A method and device for overhauling and managing a switch cabinet

Abstract

The application provides a kind of overhauling management method and system for switch cabinet, belong to overhauling management technical field, specifically include: according to the correlation degree of the operating load between different overhauling management goals and overhauling management goal data, determine the overhauling optimization processing strategy of switch cabinet except overhauling management goal, to determine the overhauling optimization processing target in switch cabinet except overhauling management goal with overhauling optimization processing strategy, according to the correlation degree of the operating load between overhauling optimization processing target and different overhauling management goals, and combined with the constituent data of overhauling optimization processing target of the similar coefficient of overhauling management goal being less than preset coefficient threshold, determine the update processing strategy of overhauling management goal, improve the identification reliability degree of identification missing condition.

Description

Technical Field

[0001] This invention belongs to the field of maintenance management technology, and particularly relates to a maintenance management method and device for switchgear. Background Technology

[0002] Switchgear inevitably encounters various problems during operation. Failure to identify and repair these problems promptly can severely impact the safety and reliability of electricity supply for users. Current technical solutions often employ testing or periodic maintenance to identify and address potential fault risks, but these methods suffer from the following technical issues: When identifying and processing faults, the model will inevitably have a certain degree of omission. Therefore, it is an urgent technical problem to determine the maintenance and management strategies for different switchgear based on the omissions under different fault types and the degree of correlation with different switchgear, so as to determine the risk of omissions in different switchgear.

[0003] Specifically, this application provides a method and apparatus for the maintenance and management of switchgear. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a maintenance management method for switchgear, which includes: S1 uses the maintenance data of the switch cabinets in the switch room to determine the missing identification data of the switch cabinets under different fault types. Based on the missing identification data and the degree of correlation between the fault types and different switch cabinets, a maintenance management strategy is determined. S2 determines the switch cabinets that are to be maintained according to a preset time cycle based on the maintenance management strategy, and takes them as maintenance management targets. When it is determined that maintenance optimization processing of switch cabinets other than maintenance management targets needs to be carried out based on the degree of matching between the maintenance management targets in the switch cabinets and the updated processing results of the fault type identification omissions of the maintenance management targets, proceed to step S3. S3 determines the maintenance optimization strategy for switchgear excluding maintenance management objectives based on the correlation between operating loads and maintenance management objective data among different maintenance management objectives. S4 uses the maintenance optimization processing strategy to determine the maintenance optimization processing targets in the switchgear, excluding the maintenance management targets. Based on the correlation degree between the maintenance optimization processing targets and different maintenance management targets regarding the operating load, and combined with the composition data of maintenance optimization processing targets whose similarity coefficient is less than a preset coefficient threshold, S4 determines the update processing strategy for the maintenance management targets.

[0005] The beneficial effects of this invention are as follows: Based on the identification omission data in different fault types and the degree of correlation with different switchgear, the identification omission risk of different fault types and the degree of impact on the identification reliability of different switchgear are determined. Based on the identification omission risk of different fault types and the degree of impact on the identification reliability of different switchgear, maintenance management strategies are determined, thus laying the foundation for timely and effective determination of the identification omission situation of the model.

[0006] Based on the correlation between the maintenance optimization processing objective and the operating load of different maintenance management objectives, the model's identification results for omissions under different operating loads under the current maintenance optimization processing objective are determined, along with the deviation from the maintenance management objective. Based on the deviation, the reliability and comprehensiveness of the model's identification of omissions under the current maintenance optimization processing objective and maintenance management objective are determined. Based on the reliability and comprehensiveness of the model's identification of omissions, an update processing strategy for the maintenance management objective is determined, thereby further improving the reliability and accuracy of the model's identification of omissions.

[0007] Furthermore, the maintenance data of the switchgear is determined based on the identification results of the fault types of the switchgear during the maintenance process.

[0008] Furthermore, the missing identification data of the switchgear under different fault types is determined based on the number of fault types that were not identified by the fault model during the maintenance process.

[0009] Furthermore, the degree of correlation between the fault type and different switchgear is determined based on the frequency of occurrence of the fault type in different switchgear.

[0010] Furthermore, the method for determining the maintenance management strategy is as follows: Based on the identified omission data, the number of times the switchgear was identified in different fault types is determined, and the identification omission risk type in the fault type is determined based on the number of identified omissions. Based on the correlation between the identified omission risk types and different switch cabinets, determine the historical number of failures of the switch cabinets under different identified omission risk types; The maintenance management strategy is determined based on the identified omission risk types and the historical number of failures of the switchgear in different identified omission risk types.

[0011] Specifically, the identified omission risk type is the fault type where the number of omissions does not meet the requirements, that is, the fault type where the number of omissions exceeds a certain threshold.

[0012] Furthermore, the method for determining the update processing strategy for the maintenance management objectives is as follows: Based on the degree of correlation between the maintenance optimization processing target and different maintenance management targets in terms of operating load, a similarity coefficient of the operating load between the maintenance optimization processing target and different maintenance management targets is determined. Based on the average value of the similarity coefficient of the operating load between the maintenance optimization processing target and different maintenance management targets, a reference matching weight coefficient of the maintenance optimization processing target is determined. By using the similarity coefficient of the operating load between the maintenance processing optimization target and different maintenance management targets, maintenance optimization processing targets with similarity coefficients less than a preset coefficient threshold are identified and used as matching processing targets. The update processing strategy for the maintenance management objectives is determined by the number of matching processing objectives for different maintenance management objectives and the reference matching weight coefficients for different maintenance optimization processing objectives.

[0013] In a second aspect, the present invention provides a computer device comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for maintenance management of switchgear when running the computer program.

[0014] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of a maintenance management method for switchgear; Figure 2 This is a flowchart illustrating the methods for determining maintenance management strategies; Figure 3 This is a flowchart illustrating the method for determining the maintenance optimization strategy for switchgear, excluding maintenance management objectives. Detailed Implementation

[0018] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0019] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0020] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a maintenance management method for switchgear is provided, specifically including: S1 uses the maintenance data of the switch cabinets in the switch room to determine the missing identification data of the switch cabinets under different fault types. Based on the missing identification data and the degree of correlation between the fault types and different switch cabinets, a maintenance management strategy is determined. Furthermore, the maintenance data of the switchgear is determined based on the identification results of the fault types of the switchgear during the maintenance process.

[0021] Specifically, the missing identification data of the switchgear under different fault types is determined based on the number of fault types that were not identified by the fault model during the maintenance process.

[0022] It is understood that the degree of correlation between the fault type and different switchgear is determined based on the frequency of occurrence of the fault type in different switchgear.

[0023] Specifically, such as Figure 2 As shown, the method for determining the maintenance management strategy is as follows: In this embodiment, based on the identification omission data in different fault types and the degree of correlation with different switchgear, the identification omission risk of different fault types and the degree of impact on the identification reliability of different switchgear are determined. Based on the identification omission risk of different fault types and the degree of impact on the identification reliability of different switchgear, maintenance management strategies are determined, thus laying the foundation for timely and effective determination of the identification omission situation of the model.

[0024] The maintenance management strategy refers to the decision-making scheme for determining which switchgear cabinets should be maintained according to a preset time cycle; the risk of omission refers to the risk of the fault model failing to identify a specific fault type; the degree of impact of identification reliability refers to the magnitude of the impact of identification omission on the reliability of switchgear fault identification.

[0025] If a certain type of fault has a high risk of being missed in identification and is highly associated with multiple switchgear cabinets, then this type of fault will have a significant impact on the reliability of switchgear identification, and more switchgear cabinets need to be included in the periodic maintenance.

[0026] By identifying omission risks and their correlation, maintenance management strategies are determined, and the omission information identified in the fault model is transformed into a basis for maintenance management decisions, thereby enabling timely and effective identification of omissions in the model.

[0027] S11 Based on the identified omission data, determine the number of times the switch cabinet was identified in different fault types, and determine the identification omission risk type in the fault type based on the number of identified omissions; The number of times a fault type was missed refers to the total number of times a fault type was not identified by the fault model during historical maintenance; the risk type of the number of times the number of times the fault type was missed refers to the fault type whose number of times the number of times the fault type was missed does not meet the requirements, that is, the fault type whose number of times the fault type was missed is too high and exceeds the preset number of times the fault type was missed.

[0028] If a certain fault type is not identified multiple times in historical maintenance, exceeding a preset threshold for the number of times it has been missed, then that fault type is identified as a type with a risk of being missed.

[0029] By identifying the number of omissions, we can filter out the types of omission risks and identify the types with higher omission risks from all fault types, thus providing a risk type-level basis for determining subsequent maintenance management strategies.

[0030] In the above steps: if the proportion of the number of identified omission risk types in the fault types does not meet the requirements, that is, when it is greater than a certain proportion threshold, then the number of identified omission risk types is too large. In this case, all switch cabinets with fewer faults in the most recent preset time period than the preset fault number threshold will be treated as switch cabinets to be inspected and processed according to the preset time cycle.

[0031] The term "not meeting the requirements" refers to the situation where the proportion of identified omission risk types exceeds the preset risk type proportion threshold, i.e., the number of risk types is large and the overall identified omission risk is relatively large; the preset risk type proportion threshold is the critical value for judging whether the proportion of risk types is high; the preset failure number threshold is the critical value for judging whether the number of recent failures of the switchgear is low.

[0032] If the proportion of identified omission risk types is high, it indicates that the overall risk of omission is relatively large. In this case, switchgear with fewer recent failures will be included in the periodic maintenance to promptly detect failures that the model has not identified.

[0033] When the proportion of risk types is high, a lenient inclusion strategy is adopted. When the overall identification of risks is relatively large, more switchgear is included in the periodic maintenance to promptly detect faults missed by the model, thereby ensuring the comprehensiveness of the maintenance.

[0034] It is also understandable that if the proportion of the number of identified risk types in the fault types meets the requirements, the process proceeds to step S12.

[0035] The phrase "meeting the requirements" refers to the situation where the proportion of the number of identified omission risk types does not exceed the preset risk type proportion threshold, meaning that the number of risk types is small and the overall identified omission risk is controllable.

[0036] If the proportion of identified omission risk types does not exceed the preset risk type proportion threshold, it indicates that the overall identified omission risk is controllable, and then proceed to S12 for more detailed analysis.

[0037] S12 determines the number of historical failures of the switch cabinet in different identified omission risk types based on the degree of correlation between the identified omission risk types and different switch cabinets; The historical failure count refers to the total number of times a certain switchgear has experienced a certain type of failure under a certain identified omission risk type.

[0038] Suppose that a certain switchgear has experienced this fault multiple times in the past under a certain type of identified omission risk. Then, counting these occurrences is the historical number of faults of the switchgear under that risk type.

[0039] By determining the degree of correlation, the number of historical failures is determined, and the historical occurrence of each identified and missed risk type is quantified from the perspective of switch cabinets, providing a basis for the subsequent calculation of correlation weight values.

[0040] S13 determines the maintenance management strategy based on the identified omission risk types and the historical number of failures of the switchgear in different identified omission risk types.

[0041] Assuming that the types of risks that have been identified and the number of historical failures of the switchgear have been determined, maintenance management strategies are determined based on the judgment results of S131 and S132.

[0042] Specifically, the identified omission risk type is the fault type whose omission count does not meet the requirements, that is, the fault type whose omission count is greater than a certain threshold. The judgment conditions for identifying omission risk types are clarified, ensuring that the identification of omission risk types has clear quantitative conditions.

[0043] Specifically, the maintenance management strategy is determined based on the identified omission risk types and the historical failure count of the switchgear in different identified omission risk types, including: S131, determine the association weight value between the switch cabinet and the identified omission risk type by multiplying the historical failure number of the switch cabinet in the identified omission risk type by a preset proportional factor, and determine whether there are identified omission risk types with association weight values ​​above the preset weight threshold. If yes, proceed to step S132. If no, treat all switch cabinets that have no failures in the most recent preset time period as switch cabinets to be repaired according to the preset time cycle. The correlation weight value refers to the weight value calculated based on the number of historical failures and a preset proportional factor, which quantifies the correlation between the switchgear and the identified omission risk type; the preset weight threshold refers to the critical value for judging whether the sum of the correlation weight values ​​is too high.

[0044] The calculation method for the correlation weight value is as follows: Let Fij be the number of historical failures of the i-th switchgear in the j-th identified omission risk type, and let K be the preset proportional factor. Then: the association weight value between the i-th switchgear and the j-th identified omission risk type = Fij × K.

[0045] If there are no identified risk types whose total associated weight values ​​exceed the preset weight threshold, it indicates that the correlation between each risk type is not high. In this case, switchgear that has not experienced any recent failures will be included in the periodic maintenance.

[0046] The presence of high-risk types is determined by summing the associated weight values. If no high-risk types exist, a simpler strategy is adopted to improve efficiency. If high-risk types exist, a more refined analysis is used to determine the strategy.

[0047] S132 determines the identified omission risk value by summing the association weight values ​​of different identified omission risk types with different switch cabinets and the proportion of the number of identified omission risk types in the fault types. It then determines whether the identified omission risk value is greater than a preset risk threshold. If so, switch cabinets with fewer than a preset fault number threshold in the most recent preset time period are all considered switch cabinets to be inspected and processed according to a preset time cycle. If not, switch cabinets with no faults in the most recent preset time period, as well as switch cabinets with fewer than a preset fault number threshold in the most recent preset time period and whose association weight values ​​with different identified omission risk types are greater than a preset weight value, are all considered switch cabinets to be inspected and processed according to a preset time cycle.

[0048] The identified omission risk value refers to a quantitative indicator calculated by combining the total associated weight values ​​and the proportion of risk types; the preset risk threshold refers to a critical value for judging whether the identified omission risk value is high; the preset weight value refers to a critical value for judging whether the total associated weight values ​​of a single switch cabinet are high.

[0049] The method for calculating the identified omission risk value is as follows: Let Wsum be the sum of the association weights between different types of identified omission risks and different switch cabinets, and Prisk be the proportion of the number of identified omission risks. Then: Identification omission risk value = Wsum × Prisk.

[0050] If the calculated risk value for identification omission is greater than the preset risk threshold, it indicates that the overall risk of identification omission is high. In this case, switchgear with fewer recent failures will be included in the periodic maintenance. If it is not greater than the threshold, switchgear with no recent failures and switchgear with fewer recent failures and a higher total correlation weight value will be included in the periodic maintenance.

[0051] By identifying omission risk values, maintenance management strategies can be further differentiated. When high-risk types exist, the scope of inclusion can be dynamically adjusted based on the overall risk value, thereby achieving a refined balance between maintenance coverage and resource efficiency.

[0052] This embodiment realizes a complete process from identifying and counting the number of omissions to identifying and screening the types of omission risks and determining the maintenance management strategy. Its core value is reflected in three aspects: First, by judging the proportion of risk types, a lenient inclusion strategy is adopted when the overall risk is high to ensure the comprehensiveness of maintenance; second, by judging the sum of the associated weight values, a simple strategy is adopted to improve efficiency when there are no high-risk types; and third, by judging the classification of the identified omission risk values, the inclusion scope is dynamically adjusted when there are high-risk types, thereby achieving an adaptive balance between maintenance coverage and resource efficiency.

[0053] A switch room contains 10 switchgear cabinets C1 to C10. The fault model can identify six fault types F1 to F6. In 30 historical maintenance operations, the number of times each fault type was missed in identification is as follows: F1: 5 missed, F2: 2 missed, F3: 8 missed, F4: 1 missed, F5: 6 missed, F6: 3 missed.

[0054] Step S11: Determine whether each fault type is a type of risk that has been missed (number of missed identifications > 4).

[0055] F1: 5 > 4, identify the type of omitted risk. F2: 2 not greater than 4, not applicable. F3: 8 > 4, identify the type of omitted risk. F4: 1 not greater than 4, not applicable. F5: 6 > 4, identify the type of omitted risk. F6: 3 not greater than 4, not applicable.

[0056] The identified omission risk types are F1, F3, and F5, a total of 3. The proportion of each type is 3 / 6 = 0.50.

[0057] Determine if the proportion of 0.50 does not meet the requirements (i.e., exceeds the preset risk type proportion threshold of 0.40): If 0.50 is greater than 0.40, it does not meet the requirements, and switchgear with fewer than 3 failures in the last 30 days will be considered as switchgear for periodic maintenance.

[0058] S2 determines the switch cabinets that are to be maintained according to a preset time cycle based on the maintenance management strategy, and takes them as maintenance management targets. When it is determined that maintenance optimization processing of switch cabinets other than maintenance management targets needs to be carried out based on the degree of matching between the maintenance management targets in the switch cabinets and the updated processing results of the fault type identification omissions of the maintenance management targets, proceed to step S3. It is understandable that the switchgear maintenance optimization process, excluding maintenance management objectives, needs to be implemented, specifically including: In this embodiment, based on the updated processing results of the fault type identification omission situation of the maintenance management target, the overall identification omission risk is determined. Based on the overall identification omission risk, it is determined whether maintenance optimization processing of switchgear other than the maintenance management target is required, thereby further ensuring the reliability of the model's identification omission risk.

[0059] The maintenance optimization process refers to a supplementary strategy for the maintenance of switchgear excluding maintenance management objectives; the update process result for identifying omissions refers to the latest result after updating the fault model to identify omissions after periodic maintenance.

[0060] If the maintenance management objectives are found to have many omissions after the periodic maintenance, it indicates that the maintenance management objectives may have difficulty effectively identifying the types of faults that are at risk of being missed. In this case, maintenance optimization processing is required for non-maintenance management objectives.

[0061] By identifying omissions and updating the results of maintenance management objectives, it is determined whether maintenance optimization is needed. The maintenance feedback information of maintenance management objectives is transformed into a decision-making basis for whether to expand the maintenance scope, thereby ensuring the reliability of the model in identifying omission risks.

[0062] S21, based on the update processing result of the fault type identification omission of the maintenance management target, determine the maintenance management target with fault type identification omission during the maintenance process, and use it as the omission update target; The omission update target refers to the maintenance management target that still has omissions in fault type identification during the latest maintenance process, that is, the maintenance of this target has not completely eliminated the omissions in identification.

[0063] If a maintenance management objective is found to have a fault type that was not identified by the fault model during the most recent periodic maintenance, then the objective is identified as an object that has been omitted from the update.

[0064] By updating the processing results, missing update targets are filtered out, and targets that are still missing from the maintenance management targets are identified, providing a basis for determining whether subsequent maintenance and optimization processing is needed.

[0065] S22, Based on the maintenance management objectives in the switchgear, determine the proportion of the number of maintenance management objectives in the switchgear and use it as the proportion of related objectives; The percentage of associated targets refers to the proportion of the number of maintenance management targets to the total number of all switchgear.

[0066] Assuming that 6 out of 10 switchgear units are maintenance management targets, then the proportion of related targets = 6 / 10 = 0.60.

[0067] By calculating the proportion of associated targets, the coverage of maintenance management targets on the overall switchgear is quantified, providing a quantitative basis for the subsequent calculation of adaptation values ​​in terms of proportion.

[0068] S23, based on the degree of matching between the proportion of the associated targets and the missing update targets, determine whether it is necessary to carry out maintenance optimization processing for switchgear excluding maintenance management targets.

[0069] If the matching degree between the proportion of associated targets and the proportion of missing update targets is low, it indicates that the maintenance management objectives may not be able to effectively identify the risk of omission, and maintenance optimization is required.

[0070] The degree of matching determines whether maintenance and optimization are needed, thus enabling adaptive judgment in the determination of maintenance and optimization.

[0071] It is understandable that, based on the degree of matching between the aforementioned proportion of related targets and the omission of update targets, it is determined whether maintenance optimization processing of switchgear, excluding maintenance management targets, is required. Specifically, this includes: The adaptation value is determined by the difference between the proportion of the associated target and the proportion of the omitted update target in the maintenance management target. When the adaptation value is below a preset adaptation threshold, it is determined that the maintenance management target may not be able to effectively and accurately identify fault types with the risk of omission. Therefore, it is determined that maintenance optimization processing of switchgear other than the maintenance management target is required. The adaptation value is confirmed by preliminary verification as the absolute value of the proportion difference, which is explicitly stated here.

[0072] The adaptation value refers to the absolute difference between the proportion of associated targets and the proportion of missed update targets in the maintenance management targets; the preset adaptation threshold refers to the critical value for judging whether the adaptation value is too low.

[0073] The adaptation value is calculated as follows: Let Ptarget be the proportion of associated targets and Pmiss be the proportion of missed update targets in maintenance management targets. Then: Fit value = Ptarget - Pmiss.

[0074] If the calculated adaptation value is below the preset adaptation threshold, it indicates that the proportion of associated targets is close to the proportion of missing update targets, and the maintenance management target may be difficult to effectively identify the risk of omission. In this case, maintenance optimization processing is required.

[0075] When the fit value is low, maintenance optimization processing is triggered. When the coverage ratio of maintenance management targets is close to the omission update ratio, it indicates that the maintenance management targets themselves have the risk of omission in identification and need to supplement the identification capability through maintenance optimization processing of non-maintenance management targets.

[0076] This embodiment realizes a complete process from determining the omission update target to calculating the proportion of associated targets and then judging the maintenance optimization process. Its core value is reflected in two aspects: First, it identifies targets that are still missing from the maintenance management targets by identifying omission update targets; second, it triggers maintenance optimization process when the maintenance management targets may not be able to effectively identify risks by judging the fit value, thereby ensuring the reliability of the model in identifying omission risks.

[0077] Continuing with the calculation results of S1, the maintenance management targets are C1, C3, C5, C6, C8, and C10, a total of 6 units.

[0078] Step S21: After the most recent periodic maintenance, update the identification omissions of each maintenance management target. Assuming that there are still omissions in the identification of fault types for C1, C5, and C8 among the 6 maintenance management targets during the maintenance process, then the omission update targets are C1, C5, and C8, a total of 3.

[0079] Step S22: The proportion of associated targets = 6 / 10 = 0.60.

[0080] Step S23: The percentage of missed update targets in maintenance management targets = 3 / 6 = 0.50.

[0081] The adaptation value is 0.60 - 0.50 = 0.10. It is determined whether the adaptation value of 0.10 is below the preset adaptation threshold of 0.15: 0.10 is less than 0.15. If yes, it is determined that the maintenance optimization process of the switchgear, excluding the maintenance management target, needs to be carried out, and the process is entered into S3.

[0082] S3 determines the maintenance optimization strategy for switchgear excluding maintenance management objectives based on the correlation between operating loads and maintenance management objective data among different maintenance management objectives. Specifically, such as Figure 3As shown, the method for determining the maintenance optimization strategy for switchgear, excluding maintenance management objectives, is as follows: In this embodiment, based on the correlation between the operating loads of different maintenance management objectives and the maintenance-related objective data, the reliability of identifying omissions of fault types under different operating loads under the current maintenance management objective is determined. Based on the reliability of identifying omissions of fault types under different operating loads under the current maintenance management objective, an optimized maintenance processing strategy for switchgear excluding maintenance management objectives is determined. This reduces the difficulty of maintenance processing while ensuring the reliability of identifying omissions of fault types under different operating loads.

[0083] The maintenance optimization strategy refers to the decision-making scheme for determining which non-maintenance management target switchgear should be used as maintenance optimization targets; the correlation degree of operating load refers to the similarity of operating load distribution among different maintenance management targets.

[0084] Assuming that the operating loads of maintenance management objectives are highly correlated, the current maintenance management objectives already cover many load scenarios. Among the non-maintenance management objectives, only switchgear with significant load differences needs to be selected as optimization objectives.

[0085] By determining the degree of correlation between operating loads, maintenance optimization strategies are determined, and operating load information is transformed into a basis for selecting optimization targets, thereby reducing the difficulty of maintenance while ensuring the reliability of identification under different loads.

[0086] S31, Based on the degree of correlation between the operating loads of different maintenance management objectives, determine the similarity coefficient of the operating loads between different maintenance management objectives; The similarity coefficient of the operating load refers to the degree of similarity between the two maintenance management objectives in the proportion of operating time in different operating load ranges.

[0087] By calculating the similarity coefficient based on the degree of correlation, the similarity of the operating load distribution is quantified into comparable coefficient values, providing a quantitative basis for determining subsequent maintenance and optimization strategies.

[0088] It is understood that the similarity coefficient of the operating load between the maintenance management targets is determined based on the average of the similarity coefficients of the proportion of operating time in different operating load ranges.

[0089] The similarity coefficient of the operating load is calculated as follows: Let the proportions of the operating time of the two maintenance management targets in different operating load intervals be P1k and P2k (k=1 to N, where N is the number of load intervals), respectively, then: The similarity coefficient of the operating load is the complement of Σ|P1k - P2k| / N, that is, the similarity coefficient is 1 - Σ|P1k - P2k| / N.

[0090] S32, using the maintenance management target data, determine the proportion of maintenance management targets in the switchgear, and use it as the proportion of related targets; Assuming that 6 out of 10 switchgear units are maintenance management targets, then the proportion of related targets = 6 / 10 = 0.60. By calculating the proportion of related targets, we can provide a basis for determining the magnitude of maintenance optimization strategies based on the differences in the proportions.

[0091] It is understood that the above steps include the following: Determine whether the proportion of the associated targets is greater than the preset threshold for the proportion of associated targets. If so, the number of maintenance management targets is large. Therefore, determine that the maintenance optimization processing strategy for switch cabinets other than maintenance management targets is to take the switch cabinets of the target type as maintenance optimization processing targets. If not, proceed to step S33. The preset threshold for the proportion of associated targets refers to the critical value for judging whether the proportion of associated targets is high; the switch cabinet of the target type is the switch cabinet whose maximum value of the similarity coefficient of the operating load between it and the maintenance management target is less than the preset coefficient threshold, that is, the operating load of the switch cabinet is not similar to all maintenance management targets.

[0092] If the proportion of associated targets is greater than the preset threshold for the proportion of associated targets, it indicates that there are a large number of maintenance management targets and good coverage. In this case, only the non-maintenance management targets with the largest load difference need to be selected as optimization targets.

[0093] When the proportion of related targets is high, a target type screening strategy is adopted. When the coverage of maintenance management targets is good, it is only necessary to select switch cabinets with completely different loads as optimization targets to supplement the identification capabilities of different load scenarios.

[0094] S33, using the similarity coefficient of the operating load between different maintenance management objectives and the proportion of related objectives, determine the maintenance optimization processing strategy for the switchgear excluding maintenance management objectives.

[0095] For example, assuming that the similarity coefficient of operating load and the proportion of related targets among various maintenance management objectives have been determined, maintenance optimization strategies are determined based on the judgment results of S331, S332 and S333 respectively.

[0096] It is understandable that, by utilizing the similarity coefficient of operating load between different maintenance management objectives and the proportion of related objectives, the maintenance optimization strategy for the switchgear excluding maintenance management objectives is determined, specifically including: S331, determine whether the average value of the similarity coefficient of the operating load between different maintenance management targets is greater than the preset similarity coefficient threshold. If yes, determine that the maintenance optimization processing strategy for switchgear excluding maintenance management targets is to take the switchgear of the second target type as the maintenance optimization processing target. If no, proceed to step S332. The preset similarity coefficient threshold refers to the critical value for judging whether the average similarity coefficient of the operating load between maintenance management targets is high; the switch cabinet of the second target type is the switch cabinet whose average similarity coefficient of the operating load between it and the maintenance management target is less than the preset coefficient threshold, that is, the operating load of the switch cabinet is less similar to the average similarity of the maintenance management target as a whole.

[0097] If the average similarity coefficient of the operating load among the maintenance management targets is greater than the preset similarity coefficient threshold, it indicates that the loads among the maintenance management targets are highly similar. In this case, the non-maintenance management targets with lower average similarity are selected as the optimization targets.

[0098] When the average similarity coefficient is high, a second target type screening strategy is adopted. When the loads between maintenance management targets are highly similar, switchgear with a large difference from the overall load of the maintenance management targets is selected to supplement different load scenarios.

[0099] S332, using the similarity coefficient of the operating load between different maintenance management objectives, determine the maintenance management objectives whose similarity coefficient with other maintenance management objectives is greater than a preset similarity coefficient threshold, and use them as load association objectives. Based on the number of load association objectives among different maintenance management objectives, determine the load overlap weight value of the maintenance management objectives. Determine whether the average value of the load overlap weight values ​​of different maintenance management objectives is less than a preset weight threshold. If yes, proceed to step S333. If no, determine that the maintenance optimization processing strategy for switchgear excluding maintenance management objectives is to use switchgear of the second objective type as the maintenance optimization processing objective.

[0100] The load-related target refers to other maintenance management targets whose operating load similarity coefficient with a certain maintenance management target is greater than a preset similarity coefficient threshold; the load overlap weight value refers to the number of load-related targets among the maintenance management targets, reflecting the degree of load overlap between the target and other targets.

[0101] If the average load overlap weight value of the maintenance management objectives is less than the preset weight threshold, it indicates that the load overlap among the maintenance management objectives is low, and the process proceeds to S333 for further analysis; if the average value is not less than the threshold, it indicates that the load overlap is high, and the second objective type screening strategy is adopted.

[0102] The significance of further differentiating strategies by using load overlap weight values ​​lies in dynamically adjusting the screening strategy based on the degree of load overlap when the mean similarity coefficient is not high.

[0103] S333, determine whether the proportion of the associated target meets the requirements. If yes, then the number of maintenance management targets is large and the similarity is low. Therefore, determine that the maintenance optimization processing strategy for switch cabinets other than maintenance management targets is to take the switch cabinets of the target type as maintenance optimization processing targets. If not, determine that the maintenance optimization processing strategy for switch cabinets other than maintenance management targets is to take the switch cabinets of the third target type as maintenance optimization processing targets.

[0104] The term "meeting the requirements" refers to the situation where the proportion of associated targets is not lower than the lower limit of the preset threshold for the proportion of associated targets; the switchgear of the third target type is a switchgear whose average value of the similarity coefficient of the operating load between it and the maintenance management target is less than the preset threshold, and whose maximum value of the similarity coefficient of the operating load between it and the maintenance management target is less than the preset threshold, and whose number is above the preset number threshold. That is, the load similarity between the switchgear and the maintenance management target as a whole and the individual target is low.

[0105] If the proportion of related targets meets the requirements and the similarity between maintenance management targets is low, it indicates that the number of maintenance management targets is sufficient but the load coverage is dispersed. In this case, switchgear of target types with completely different loads will be selected as the optimization target. If the proportion does not meet the requirements, a third target type screening strategy will be adopted to select switchgear with low similarity to both the overall and individual loads of maintenance management targets.

[0106] The strategy is further differentiated by the proportion of associated targets, and the strictness of the screening strategy is dynamically adjusted according to the proportion when the degree of overlap in load is low.

[0107] It should be noted that if the switchgear is a target for maintenance and optimization, maintenance will be carried out if there is no maintenance data in the most recent second preset time period (greater than the preset time period).

[0108] Suppose a switchgear is identified as a maintenance optimization target, and it has not been maintained in the most recent second preset time period (i.e., there is no situation where a fault was found and maintenance was carried out), then maintenance is carried out on the switchgear. This clarifies the maintenance triggering conditions for the maintenance optimization target. Its significance is that by using a longer maintenance interval than the maintenance management target, the optimization target is ensured to be maintained when necessary, thereby ensuring the reliability of identification while avoiding over-maintenance.

[0109] This embodiment realizes a complete process from calculating the similarity coefficient of operating load to analyzing the proportion of associated targets and determining the maintenance optimization strategy. Its core value is reflected in three aspects: First, by judging the proportion of associated targets, a target type screening strategy is adopted to improve efficiency when the coverage of maintenance management targets is good. Second, by judging the hierarchical nature of the average similarity coefficient and the load overlap weight value, the screening strategy is dynamically adjusted under different load correlation levels. Third, by further judging the proportion of associated targets, the strategy is refined when the load overlap is low, thereby achieving an adaptive balance between maintenance coverage and resource efficiency.

[0110] Continuing with the calculation results of S2, maintenance optimization is required. Maintenance management targets are C1, C3, C5, C6, C8, and C10, a total of 6 units. Non-maintenance management targets are C2, C4, C7, and C9, a total of 4 units. The proportion of related targets = 6 / 10 = 0.60.

[0111] The operating load is divided into three ranges based on the load rate: H1 (0 to 40%), H2 (40% to 70%), and H3 (70% to 100%).

[0112] Step S31: Calculate the similarity coefficient of operating load between each maintenance management objective. Taking C1 and C3 as examples, assuming that the operating time ratio of C1 in H1 / H2 / H3 is 0.2 / 0.5 / 0.3 and that of C3 is 0.3 / 0.4 / 0.3, then the similarity coefficient = 1 - (|0.2-0.3| + |0.5-0.4| + |0.3-0.3|) / 3 = 1 - (0.1 + 0.1 + 0) / 3 = 1 - 0.067 ≈ 0.93.

[0113] Similarly, calculate the similarity coefficient among all maintenance management objectives, assuming a mean of 0.72.

[0114] Step S32: The proportion of associated targets = 0.60.

[0115] Determine if the proportion of associated targets (0.60) is greater than the preset threshold of 0.50: If 0.60 is greater than 0.50, then yes.

[0116] According to the path that meets the conditions in S32, the maintenance optimization processing strategy is to take the switchgear of the target type as the maintenance optimization processing target, that is, to take the non-maintenance management target whose maximum value of the operating load similarity coefficient with all maintenance management targets is less than the preset coefficient threshold of 0.40 as the maintenance optimization processing target.

[0117] The targets for overhaul and optimization are C2 and C7, a total of 2 units.

[0118] S4 uses the maintenance optimization processing strategy to determine the maintenance optimization processing targets in the switchgear, excluding the maintenance management targets. Based on the correlation degree between the maintenance optimization processing targets and different maintenance management targets regarding the operating load, and combined with the composition data of maintenance optimization processing targets whose similarity coefficient is less than a preset coefficient threshold, S4 determines the update processing strategy for the maintenance management targets.

[0119] Furthermore, the method for determining the update processing strategy for the maintenance management objectives is as follows: In this embodiment, based on the correlation between the maintenance optimization processing target and the operating load of different maintenance management targets, the model's identification results for omissions under different operating loads under the current maintenance optimization processing target are determined, along with the deviation from the maintenance management target. Based on the deviation, the reliability and comprehensiveness of determining the model's identification omissions under the current maintenance optimization processing target and maintenance management target are determined. Based on the reliability and comprehensiveness of determining the model's identification omissions, an update processing strategy for the maintenance management target is determined, thereby further improving the reliability and accuracy of determining the model's identification omissions.

[0120] The update processing strategy refers to the decision-making scheme that determines under what conditions the maintenance optimization processing target is upgraded to the maintenance management target; the deviation refers to the degree of difference between the maintenance optimization processing target and the maintenance management target in the dimension of operating load.

[0121] If the correlation between the maintenance optimization target and the maintenance management target in terms of operating load is low, it indicates that the maintenance optimization target can supplement different load scenarios. Therefore, if there is any omission in its identification, it will be upgraded to the maintenance management target.

[0122] By determining the degree of load correlation, the update processing strategy is determined, and the load correlation information of the maintenance optimization processing target is transformed into the decision basis for updating the maintenance management target, thereby improving the reliability and comprehensiveness of the model in identifying omissions.

[0123] S41, based on the degree of correlation between the maintenance optimization processing target and the operating load of different maintenance management targets, determine the similarity coefficient of the operating load between the maintenance optimization processing target and different maintenance management targets, and determine the reference matching weight coefficient of the maintenance optimization processing target based on the average value of the similarity coefficient of the operating load between the maintenance optimization processing target and different maintenance management targets. The reference matching weight coefficient refers to the average value of the similarity coefficient of operating load between the maintenance optimization target and all maintenance management targets, reflecting the degree of load correlation between the optimization target and the overall maintenance management targets.

[0124] The reference matching weight coefficient is calculated as follows: Let Ski be the similarity coefficient of the operating load between the k-th maintenance optimization target and the ith maintenance management target, and let Ntarget be the total number of maintenance management targets. Then: The reference matching weight coefficient for the kth maintenance optimization target is ΣSki / Ntarget.

[0125] If the average similarity coefficient of the operating load between a certain maintenance optimization target and all maintenance management targets is 0.35, then its reference matching weight coefficient is 1-0.35=0.65, indicating that the overall load correlation between the optimization target and the maintenance management targets is low.

[0126] The reference matching weight coefficient is determined by the mean of the similarity coefficient, which quantifies the load correlation between the maintenance optimization and maintenance management objectives from an overall perspective, providing a quantitative basis for determining the subsequent update and processing strategies.

[0127] It is understood that if the sum of the reference matching weight coefficients of different maintenance optimization processing targets is greater than the preset matching weight coefficient threshold in the above steps, then the update processing strategy for the maintenance management target is determined to be that as long as there is a fault type that has been missed in the maintenance optimization processing target, the maintenance optimization processing target is taken as the maintenance management target.

[0128] The preset matching weight coefficient threshold refers to the critical value for judging whether the sum of the reference matching weight coefficients is low. If the sum of the reference matching weight coefficients is greater than the preset matching weight coefficient threshold, it indicates that the overall load correlation between the maintenance optimization processing target and the maintenance management target is low and the coverage of the maintenance management target under different load scenarios is insufficient. Therefore, as long as there is an identification omission in the optimization target, it will be upgraded to the maintenance management target.

[0129] When the total weight coefficient is high, a relaxed upgrade strategy is adopted. When the maintenance management target load coverage is insufficient, the optimization target is incorporated into the management target through a lower upgrade threshold to supplement the identification capability of different load scenarios.

[0130] Additionally, it is understood that if the sum of the reference matching weight coefficients of different maintenance optimization processing targets is not less than the preset matching weight coefficient threshold, then proceed to step S42. If the sum of the reference matching weight coefficients is not less than the preset matching weight coefficient threshold, it indicates that the overall load correlation between the maintenance optimization treatment target and the maintenance management target is acceptable, and then proceed to S42 for more detailed analysis.

[0131] S42 uses the similarity coefficient of the operating load between the maintenance processing optimization target and different maintenance management targets to determine the maintenance optimization processing target whose similarity coefficient is less than a preset coefficient threshold, and uses it as the matching processing target; The matching processing target refers to the maintenance optimization processing target whose operating load similarity coefficient with a certain maintenance management target is less than a preset coefficient threshold, that is, the load difference between the optimization target and the maintenance management target is large.

[0132] If the similarity coefficient of the operating load between a certain maintenance optimization target and a certain maintenance management target is 0.25, which is less than the preset coefficient threshold of 0.40, then the optimization target is the matching target of the maintenance management target.

[0133] By screening and matching processing targets using similarity coefficients, optimization targets with significant differences in load from various maintenance management targets are identified from the maintenance optimization processing targets, providing a basis for the refined determination of subsequent update processing strategies.

[0134] It is understood that if the average number of matching processing targets for different maintenance management objectives is less than the preset threshold for the number of matching processing targets in the above steps, the update processing strategy for the maintenance management objective is determined to be that the maintenance optimization processing objective is used as the maintenance management objective as long as there is a fault type that has been missed in the identification of the maintenance optimization processing objective.

[0135] The preset target number threshold for matching processing refers to the critical value used to determine whether the average number of target matching processes is sufficient.

[0136] If the average number of matching targets is less than the preset threshold for the number of matching targets, it indicates that the load coverage of the maintenance management targets is insufficient and the load difference with the optimization targets is large. In this case, as long as there is an omission in the identification of the optimization targets, they will be upgraded to maintenance management targets.

[0137] When the average value of the matching processing target is low, a relaxed upgrade strategy is adopted. When the maintenance management target load coverage is insufficient, the identification capability of different load scenarios is supplemented by a lower upgrade threshold.

[0138] It should also be noted that if the average number of matching processing targets for different maintenance management objectives is not less than the preset threshold for the number of matching processing targets, proceed to step S43. If the average number of matching processing targets is not less than the preset threshold for the number of matching processing targets, it indicates that the load coverage of the maintenance management targets is acceptable, and then proceed to S43 for more detailed analysis.

[0139] S43, determine the update processing strategy for the maintenance management objective based on the number of matching processing objectives for different maintenance management objectives and the reference matching weight coefficients for different maintenance optimization processing objectives.

[0140] Assuming that the number of matching processing targets for each maintenance management objective and the reference matching weight coefficient for each maintenance optimization processing objective have been determined, the update processing strategy is determined based on the judgment results of S431 and S432.

[0141] The update processing strategy is determined by combining the number of target matching processes and the reference matching weight coefficients, thus achieving adaptive hierarchical determination of the update processing strategy.

[0142] Furthermore, in the above steps, maintenance management targets whose number of matching processing targets is less than a preset threshold for the number of matching processing targets are taken as matching deviation management targets. It is determined whether there is a matching deviation management target. If yes, proceed to step S432. If no, determine that the update processing strategy for the maintenance management target is that as long as the number of identified and missed fault types of the maintenance optimization processing target is above a preset value, or the number of identification omissions in any fault type is above a preset value, the maintenance optimization processing target is taken as the maintenance management target.

[0143] The matching deviation management target refers to the maintenance management target with insufficient matching processing targets, that is, the maintenance management target has a deviation in load coverage; the quantity preset value refers to the critical value for judging whether the number of missed fault types is too large; the missed number preset value refers to the critical value for judging whether the number of missed identifications of a single type is too large.

[0144] If there is no matching deviation management target, it means that the load coverage of all maintenance management targets is good. In this case, stricter upgrade conditions are adopted, requiring that the optimization target be upgraded only when there are multiple identified missed fault types or a large number of missed faults of a single type.

[0145] By matching the deviation management target, we determine whether more refined analysis is needed. If there is no deviation target, we adopt stricter upgrade conditions to avoid over-upgrade. If there is a deviation target, we conduct further analysis through the fit value.

[0146] S432, based on the proportion of the matching deviation management target in the maintenance management target and the reference matching weight coefficient of different maintenance optimization processing targets, determine the adaptation value of the maintenance optimization processing target, and determine whether the adaptation value of the maintenance optimization processing target is greater than a preset adaptation threshold. If not, determine that the update processing strategy for the maintenance management target is to use the maintenance optimization processing target as the maintenance management target as long as the number of identified but missed fault types of the maintenance optimization processing target is greater than the preset number. If yes, determine that the update processing strategy for the maintenance management target is to use the maintenance optimization processing target as the maintenance management target as long as the maintenance optimization processing target has identified but missed fault types.

[0147] The adaptation value of S432 refers to the quantitative indicator calculated by combining the proportion of the comprehensive matching deviation management target and the reference matching weight coefficient; the preset adaptation threshold refers to the critical value for judging whether the adaptation value is too high.

[0148] The adaptation value of S432 is calculated as follows: Let P be the proportion of the matching deviation management target in the maintenance management target, and let Wsum_opt be the mean of the reference matching weight coefficient of different maintenance optimization treatment targets. Then: Fit value = (P deviation + Wsum_opt) / 2.

[0149] If the calculated fit value is greater than the preset fit threshold, it indicates that the proportion of the matching deviation management target is low and the weight coefficient of the optimization target is low. In this case, a stricter upgrade condition is adopted, requiring the optimization target to have multiple identified missing fault types before upgrading. If the fit value is greater than the threshold, a more lenient upgrade condition is adopted, and the upgrade is carried out as long as there is an identification omission.

[0150] By further differentiating upgrade strategies through adaptation values, and dynamically adjusting the strictness of upgrade conditions based on the overall adaptation degree when there are matching deviation management targets, a fine balance is achieved between upgrade coverage and screening accuracy.

[0151] This embodiment realizes a complete process from calculating the reference matching weight coefficient to analyzing the matching processing target and determining the maintenance management target update processing strategy. Its core value is reflected in three aspects: First, by judging the sum of the reference matching weight coefficients, a lenient upgrade strategy is adopted to ensure coverage when the load correlation is low; second, by judging the average number of matching processing targets, a lenient upgrade strategy is adopted to ensure identification reliability when the load coverage is insufficient; third, by judging the hierarchical nature of the matching deviation management target and the adaptation value, the strictness of the upgrade conditions is dynamically adjusted when the load coverage is still acceptable, thereby achieving an adaptive balance between upgrade coverage and screening accuracy.

[0152] Continuing with the calculation results from S3, the maintenance optimization targets are C2 and C7, a total of 2 units. The maintenance management targets are C1, C3, C5, C6, C8, and C10, a total of 6 units.

[0153] Step S41: Calculate the reference matching weight coefficient for each maintenance optimization target (the difference between the average operating load similarity coefficients of 1 and 6 maintenance management targets).

[0154] The reference matching weight coefficient for C2 is 0.70, and the reference matching weight coefficient for C7 is 0.75.

[0155] The sum of the reference matching weight coefficients is 1.45.

[0156] Determine if the sum of 1.45 and 1.45 is greater than the preset matching weight coefficient threshold of 1.3. If yes, then yes.

[0157] Based on the path that satisfies the conditions in S41, the update processing strategy for maintenance management objectives is determined to be that as long as there are identified but omitted fault types in the maintenance optimization processing objectives, the maintenance optimization processing objectives will be used as maintenance management objectives.

[0158] That is, for C2 and C7, as long as a fault type that is not identified by the fault model is found during the maintenance process, it will be upgraded to a maintenance management target.

[0159] This embodiment implements a maintenance management method for switchgear. S1 determines the maintenance management strategy and objectives through hierarchical analysis of the correlation between the number of missed faults and the type of fault, achieving an adaptive balance between maintenance coverage and resource efficiency. S2 determines whether maintenance optimization is needed through adaptation value analysis, triggering optimization when the maintenance management objectives may not effectively identify risks. S3 determines the maintenance optimization strategy and objectives through hierarchical analysis of the similarity coefficient of operating load and the proportion of associated objectives, achieving an adaptive balance between maintenance coverage and resource efficiency. S4 determines the update strategy for maintenance management objectives through hierarchical analysis of the reference matching weight coefficient and the matching processing objectives, achieving an adaptive balance between upgrade coverage and screening accuracy. Overall, this method constructs a complete technical closed loop from data collection to update strategy determination through the coordinated linkage of maintenance data analysis, risk assessment for identifying omissions, determination of maintenance management strategies, hierarchical determination of maintenance optimization strategies, and dynamic management of maintenance management target update strategies. Its core value is reflected in four aspects: First, it ensures the objectivity of maintenance management strategy determination through quantitative analysis of the number of omissions and associated weight values; second, it triggers timely maintenance optimization when maintenance management targets may not effectively identify risks through the judgment of fit values; third, it achieves adaptive selection of maintenance optimization targets through hierarchical judgment of operating load similarity coefficients and the proportion of associated targets; and fourth, it achieves dynamic determination of maintenance management target update strategies through feedback analysis of reference matching weight coefficients and matching targets, thereby ensuring the reliability of the model in identifying omission risks while reasonably controlling maintenance resource consumption.

[0160] Example 2 In a second aspect, the present invention provides a computer device comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for maintenance management of switchgear when running the computer program.

[0161] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0162] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0163] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for maintenance management of switchgear, characterized in that, Specifically, it includes: Based on the maintenance data of the switchgear in the switch room, identify the missing identification data of the switchgear under different fault types. Based on the missing identification data and the degree of correlation between the fault type and different switchgear, determine the maintenance management strategy. Based on the maintenance management strategy, the switch cabinets that are to be maintained according to a preset time cycle are identified and used as maintenance management targets. The degree of matching between the maintenance management targets in the switch cabinets and the updated processing results of the fault type identification omissions of the maintenance management targets is used to determine whether maintenance optimization processing of switch cabinets other than the maintenance management targets is required, and then proceed to the next step. Based on the correlation between the operating loads of different maintenance management objectives and the maintenance management objective data, determine the maintenance optimization strategy for switchgear excluding maintenance management objectives. The maintenance optimization processing strategy is used to determine the maintenance optimization processing targets in the switchgear, excluding the maintenance management targets. Based on the correlation between the maintenance optimization processing targets and different maintenance management targets in terms of operating load, and combined with the composition data of maintenance optimization processing targets whose similarity coefficient is less than a preset coefficient threshold, the update processing strategy of the maintenance management targets is determined.

2. The maintenance management method for switchgear as described in claim 1, characterized in that, The maintenance data of the switchgear is determined based on the identification results of the fault type during the maintenance process.

3. The maintenance management method for switchgear as described in claim 1, characterized in that, The missing identification data of the switchgear under different fault types are determined based on the number of fault types that the fault model failed to identify during the maintenance process.

4. The maintenance and management method for switchgear as described in claim 1, characterized in that, The method for determining the maintenance management strategy is as follows: Based on the identified omission data, the number of times the switchgear was identified in different fault types is determined, and the identification omission risk type in the fault type is determined based on the number of identified omissions. Based on the correlation between the identified omission risk types and different switch cabinets, determine the historical number of failures of the switch cabinets under different identified omission risk types; The maintenance management strategy is determined based on the identified omission risk types and the historical number of failures of the switchgear in different identified omission risk types.

5. The maintenance management method for switchgear as described in claim 4, characterized in that, If the proportion of identified risk types among the fault types does not meet the requirements, then all switchgear with a number of faults less than the preset fault threshold in the most recent preset time period will be treated as switchgear to be inspected and processed according to the preset time cycle.

6. The maintenance and management method for switchgear as described in claim 4, characterized in that, The identified omission risk type is the fault type that identifies the number of omissions that does not meet the requirements.

7. The maintenance management method for switchgear as described in claim 1, characterized in that, The switchgear that requires maintenance optimization beyond the scope of maintenance management objectives has been identified, specifically including: Based on the update processing results of the fault type identification omissions of the maintenance management targets, the maintenance management targets with fault type identification omissions during the maintenance process are identified and used as omission update targets. Based on the maintenance management objectives in the switchgear, determine the proportion of the number of maintenance management objectives in the switchgear and use it as the proportion of related objectives; Based on the degree of matching between the proportion of the associated targets and the missing update targets, it is determined whether maintenance optimization processing of switchgear, excluding maintenance management targets, is required.

8. The maintenance management method for switchgear as described in claim 7, characterized in that, Based on the degree of matching between the aforementioned proportion of related targets and the omitted update targets, it is determined whether maintenance optimization processing of switchgear, excluding maintenance management targets, is required. Specifically, this includes: The adaptation value is determined by the difference between the proportion of the associated target and the proportion of the omitted update target in the maintenance management target. When the adaptation value is below the preset adaptation threshold, it is determined that maintenance optimization processing of switchgear other than maintenance management target needs to be carried out.

9. The maintenance management method for switchgear as described in claim 1, characterized in that, The method for determining the update strategy for the maintenance management objectives is as follows: Based on the degree of correlation between the maintenance optimization processing target and different maintenance management targets in terms of operating load, a similarity coefficient of the operating load between the maintenance optimization processing target and different maintenance management targets is determined. Based on the average value of the similarity coefficient of the operating load between the maintenance optimization processing target and different maintenance management targets, a reference matching weight coefficient of the maintenance optimization processing target is determined. By using the similarity coefficient of the operating load between the maintenance processing optimization target and different maintenance management targets, maintenance optimization processing targets with similarity coefficients less than a preset coefficient threshold are identified and used as matching processing targets. The update processing strategy for the maintenance management objectives is determined by the number of matching processing objectives for different maintenance management objectives and the reference matching weight coefficients for different maintenance optimization processing objectives.

10. A computer device comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a maintenance management method for switchgear as described in any one of claims 1-9.

Images