Assessment informatization management system for military equipment maintenance support equipment

Abstract

The invention belongs to the technical field of maintenance support equipment assessment management, and particularly discloses and provides a military equipment maintenance support equipment assessment informatization management system which comprises a maintenance equipment distribution module, a maintenance diagnosis analysis module, a maintenance process analysis module, a maintenance result analysis module and a maintenance management feedback terminal. According to the invention, based on the maintenance diagnosis qualification coefficient, the maintenance process qualification coefficient and the maintenance result qualification coefficient of each maintenance device, the assessment score of each maintenance device is confirmed, full-chain closed-loop assessment of diagnosis-process-result is realized, and the state of the maintenance device is comprehensively assessed, so that the accuracy of an analysis result is improved, and the service life of the maintenance device is prolonged. And meanwhile, the maintenance process qualification coefficient of the maintenance equipment is analyzed according to the operation time sequence log of the maintenance equipment and the historical maintenance operation information of the maintenance personnel, so that the two-way evaluation of the equipment efficiency and the personnel operation is realized, and the scientificity and the performability of the evaluation result are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of assessment and management of maintenance support equipment, and relates to an information-based management system for the assessment of military equipment maintenance support equipment. Background Art

[0002] Military equipment maintenance support equipment is the key equipment to ensure the normal operation of military equipment and maintain its performance, covering detection and diagnosis equipment, maintenance tools, spare parts storage equipment, etc. Its operating status and maintenance capabilities directly affect the execution of military tasks. In order to keep track of the equipment status in real time, quickly obtain maintenance data, accurately evaluate the maintenance support capabilities, and achieve efficient and accurate equipment management, it is necessary to conduct information-based management of its assessment.

[0003] For example, Chinese invention patent with publication number CN119273185A discloses a diagnostic assessment method for military equipment maintenance support equipment. The present invention relates to the field of diagnostic assessment of military support equipment, and specifically discloses a diagnostic assessment method for military equipment maintenance support equipment. By obtaining the diagnostic accuracy coefficient and detection rapidity coefficient of military support equipment, the performance index compliance index of each military support equipment is analyzed. Obtain the failure rate, mean time between failures, and maintainability coefficient of each military support equipment, and analyze the reliability index compliance index of each military support equipment. Obtain the maintenance cycle trend curve and maintenance cost trend curve of each military support equipment, and analyze the maintenance and support index compliance index of each military support equipment. Further evaluate the comprehensive assessment index of each military support equipment. Qualitative and quantitative analysis and comprehensive assessment of military equipment maintenance support equipment are carried out from multi-dimensional and multi-faceted indicators, so as to realize a comprehensive and objective diagnostic assessment of military equipment maintenance support equipment, thereby providing a scientific basis for the selection, use, and improvement of military support equipment.

[0004] The above prior art has the following deficiencies in several aspects: 1. When conducting the assessment and analysis of maintenance equipment at present, it mainly relies on discrete index analysis, lacking a linkage mechanism between data, making it difficult to discover potential problems hidden behind different indicators, and thus unable to comprehensively evaluate the status of maintenance equipment, which affects the accuracy of the analysis results.

[0005] 2. When conducting the assessment and evaluation of maintenance equipment at present, only the parameters of the maintenance equipment itself are considered, without considering the influence of personnel operation, and it is impossible to comprehensively evaluate the overall efficiency of the completion of maintenance tasks, resulting in difficulty in accurately judging the timeliness and effectiveness of the work of maintenance equipment, thus affecting the accuracy of the assessment results of maintenance equipment. Summary of the Invention

[0006] In view of this, in order to solve the problems raised in the above background art, an information-based management system for the assessment of military equipment maintenance support equipment is proposed.

[0007] The object of the present invention can be achieved by the following technical solutions: The present invention provides an assessment information management system for military equipment maintenance support equipment, including: a maintenance equipment allocation module, which is used to allocate the equipment to be maintained based on the prior fault diagnosis reports of each piece of equipment to be maintained through a dynamic batch association algorithm.

[0008] A maintenance diagnosis analysis module, which is used to detect the on-site fault diagnosis reports of each maintenance equipment for its respective equipment to be maintained, and then analyze the maintenance diagnosis scores of each maintenance equipment.

[0009] A maintenance process analysis module, which is used to extract prior fault causes from the prior fault diagnosis reports, perform maintenance on the equipment to be maintained accordingly, record the operation time sequence logs of each maintenance equipment, and then analyze the maintenance process scores of each maintenance equipment.

[0010] A maintenance result analysis module, which is used to conduct a running test on the maintained equipment after the maintenance is completed, detect the running test information of each maintained equipment, and then analyze the maintenance result scores of each maintenance equipment.

[0011] A maintenance assessment feedback terminal, which is used to confirm the assessment scores of each maintenance equipment based on the maintenance diagnosis scores, maintenance process scores, and maintenance result scores of each maintenance equipment, and give corresponding feedback on the maintenance equipment management platform.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By confirming the assessment scores of each maintenance equipment based on the maintenance diagnosis scores, maintenance process scores, and maintenance result scores of each maintenance equipment, the present invention realizes a full-chain closed-loop assessment of "diagnosis - process - result", comprehensively evaluates the status of maintenance equipment, and thus improves the accuracy of the analysis results.

[0013] (2) By analyzing the maintenance process scores of maintenance equipment according to the operation time sequence logs of maintenance equipment and the historical maintenance operation information of maintenance personnel, the present invention realizes a two-way assessment of equipment efficiency and personnel operation, improves the accuracy of evaluating the task execution efficiency of maintenance equipment, and further enhances the scientificity and feasibility of the evaluation results.

[0014] (3) By establishing an equipment assessment model through a dynamic batch association algorithm and establishing a maintenance efficiency evaluation model, the present invention solves the problem of inconsistent evaluation criteria caused by differences in equipment types in traditional methods, realizes the dynamic adaptation of the standardized evaluation system, and eliminates the influence of equipment heterogeneity on the evaluation results, thereby improving the evaluation efficiency of maintenance equipment in complex scenarios. Description of the Drawings

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a schematic connection diagram of each module of the system of the present invention.

[0017] Figure 2 It is a schematic connection diagram of the scoring analysis steps in the maintenance process of the present invention.

[0018] Figure 3 It is a schematic connection diagram of the scoring analysis steps of the maintenance result of the present invention. Specific embodiments

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0020] Please refer to Figure 1 As shown, the present invention provides an assessment information management system for military equipment maintenance support equipment. The system includes: a maintenance equipment allocation module, a maintenance diagnosis and analysis module, a maintenance process analysis module, a maintenance result analysis module, and a maintenance management feedback terminal.

[0021] Among the above, the maintenance diagnosis and analysis module is respectively connected to the maintenance equipment allocation module, the maintenance process analysis module, and the maintenance management feedback terminal. The maintenance process analysis module is respectively connected to the maintenance result analysis module and the maintenance management feedback terminal. The maintenance result analysis module is also connected to the maintenance management feedback terminal.

[0022] The maintenance equipment allocation module is used to allocate the equipment to be maintained based on the prior fault diagnosis reports of each equipment to be maintained through a dynamic batch association algorithm.

[0023] It should be added that the dynamic batch association algorithm refers to: accurately and efficiently assigning each piece of equipment to be repaired in the same batch to the most suitable maintenance equipment. The core lies in comprehensively considering the multi-dimensional characteristics of equipment and devices to achieve optimal resource allocation, while ensuring the consistency of equipment assessment difficulty. The specific steps are as follows: (1) Feature extraction of equipment to be repaired: Extract the fault type and fault severity from the prior fault diagnosis report of the equipment to be repaired. For the fault type, detailed records are made, such as gear wear and bearing damage for mechanical equipment, component burning and line breakage for electronic equipment, etc. The fault severity is divided into three levels: mild, moderate and severe according to factors such as the scope of equipment damage and the degree of impact on the core functions of the equipment. For example, damage to only small parts that does not affect the core functions is judged as mild, damage to some important functions that can be repaired by replacing parts is judged as moderate, and complete loss of the core functions of the equipment, requiring replacement of a large number of parts or complex system repairs, is judged as severe.

[0024] (2) Matching calculation: A matching matrix is ​​established, where rows represent the equipment to be repaired and columns represent the maintenance equipment. For each piece of equipment to be repaired, the matching degree is calculated based on the fault severity score and the normalized index of the maintenance equipment's ability to handle faults of corresponding severity. The matching degree calculation formula can be set as: ,in, For the matching degree, is the fault severity, It is an indicator of the ability of maintenance equipment to handle severe failures. and is the weight coefficient adjusted according to the actual situation, for example, , , to balance the impact of fault severity and repair equipment capability in the matching degree.

[0025] (3) Matching decision: Sort the combinations of equipment to be repaired and maintenance equipment from high to low according to the matching degree. To ensure the consistency of equipment assessment difficulty, try to make the equipment failure severity assigned to each maintenance equipment evenly distributed during the allocation process. For example, if a large number of equipment with severe failures has been assigned to a certain maintenance equipment, in subsequent allocations, equipment with mild or moderate failures will be assigned to that maintenance equipment first, until the equipment failure severity distribution of each maintenance equipment reaches a relatively balanced state, thereby ensuring the validity of the assessment results.

[0026] It should be added that the prior fault diagnosis reports of each piece of equipment to be repaired are obtained through sensor acquisition technology and a fault diagnosis system. The sensor acquisition technology, based on the structure and operation characteristics of military equipment, installs various sensors at key positions, such as temperature and pressure sensors for ship power systems, displacement and angle sensors for flight control systems, etc. When the equipment is running, the sensors collect physical quantity data in real time, such as vibration signals and current values, and transmit them to the data processing center through wired or wireless means. During transmission, the data is preprocessed, such as denoising and filtering. Then, signal processing technology is used to extract parameters reflecting the equipment status and fault characteristics, such as vibration frequency and temperature change trend. By comparing these characteristic parameters with preset thresholds, preliminary fault judgments are made, and abnormal information is recorded to provide basic data for the report. Furthermore, multi-source information such as sensor data, equipment operation time, and maintenance records is input into the fault diagnosis system through the fault diagnosis system, and a unified data model is established through integration. The system uses the physical or mathematical model of the equipment to analyze the difference between the input and output signals to locate faults, and uses machine learning and deep learning algorithms to mine data, such as neural networks learning fault patterns, fault trees analyzing fault causes and propagation paths, and combining expert knowledge, rule bases, and case bases for knowledge reasoning and decision-making to judge the fault type and severity. Finally, the system generates a prior fault diagnosis report according to the specifications, covering content such as equipment information, fault phenomena, cause analysis, and maintenance suggestions.

[0027] In the embodiment of the present invention, an equipment assessment model is established through a dynamic batch association algorithm, and a maintenance efficiency evaluation model is established to solve the problem of inconsistent evaluation criteria caused by equipment type differences in traditional methods, and to dynamically adapt the standardized evaluation system. At the same time, the impact of equipment heterogeneity on the evaluation results is eliminated, thereby improving the evaluation efficiency of maintenance equipment in complex scenarios.

[0028] The maintenance diagnosis analysis module is used to detect the on-site fault diagnosis reports of each maintenance equipment for its respective equipment to be repaired, and then analyze the maintenance diagnosis scores of each maintenance equipment.

[0029] It should be added that the way each maintenance equipment obtains the on-site fault diagnosis report of each equipment to be repaired is as follows: first, use the general detection instruments such as multimeters and oscilloscopes carried by the maintenance equipment, as well as professional detection equipment such as ultrasonic flaw detectors, to conduct on-site inspections of the equipment to be repaired, and obtain data such as its physical parameters, signal characteristics, and internal conditions of components. Then, use the fault diagnosis expert system (including rule-based and case-based diagnosis methods) and intelligent diagnosis technologies such as machine learning and deep learning to analyze, infer, and learn the collected data to explore the fault characteristics and laws. Finally, through wired communication (such as cables and optical fibers) and wireless communication (such as Bluetooth, Wi-Fi, 4G / 5G) technologies, the detection and analysis data are transmitted to the data processing center of the maintenance equipment. After comprehensive processing and evaluation, an on-site fault diagnosis report is generated.

[0030] Exemplarily, the analysis of the maintenance diagnosis score of each maintenance equipment includes: G1, matching and comparing the actual inspection fault diagnosis report of each maintenance equipment for each equipment to be repaired with the prior fault diagnosis report, and recording the maintenance diagnosis whose results are consistent with the prior fault diagnosis report as a correct diagnosis.

[0031] G2. Count the correct diagnosis times and total diagnosis times of each maintenance equipment, and take the ratio of the two as the fault location accuracy of each maintenance equipment, recorded as , Number the repair equipment. .

[0032] G3. Extract the maintenance diagnosis time of each maintenance equipment for each equipment to be repaired from the on-site fault diagnosis report of each maintenance equipment, and calculate the compliance coefficient of the diagnosis time of each maintenance equipment .

[0033] Furthermore, the statistical diagnosis time compliance coefficient of each maintenance equipment includes: G3-1, extracting the fault type of each equipment to be repaired from the prior fault diagnosis report of each equipment to be repaired, and then extracting the benchmark diagnosis time of each fault type from the maintenance equipment management platform, and using it as the benchmark diagnosis time of each equipment to be repaired.

[0034] G3-2. Match and compare the diagnostic time of each maintenance equipment with its benchmark diagnostic time.

[0035] G3-3. If the diagnosis time of a certain equipment to be repaired belonging to a certain maintenance device is less than its benchmark diagnosis time, 1 will be used as the diagnosis time compliance coefficient of the equipment to be repaired belonging to the maintenance device; otherwise, the ratio of the benchmark diagnosis time to the diagnosis time will be used as the diagnosis time compliance coefficient of the equipment to be repaired belonging to the maintenance device, and then the diagnosis time compliance coefficient of each equipment to be repaired belonging to each maintenance device will be obtained.

[0036] G3-4. Calculate the average value of the diagnostic duration compliance coefficients of each piece of equipment to be repaired belonging to each maintenance equipment, and obtain the diagnostic duration compliance coefficient of each maintenance equipment.

[0037] G4. Statistically analyze the maintenance diagnosis scores of each maintenance equipment , , where is the weight adjustment coefficient set as a reference.

[0038] It should be added that the above formula combines the fault location accuracy rate and the diagnostic duration compliance coefficient to comprehensively evaluate the working quality of the maintenance equipment. If the fault location is accurate but the maintenance time is too long, or the maintenance time is short but the fault location is inaccurate, it cannot be considered high-quality maintenance work. Among them, the role of the weight adjustment coefficient is: according to actual needs and focuses, it can flexibly adjust to change the influence degree of the diagnostic duration compliance coefficient in the final qualification coefficient. If more emphasis is placed on the maintenance duration, can be appropriately increased. If more emphasis is placed on the accuracy of fault location, can be decreased. When (i.e., the maintenance duration is compliant), , and at this time, the maintenance diagnosis score is equal to the fault location accuracy rate. When (the maintenance duration is better), will be amplified and adjusted to reflect the reward. When (the maintenance duration does not meet the standard), will be adjusted to reflect the punishment.

[0039] In a specific embodiment, when , assume that there are three maintenance equipments, namely maintenance equipment A, maintenance equipment B, and maintenance equipment C, and their specific data are shown in Table 1.

[0040] Table 1: Statistical table of fault diagnosis and maintenance related coefficients of each maintenance equipment

[0041]

[0042] It should be added that plays a role in balancing the weights of these two factors in the score. When , it means that in this evaluation, the influence degrees of the fault location accuracy rate and the diagnostic duration compliance on the maintenance diagnosis score are equivalent. It not only pays attention to whether the maintenance equipment can accurately diagnose faults, but also equally values whether its diagnostic time is compliant, and will not overly favor a certain factor.

[0043] The maintenance equipment A was correctly diagnosed 8 times out of a total of 10 diagnoses. The fault location accuracy rate was 0.8, which is at a relatively high level. It can accurately judge faults, but the compliance coefficient of the diagnosis duration was only 0.5, indicating that the average diagnosis time was relatively long and the efficiency was not high. Finally, the maintenance diagnosis score was 40, and it did not reach a higher score due to the influence of the diagnosis duration.

[0044] The maintenance equipment B was correctly diagnosed 6 times out of a total of 8 diagnoses. The fault location accuracy rate was 0.75, slightly lower than that of A and C. However, its compliance coefficient of the diagnosis duration reached 1, meaning that the diagnosis durations all met the benchmark requirements and the efficiency was high. As a result, its maintenance diagnosis score reached 75.

[0045] The maintenance equipment C was correctly diagnosed 7 times out of a total of 9 diagnoses. The fault location accuracy rate was 0.778, with a certain degree of accuracy. The compliance coefficient of the diagnosis duration was 0.8, indicating that the diagnosis duration efficiency was good. The final score was 62, and the overall performance was relatively balanced.

[0046] The maintenance process analysis module is used to extract the prior fault causes from the prior fault diagnosis report, perform maintenance on the equipment to be maintained accordingly, record the operation timing logs of each maintenance equipment, and then analyze the maintenance process scores of each maintenance equipment.

[0047] It should be added that the operation timing logs of each maintenance equipment include: each actual maintenance step and the actual maintenance duration of each maintenance equipment for its respective equipment to be maintained.

[0048] It should be added that the acquisition of the actual maintenance steps: Install various types of sensors at the key parts of the maintenance equipment, such as pressure sensors, displacement sensors, angle sensors, etc. These sensors can real-time sense information such as the operating state of the equipment and the position change of components. For example, when disassembling a component, the displacement sensor can detect the movement of the component, thereby judging the start and end of the disassembly action. Data collection and transmission: The data collected by the sensors is transmitted to the data collection terminal through Internet of Things communication protocols, such as Zigbee, Bluetooth, Wi-Fi, etc. The data collection terminal performs preliminary processing and packaging on the data, and then uploads it to the local server. Data analysis and step identification: On the server side, use data analysis algorithms to analyze the sensor data. By extracting the features and pattern matching of the data, different maintenance steps are identified. For example, according to the change law of the pressure sensor data, operation steps such as tightening screws and plugging and unplugging cables can be judged.

[0049] It should be added that the actual maintenance duration is detected by the time sensors deployed on the maintenance equipment.

[0050] Please refer to Figure 2As shown, exemplarily, the scoring of the maintenance process of each maintenance device includes: H1. Based on the prior fault diagnosis reports of each equipment to be maintained, extract each standard maintenance step corresponding to the prior fault diagnosis report of each equipment to be maintained from the maintenance device management platform.

[0051] H2. Extract each actual maintenance step of each maintenance device for each equipment to be maintained under its jurisdiction from the operation time sequence log of each maintenance device, and match and compare it with the corresponding standard maintenance step.

[0052] H3. Count the number of steps with consistent maintenance order and the total number of maintenance steps of each maintenance device, and use the ratio of the two as the maintenance step compliance coefficient of each maintenance device, denoted as .

[0053] H4. Extract the actual maintenance duration of each maintenance device for each equipment to be maintained under its jurisdiction from the operation time sequence log of each maintenance device, and then count the maintenance duration compliance coefficient of each maintenance device .

[0054] Furthermore, the counting of the maintenance duration compliance coefficient of each maintenance device includes: H4-1. Extract the reference maintenance duration interval corresponding to the prior fault diagnosis report of each equipment to be maintained from the maintenance device management platform.

[0055] H4-2. Based on the actual maintenance duration of each maintenance device for each equipment to be maintained under its jurisdiction and the corresponding reference maintenance duration interval, use the normal distribution model to count the maintenance duration compliance coefficient of each maintenance device.

[0056] It should be added that the normal distribution model is a very important continuous probability distribution model in the field of probability statistics, and its probability density function is , is the natural constant, denoted by indicating that the random variable follows a normal distribution, where is the mean value, which determines the central position of the distribution, is the standard deviation, which determines the degree of dispersion of the distribution. Its graph is bell-shaped, with characteristics such as symmetry and unimodality. The probability of taking values near the mean is relatively large, and the probability of taking values farther away from the mean is smaller. The normal distribution model is used in the management of maintenance equipment because the actual maintenance duration is usually affected by various factors, such as the skill level of maintenance personnel, the status of maintenance equipment, and the complexity of the faults of the equipment to be maintained. Under the combined action of these factors, the distribution of the actual maintenance duration often shows characteristics similar to the normal distribution, that is, in most cases, the actual maintenance duration will be concentrated near the reference maintenance duration interval, and only in a few cases will it deviate far. Using the normal distribution model can well model and analyze the distribution law of the actual maintenance duration, so as to reasonably calculate the maintenance duration qualification coefficient of each maintenance equipment by comparing the actual maintenance duration with the reference maintenance duration interval.

[0057] In a specific embodiment, the statistical process of calculating the maintenance duration qualification coefficient of each maintenance equipment through the normal distribution model is as follows: First, calculate the mean of the maintenance durations of all similar faults and the standard deviation . For the current equipment to be maintained, if the maintenance duration corresponding to its prior fault diagnosis report is within ( is a constant determined according to the actual situation, such as or ), it is considered qualified. Let the actual maintenance duration be , and the calculation method of the maintenance duration qualification coefficient can be: when , the maintenance duration qualification coefficient is . In the normal distribution, the said interval is considered a reasonable reference range, which means that the actual maintenance duration is within the expected normal fluctuation range. At this time, it is considered that the maintenance work is completed on time and the maintenance duration is completely qualified. Therefore, the qualification coefficient is set to the maximum value of 1. When , , indicating that the actual maintenance duration is less than the lower limit of the reference interval, that is, the maintenance work is completed in advance. In the formula, the exponential part . When is smaller (that is, the maintenance duration is shorter), this exponent approaches negative infinity. According to the properties of the exponential function, the result of the negative exponent power of approaches 0, but due to the setting of the previous coefficient, approaches 1, reflecting that the shorter the maintenance duration, the higher the qualification coefficient and the closer to the perfect qualified state. When , , indicating that the actual maintenance duration exceeds the upper limit of the reference interval. The exponential part is larger (the more it exceeds the reference duration), this exponent approaches negative infinity, The result of the negative exponential power approaches 0 more closely, which means that the longer the time exceeds the reference duration, the lower the qualification coefficient, and the worse the qualification degree of the maintenance duration.

[0058] H5. Extract the historical maintenance operation information of each maintenance personnel from the maintenance equipment management platform, and then set the personnel operation influence coefficient of each maintenance equipment .

[0059] Furthermore, the setting of the personnel operation influence coefficient of each maintenance equipment includes: H5-1. Extract the maintenance operation score and the maintenance fault location accuracy rate of each historical maintenance of each maintenance personnel from the historical maintenance operation information of each maintenance personnel, and record the maintenance fault location accuracy rate of each maintenance personnel as , is the maintenance personnel number, .

[0060] H5-2. Calculate the average value of the maintenance operation scores of each maintenance of each maintenance personnel to obtain the average maintenance operation score of each maintenance personnel. Then, match and compare the average maintenance operation score of each maintenance personnel with the maintenance operation score interval corresponding to each operation specification compliance degree to obtain the operation specification compliance degree of each maintenance personnel, and record it as .

[0061] H5-3. Statistically calculate the personnel operation influence coefficient of each maintenance personnel , , and are the weights of the set reference maintenance fault location accuracy rate and operation specification compliance degree respectively, , .

[0062] It should be added that the maintenance fault location accuracy rate directly determines the correctness of the maintenance direction, which is the fundamental prerequisite for solving problems and plays a decisive role in the success or failure of maintenance work and the safe operation of equipment. The operation specification compliance degree is an important factor to ensure the smooth implementation of maintenance work and the guarantee of maintenance quality on the basis of correct diagnosis. Therefore, in the personnel operation influence coefficient of maintenance personnel, the weight of the maintenance fault location accuracy rate should usually be greater than the operation specification compliance degree. Therefore, set , for the convenience of analysis, can be specifically taken as 0.6, can be specifically taken as 0.4.

[0063] H5-4. Extract the maintenance personnel corresponding to each maintenance equipment from the on-site inspection fault diagnosis reports of each maintenance equipment for its affiliated equipment to be maintained, and then screen out the personnel operation influence coefficient of each maintenance equipment.

[0064] H6. Calculate the repair process scores of each maintenance equipment through the geometric mean algorithm .

[0065] It should be added that the statistical formula for calculating the repair process scores of each maintenance equipment is . The geometric mean algorithm is a calculation method that multiplies multiple numbers and then takes the corresponding root. It is often used to measure the comprehensive level of multiple indicators. Compared with the arithmetic mean, it emphasizes the mutual relationship and comprehensive effect among indicators more. When calculating the repair process scores of maintenance equipment , multiply the compliance coefficient of repair steps , the qualified coefficient of repair duration and these three factors. In essence, it is a variant application of the geometric mean algorithm. This method combines the compliance coefficient of repair steps, the qualified coefficient of repair duration, and the influence coefficient of personnel operation to comprehensively evaluate the repair process of maintenance equipment. Only when the repair steps are compliant, the duration is qualified, and the influence of personnel operation is small, can a high repair process score be obtained. Among them, this term is used to adjust the influence of the personnel operation factor. The larger the value, the greater the possible negative impact of personnel operation on the repair process, and then the value will be smaller, which will reduce the final qualified coefficient. On the contrary, if the influence of personnel operation is small, is close to 1, and the influence on the product of the first two coefficients is small, thus comprehensively reflecting the influence of various factors on the qualified degree of the repair process.

[0066] In the embodiments of the present invention, by analyzing the repair process scores of maintenance equipment based on the operation sequence logs of the maintenance equipment and the historical maintenance operation information of maintenance personnel, a two-way evaluation of equipment efficiency and personnel operation is realized, improving the accuracy of evaluating the task execution efficiency of maintenance equipment, and further enhancing the scientificity and feasibility of the evaluation results.

[0067] The repair result analysis module is used to conduct an operation test on the repaired equipment and detect the operation test information of each repaired equipment after the repair is completed, and then analyze the repair result scores of each maintenance equipment.

[0068] It should be added that the operation test information of each repaired equipment includes: the test data of each repaired equipment in each function test and the test results of each repaired equipment in each fault reproducibility test.

[0069] It should be added that the test data of each repaired equipment in various function tests include: the test values and test response durations of each repaired equipment in various functions. The acquisition method is as follows: Enter the monitoring system operation interface of the repaired equipment itself, activate the monitoring module related to the function test, and set the monitoring parameters according to specific test requirements, such as selecting the function indicators to be monitored, setting the time interval for data recording, etc. Function operation and monitoring: Let the repaired equipment perform various function test tasks, and its own monitoring system will run automatically in the background, continuously monitoring and recording various data during the operation of the equipment, including the test values of various functions. At the same time, the monitoring system will record the timestamp of each function operation, and by calculating the difference between adjacent timestamps, the test response duration of the corresponding function can be obtained.

[0070] It should be added that the test results of each repaired equipment in each fault reproducibility test are either fault reproduction or no fault reproduction. The acquisition method is as follows: Install various sensors, such as temperature sensors, pressure sensors, vibration sensors, current sensors, etc., at the key parts and possible fault parts of the repaired equipment. Through the sensors, the physical quantity changes during the operation of the equipment are sensed in real time and converted into electrical signals or digital signals. Then, through the data acquisition system, the signals output by the sensors are collected at a certain sampling frequency, and the collected data is transmitted to the data processing center or monitoring terminal through a wired or wireless communication network. Thus, special data analysis software is used to process and analyze the collected data, set the threshold range of normal operation data and the fault feature data model. When the data exceeds the normal range and conforms to the fault feature model, the system automatically judges that the fault is reproduced; otherwise, it is judged that the fault is not reproduced.

[0071] Please refer to Figure 3 As shown, exemplarily, the scoring of the repair results of each repaired equipment includes: P1. Extract the test data of each repaired equipment in various function tests from the operation test information of each repaired equipment, and then statistically calculate the qualified scores of the function tests of each repaired equipment.

[0072] Furthermore, the statistical calculation of the qualified scores of the function tests of each repaired equipment includes: P1-1. Extract the test values of each repaired equipment in various function tests from the test data of each repaired equipment in various function tests.

[0073] P1-2. Match and compare the test values of each repaired equipment in various functions with the corresponding test scores of the set reference test values of various functions respectively to obtain the test scores of each repaired equipment in various functions.

[0074] P1-3. Calculate the average value of the test scores of each repaired equipment in various functions, and take the calculation result as the qualified score of the function test of each repaired equipment.

[0075] P2. Extract the response time of each repaired equipment in each function test from the function test data of each repaired equipment, and then count the qualified score of the response time of each repaired equipment.

[0076] Further, the counting of the qualified score of the response time of each repaired equipment includes: P2-1. Record the test response time of each repaired equipment in each function test as , is the number of the repaired equipment, , is the function test number, .

[0077] P2-2. Count the qualified score of the response time of each repaired equipment , , is the th set reference response time of the

[0078] It should be noted that The acquisition method of: Extract the historical operation data of each repaired equipment from the maintenance equipment management platform, and then extract the historical response time of each function in each repaired equipment from it, and then calculate its mean value to obtain the average response time of each function in each repaired equipment, and use it as the reference response time of each function in each set of repaired equipment.

[0079] P3. Extract the test results of each repaired equipment in each fault reproducibility test from the operation test information of each repaired equipment.

[0080] P4. If the test results of the repaired equipment in each fault reproducibility test are all that the fault does not reappear, record the maintenance reliability score as 1, otherwise, record the maintenance reliability score of the repaired equipment as 0, and then obtain the maintenance reliability score of each repaired equipment.

[0081] P5. Select the minimum value from the function test qualified score, response time qualified score and maintenance reliability score of each repaired equipment as the maintenance result score of each repaired equipment.

[0082] P6. Calculate the mean value of the maintenance result scores of each repaired equipment corresponding to each maintenance equipment to obtain the maintenance result score of each maintenance equipment, denoted as .

[0083] The maintenance assessment feedback terminal is used to confirm the assessment score of each maintenance equipment based on the maintenance diagnosis score, maintenance process score and maintenance result score of each maintenance equipment, and perform corresponding feedback on the maintenance equipment management platform.

[0084] Exemplarily, the assessment scores for each maintenance device include: extracting the maintenance diagnosis scores of each maintenance device , the maintenance process scores of each maintenance device and the maintenance result scores of each maintenance device .

[0085] Statistical assessment scores for each maintenance device , , , and are the weights of the set reference maintenance diagnosis scores, maintenance process scores, and maintenance result scores respectively, , .

[0086] It should be added that maintenance diagnosis is the starting point and key link of maintenance work. Accurate diagnosis can determine key information such as the specific location, cause, and degree of faults in military equipment, providing a clear direction for subsequent maintenance work. If the diagnosis is incorrect, regardless of the standardization of the subsequent maintenance process and the qualification of the final result, it may only be a temporary appearance and cannot fundamentally solve the problem. The military equipment is likely to malfunction again in a short time. For key areas such as military maintenance equipment, accurate diagnosis can timely detect potential safety hazards, so its weight is the highest. A qualified maintenance process is the key link to ensure maintenance quality. A standardized and standard maintenance process can ensure that every step of the maintenance work meets technical requirements and operating specifications, enabling the maintenance work to proceed in an orderly manner, thus accurately implementing the plan determined by the maintenance diagnosis and ensuring that the maintenance, replacement, etc. of each component meet quality standards. At the same time, operating according to the correct maintenance process can avoid causing new damage to the equipment or introducing new fault factors during the maintenance process, so its weight is second only to maintenance diagnosis. A qualified maintenance result usually determines whether the equipment has returned to normal operation through a series of tests and inspections of the equipment. However, only focusing on the result has certain limitations because some faults may be latent or intermittent and may not be immediately apparent under the current test conditions. At the same time, the maintenance result largely depends on the correctness and standardization of the maintenance diagnosis and maintenance process. A qualified maintenance result is only a test of the current state and cannot fully represent the overall quality of the maintenance work and the long-term reliability of the device, so its weight is the lowest. Therefore, set For ease of analysis, it can be specifically set to 0.5, it can be specifically set to 0.3, it can be specifically set to 0.2.

[0087] In the embodiments of the present invention, by confirming the assessment scores of each maintenance device based on the maintenance diagnosis scores, maintenance process scores, and maintenance results scores of each maintenance device, a full-chain closed-loop assessment of "diagnosis - process - result" is achieved, comprehensively evaluating the status of the maintenance device, thereby improving the accuracy of the analysis results.

[0088] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. An assessment information management system for military equipment maintenance support equipment, characterized in that: The system includes: A maintenance equipment allocation module, which is used to allocate the equipment to be maintained based on the prior fault diagnosis reports of the equipment to be maintained through a dynamic batch association algorithm; A maintenance diagnosis analysis module, which is used to detect the on-site fault diagnosis reports of each maintenance equipment for the equipment to be maintained under its jurisdiction, and then analyze the maintenance diagnosis scores of each maintenance equipment; A maintenance process analysis module, which is used to extract prior fault causes from the prior fault diagnosis reports, perform maintenance on the equipment to be maintained accordingly, record the operation time sequence logs of each maintenance equipment, and then analyze the maintenance process scores of each maintenance equipment; A maintenance result analysis module, which is used to conduct an operation test on the maintained equipment after the maintenance is completed, detect the operation test information of each maintained equipment, and then analyze the maintenance result scores of each maintenance equipment; A maintenance assessment feedback terminal, which is used to confirm the assessment scores of each maintenance equipment based on the maintenance diagnosis scores, maintenance process scores and maintenance result scores of each maintenance equipment, and give corresponding feedback on the maintenance equipment management platform.

2. The assessment information management system for a military equipment maintenance support device according to claim 1, wherein: The analysis of the maintenance diagnosis scores of each maintenance equipment includes: G1. Matching and comparing the on-site fault diagnosis reports of each maintenance equipment for the equipment to be maintained under its jurisdiction with the prior fault diagnosis reports, and recording the maintenance diagnosis with the same result between the on-site fault diagnosis report and the prior fault diagnosis report as a correct diagnosis; G2. Count the number of correct diagnoses and the total number of diagnoses for each maintenance device, and use the ratio of the two as the fault location accuracy rate of each maintenance device, denoted as , is the maintenance device number, ; G3. Extract the diagnostic duration of each maintenance equipment for each piece of equipment to be maintained under its jurisdiction from the on-site fault diagnosis reports of each maintenance equipment, and count the compliance coefficient of the diagnostic duration of each maintenance equipment. ; G4. Statistically calculate the maintenance diagnosis scores of each maintenance equipment , , where is the weight adjustment coefficient set for reference.

3. The assessment information management system for a military equipment maintenance support device according to claim 2, characterized in that: The statistics of the compliance coefficient of the diagnosis duration of each maintenance equipment includes: Extracting the fault types of the equipment to be maintained from the prior fault diagnosis reports of the equipment to be maintained, and then extracting the reference diagnosis duration of each fault type from the maintenance equipment management platform as the reference diagnosis duration of the equipment to be maintained; Matching and comparing the diagnosis duration of each maintenance equipment for the equipment to be maintained under its jurisdiction with its reference diagnosis duration; If the diagnosis duration of a certain maintenance equipment for a certain equipment to be maintained is less than its reference diagnosis duration, then take 1 as the compliance coefficient of the diagnosis duration of this maintenance equipment for this equipment to be maintained. Otherwise, take the ratio of the reference diagnosis duration to the diagnosis duration as the compliance coefficient of the diagnosis duration of this maintenance equipment for this equipment to be maintained, and then obtain the compliance coefficients of the diagnosis durations of each maintenance equipment for the equipment to be maintained under its jurisdiction; Calculating the average value of the compliance coefficients of the diagnosis durations of each maintenance equipment for the equipment to be maintained under its jurisdiction to obtain the compliance coefficient of the diagnosis duration of each maintenance equipment.

4. The assessment information management system for a military equipment maintenance support device according to claim 1, characterized in that: The analysis of the maintenance process scores of each maintenance equipment includes: H1. Based on the prior fault diagnosis reports of the equipment to be maintained, extracting the standard maintenance steps corresponding to the prior fault diagnosis reports of the equipment to be maintained from the maintenance equipment management platform; H2. Extracting the actual maintenance steps of each maintenance equipment for the equipment to be maintained under its jurisdiction from the operation time sequence logs of each maintenance equipment, and matching and comparing them with the corresponding standard maintenance steps; H3. Count the number of steps with consistent repair sequences and the total number of repair steps for each maintenance device, and use the ratio of the two as the compliance coefficient of the repair steps for each maintenance device, denoted as ; H4. Extract the actual repair duration of each maintenance equipment for each piece of equipment to be maintained under its jurisdiction from the operation timing logs of each maintenance equipment, and then calculate the repair duration qualification coefficient of each maintenance equipment. ; H5. Extract the historical maintenance operation information of each maintenance personnel from the maintenance equipment management platform, and then set the personnel operation influence coefficient of each maintenance equipment ; H6. Statistically evaluate the repair process scores of each maintenance equipment through the geometric mean algorithm .

5. The assessment information management system for a military equipment maintenance support device according to claim 4, characterized in that: The statistics of the qualified coefficient of the maintenance duration of each maintenance equipment includes: Extracting the reference maintenance duration interval corresponding to the prior fault diagnosis report of the equipment to be maintained from the maintenance equipment management platform; Based on the actual maintenance duration of each maintenance equipment for the equipment to be maintained under its jurisdiction and the corresponding reference maintenance duration interval, statistically calculating the qualified coefficient of the maintenance duration of each maintenance equipment through a normal distribution model.

6. The assessment information management system for a military equipment maintenance support device according to claim 4, characterized in that: The setting of the personnel operation influence coefficient for each maintenance device includes: Extract the maintenance operation scores and maintenance fault location accuracy rates of each maintenance personnel's historical maintenance operations from the historical maintenance operation information of each maintenance personnel, and record the maintenance fault location accuracy rate of each maintenance personnel as , is the maintenance personnel number, ; Calculate the mean of the maintenance operation scores for each maintenance personnel's each maintenance, obtain the average maintenance operation score of each maintenance personnel, and then match and compare the average maintenance operation score of each maintenance personnel with the maintenance operation score interval corresponding to each operation specification compliance degree to obtain the operation specification compliance degree of each maintenance personnel, and record it as ; Statistically analyze the personnel operation influence coefficients of each maintenance staff , , and are respectively the weights of the maintenance fault location accuracy rate and the operation specification compliance rate set as references, , ; Extracting the maintenance personnel corresponding to each maintenance device from the on-site fault diagnosis reports of each maintenance device for its respective to-be-maintained equipment, and then screening out the personnel operation influence coefficient for each maintenance device.

7. The assessment information management system for a military equipment maintenance support device according to claim 1, characterized in that: The analysis of the maintenance result score for each maintenance device includes: P1. Extracting the functional test data of each maintained equipment from the operation test information of each maintained equipment, and then statistically calculating the qualified score of the functional test for each maintained equipment; P2. Extracting the response time of each maintained equipment in each functional test from the functional test data of each maintained equipment, and then statistically calculating the qualified score of the response time for each maintained equipment; P3. Extracting the test results of each maintained equipment in each fault reproducibility test from the operation test information of each maintained equipment; P4. If the test results of each maintained equipment in each fault reproducibility test are all that the fault does not reappear, then recording the maintenance reliability score as 1, otherwise, recording the maintenance reliability score of the maintained equipment as 0, and then obtaining the maintenance reliability score of each maintained equipment; P5. Screening out the minimum value from the qualified score of the functional test, the qualified score of the response time, and the maintenance reliability score of each maintained equipment as the maintenance result score of each maintained equipment; P6. Calculate the mean of the maintenance result scores of each maintenance equipment corresponding to each maintained equipment to obtain the maintenance result scores of each maintenance equipment, denoted as .

8. The assessment information management system for a military equipment maintenance support device according to claim 7, characterized in that: The statistical calculation of the qualified score of the functional test for each maintained equipment includes: Extracting the test values of each function of each maintained equipment from the test data of each function test of each maintained equipment; Matching and comparing the test values of each function of each maintained equipment with the test scores corresponding to the set reference test values of each function test respectively to obtain the test scores of each function of each maintained equipment; Calculating the average value of the test scores of each function of each maintained equipment, and taking the calculation result as the qualified score of the functional test for each maintained equipment.

9. The assessment information management system for a military equipment maintenance support device according to claim 7, characterized in that: The statistical calculation of the qualified score of the response time for each maintained equipment includes: Record the test response duration of each repaired equipment in each function test as , is the repaired equipment number, , is the function test number, ; Statistically evaluate the qualified scores of the response times of each repaired equipment , , is the reference response time for the th function among the repaired equipments.

10. The assessment information management system for a military equipment maintenance support device according to claim 7, characterized in that: The confirmation of the assessment score for each maintenance device includes: Extract the maintenance diagnosis scores of each maintenance equipment and the maintenance process scores of each maintenance equipment and the maintenance result scores of each maintenance equipment ; Statistically evaluate the assessment scores of each maintenance equipment , , , and are the weights of the maintenance diagnosis score, maintenance process score, and maintenance result score set as references respectively, , .

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