experience. Experts participated in the evaluation. Experts on i Evaluation indicators The evaluation level is recorded as , multiply it by the weight of the corresponding evaluation index , the basic probability mass can be obtained as follows.

[0048] .

[0049] in, is the basic probability mass, , It is the evaluation value of the experts on the level of the evaluation index.

[0050] Based on the expert's judgment, the ER rules are used to integrate various evaluation indicators to obtain the distributed comprehensive evaluation results: ;in, represents the distributed evaluation results, Indicates n Assessment level, Indicates the evaluation level of the entire set, represents the residual ignorance of the entire set, that is, the residual confidence points to the global ignorance of the evaluation framework, Indicates pointing to n The confidence level of the evaluation level, that is, the fusion Experts pointed to n The confidence level of the assessment level.

[0051] .

[0052] .

[0053] .

[0054] in, L Indicates shared The amount of evidence given by the experts, represents a mixed weight of the reliability and weight of the evidence, Indicates k The weight of the evidence, Indicates that the n The initial confidence level of the evaluation level (given by experts), Represents the discount factor.

[0055] Step (25), determining the comprehensive evaluation value of the component according to the confidence distribution of each evaluation indicator and the global ignorance of the residual confidence pointing to the evaluation framework.

[0056] In practical applications, in order to obtain more intuitive evaluation results, the distributed credibility evaluation results can be converted into numerical outputs. Introducing the expected utility of the credibility evaluation level, if the residual confidence points to the global ignorance of the evaluation framework is equal to 0 ( , that is, the confidence distribution is complete), then the following formula is used to determine the comprehensive evaluation value of the component, that is, the expected utility of the reliability assessment result.

[0057] .

[0058] If the remaining confidence points to the global ignorance of the evaluation framework is not equal to 0 ( , i.e., the confidence distribution is incomplete), then global ignorance can be assigned to any evaluation level.

[0059] when Assign to the most preferred n Assessment level When , the expected utility of the evaluation result reaches the maximum value: .

[0060] when Assigned to 1st Assessment Level When , the expected utility of the evaluation result reaches the minimum value: .

[0061] Then the formula can be used , that is, the expected utility average value of the evaluation results determines the comprehensive evaluation value of the component.

[0062] in, y is the comprehensive evaluation value of the parts. N is the number of evaluation levels, To point to n The confidence level of the assessment level, is the confidence level pointing to the first assessment level, To point to N The confidence level of the assessment level, For the n The expected utility of the evaluation level, is the expected utility of the first evaluation level, For the N The expected utility of the evaluation level, is the maximum expected utility, is the minimum expected utility, The remaining confidence points to the global ignorance of the evaluation framework.

[0063] Step 202, determining the types of spare parts according to the comprehensive evaluation values ​​of each component in the complex equipment.

[0064] Specifically, for any component in the complex equipment, it is determined whether the comprehensive evaluation value of the component is less than a set threshold. If so, the component is not used as a spare part. If not, the component is listed as a spare part.

[0065] According to the comprehensive evaluation value, determine whether to set the component as a spare part of complex equipment according to certain rules, and qualitatively describe its reserve. Since the comprehensive evaluation value is between 1 and 5, based on people's thinking habits of judging things and the opinions of relevant experts, 3 is determined as the critical value for judging whether the component is used as a spare part, and the following strategy is obtained: when When, such parts should not be listed as spare parts; when When , this type of parts can be listed as spare parts, and the consumption takes a large integer value; when When such parts are consumed, they should be listed as spare parts and the consumption should take the largest integer value.

[0066] Step 203, according to the failure rate of spare parts and the number of replacement times, the fuzzy reasoning method is used to determine the inventory of spare parts.

[0067] The spare parts inventory of complex equipment is not only related to its failure rate, but also to the frequency of spare parts replacement. This application uses spare parts failure rate and replacement frequency as indicators to determine the spare parts inventory based on fuzzy reasoning. Step 203 includes the following steps (31) to (34).

[0068] In step (31), the failure rate, the number of replacements and the inventory are divided into multiple levels, each level corresponds to a fuzzy subset, and multiple fuzzy subsets are obtained.

[0069] The failure rate is divided into three levels: "high", "medium" and "low", corresponding to three fuzzy subsets respectively , , ,The number of replacements is also divided into three levels: "many", "normal", and "few", corresponding to the three fuzzy subsets , , The inventory is divided into three levels: "large", "medium" and "small", corresponding to , , Three fuzzy subsets.

[0070] Step (32), determine the fuzzy inference rules.

[0071] Assume that the failure rate of a spare part of a complex equipment is , the number of spare parts replacement is The inventory is .

[0072] Before fuzzy rule reasoning, the fuzzy reasoning rules are determined based on expert experience as follows.

[0073] Rule 1: If the failure rate is {H} or the number of replacements is {La}, then the spare parts inventory is {La}.

[0074] Rule 2: If the failure rate is {M} and the number of replacements is {A}, then the spare parts inventory is {A}.

[0075] Rule 3: If the failure rate is {L} and the number of replacements is {A}, then the spare parts inventory is {A}.

[0076] Rule 4: If the failure rate is {M} and the number of replacements is {S}, then the spare parts inventory is {S}.

[0077] Rule 5: If the failure rate is {L} and the number of replacements is {S}, then the spare parts inventory is {S}.

[0078] Step (33), based on the fuzzy inference rules and multiple fuzzy subsets, according to the failure rate and replacement times of spare parts, determine the membership degree of each level of inventory.

[0079] In a specific example, the failure rate of a spare part is g=0.00045 ( / hour), and the number of replacements is h=10 (pieces / 100 days). According to the failure rate membership function and the number of replacements membership function given by experts, we can calculate: , , , , , .

[0080] According to the established fuzzy reasoning rules, the following fuzzy reasoning is performed.

[0081] (1) Perform the MIN reasoning process: the membership of the spare parts inventory {La} is equal to max(0.75,0.20)=0.75; the membership of the spare parts inventory {A} is min(0.8,0.25)=0.25; the membership of the spare parts inventory {A} is min(0.8,0.0)=0.0; the membership of the spare parts inventory {S} is min(0.0,0.25)=0; the membership of the spare parts inventory {S} is min(0.0,0.0)=0.0.

[0082] (2) Perform MAX process reasoning: The membership degree of the spare parts inventory {La} is equal to 0.75; the membership degree of the spare parts inventory {A} is max(0.25,0.8)=0.8; the membership degree of the spare parts inventory {S} is max(0.0,0.0)=0.0.

[0083] Step (34), calculating the inventory of spare parts using the area centroid method according to the membership degree of each level of the inventory.

[0084] Specifically, the area centroid method is used to calculate the inventory of spare parts: ,get Z 0=10.28, round up Z 0=11, indicating that the appropriate spare parts inventory is 11. Z c is the value range of inventory quantity, for Z c The degree of membership atC Represents the output quantization level.

[0085] Step 204: managing the spare parts of the complex equipment based on the types of the spare parts and the inventory of the spare parts.

[0086] The spare parts management of complex equipment includes two aspects: the determination of spare parts varieties and the determination of spare parts inventory. In order to provide maintenance support for complex equipment, this application obtains a judgment matrix by giving a set of evaluation indicators and combining five indicators that affect the determination of spare parts. The weight values ​​of the evaluation indicators are obtained by using a fuzzy comprehensive evaluation method. Then, the five evaluation indicators are integrated based on the evidence reasoning algorithm to finally determine the spare parts varieties of the complex equipment. By dividing the membership of the spare parts failure rate, the number of replacement parts, and the membership of the spare parts inventory, fuzzy reasoning is used to obtain the spare parts inventory. As a method of uncertain information processing, evidence reasoning can effectively integrate multi-source information, thereby effectively determining the spare parts varieties.

[0087] Based on the same inventive concept, the embodiment of the present application also provides a system for implementing the spare parts management method for complex equipment involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the spare parts management system for one or more complex equipment provided below can refer to the limitations of the spare parts management method for complex equipment above, and will not be repeated here.

[0088] In an exemplary embodiment, Figure 3 As shown, a spare parts management system for complex equipment is provided, including: a parts evaluation module 301, a spare parts variety determination module 302, a spare parts inventory determination module 303 and a spare parts management module 304.

[0089] The component evaluation module 301 is used to obtain the values ​​of multiple evaluation indicators of any component in the complex equipment, and determine the comprehensive evaluation value of the component based on the values ​​of the multiple evaluation indicators of the component by using a fuzzy comprehensive evaluation method and an evidence reasoning algorithm.

[0090] The spare parts variety determination module 302 is used to determine the variety of spare parts according to the comprehensive evaluation values ​​of each component in the complex equipment.

[0091] The spare parts inventory determination module 303 is used to determine the spare parts inventory based on the failure rate and replacement times of the spare parts by using a fuzzy reasoning method.

[0092] The spare parts management module 304 is used to manage the spare parts of the complex equipment based on the types of the spare parts and the inventory of the spare parts.

[0093] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the values ​​of multiple evaluation indicators of each component in the complex equipment, and the input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a spare parts management method for complex equipment is implemented.

[0094] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0095] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0096] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0098] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0099] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0100] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for managing spare parts of complex equipment, characterized in that: The spare parts management method for complex equipment includes: For any component in the complex equipment, the values ​​of multiple evaluation indicators of the component are obtained, and according to the values ​​of the multiple evaluation indicators of the component, a fuzzy comprehensive evaluation method and an evidence reasoning algorithm are used to determine the comprehensive evaluation value of the component; Among them, according to the values ​​of multiple evaluation indicators of the parts, a fuzzy comprehensive evaluation method and an evidence reasoning algorithm are used to determine the comprehensive evaluation value of the parts, specifically including: According to the values ​​of the multiple evaluation indicators of the parts, a scaling method is used to determine the relative importance of the evaluation indicators to obtain a judgment matrix; According to the judgment matrix, determining the weight of each evaluation index; Obtain the evaluation level of each evaluation indicator from multiple experts; According to the evaluation level of each evaluation indicator and the weight of each evaluation indicator by multiple experts, the confidence distribution of each evaluation indicator and the residual confidence pointing to the global ignorance of the evaluation framework are determined by using the evidential reasoning algorithm; the confidence distribution includes the confidence pointing to each evaluation level and the expected utility of each evaluation level; Among them, the ER rules are used to integrate various evaluation indicators according to the expert's judgment to obtain the distributed comprehensive evaluation results: ;in, represents the distributed evaluation results, Indicates n Assessment level, Indicates the evaluation level of the entire set, represents the residual ignorance of the whole set, Indicates pointing to n The confidence level of each assessment level; ； ； ； in, L Indicates shared The amount of evidence given by the experts, represents a mixed weight of the reliability and weight of the evidence, Indicates k The weight of the evidence, Indicates that the n The initial confidence level of the evaluation level, represents the discount factor; According to the confidence distribution of each evaluation index and the global ignorance of the residual confidence point evaluation framework, the comprehensive evaluation value of the component is determined, which specifically includes: If the residual confidence points to the global ignorance of the evaluation framework equal to 0, the comprehensive evaluation value of the component is determined by the following formula: ； If the residual confidence points to the global ignorance of the evaluation framework and is not equal to 0, the comprehensive evaluation value of the component is determined by the following formula: ； ； ； in, y is the comprehensive evaluation value of the parts. N is the number of evaluation levels, To point to n The confidence level of the assessment level, is the confidence level pointing to the first assessment level, To point to N The confidence level of the assessment level, For the n The expected utility of the evaluation level, is the expected utility of the first evaluation level, For the N The expected utility of the evaluation level, For the n Assessment level, For the first assessment level, For the N Assessment level, is the maximum expected utility, is the minimum expected utility, The remaining confidence points to the global ignorance of the evaluation framework; Determine the types of spare parts based on the comprehensive evaluation values ​​of each component in complex equipment; According to the failure rate and replacement times of spare parts, the fuzzy reasoning method is used to determine the inventory of spare parts; The spare parts of the complex equipment are managed based on the types of the spare parts and the inventory of the spare parts.

2. The method for managing spare parts of complex equipment according to claim 1, characterized in that: The evaluation indicators include: criticality, consumability, availability, replaceability and economy.

3. The method for managing spare parts of complex equipment according to claim 1, characterized in that: Use the following formula to determine the i The weight of the evaluation indicators: ； in, For the i The weight of the evaluation index, For the i The evaluation index is relative to j The importance of the evaluation indicators, M is the number of evaluation indicators.

4. The method for managing spare parts of complex equipment according to claim 1, characterized in that: According to the comprehensive evaluation value of each component in the complex equipment, the types of spare parts are determined, including: For any component in the complex equipment, determine whether the comprehensive evaluation value of the component is less than a set threshold. If so, the component is not used as a spare part. If not, the component is listed as a spare part.

5. The method for managing spare parts of complex equipment according to claim 1, characterized in that: According to the failure rate and replacement times of spare parts, the fuzzy reasoning method is used to determine the inventory of spare parts, including: The failure rate, replacement times and inventory are divided into multiple levels respectively, each level corresponds to a fuzzy subset, and multiple fuzzy subsets are obtained; Determine fuzzy inference rules; Based on the fuzzy inference rules and multiple fuzzy subsets, the membership degree of each level of inventory quantity is determined according to the failure rate and replacement times of spare parts; According to the membership degree of each level of the inventory quantity, the inventory quantity of spare parts is calculated by using the area centroid method.

6. A spare parts management system for complex equipment, applied to the spare parts management method for complex equipment according to any one of claims 1 to 5, characterized in that: The spare parts management system of the complex equipment includes: A component evaluation module is used to obtain the values ​​of multiple evaluation indicators of any component in the complex equipment, and determine the comprehensive evaluation value of the component by using a fuzzy comprehensive evaluation method and an evidence reasoning algorithm according to the values ​​of the multiple evaluation indicators of the component; The spare parts variety determination module is used to determine the variety of spare parts based on the comprehensive evaluation values ​​of each component in the complex equipment; The spare parts inventory determination module is used to determine the spare parts inventory based on the failure rate and replacement times of spare parts using fuzzy reasoning method; The spare parts management module is used to manage the spare parts of the complex equipment based on the types of the spare parts and the inventory of the spare parts.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the spare parts management method for complex equipment according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for managing spare parts for complex equipment according to any one of claims 1 to 5 is implemented.

Images