Safety management method and system for power equipment maintenance data analysis
By establishing a database of maintenance personnel and predicting fault cascading relationships, the allocation of maintenance resources has been optimized, solving the problems of mismatched personnel capabilities and low resource allocation efficiency in power equipment maintenance, and improving the maintenance efficiency and safety of the power system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-01
- Publication Date
- 2026-03-27
AI Technical Summary
In the maintenance of power equipment, there are problems such as mismatch between personnel capabilities, low efficiency of resource allocation, lack of in-depth mining of historical fault data, and difficulty in predicting potential chain risks, resulting in low maintenance efficiency and safety hazards.
Establish a maintenance personnel database, analyze the compatibility between maintenance personnel and equipment failures, predict failure cascading relationships, monitor task status in real time, and optimize maintenance resource allocation.
It achieves a scientific match between maintenance personnel's capabilities and tasks, predicts potential fault propagation risks, improves maintenance efficiency and system resilience, and ensures the efficiency and safety of resource allocation.
Smart Images
Figure CN121745895A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of maintenance data analysis technology, specifically to a safety management method and system for analyzing maintenance data of power equipment. Background Technology
[0002] In power system operation and maintenance, equipment maintenance is a crucial link in ensuring stable system operation, and its management efficiency directly affects the reliability and security of power supply. Currently, there are still many pain points in power equipment maintenance management. In the traditional model, the assignment of maintenance personnel relies heavily on the experience and judgment of managers, lacking a systematic and quantitative analysis of personnel capabilities. Because different maintenance personnel have varying levels of proficiency in handling different types of equipment and faults, assigning tasks based solely on subjective experience often leads to a mismatch between personnel capabilities and fault requirements. This not only reduces maintenance efficiency but may also create safety hazards due to improper handling. Furthermore, there are complex interrelationships between equipment and faults in power systems; a single equipment fault or a specific fault type can trigger a chain reaction in other equipment or faults. However, existing management methods often focus on the immediate handling of current faults, lacking in-depth analysis of historical fault data. This makes it difficult to predict potential cascading risks, easily allowing local faults to spread into systemic problems and expand the scope of the fault's impact. In addition, the dynamic management capability of maintenance resources is insufficient. Delayed updates to personnel task status and a disconnect between database information and actual conditions make it difficult for managers to grasp the real-time distribution of human resources. This often results in a coexistence of idle personnel and backlogged tasks, leading to inefficient resource allocation and impacting fault response speed. Summary of the Invention
[0003] The main objective of this invention is to provide a safety management method and system for power equipment maintenance data analysis, thereby solving the problems mentioned in the background art.
[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a safety management method for power equipment maintenance data analysis, comprising the following steps: S1. Collect historical maintenance personnel data and establish a maintenance personnel database; S2. Extract the characteristics of maintenance personnel and analyze the degree of fit between maintenance personnel and power equipment types and power equipment fault types; S3. Retrieve historical equipment fault data and coordinate power equipment data within the same power system. S4. Analyze the fault cascading relationships of power equipment in the same power system and predict the risk of other power equipment failing when a power equipment fails. S5. When power equipment fails, priority analysis is performed on the maintenance personnel based on their matching degree and fault cascading relationship. S6. Monitor the task status of maintenance personnel in real time and update their busy status.
[0005] Furthermore, in step S1, after the maintenance personnel finish their maintenance of the power equipment, the maintenance records of the power equipment are stored in the log, thereby forming a maintenance personnel database about the maintenance personnel's maintenance records. The number of maintenance personnel in the database is X, the type of equipment is Y, and the type of equipment failure is W. The personnel data of the xth maintenance personnel is extracted to obtain the maintenance record of the xth maintenance personnel.
[0006] Furthermore, in step S2, the extracted features include: equipment maintenance proficiency features, equipment maintenance success rate features, fault repair proficiency features, and fault repair success rate features. The proficiency of the xth maintenance worker in the maintenance of the yth type of electrical equipment is denoted as . ; The success rate of the x-th maintenance worker in maintaining the y-th type of electrical equipment is denoted as . ; The equipment maintenance proficiency of the xth maintenance worker for the wth type of electrical equipment is denoted as . ; The success rate of the x-th maintenance worker in maintaining the w-th type of electrical equipment is denoted as . .
[0007] Furthermore, in step S2, the equipment type proficiency features and fault type proficiency features of the power system are also extracted. The extraction process is as follows: Query the historical fault records of any power system, and record the frequency of faults occurring in the y-th type of equipment in the power system as follows: The frequency of the w-th type of fault in the power system is denoted as . ; The xth maintenance worker's familiarity with the types of equipment in the power system The expression is as follows: (1); in, Weighting of proficiency in the maintenance of power equipment. Weighting of the success rate of power equipment maintenance; The xth maintenance worker's familiarity with power system fault types The expression is as follows: (2); in Weighting of equipment malfunction repair proficiency. Weighting of the success rate of equipment failure repair.
[0008] Furthermore, in step S3, historical equipment fault data is retrieved, and power equipment fault data for any power system is extracted, including equipment fault data and fault type data, with timestamps added to the data during extraction.
[0009] Furthermore, in step S4, the first in the power system The following is an analysis of the equipment malfunction: Set a monitoring cycle, in the power system When a certain type of equipment fails, the monitoring power system will... Within a monitoring cycle starting from the time when the equipment malfunctions, the first... The probability of equipment failure Set equipment cascading failure thresholds ,like Then judge Equipment failure The equipment failures are not interconnected, causing Equipment Interlocking coefficient of equipment =0; otherwise, determine Equipment failure Equipment failures have a cascading effect, causing for ; will the first The interlocking coefficient of this type of equipment Set it to 1.
[0010] Furthermore, regarding the occurrence of the first event in the power system The analysis of various fault types is as follows: Set a monitoring cycle, and when the first occurrence of a certain event occurs in the power system... When a certain type of fault occurs, the power system is monitored to detect when the fault occurs. The fault type occurs within a monitoring cycle starting from the time of the fault. The probability of each type of failure Set fault cascading threshold ,like Then judge Fault type The fault types are not cascading, making Fault pair Fault cascading coefficient =0; otherwise, determine Fault type The types of faults are interconnected, causing for ; The first The linkage coefficient of a type to itself Set it to 1.
[0011] Furthermore, in step S5, for any power system, when the first occurrence occurs... The equipment's first When a failure of type occurs, the call occurs. When one type of equipment fails, the cascading effect on other equipment is determined by calling the cascading effect of the first equipment failure. The fault cascading coefficient of this type to other fault types; The xth maintenance worker inspected the equipment that had this malfunction. Preferred degree for: (3); The xth maintenance worker investigated the type of this fault. Preferred degree for: (4); Finally, the equipment selection degree of the xth maintenance personnel will be optimized. and fault optimization degree The priority of the xth maintenance worker is obtained by performing a weighted summation.
[0012] Furthermore, in step S6, the task status of maintenance personnel is monitored in real time, and the maintenance personnel database is updated accordingly. When maintenance personnel receive a task, they are categorized as busy personnel; once the task is completed, their information is removed from the busy personnel category.
[0013] This invention also provides a safety management system for power equipment maintenance data analysis, used to implement the steps in the above method, the system comprising: The system includes a database module for maintenance personnel, a module for adapting and analyzing maintenance personnel, a module for integrating equipment fault data, a module for predicting fault cascading risks, a module for priority assessment, and a module for real-time status updates. The maintenance personnel data database module is used to collect historical maintenance personnel data and establish a maintenance personnel database. The maintenance personnel adaptation analysis module is used to analyze the characteristics of maintenance personnel and the degree of adaptation of maintenance personnel to the types of power equipment and the types of power equipment faults. The equipment fault data integration module is used to retrieve historical equipment fault data and to uniformly mobilize power equipment data within the same power system. The fault cascading risk prediction module is used to analyze the fault cascading relationships of power equipment in the same power system and predict the risk of other power equipment failing when a power equipment fails. The priority assessment module is used to analyze the priority of maintenance personnel based on their matching degree and fault cascading relationship when power equipment fails. The real-time status update module is used to monitor the task status of maintenance personnel in real time and update their busy status.
[0014] Beneficial effects: (1) By systematically reviewing the historical maintenance records of maintenance personnel, key characteristics are extracted from both equipment maintenance capabilities and fault handling levels. Combined with the actual characteristics of equipment and faults in the power system, a scientific match between personnel capabilities and task requirements is achieved. This process eliminates the arbitrariness of traditional experience-based assignment, allowing the most suitable personnel to handle the corresponding faults, reducing maintenance delays or oversights caused by capability mismatches, and strengthening the power system's safety defense line from the perspective of personnel allocation.
[0015] (2) By deeply mining historical data, we can sort out the chain relationship between equipment failures and failure types, and form a predictive mechanism for potential derivative problems. When a failure occurs, we can plan the key points of maintenance in advance based on the chain relationship, guide personnel to deal with the current problem while taking into account the equipment or failure type that may be affected, avoid the spread of a single failure into a systemic problem, and greatly improve the resilience of the power system to cope with complex failures.
[0016] (3) Real-time monitoring of the task status of maintenance personnel and synchronous updating of the database enable managers to keep track of the distribution of human resources at any time and flexibly adjust task allocation according to the urgency of the fault and the busyness of personnel. This dynamic adjustment mechanism avoids the situation of idle or overloaded manpower, ensures that maintenance resources are always concentrated on the most needed links, improves the smoothness and response speed of the overall maintenance process, and provides efficient management support for the stable operation of the power system. Attached Figure Description
[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of the safety management system for power equipment maintenance data analysis according to the present invention; Figure 2 This is a flowchart of the safety management method for power equipment maintenance data analysis according to the present invention. Detailed Implementation
[0018] Example 1 like Figures 1-2 As shown, a safety management method for analyzing power equipment maintenance data includes the following steps: S1. Collect historical maintenance personnel data and establish a maintenance personnel database. After maintenance personnel finish their maintenance of power equipment, they store the maintenance records in the log, thus forming a maintenance personnel database about maintenance personnel records. The number of maintenance personnel in the database is X, the type of equipment is Y, and the type of equipment failure is W. Extract personnel data for the xth maintenance personnel to obtain the maintenance record of the xth maintenance personnel.
[0019] S2. Extract the characteristics of maintenance personnel and analyze the degree of fit between maintenance personnel and power equipment types and power equipment fault types; The extracted features include: equipment maintenance proficiency features, equipment maintenance success rate features, fault repair proficiency features, and fault repair success rate features. The proficiency of the xth maintenance worker in the maintenance of the yth type of electrical equipment is denoted as . The equipment maintenance proficiency here represents the ratio of the number of times the xth maintenance worker successfully maintains the yth type of power equipment to the average number of times all X maintenance workers successfully maintain the yth type of power equipment. The success rate of the x-th maintenance worker in maintaining the y-th type of electrical equipment is denoted as . ; The equipment maintenance proficiency of the xth maintenance worker for the wth type of electrical equipment is denoted as . The troubleshooting proficiency here represents the ratio of the number of times the xth maintenance worker successfully repairs the wth type of equipment to the average number of times all the x maintenance workers successfully repair the wth type of equipment. The success rate of the x-th maintenance worker in maintaining the w-th type of electrical equipment is denoted as . .
[0020] The extracted features also include proficiency features of power system equipment types and proficiency features of power system fault types. The extraction process is as follows: Query the historical fault records of any power system, and record the frequency of faults occurring in the y-th type of equipment in the power system as follows: The frequency of the w-th type of fault in the power system is denoted as . ; The xth maintenance worker's familiarity with the types of equipment in the power system The expression is as follows: (1); in, Weighting of proficiency in the maintenance of power equipment. Weighting of the success rate of power equipment maintenance; The xth maintenance worker's familiarity with power system fault types The expression is as follows: (2); in Weighting of equipment malfunction repair proficiency. Weighting of the success rate of equipment failure repair.
[0021] S3. Retrieve historical equipment failure data and uniformly mobilize power equipment data of the same power system. The specific process is as follows: retrieve historical equipment failure data and extract power equipment failure data for any power system, including equipment failure data and failure type data. During extraction, timestamps are marked on the data.
[0022] S4. Analyze the fault cascading relationships of power equipment in the same power system and predict the risk of other power equipment failing when a power equipment fails. For the first in the power system The following is an analysis of the equipment malfunction: Set a monitoring cycle, in the power system When a certain type of equipment fails, the monitoring power system will... Within a monitoring cycle starting from the time when the equipment malfunctions, the first... The probability of equipment failure Set equipment cascading failure thresholds ,like Then judge Equipment failure The equipment failures are not interconnected, causing Equipment Interlocking coefficient of equipment =0; otherwise, determine Equipment failure Equipment failures have a cascading effect, causing for ; will the first The interlocking coefficient of this type of equipment Let's set it to 1, here Set as the first in the total of historical data The number of equipment failures and the occurrence of the first The ratio of the number of equipment failures.
[0023] For the first occurrence in the power system The analysis of various fault types is as follows: Set a monitoring cycle, and when the first occurrence of a certain event occurs in the power system... When a certain type of fault occurs, the power system is monitored to detect when the fault occurs. The fault type occurs within a monitoring cycle starting from the time of the fault. The probability of each type of failure Set fault cascading threshold ,like Then judge Fault type The fault types are not cascading, making Fault pair Fault cascading coefficient =0; otherwise, determine Fault type The types of faults are interconnected, causing for ; The first The linkage coefficient of a type to itself Set to 1, the fault cascading fault threshold here represents the ratio of the total number of times the w2th fault type occurred to the total number of times the w1th fault type occurred in the historical data.
[0024] S5. When power equipment fails, priority analysis is performed on the maintenance personnel based on their matching degree and fault cascading relationship. For any power system, when the first... The equipment's first When a failure of type occurs, the call occurs. When one type of equipment fails, the cascading effect on other equipment is determined by calling the cascading effect of the first equipment failure. The fault cascading coefficient of this type to other fault types; The xth maintenance worker inspected the equipment that had this malfunction. Preferred degree for: (3); The xth maintenance worker investigated the type of this fault. Preferred degree for: (4); Finally, the equipment selection degree of the xth maintenance personnel will be optimized. and fault optimization degree After performing a weighted summation, the task priority of the x-th maintenance worker is obtained, where the equipment priority is... Corresponding weights for: (5); Fault optimization Corresponding weights for: (6).
[0025] S6. Monitor the task status of maintenance personnel in real time and update their busy status. When maintenance personnel receive a task, they are classified as busy; when they complete the task, their information is removed from the busy list.
[0026] This embodiment also provides a method for using the system: First, after maintenance personnel complete equipment maintenance, their work records will be stored in the system, gradually forming an information database that includes all maintenance personnel, the types of equipment involved, and common fault types, making it easier to retrieve each person's past maintenance information later.
[0027] Next, features are extracted from the database to analyze the maintenance capabilities of each personnel. By comparing the number of successful maintenance attempts for various types of equipment by an individual with the group average, relative proficiency is determined; combined with the actual success rate, the absolute success rate is defined. Simultaneously, considering the frequency of various types of equipment in the system, a reasonable weighting is used to calculate the personnel's fit with the system's equipment requirements. For fault types, a similar logic is used to analyze the proficiency and success rate in handling different faults, and combined with the prevalence of the faults, the fit with the system's fault requirements is calculated, thus objectively presenting the match between personnel capabilities and system needs.
[0028] Subsequently, historical fault data (labeled with occurrence time) is retrieved to analyze potential correlations between faults. When a certain type of equipment fails, the probability of other equipment failing within a certain period is monitored. Based on set criteria, it is determined whether there is a cascading effect, and the correlation strength is labeled with coefficients. For each fault type, the probability of other fault types occurring in the same period after a certain type of fault occurs is analyzed to filter out meaningful cascading relationships and quantify them, thus clarifying the fault propagation path.
[0029] When a specific type of fault occurs in a certain type of equipment in the system, the chain correlation coefficient corresponding to the equipment and the fault type is retrieved. Combined with the previously calculated personnel suitability, the suitability of each person to handle the current fault and the possible chain problems is comprehensively evaluated. The priority order of task handling is determined through weighted calculation to ensure that the most suitable personnel intervene first.
[0030] Meanwhile, the system tracks the task status of maintenance personnel in real time, updates the information database in a timely manner, distinguishes between personnel who are busy and those who are idle, and ensures that personnel dynamics can be accurately grasped when assigning tasks, thereby improving overall maintenance efficiency.
[0031] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.
Claims
1. A security management method for power equipment overhaul data analysis, characterized in that: Comprise the following steps: S1, collect historical maintenance personnel data, establish a maintenance personnel database; S2, the characteristics of maintenance personnel are extracted, and the adaptation degree of maintenance personnel to the type of electric power equipment and the type of electric power equipment fault is analyzed; S3, the historical equipment fault data is called, and the electric power equipment data of the same electric power system is uniformly mobilized; S4, analyze the fault chain relationship of electric power equipment under the same electric power system, and predict the risk of other electric power equipment failure when electric power equipment failure occurs; S5, when the electric power equipment fails, the priority of the maintenance personnel is analyzed according to the matching degree of the maintenance personnel and the fault chain relationship; S6, real-time monitoring of the task situation of maintenance personnel, updating the busy state of maintenance personnel.
2. The security management method for power equipment overhaul data analysis according to claim 1, characterized in that: In step S1, after the maintenance personnel completes the maintenance of the electric power equipment, the electric power equipment maintenance record is stored in the log, and then the maintenance personnel database of the maintenance record of the maintenance personnel is formed, the number of maintenance personnel in the database is X, the type of equipment is Y, the type of equipment fault is W, the personnel data of the xth maintenance personnel is extracted, and the maintenance record of the xth maintenance personnel is obtained.
3. The security management method for power equipment overhaul data analysis according to claim 2, characterized in that: In step S2, the extracted features include: equipment maintenance proficiency feature, equipment maintenance success rate feature, fault maintenance proficiency feature, fault maintenance success rate feature; The xth maintenance worker's proficiency in the yth power equipment is denoted as ; The success rate of the xth maintenance personnel in the maintenance of the yth power equipment is recorded as ; The xth maintenance personnel's proficiency in the maintenance of the wth power equipment is denoted as ; The success rate of the xth maintenance personnel in the maintenance of the wth power equipment is denoted as .
4. The security management method for power equipment overhaul data analysis according to claim 3, characterized in that: In step S2, the device type proficiency feature of the electric power system and the fault type proficiency feature of the electric power system are also extracted, and the extraction process is as follows: Querying the historical fault records of any power system, the frequency of the yth equipment failure in the power system is denoted as , and the frequency of the wth fault in the power system is denoted as ; xthinspector proficiency in type of equipment of the power system The expression is as follows: （1）； wherein, is a weight of the proficiency of the maintenance of the power equipment, is a weight of the success rate of the maintenance of the power equipment; xth maintenance worker's proficiency in a fault type of the power system The expression is as follows: （2）； wherein is a repair proficiency weight for equipment failure, is a repair success rate weight for equipment failure.
5. The security management method for power equipment overhaul data analysis according to claim 4, characterized in that: In step S3, the historical equipment fault data is called, and the electric power equipment fault data of any electric power system is extracted, including equipment fault data and fault type data, and the data is labeled with time stamp during extraction.
6. The security management method for power equipment overhaul data analysis according to claim 5, characterized in that: In step S4, the first in the power system The following is an analysis of the equipment malfunction: Set a monitoring cycle, in the power system When a certain type of equipment fails, the monitoring power system will... Within a monitoring cycle starting from the time when the equipment malfunctions, the first... The probability of equipment failure Set equipment cascading failure thresholds ,like Then judge Equipment failure The equipment failures are not interconnected, causing Equipment Interlocking coefficient of equipment =0; otherwise, determine Equipment failure Equipment failures have a cascading effect, causing for ; will the first The interlocking coefficient of this type of equipment Set it to 1.
7. The security management method for power equipment overhaul data analysis according to claim 5, characterized in that: For the first occurrence in the power system The analysis of various fault types is as follows: Set a monitoring period, when a first fault type occurs in the power system, monitor the power system, monitor the probability of the power system occurring the first fault type within a monitoring period starting from the time of the first fault type The first type of linkage coefficient to itself is set to 1.
8. The security management method for power equipment overhaul data analysis according to claim 6 or 7, characterized in that: In step S5, for any power system, when the first occurrence occurs... The equipment's first When a failure of type occurs, the call occurs. When one type of equipment fails, the cascading effect on other equipment is determined by calling the cascading effect of the first equipment failure. The fault cascading coefficient of this type to other fault types; the xth maintenance person's preference for the equipment that failed this time is: （3）； The xth maintenance person's preference for the type of fault is: （4）； Finally the xth maintenance person device preference and fault preference are weighted summed to get the xth maintenance person task priority.
9. The security management method for power equipment overhaul data analysis according to claim 1, characterized in that: In step S6, the task situation of the maintenance personnel is monitored in real time, and the maintenance personnel database is updated: When the maintenance personnel receives the task, the maintenance personnel is divided into busy personnel; when the maintenance personnel completes the task, the maintenance personnel information is removed from the busy personnel.
10. A safety management system for power equipment overhaul data analysis to implement the steps of the method of any one of claims 1 to 9, characterized in that: Comprise maintenance personnel data library module, maintenance personnel adaptation analysis module, equipment fault data integration module, fault chain risk prediction module, priority research module and state real-time update module; The maintenance personnel data library module is used for collecting historical maintenance personnel data and establishing a maintenance personnel database; The maintenance personnel adaptation analysis module is used for analyzing the characteristics of maintenance personnel, and the adaptation degree of maintenance personnel to the type of electric power equipment and the type of electric power equipment fault; The equipment fault data integration module is used for calling the historical equipment fault data, and the electric power equipment data of the same electric power system is uniformly mobilized; The fault chain risk prediction module is used for analyzing the fault chain relationship of electric power equipment under the same electric power system, and predicting the risk of other electric power equipment failure when electric power equipment failure occurs; The priority research module is used for analyzing the priority of the maintenance personnel according to the matching degree of the maintenance personnel and the fault chain relationship when the electric power equipment fails; The state real-time update module is used for real-time monitoring of the task situation of maintenance personnel, updating the busy state of maintenance personnel.