Military equipment maintenance support equipment assessment optimization method

Abstract

The invention relates to the technical field of military equipment maintenance, in particular to a military equipment maintenance support equipment assessment optimization method, which comprises the following steps: acquiring maintenance information of military equipment; preprocessing the maintenance information, inputting the preprocessed maintenance information into a maintenance detection model, and identifying a maintenance demand and a target maintenance node during maintenance of the military equipment; obtaining the use probability of the same maintenance device and the failure rate of the military device in two adjacent maintenance under the maintenance demand corresponding to the target maintenance node, and obtaining a maintenance probability distribution coefficient; determining the difference value and the repair time of the maintenance information after each military equipment maintenance, and setting a maintenance average coefficient of the military equipment maintenance; the maintenance average coefficient and the maintenance probability distribution coefficient are combined with the target maintenance node, a maintenance decision tree corresponding to the target maintenance node is generated, and a maintenance strategy of the current military equipment is generated according to the maintenance decision tree; and the maintenance efficiency and the reliability of equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of military equipment maintenance, and specifically, to a method for optimizing the assessment of military equipment maintenance support equipment. Background Art

[0002] With the rapid development of modern military technology, the complexity and intelligence level of military equipment have been continuously improved, which puts forward higher requirements for maintenance support work. The traditional maintenance support method has been difficult to meet the needs of modern warfare for quickly restoring the combat effectiveness of equipment. Therefore, optimizing the assessment of military equipment maintenance support equipment to improve maintenance efficiency and support capabilities has become an important issue in the current military field.

[0003] For example, Chinese Patent Publication No. CN114841656A discloses a method and system for fault detection of military aircraft based on edge computing, which relates to the technical field of detection. This method collects different detection requirements of different aircraft; according to the different detection requirements of different aircraft collected, different detection targets are identified and judged through edge computing nodes; subsequently, according to the different characteristics of different aircraft, the detection targets are automatically classified to obtain preliminary detection results; then, based on the preliminary detection results, refined detection of corresponding preset items is carried out for different aircraft to obtain comprehensive fault detection results.

[0004] In the prior art, a military equipment repair model is constructed by setting permission control and corresponding image data viewing. However, when setting this model, it is also necessary to consider the types of equipment used for repairing military equipment and the specific content represented by these repair tasks. Further limit the repair of military equipment according to the time and cost spent under these repair tasks and equipment types to improve the maintenance support efficiency of military equipment. Summary of the Invention

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: a method for optimizing the assessment of military equipment maintenance support equipment, including: S1, obtaining the maintenance information of military equipment, where the maintenance information includes the equipment type, maintenance task, maintenance time, and maintenance cost of the maintenance equipment for repairing military equipment when repairing military equipment.

[0006] S2, preprocess the maintenance information, and input the preprocessed maintenance information into the maintenance detection model to identify the maintenance requirements and target maintenance nodes when repairing military equipment.

[0007] S3, combine the maintenance information according to the target maintenance nodes, and obtain the usage probability of the same maintenance equipment of military equipment in two adjacent repairs and the failure rate of military equipment under the maintenance requirements corresponding to the target maintenance nodes, so as to obtain the maintenance probability distribution coefficient.

[0008] S4. Compare the maintenance information, determine the difference value of the maintenance information and the repair time after each military equipment repair, and set the average maintenance coefficient for military equipment repair.

[0009] S5. Combine the average maintenance coefficient and the maintenance probability distribution coefficient with the target maintenance node to generate a maintenance decision tree corresponding to the target maintenance node, and generate a maintenance strategy for the current military equipment according to the maintenance decision tree.

[0010] The beneficial effects of the present invention are as follows: First, by constructing a maintenance detection model and generating a maintenance strategy matrix, the present invention can accurately identify maintenance requirements and target maintenance nodes, and formulate an optimal maintenance strategy accordingly; it solves the problem that traditional maintenance decisions rely on empirical judgments, reduces the influence of subjective factors, and ensures the scientificity and accuracy of maintenance decisions.

[0011] Second, by preprocessing and continuous analysis of maintenance information, the present invention can give early warnings of potential problems, reasonably arrange maintenance tasks, and avoid unnecessary resource waste; it solves the problems of increased costs and low efficiency caused by unreasonable resource allocation, and realizes the efficient use of resources and cost control.

[0012] Third, by selecting the decision path with the fewest nodes as the maintenance strategy, the present invention simplifies the operation process, reduces complexity and error probability, and improves the efficiency of maintenance work; it solves the problems of time delay and low efficiency caused by complex processes during maintenance, and enhances the emergency handling ability and response speed.

[0013] Fourth, by continuously monitoring and optimizing the maintenance strategy, the present invention ensures that the equipment operates in the best state, extends the service life, and reduces the failure rate; it solves the problems of frequent equipment failures and excessive downtime, and improves the reliability of the equipment and the task completion rate. Description of the Drawings

[0014] The present invention will be further described below with reference to the drawings and embodiments.

[0015] Figure 1 is a schematic flowchart of a method for optimizing the assessment of military equipment maintenance support equipment.

[0016] Figure 2 is a schematic flowchart of step S2 of a method for optimizing the assessment of military equipment maintenance support equipment.

[0017] Figure 3 is a schematic flowchart of step S3 of a method for optimizing the assessment of military equipment maintenance support equipment.

[0018] Figure 4 is a schematic flowchart of step S4 of a method for optimizing the assessment of military equipment maintenance support equipment.

[0019] Figure 5 It is a schematic flow diagram of step S5 of an optimization method for assessing military equipment maintenance support equipment. Specific implementation manner

[0020] The embodiments of the present invention will be described in detail below. The embodiments described below are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention. For those not specified in the embodiments in terms of specific technologies or conditions, they shall be carried out according to the technologies or conditions described in the literature in this field or according to the product instructions.

[0021] Refer to Figure 1 , an optimization method for assessing military equipment maintenance support equipment, including: S1, obtaining the maintenance information of military equipment, where the maintenance information includes the equipment type, maintenance task, maintenance time, and maintenance cost of the maintenance equipment for maintaining military equipment when maintaining military equipment.

[0022] S2, preprocessing the maintenance information, and inputting the preprocessed maintenance information into a maintenance detection model to identify the maintenance requirements and target maintenance nodes when maintaining military equipment.

[0023] S3, combining the maintenance information according to the target maintenance nodes, obtaining the usage probability of the same maintenance equipment of military equipment in two adjacent maintenances and the failure rate of military equipment under the maintenance requirements corresponding to the target maintenance nodes, and obtaining a maintenance probability distribution coefficient.

[0024] S4, comparing the maintenance information, determining the difference value and repair time of the maintenance information after each maintenance of military equipment, and setting a maintenance average coefficient for the maintenance of military equipment.

[0025] S5, combining the maintenance average coefficient and the maintenance probability distribution coefficient with the target maintenance nodes to generate a maintenance decision tree corresponding to the target maintenance nodes, and generating a maintenance strategy for the current military equipment according to the maintenance decision tree.

[0026] The equipment type of the maintenance equipment for maintaining military equipment refers to the tools or systems used to describe the execution of specific maintenance tasks. There will be different types of maintenance equipment for different equipment types of military equipment. For example, for an aircraft, the maintenance equipment that may be involved includes special testers, hydraulic tools, electronic diagnostic equipment, etc. Understanding the equipment types used helps to evaluate their applicability and efficiency, and also helps to train technicians to use these equipment correctly; when different equipment types are used, the maintenance time, cost, and content that can be repaired will change. By linking these used equipment with military equipment, the efficiency of military equipment maintenance can be found to evaluate the maintenance work.

[0027] A maintenance task refers to a specific maintenance activity that needs to be carried out, which can be preventive regular maintenance or corrective repair of faulty components. Each task has a clear goal, such as replacing worn parts, calibrating sensors, updating software, etc. Recording specific maintenance tasks can help track the historical maintenance of the equipment, identify common problem points, and provide a reference for future preventive measures. When identifying these maintenance tasks, in addition to identifying these types and tasks, the corresponding records of the maintenance personnel, such as obtaining the maintenance time and maintenance cost, also need to be recorded.

[0028] The maintenance time is the time required from the start of maintenance to completion and the restoration of the equipment to normal operation. This is usually divided into several stages, including preparation time, actual hands-on time, and subsequent inspection time. The maintenance time is an important indicator to measure maintenance efficiency. A shorter maintenance time means a faster turnover rate and a higher state of combat readiness; while a longer maintenance time may indicate the existence of bottlenecks or insufficient resources.

[0029] The maintenance cost covers all costs related to maintenance activities, including but not limited to labor costs, material costs, equipment rental costs, transportation costs, etc. In addition, indirect costs, such as losses caused by downtime, should also be considered. Accurately calculating the maintenance cost is crucial for budget planning. It not only affects the current financial expenditure but also relates to long-term cost-benefit analysis and resource allocation decisions.

[0030] The selection of maintenance time is to verify the corresponding conditions during the maintenance of military equipment and the average time required for repair under such conditions, so as to distinguish and show the repair situations of different tasks. The selection of maintenance cost is to verify the impacts caused by different types of problems, so as to verify various aspects such as the usage situation, maintenance quality, and response time of military equipment at this time.

[0031] In an embodiment of the present invention, after preprocessing the maintenance information in step S2, the maintenance requirements and target maintenance nodes during the maintenance of military equipment are identified.

[0032] The preprocessing methods for maintenance information include but are not limited to data deduplication and data standardization. After converting all data into the same format and eliminating the dimensions existing in these formats, and checking whether these data are consistent, these maintenance information are used for subsequent analysis and processing.

[0033] The maintenance requirements during the maintenance of military equipment refer to the specific repair or maintenance requirements determined based on the current equipment status and historical maintenance records. This includes but is not limited to replacing parts, correcting parameters, repairing software failures, etc. Through the analysis of the maintenance detection model, it is possible to predict which components may have problems and in which stage or conditions these problems may occur. Based on this, the corresponding tools and technical support can be prepared in advance.

[0034] The target maintenance node refers to the key inspection point or milestone established to complete the entire maintenance process. Each node represents a specific task or stage. When all nodes are correctly executed, it means that the maintenance work has been successfully completed. The maintenance detection model can help identify which nodes are most critical for the successful completion of maintenance, and at the same time can also point out which nodes are prone to become bottlenecks or risk points. In this way, targeted monitoring and management can be strengthened during actual maintenance to improve efficiency and reduce uncertainty.

[0035] For the selected maintenance detection model used to identify maintenance requirements and locate target maintenance nodes, this model discovers the existing maintenance requirements and target maintenance nodes at this time by extracting and identifying maintenance information.

[0036] The existing maintenance requirements can be set according to the fault points and maintenance cycles in historical data; for the target maintenance nodes, multiple target maintenance nodes during maintenance can be obtained according to the comments and content descriptions of the corresponding maintenance personnel in the maintenance records, so as to complete the acquisition of the current target maintenance nodes.

[0037] At this time, the maintenance detection model can be set in the form of a fault analysis tree or a decision tree to select the points that need to be focused on observing through maintenance information.

[0038] Such as Figure 2 As shown, the implementation method of step S2 also includes: S21, using the equipment type and maintenance task of the maintenance equipment as the input feature vector to construct a maintenance detection model.

[0039] S22, using the maintenance detection model to locate the maintenance scope of the maintenance information; obtaining the maintenance scope under different equipment types. The current maintenance scope is obtained by extracting the corresponding military equipment for maintenance from the equipment type and maintenance task of the military equipment for maintenance, dividing it according to the equipment type during military equipment maintenance, and then further extracting according to the maintenance task to determine the faulty parts of the military equipment at this time, and finally determining the current maintenance scope based on this faulty part.

[0040] S23, dividing the maintenance scope according to the values of the feature vector, determining the maintenance requirements during maintenance, and setting the maintenance requirement with the most output times as the target maintenance node. At this time, the maintenance detection model will output multiple leaf nodes in the form of a fault analysis tree or a decision tree. This will involve multiple groups of feature vectors. These data will be output after the maintenance detection model completes processing. Then, the part with the most times in the output maintenance scope at this time will be considered the key point identified at this time, and this key point is considered the target maintenance node.

[0041] The maintenance detection model includes a root node, several branch nodes and several leaf nodes; each root node represents a type of equipment for maintaining military equipment, each leaf node represents an output maintenance scope, each branch node represents a maintenance task, and the path from the root node to each leaf node represents a set of detection and positioning paths.

[0042] When constructing the maintenance detection model, look up the initial decision tree model that matches the current military equipment model in the model library, and determine the working environment during the maintenance of the military equipment according to the maintenance time and maintenance cost of the military equipment; obtain the parts list of the military equipment, compare the working environment and maintenance tasks of the military equipment with the parts list of the military equipment, obtain the associated parts during the maintenance of the military equipment, and screen the initial decision tree model according to the associated parts to obtain the set maintenance detection model. When knowing what kind of equipment a military equipment is, if the maintenance time and maintenance cost of this military equipment are obtained, then the approximate degree of repair of this military equipment can be deduced, so that the required maintenance environment at this time can be known. Then, according to the maintenance tasks, find the corresponding parts, select the decision tree model related to these parts, and finally can assist in dealing with the specific performance of these maintenance military equipment under different types.

[0043] When dividing the branch nodes for the detection and positioning paths, according to the maintenance tasks existing on the branch nodes, divide the branch nodes into multiple maintenance task nodes. Each maintenance task node corresponds to at least one part existing in the military equipment, and calculate the Gini impurity corresponding to each maintenance task, and compare the Gini impurity of the current maintenance task node with the Gini impurity of the upper-level node. When the Gini impurity of the previous node is greater than the Gini impurity of the current maintenance task node, delete the current maintenance task node, and loop to judge all the branch nodes in the maintenance detection model until the remaining branch nodes meet the requirements, and finally complete the judgment and output of the maintenance detection model.

[0044] In an embodiment of the present invention, step S3 combines the maintenance information according to the target maintenance node, and obtains the maintenance probability distribution coefficient by identifying the usage probability and failure rate corresponding to the military equipment.

[0045] This part records the corresponding data of the target maintenance node in the corresponding situation to complete the overall usage frequency and failure rate; according to these two values, the tasks existing on the target maintenance node and the corresponding situations of these tasks can be described.

[0046] The maintenance requirements corresponding to the target maintenance node here represent the content output by using the maintenance detection model, determine the probability of the military equipment using the equipment type of the same maintenance equipment on this content, and the specific content of the failure of this military equipment.

[0047] As Figure 3 shown, the implementation method of step S3 further includes: S31. Taking the target maintenance node as the basic condition, generating a maintenance strategy matrix for military equipment maintenance according to the target maintenance node and the maintenance requirements corresponding to the target maintenance node; the maintenance strategy matrix starts from the target maintenance node, quantifies the maintenance requirements corresponding to this point, and forms a maintenance strategy matrix. Each row in the matrix represents different execution contents, such as replacing components and calibrating parameters, and each column in the matrix represents the factors that may be affected by the execution content, such as equipment type, usage frequency, and failure rate.

[0048] S32. Obtaining the maintenance reason when the maintenance strategy matrix is formulated, and constructing the maintenance reason for each maintenance into a maintenance vector; at this time, the reason for military equipment maintenance is set as a vector to record the situation under the corresponding maintenance.

[0049] S33. Obtaining the maintenance time and maintenance probability of the maintenance vector at the target maintenance node, and setting the priority of the maintenance strategy matrix; the priority will be set by using the maintenance time and the probability of the corresponding maintenance used in the content of the target maintenance node. For example, the priority can be expressed as obtaining the ratio of the maintenance time and maintenance probability of the corresponding element in the maintenance strategy matrix to the sum of the maintenance time and maintenance probability of all elements in the maintenance strategy matrix respectively, and weighted summing the obtained ratios of the maintenance time and maintenance probability to obtain the priority of the corresponding element in the maintenance strategy matrix. In this way, the priority of the corresponding element in the maintenance strategy matrix is obtained in turn, and finally the priority of the entire maintenance strategy matrix is obtained.

[0050] For the above-mentioned maintenance probability, it will be set according to the probability that the maintenance time of the maintenance vector at the target maintenance node is less than the average value of the maintenance time in the historical data.

[0051] S34. Using the priority of the maintenance strategy matrix and the maintenance strategy matrix to statistically analyze the usage probability and failure rate of military equipment under the corresponding equipment type, and obtaining the maintenance probability distribution coefficient.

[0052] At this time, the maintenance probability distribution coefficient will combine the usage probability, failure rate, priority, and maintenance probability under the corresponding equipment type to obtain the maintenance probability distribution coefficient, so as to determine whether the maintenance reason set for military equipment during maintenance can form a matching relationship with the usage probability of the maintenance equipment and the failure rate of military equipment.

[0053] The maintenance probability distribution coefficient can be as follows: traverse the maintenance strategy matrix, comprehensively process the usage probability, failure rate, priority, and maintenance probability corresponding to each element in the maintenance strategy matrix, obtain the maintenance distribution coefficient corresponding to each element in the maintenance strategy matrix, then judge the error ratio between adjacent maintenance distribution coefficients, and comprehensively calculate the error ratio between adjacent maintenance distribution coefficients and the maintenance distribution coefficient to obtain the maintenance probability distribution coefficient.

[0054] The maintenance distribution coefficient is expressed as: ; where represents the maintenance distribution coefficient of the i-th element in the maintenance strategy matrix, represents the usage probability corresponding to the i-th element in the maintenance strategy matrix, represents the failure rate corresponding to the i-th element in the maintenance strategy matrix, represents the priority corresponding to the i-th element in the maintenance strategy matrix, represents the maintenance probability corresponding to the i-th element in the maintenance strategy matrix, represents the exponential constant.

[0055] The error ratio between adjacent maintenance distribution coefficients is expressed as: ; where represents the error ratio between adjacent maintenance distribution coefficients of the i-th element in the maintenance strategy matrix; represents the error function, , represents the maintenance distribution coefficient of the (i - 1)-th element in the maintenance strategy matrix. When i takes 1, the value of the maintenance distribution coefficient at this time is the average value of the maintenance distribution coefficient calculated last time.

[0056] The maintenance probability distribution coefficient is expressed as: ; where represents the number of elements in the maintenance strategy matrix, and the value range of i is from 1 to n, represents the maintenance probability distribution coefficient.

[0057] The finally output maintenance probability distribution coefficient will represent the corresponding situation of military equipment during maintenance and the corresponding data processing content after maintenance.

[0058] In an embodiment of the present invention, step S4 mainly compares the maintenance information and quantifies the maintenance situation of military equipment by comparing the difference values on the maintenance information of military equipment.

[0059] At this time, it is necessary to combine the specific maintenance information to obtain the difference value of the recorded information of the same equipment in different maintenance operations under the maintenance information, as well as the repair time required for each maintenance. Finally, summarize the maintenance information under all equipment types to obtain the average maintenance coefficient of military equipment during maintenance.

[0060] The difference value of maintenance information represents the difference in corresponding values of maintenance time, maintenance cost, maintenance tasks, and equipment type when maintaining military equipment. At this time, equipment types, etc. are converted into numerical representations to calculate the difference. When setting the average maintenance coefficient, these difference values are combined to obtain the corresponding average maintenance coefficient, which is used to measure the average maintenance efficiency or cost of maintenance events.

[0061] As Figure 4 shown, the implementation method of step S4 also includes: S41, fitting the difference values of maintenance information after each maintenance of military equipment to obtain the fitting error of maintenance information relative to the maintained equipment. At this time, the difference values are mapped according to the maintained equipment, and these errors are fitted to a corresponding curve to represent the specific magnitude of this difference value.

[0062] S42, determining whether the fitting error meets the preset threshold, and identifying the part higher than the preset threshold to obtain the probability frequency array of the fitting error higher than the preset threshold. The preset threshold is set by using the average value of the fitting error existing in historical data, and the part of the data greater than the preset threshold is identified to determine the comprehensive occurrence situation of the difference value when the corresponding difference value appears.

[0063] S43, performing a continuity analysis on the probability frequency array to determine the occurrence probability and repair time of the corresponding data in the probability frequency array, and calculating to obtain the average maintenance coefficient. At this time, the repair time represents the time period from the start to the end of each maintenance of military equipment. The repair time is more inclined to the time during the maintenance of military equipment compared to the maintenance time, and the maintenance time represents the time from the occurrence of a problem to the completion of maintenance, and the maintenance time is greater than the repair time. At this time, the probability frequency array will contain multiple fitting errors, and each fitting error will correspond to a repair time, that is, the error generated for each corresponding maintenance. What form will this repair time show?

[0064] The average maintenance coefficient is expressed as obtaining the occurrence probability and repair time corresponding to each element in the probability frequency array, and calculating to obtain the average maintenance coefficient.

[0065] ; where represents the average maintenance coefficient, represents the number of elements in the probability frequency array, and the value range of j is from 1 to m; represents the repair time of the j-th element in the probability frequency array, represents the occurrence probability of the j-th element in the probability frequency array, represents the average value of the repair time, represents the pi.

[0066] The final output of the average maintenance coefficient will represent the corresponding time during the maintenance of military equipment and the corresponding operation during maintenance. Based on this value, the maintenance of military equipment can be continuously monitored to ensure that the downtime of military equipment is minimized and the repair of military equipment is completed as efficiently as possible.

[0067] In one embodiment of the present invention, step S5 mainly combines the average maintenance coefficient and the maintenance probability distribution coefficient with the target maintenance node, and obtains a maintenance decision tree according to the combination form of these values and the target maintenance node, so as to output the maintenance strategy during the maintenance of military equipment.

[0068] In the maintenance decision tree, the average maintenance coefficient and the maintenance probability distribution coefficient are compared with the previous target maintenance node. The maintenance decision tree will set multiple nodes to describe the relationship between the average maintenance coefficient and the maintenance probability distribution coefficient and the target maintenance node, and use these relationships to complete the output of the final maintenance decision tree. The content output by this maintenance decision tree will be the maintenance strategy of the current military equipment.

[0069] At this time, the maintenance decision tree will analyze the relationship between the target maintenance node and the average maintenance coefficient and the maintenance probability distribution coefficient to determine whether there are strong correlation and weak correlation contents at this time, so as to construct the maintenance decision tree.

[0070] When describing these correlations, the effective relationship data and effective scoring data under each equipment type and maintenance task will be recorded, so as to obtain the finally required constructed maintenance decision tree and the output decision strategy.

[0071] As Figure 5 shown, when step S5 generates the maintenance decision tree, the specific processing methods include: S51, forming a node list corresponding to the target maintenance node, and mapping the average maintenance coefficient and the maintenance probability distribution coefficient into the node list. At this time, the mapping is to set the target maintenance node into multiple nodes, and these nodes represent the corresponding content on the maintenance decision tree, that is, the key points to be noted during the maintenance of military equipment are divided into multiple points that can specifically describe the current military equipment, and these points will form a node list, and the previously calculated average maintenance coefficient and maintenance probability distribution coefficient are corresponding to these nodes to complete the mapping.

[0072] S52, using the target maintenance nodes in the node list to form a maintenance decision tree, and judging the position of each target maintenance node on the maintenance decision tree. The judgment method can be to judge according to the values of the average maintenance coefficient and the maintenance probability distribution coefficient on the target maintenance node.

[0073] Therefore, the implementation method for judging the position of each target maintenance node in the maintenance decision tree in step S52 is as follows: judge the values of the maintenance average coefficient and the maintenance probability distribution coefficient corresponding to the upper-level node and the lower-level node in the maintenance decision tree. When the values of the maintenance average coefficient and the maintenance probability distribution coefficient of the upper-level node in the maintenance decision tree are both greater than those of the lower-level node, construct a conditional judgment node and a specific operation node for the lower-level node; when the values of the maintenance average coefficient and the maintenance probability distribution coefficient of the upper-level node in the maintenance decision tree are both less than those of the lower-level node, set a decision node for the upper-level node.

[0074] When the composition of the maintenance decision tree is completed, the maintenance decision tree will be composed of various nodes, including conditional judgment nodes, specific operation nodes, and decision nodes. These nodes will be composed of the content on the target maintenance node at this time, and these nodes will be connected by arrows; the conditional judgment node represents a description for judging a certain condition, the specific operation node explains the content of the corresponding specific operation on this node, and the decision node is the node that divides the relevant content in the maintenance decision tree.

[0075] S53. Identify the decision path on the completed maintenance decision tree and output the decision path with the fewest nodes as the current maintenance strategy for military equipment.

[0076] By selecting the decision path with the fewest nodes as the maintenance strategy, not only can the efficiency and accuracy of the maintenance work be significantly improved, but also the operation process can be simplified, and the complexity and error probability can be reduced. In addition, this method also supports data-driven decision-making and the integration of automated systems, which helps to achieve more scientific and efficient maintenance management of military equipment.

[0077] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention, and still be covered by the protection scope of the present invention.

Claims

1. An optimization method for the assessment of military equipment maintenance support equipment, characterized in that, Including: S1. Obtain the maintenance information of military equipment. The maintenance information includes the equipment type, maintenance task, maintenance time, and maintenance cost of the maintenance equipment when maintaining military equipment; S2. Preprocess the maintenance information, input the preprocessed maintenance information into the maintenance detection model, and identify the maintenance requirements and target maintenance nodes when maintaining military equipment; S3. Combine the maintenance information according to the target maintenance nodes, obtain the usage probability of the same maintenance equipment of military equipment in two adjacent maintenances and the failure rate of military equipment under the maintenance requirements corresponding to the target maintenance nodes, and obtain the maintenance probability distribution coefficient; S4. Compare the maintenance information, determine the difference value and repair time of the maintenance information after each maintenance of military equipment, and set the maintenance average coefficient for the maintenance of military equipment; S5. Combine the maintenance average coefficient and the maintenance probability distribution coefficient with the target maintenance nodes to generate a maintenance decision tree corresponding to the target maintenance nodes, and generate a maintenance strategy for the current military equipment according to the maintenance decision tree.

2. The assessment and optimization method for military equipment maintenance support equipment according to claim 1, characterized in that Step S2 includes: S21. Use the equipment type and maintenance task of the maintenance equipment as the input feature vector to construct a maintenance detection model; S22. Use the maintenance detection model to locate the maintenance scope of the maintenance information; obtain the maintenance scope under different equipment types; S23. Divide the maintenance scope according to the values of the feature vector, determine the maintenance requirements during maintenance, and set the maintenance requirement with the most output times as the target maintenance node.

3. The assessment optimization method for military equipment maintenance support equipment according to claim 2, characterized in that, The maintenance detection model includes a root node, several branch nodes, and several leaf nodes; each root node represents the equipment type of a maintenance military equipment, each leaf node represents an output maintenance scope, each branch node represents a maintenance task, and the path from the root node to each leaf node represents a set of detection and location paths.

4. A method for optimizing the assessment of military equipment maintenance support equipment according to claim 1, characterized in that, Step S3 includes: S31. Use the target maintenance node as the basic condition, and generate a maintenance strategy matrix for the maintenance of military equipment according to the target maintenance node and the maintenance requirements corresponding to the target maintenance node; S32. Obtain the maintenance reason when formulating the maintenance strategy matrix, and construct the maintenance reasons for each maintenance into a maintenance vector; S33. Obtain the maintenance time and maintenance probability of the maintenance vector at the target maintenance node, and set the priority of the maintenance strategy matrix; S34. Use the priority of the maintenance strategy matrix and the maintenance strategy matrix to statistically calculate the usage probability and failure rate of military equipment under the corresponding equipment type to obtain the maintenance probability distribution coefficient.

5. A method for optimizing the assessment of military equipment maintenance support equipment according to claim 4, characterized in that, The maintenance probability distribution coefficient is expressed as: traverse the maintenance strategy matrix, comprehensively process the usage probability, failure rate, priority, and maintenance probability corresponding to each element in the maintenance strategy matrix, obtain the maintenance distribution coefficient corresponding to each element in the maintenance strategy matrix, and then judge the error ratio between adjacent maintenance distribution coefficients, and comprehensively calculate the error ratio between adjacent maintenance distribution coefficients and the maintenance distribution coefficient to obtain the maintenance probability distribution coefficient.

6. The assessment and optimization method for military equipment maintenance support equipment according to claim 5, characterized in that The maintenance distribution coefficient is expressed as: ； Among them, represents the maintenance distribution coefficient of the i-th element in the maintenance strategy matrix, represents the usage probability corresponding to the i-th element in the maintenance strategy matrix, represents the failure rate corresponding to the i-th element in the maintenance strategy matrix, represents the priority corresponding to the i-th element in the maintenance strategy matrix, represents the maintenance probability corresponding to the i-th element in the maintenance strategy matrix, represents the exponential constant; The error ratio between adjacent maintenance distribution coefficients is expressed as: ； Among them, represents the error ratio between the i-th element in the maintenance strategy matrix and the adjacent maintenance distribution coefficients; represents the error function, , represents the maintenance distribution coefficient of the (i - 1)-th element in the maintenance strategy matrix; The maintenance probability distribution coefficient is expressed as: ； Among them, represents the number of elements in the maintenance strategy matrix, and the value range of i is from 1 to n, represents the maintenance probability distribution coefficient.

7. An optimization method for evaluating a military equipment maintenance support device according to claim 1, characterized in that Step S4 includes: S41. Fit the difference values of the maintenance information after each military equipment maintenance to obtain the fitting error of the maintenance information relative to the maintained equipment. S42. Determine whether the fitting error meets the preset threshold, and identify the part higher than the preset threshold to obtain the probability frequency array of the fitting error above the preset threshold. S43. Conduct a continuity analysis on the probability frequency array, determine the occurrence probability and repair time of the corresponding data in the probability frequency array, and calculate the average maintenance coefficient.

8. A method for optimizing the assessment of military equipment maintenance support equipment according to claim 7, characterized in that, The average maintenance coefficient is expressed as follows: Obtain the occurrence probability and repair time corresponding to each element in the probability frequency array, and calculate the average maintenance coefficient. ； Among them, represents the average maintenance coefficient, represents the number of elements in the probability frequency array, and the value range of j is from 1 to m; represents the repair time of the j-th element in the probability frequency array, represents the occurrence probability of the j-th element in the probability frequency array, represents the average value of the repair time, represents the pi.

9. The assessment optimization method for military equipment maintenance support equipment according to claim 1, characterized in that Step S5 includes: S51. Form a node list corresponding to the target maintenance node, and map the average maintenance coefficient and the maintenance probability distribution coefficient to the node list. S52. Use the target maintenance nodes in the node list to form a maintenance decision tree, and determine the position of each target maintenance node on the maintenance decision tree. S53. Identify the decision path on the maintenance decision tree after the judgment is completed, and output the decision path with the fewest nodes as the current maintenance strategy for military equipment.

10. A method for optimizing the assessment of military equipment maintenance support equipment according to claim 9, characterized in that, The implementation method for determining the position of each target maintenance node on the maintenance decision tree in step S52 is as follows: Judge the values of the average maintenance coefficient and the maintenance probability distribution coefficient corresponding to the upper-level node and the lower-level node on the maintenance decision tree. When the values of the average maintenance coefficient and the maintenance probability distribution coefficient of the upper-level node on the maintenance decision tree are both greater than those of the lower-level node, construct a conditional judgment node and a specific operation node for the lower-level node; when the values of the average maintenance coefficient and the maintenance probability distribution coefficient of the upper-level node on the maintenance decision tree are both less than those of the lower-level node, set a decision node for the upper-level node.

Images