Hydropower station spare part classification method, system and equipment and storage medium
By combining the AHP and DEA methods, the spare parts of the hydropower station are classified and evaluated, the problems that are difficult to effectively classify and manage in existing technology have been solved, and more accurate and efficient spare parts management has been achieved.
Patent Information
- Application Number
- CN202411647066.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-18
- Publication Date
- 2025-01-24
AI Technical Summary
The existing technology is difficult to effectively classify and manage spare parts for hydropower stations, resulting in inventory backlogs and high procurement costs, and lacks specific classification methods for the hydropower industry.
The method of combining the hierarchical analysis method (AHP) and the data package method (DEA) is used to determine the indicator of the spare parts, calculate the index weight, set the main objective preference coefficient, and use a linear weighted method to determine the combined weight, and then classify and evaluate.
The accurate classification and evaluation of the spare parts of the hydropower stations have been realized, the impact of human factors has been reduced, the systemicity and accuracy of the classification results are improved, and the water and electricity enterprises optimize the inventory management and replenishment strategies.
Smart Images
Figure CN119357750A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of spare parts classification, and relates to a method, system, equipment and storage medium for classifying spare parts of a hydropower station. Background Art
[0002] With the rapid development of the hydropower industry, hydropower equipment is gradually developing in the direction of "high parameters, large units, high voltage, and large power grids", which puts higher requirements on the equipment guarantee capabilities of hydropower enterprises. Spare parts refer to various parts that are purchased and reserved in advance to replace faulty or deteriorated parts in order to shorten the downtime caused by missing parts during power equipment failure repair or planned maintenance and ensure normal power supply. They play an important role in ensuring the smooth operation of the entire hydropower enterprise's power generation process. Therefore, the scientific management of spare parts for hydropower enterprises has received more and more attention. However, in order to respond to various unexpected failures in a timely manner and continuously improve the reliability of power equipment, the phenomenon of "more preparation and less trouble" is prevalent in the reserve management of spare parts of hydropower enterprises, resulting in inventory backlogs from time to time; secondly, there are a large number of equipment in hydropower enterprises, and they cover a wide range, including turbines, hydroelectric generators, ancillary electrical equipment, mechanical equipment, etc., resulting in a wide variety of spare parts and various models; at the same time, the spare parts of hydropower enterprises are of high value, and have the characteristics of less demand and uncertain demand for general spare parts, resulting in high procurement costs and low inventory turnover.
[0003] The rapid development of the hydropower industry has put forward higher and higher requirements on the reliability of hydropower enterprise equipment. Spare parts are important materials to ensure the normal operation of power equipment, and the optimization of their management has gradually received high attention from the industry.
[0004] At present, most of the research on the classification of spare parts and materials in China adopts the ABC method. The key to the method lies in the selection of indicators and the introduction of multi-indicator evaluation methods. There are many methods for classifying spare parts for the power industry, but they are all researched and formulated by power grid companies and are only applicable to spare parts for power transmission and transformation. At present, there are very few specific classifications of spare parts in the hydropower industry. Targeted classification of spare parts for hydropower stations will lay the foundation for the management and replenishment of spare parts for hydropower stations. Effective spare parts classification methods can also provide effective replenishment support for hydropower companies, making hydropower company management more efficient and saving costs.
[0005] As the most commonly used classification method, the analytic hierarchy process (AHP) has the advantage of being able to make full use of the subjective opinions of experts, but its disadvantages are also obvious: the systematicness and accuracy of its classification and evaluation results are completely subject to the personal experience, preferences and knowledge level of the evaluation subject, so it is difficult to eliminate the errors caused by human factors; Data Envelopment Algorithm (DEA) is an evaluation method for multiple evaluation objects with multiple inputs and outputs. By constructing a linear programming model, it can optimize the efficiency of each evaluation object. Although it can be well utilized in quantitative models, it cannot reflect these qualitative indicators and decision makers' preferences well when there are subjective and fuzzy indicators. Summary of the invention
[0006] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, system, equipment and storage medium for classifying spare parts of hydropower stations, which can accurately classify and evaluate spare parts of hydropower enterprises.
[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A method for classifying spare parts for a hydropower station includes the following steps: S1, determine the indicators of spare parts; S2, using the AHP method to calculate the weights of indicators; S3, using DEA method to calculate the weight of indicators; S4, according to the proportion of subjective indicators to the total number of indicators, set the subjective and objective preference coefficients, and use the linear weighting method to determine the combined weight of the evaluation indicators; S5, evaluate various indicators on the combined weights of the spare parts after they are adopted, set thresholds for category division, and judge the category to which the spare parts belong by comparing the total score with the threshold.
[0008] Preferably, the indicators of spare parts include procurement cost, service level, demand, price, procurement lead time, versatility and degree of standardization.
[0009] Preferably, the S2 process is: obtaining an indicator judgment matrix according to an expert scoring method; and calculating a normalized importance vector of each indicator according to the indicator judgment matrix.
[0010] Furthermore, after normalization is completed, consistency verification is performed.
[0011] Preferably, the S3 process is: The importance of each evaluation object is evaluated according to its importance level. The maximum is the objective function, and the importance of all evaluation objects is not less than 0 as the constraint condition. A linear programming model is constructed and solved to obtain the evaluation object. The indicator weight with the best importance is normalized to obtain the required weight.
[0012] Furthermore, the classification indicators whose smaller values mean higher attention paid by enterprises are set as input indicators; the classification indicators whose larger values mean higher attention paid by enterprises are set as output indicators; the values of each spare part under each indicator are dimensionlessly processed, and for qualitative indicators, fuzzy methods are used to quantify them.
[0013] Preferably, in S4, the combined weight is:
[0014] In the formula, is the weight calculated by AHP method, is the weight calculated by DEA method, is the combined weight of the evaluation indicators under the bottom i evaluation objects, is the subjective preference coefficient, is the objective preference coefficient, , determined by the proportion of subjective indicators to the total number of indicators.
[0015] A method for classifying spare parts for a hydropower station, comprising: An index determination module, used to determine the index of spare parts; AHP calculation module, used to calculate the weight of indicators using the AHP method; DEA calculation module, used to calculate the weight of indicators using the DEA method; The weight combination module is used to set the subjective and objective preference coefficients according to the proportion of subjective indicators to the total number of indicators, and to determine the combined weights of the evaluation indicators using a linear weighting method; The evaluation and classification module is used to evaluate various indicators of the combined weights of spare parts after they are adopted, set thresholds for category division, and determine the category to which the spare parts belong by comparing the total score with the threshold.
[0016] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for classifying spare parts of a hydropower station are implemented.
[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for classifying spare parts for a hydropower station.
[0018] Compared with the prior art, the present invention has the following beneficial effects: In order to give full play to the advantages of AHP and DEA, the present invention introduces subjective and objective preference coefficients and uses a linear weighted method to calculate the combined weight. Compared with the AHP method alone, the systematicness and accuracy of the classification and evaluation results are not subject to the personal experience, preferences and knowledge level of the evaluation subject, and the errors caused by human factors can be eliminated; compared with the DEA method alone, when there are subjective and fuzzy indicators, these qualitative indicators and the preferences of decision makers can be better reflected. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 A flow chart of a method for classifying spare parts for a hydropower station according to the present invention; Figure 2 It is a schematic diagram of the spare parts classification hierarchy model of the present invention. DETAILED DESCRIPTION
[0020] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.
[0021] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.
[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs. The terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or can communicate with each other; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. The term "and / or" used in this article includes any and all combinations of one or more related listed items. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.
[0023] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.
[0024] The method for classifying spare parts for a hydropower station according to the present invention is as follows: Figure 1 As shown, the following process is included: S1, determine the indicators of spare parts.
[0025] S2, the AHP method is used to calculate the weights of the indicators.
[0026] S3, DEA method is used to calculate the weights of indicators.
[0027] S4, according to the proportion of subjective indicators in the total number of indicators, set the subjective and objective preference coefficients, and use the linear weighted method to determine the combined weight of the evaluation indicators.
[0028] S5, evaluate various indicators on the combined weights of the spare parts after they are adopted, set thresholds for category division, and judge the category to which the spare parts belong by comparing the total score with the threshold.
[0029] The specific process of the above scheme is as follows: 1. Build a hierarchical model The classification indicators of spare parts for hydropower enterprises can be divided into seven categories: procurement cost, service level, demand, price, procurement lead time, versatility, and standardization. Based on the above indicators, the fuzzy comprehensive evaluation method is used to classify the spare parts of hydropower enterprises. At the same time, the analytic hierarchy process is combined with the data envelopment method to determine the indicator weights.
[0030] A single-objective multi-criteria hierarchical model is constructed, in which the objective layer is the importance of each spare part of the hydropower enterprise, the criterion layer is the above seven classification indicators, and the scheme layer is the hydropower spare parts to be classified, such as Figure 2 shown.
[0031] 2. Calculation of index weights based on AHP Construct a pairwise comparison judgment matrix, a ij It represents the importance of indicator i compared with indicator j. The indicator judgment matrix is obtained according to the expert scoring method as shown in Table 1.
[0032] Table 1 Judgment matrix
[0033] According to the indicator judgment matrix, calculate the normalized importance vector of each indicator , , The results are shown in Table 2. The results are shown in Table 2.
[0034] Table 2 Importance of each indicator
[0035] Calculate consistency index
[0036] in , n=7 Calculate the consistency ratio CR = CI / RI, where RI can be obtained by querying the average random consistency index table, as shown in Table 3.
[0037] Table 3 Average random consistency index
[0038] Taking RI=1.36, then CR=CI / RI=Zone 9, CR<0.1, which meets the consistency requirements.
[0039] 3. Calculation of indicator weights based on DEA The most commonly used DEA model is the C2R model. The basic principle of the DEA model is: if there are m evaluation objects in the system (i = 1, 2..., p), n evaluation indicators, where the input and output indicators of each evaluation object are p and q respectively, and the corresponding input vector and output vector are , , there is the following relationship between the number of input and output indicators and their vectors: At the same time, vectors V and U are used to represent the weights of input indicators and output indicators respectively. , If the The importance of each evaluation object is evaluated according to its importance level. The maximum is the objective function, with all evaluation objects (including The importance of each evaluation object) is not less than 0 as a constraint condition, and the linear programming model is constructed as follows.
[0040]
[0041]
[0042]
[0043]
[0044]
[0045] By solving the above linear programming model, we can obtain the evaluation object The index weight with the best importance is normalized to obtain the required weight:
[0046] First, according to the DEA principle, the smaller the value, the higher the attention paid by enterprises to the classification indicators are set as input indicators; the larger the value, the higher the attention paid by enterprises to the classification indicators are set as output indicators. Therefore, among the seven classification indicators, the input indicators are universality and standardization, and the output indicators are price, procurement lead time, demand, service level, and procurement cost.
[0047] Secondly, the values of each spare part under each indicator are dimensionless, and the two qualitative indicators of universality and standardization are quantified using fuzzy methods, as shown in Table 4.
[0048] Table 4 Fuzzy index quantification table
[0049] Substitute the dimensionless data into the above formula to solve the linear programming problem and obtain the indicator weights under the DEA method. .
[0050] 4. Comprehensive weight calculation The advantages of both AHP and DEA are considered. According to the proportion of subjective indicators in the total number of indicators, appropriate subjective and objective preference coefficients are set. The linear weighting method is used to jointly determine the combined weights of the evaluation indicators. The specific calculation method is as follows:
[0051] In the formula, is the combined weight of the evaluation indicators under the bottom i evaluation objects, is the subjective preference coefficient, is the objective preference coefficient, , which can be determined by the proportion of subjective indicators to the total number of indicators.
[0052] 5. Spare parts classification Spare parts of hydropower enterprises are scored separately under seven indicators, and the total score of the spare parts is obtained by weighted summation. The scoring standard and score of each indicator can be determined based on data statistics and expert scoring method. Spare parts with a total score higher than 0.7 are classified as Class I, and the others are classified as Class II.
[0053] Since there are both quantitative and qualitative indicators in the classification index system of spare parts for hydropower enterprises, in order to give full play to the advantages of AHP and DEA, subjective and objective preference coefficients are introduced, and the linear weighted method is used to calculate the combined weight. Compared with the AHP method alone, the systematicness and accuracy of the classification and evaluation results are not restricted by the personal experience, preferences and knowledge level of the evaluation subject, and the errors caused by human factors can be eliminated; compared with the DEA method alone, when there are subjective and fuzzy indicators, these qualitative indicators and decision makers' preferences can be better reflected.
[0054] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.
[0055] In another embodiment of the present invention, a hydropower station spare parts classification system is provided, which can be used to implement the above-mentioned hydropower station spare parts classification method. Specifically, the hydropower station spare parts classification system includes an indicator determination module, an AHP calculation module, a DEA calculation module, a weight combination module and an evaluation classification module.
[0056] Among them, the indicator determination module is used to determine the indicators of spare parts.
[0057] The AHP calculation module is used to calculate the weights of indicators using the AHP method.
[0058] The DEA calculation module is used to calculate the weights of indicators using the DEA method.
[0059] The weight combination module is used to set the subjective and objective preference coefficients according to the proportion of subjective indicators in the total number of indicators, and to determine the combined weights of the evaluation indicators using a linear weighted method.
[0060] The evaluation and classification module is used to evaluate various indicators of the combined weights of spare parts after they are adopted, set thresholds for category division, and determine the category to which the spare parts belong by comparing the total score with the threshold.
[0061] In yet another embodiment of the present invention, a terminal device is provided. The terminal device includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the hydropower station spare parts classification method, including: S1, determining the indicators of spare parts; S2, using the AHP method to calculate the weight of the indicators; S3, using the DEA method to calculate the weight of the indicators; S4, according to the proportion of subjective indicators to the total number of indicators, setting subjective and objective preference coefficients, and using a linear weighted method to determine the combined weight of the evaluation indicators; S5, evaluating each indicator on the combined weight of the spare parts after adoption, setting a threshold for category classification, and judging the category to which the spare parts belong by comparing the total score with the threshold.
[0062] In another embodiment, the present invention further provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.
[0063] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the method for classifying spare parts for a hydropower station in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and the following steps are executed: S1, determining the index of spare parts; S2, using the AHP method to calculate the weight of the index; S3, using the DEA method to calculate the weight of the index; S4, according to the proportion of subjective indicators to the total number of indicators, setting subjective and objective preference coefficients, and using a linear weighted method to determine the combined weight of the evaluation index; S5, evaluating each index on the combined weight of the spare parts after adoption, setting a threshold for category classification, and judging the category to which the spare parts belong by comparing the total score with the threshold.
[0064] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0065] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0068] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.
[0069] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.
[0070] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.
[0071] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.
[0072] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.
[0073] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this patent should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a waiver of such subject matter, nor should it be considered that the applicant has not considered such subject matter to be part of the disclosed inventive subject matter.
Claims
1. A method for classifying spare parts for a hydropower station, characterized in that: The process includes: S1, determine the indicators of spare parts; S2, using the AHP method to calculate the weights of indicators; S3, using DEA method to calculate the weight of indicators; S4, according to the proportion of subjective indicators to the total number of indicators, set the subjective and objective preference coefficients, and use the linear weighting method to determine the combined weight of the evaluation indicators; S5, evaluate various indicators on the combined weights of the spare parts after they are adopted, set thresholds for category division, and judge the category to which the spare parts belong by comparing the total score with the threshold.
2. The method for classifying spare parts for a hydropower station according to claim 1, characterized in that: The indicators of spare parts include procurement costs, service levels, demand, prices, procurement lead time, versatility and degree of standardization.
3. The method for classifying spare parts for a hydropower station according to claim 1, characterized in that: The S2 process is: obtaining the indicator judgment matrix according to the expert scoring method; and calculating the normalized importance vector of each indicator according to the indicator judgment matrix.
4. The method for classifying spare parts for a hydropower station according to claim 2, characterized in that: After normalization is completed, consistency verification is performed.
5. The method for classifying spare parts for a hydropower station according to claim 1, characterized in that: The S3 process is: The importance of each evaluation object is evaluated according to its importance level. The maximum is the objective function, and the importance of all evaluation objects is not less than 0 as the constraint condition. A linear programming model is constructed and solved to obtain the evaluation object. The indicator weight with the best importance is normalized to obtain the required weight.
6. The method for classifying spare parts for a hydropower station according to claim 5, characterized in that: The classification indicators whose values are smaller and the enterprises pay more attention to them are set as input indicators; the classification indicators whose values are larger and the enterprises pay more attention to them are set as output indicators; the values of each spare part under each indicator are dimensionlessly processed, and for qualitative indicators, fuzzy methods are used to quantify them.
7. The method for classifying spare parts for a hydropower station according to claim 1, characterized in that: In S4, the combined weight is: In the formula, is the weight calculated by AHP method, is the weight calculated by DEA method, is the combined weight of the evaluation indicators under the bottom i evaluation objects, is the subjective preference coefficient, is the objective preference coefficient, , determined by the proportion of subjective indicators to the total number of indicators.
8. A method for classifying spare parts for a hydropower station, characterized in that: include: An index determination module is used to determine the index of spare parts; AHP calculation module, used to calculate the weight of indicators using the AHP method; DEA calculation module, used to calculate the weight of indicators using the DEA method; The weight combination module is used to set the subjective and objective preference coefficients according to the proportion of subjective indicators to the total number of indicators, and to determine the combined weights of the evaluation indicators using a linear weighting method; The evaluation and classification module is used to evaluate various indicators of the combined weights of spare parts after they are adopted, set thresholds for category division, and determine the category to which the spare parts belong by comparing the total score with the threshold.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for classifying spare parts for a hydropower station as described in any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for classifying spare parts for a hydropower station as claimed in any one of claims 1 to 7 are implemented.