A dynamic perception-based maintenance assistance device monitoring system and method
By dividing monitoring areas, establishing a fault database, and implementing scientific scheduling, the problem of uneven resource allocation in traditional maintenance auxiliary equipment management has been solved. This has enabled accurate prediction of auxiliary tool demand and efficient resource allocation, ensuring stable equipment operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGDONG QIMING MECHANICAL & ELECTRICAL EQUIPMENT CO LTD
- Filing Date
- 2025-10-24
- Publication Date
- 2026-07-24
AI Technical Summary
Traditional maintenance auxiliary equipment management lacks scientific support, leading to inaccurate judgment of auxiliary tool needs, uneven resource allocation, and affecting the timeliness and stability of equipment maintenance. Furthermore, the management model lacks a dynamic adjustment mechanism.
The maintenance auxiliary equipment monitoring system based on dynamic perception divides the monitoring area, establishes a fault database, analyzes the demand for auxiliary tools based on fault types, predicts the demand status, and performs scientific scheduling to optimize resource allocation.
It improves the accuracy of demand forecasting for auxiliary tools, optimizes resource scheduling, reduces management costs, enhances the continuity and stability of management, and ensures long-term equipment operation.
Smart Images
Figure CN121390441B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of auxiliary equipment monitoring technology, specifically to a maintenance auxiliary equipment monitoring system and method based on dynamic sensing. Background Technology
[0002] In the field of maintenance auxiliary equipment management, traditional methods for judging the demand for auxiliary tools lack scientific support and rely heavily on experience-based estimations. Because a systematic database linking faults and maintenance has not been established, and interference from invalid maintenance personnel's data has not been eliminated, it is impossible to accurately identify the actual demand for different fault types and various auxiliary tools. This often results in mismatches between predicted auxiliary tool demand and actual maintenance scenarios, leading to either excessive reserves causing idle waste or insufficient reserves to support maintenance work, thus affecting the progress of maintenance tasks and failing to guarantee the timeliness of equipment maintenance. Existing auxiliary tool resource scheduling and allocation lack overall coordination and regional collaboration. When dealing with differences in tool reserves across monitoring areas, regional allocation is often carried out directly without first assessing whether the total reserves meet the standards, and distance factors are not prioritized during allocation, often opting for long-distance transportation, increasing time and cost. Simultaneously, the lack of inter-regional resource complementarity mechanisms easily leads to imbalances where some areas have tool surpluses while others are in short supply. Managers often rely on subjective judgment during allocation, resulting in significant blind spots and further increasing resource management and operating costs. Traditional auxiliary tool management lacks a continuous and dynamic adjustment mechanism and has not established a regular evaluation system. After initial configuration, the matching of tool reserves and demand in each region is rarely re-analyzed at fixed intervals, making it difficult to detect changes in reserves and fluctuations in demand in a timely manner. When tools become insufficient due to consumption, timely replenishment is difficult, easily leading to maintenance delays due to tool shortages and affecting the stable operation of equipment such as generator sets. Furthermore, the management model lacks foresight, relying heavily on temporary emergency deployment, which increases deployment pressure and reduces the overall stability and reliability of equipment operation and maintenance. Summary of the Invention
[0003] The purpose of this invention is to provide a monitoring system and method for maintenance auxiliary equipment based on dynamic perception, so as to solve the problems mentioned in the background art.
[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a method for monitoring maintenance auxiliary equipment based on dynamic sensing, comprising the following steps: S1. Divide the monitoring area according to the monitoring range, mark the generator sets, and collect historical operating data, auxiliary tool data and fault type data; S2. Establish a fault database for the monitoring area and retrieve fault information from the monitoring area; S3. Based on historical data, analyze the demand for auxiliary tools according to different fault types; S4. For the monitoring area, predict the demand for auxiliary tools for one monitoring cycle; S5. Considering the demand and reserves of auxiliary tools, analyze the storage status of auxiliary tools within the monitoring range, and remind managers to replenish auxiliary tools until there are sufficient auxiliary tools within the monitoring range; S6. When it is found that there are sufficient auxiliary tools within the monitoring range, analyze the reserve status of auxiliary tools in the monitoring area and schedule the auxiliary tools within the monitoring range.
[0005] Furthermore, in step S1, after authorization, the monitoring range is divided into K monitoring areas, and monitoring area Z is... k The M generator sets within the area are marked, where Z k This represents the k-th monitoring area within the monitoring range. The M generator sets are denoted as {A1, A2, ..., A3}. m ,…,A M}, where A m This represents the m-th generator set; historical operating data of the M-th generator set is collected, including historical fault data and historical maintenance data; auxiliary tools within the monitoring area are marked, specifying their type and quantity. The number of auxiliary tool types is N, and the total number of N types of auxiliary tools is {B1, B2, ..., B3}. n ,…,B N}, where B n Indicates the quantity of the nth type of auxiliary tool; the monitoring area Z. k The number of historical fault types of the engine sets within the area is X. Through authorized systematic planning, a solid foundation is laid for subsequent monitoring of auxiliary maintenance equipment. Dividing the monitoring area clarifies the overall control scope, avoiding the confusion that can easily arise from directly managing a large monitoring area; marking generator sets allows for precise location of each set of equipment, facilitating subsequent tracking of its operating trajectory and status; collecting historical operating data provides a reliable basis for subsequent analysis of fault patterns and summarizing maintenance experience; clarifying the types and quantities of auxiliary tools allows for real-time monitoring of current tool resource reserves, avoiding delays caused by unclear information during subsequent deployment; statistically analyzing fault types clarifies the scope of equipment faults within the area in advance, laying the groundwork for targeted matching of tool needs, ensuring that the monitoring work has a clear structure and sufficient data support from the outset.
[0006] Furthermore, in step S2, the monitoring area Z... k A fault database is established for the generator sets. This database stores fault information and maintenance information for the generator sets. The fault information includes the fault type and the generator set where the fault occurred. The maintenance information includes maintenance personnel information, auxiliary tool information, and maintenance results. Maintenance results are categorized as successful or unsuccessful, thereby obtaining the monitoring area Z.k The number of X types of faults in the engine unit is {C1, C, ..., C}. x ,…,C X}, where C x Indicates monitoring area Z k The database records the number of times the xth type of fault occurs in the generator sets within the monitoring area. By establishing a fault database for generator sets in the monitoring area, scattered fault and maintenance information is effectively integrated, avoiding the inconvenience of querying and management chaos caused by fragmented information storage. The database clearly records fault types and corresponding faulty equipment, which helps to quickly locate related equipment for different faults and reduce fault investigation time. At the same time, the stored information on maintenance personnel, auxiliary tools, and maintenance results provides a basis for subsequent screening of effective maintenance data and analysis of the correlation between tool use and maintenance results. In addition, by statistically analyzing the occurrence of various faults, the occurrence patterns of faults in the area can be preliminarily grasped, laying a data foundation for accurate judgment of the need for auxiliary tools.
[0007] Furthermore, in step S3, in the monitoring area Z k In the process, after excluding maintenance information handled by invalid personnel, the number of occurrences of type x fault is Y. The fault database is then accessed to determine the number of times type x faults occurred in the Y occurrences, where type n auxiliary tools were carried. x_n In D x_n The success parameter for this repair was E. x_n If D x_n =0, let E x_n =0; if D x_n ≠0, E x_n D x_n The number of successful repairs in this repair and D x_n The ratio, in Y types of faults, is d, the number of times the nth type of auxiliary tool was not carried. x_n , in d x_n The success parameter for this repair is e. x_n If d x_n =0, let e x_n =0; if d x_n ≠0, e x_n For d x_n The number of successful repairs in this repair and D x_n The ratio of the two values is used to obtain the necessary parameter F for the nth auxiliary tool for the xth type of fault. x_n =D x_n -d x_n If F x_nIf the value of the auxiliary tool is greater than or equal to F0, where F0 is a pre-defined threshold for necessary parameters, then the nth type of auxiliary tool is determined to be a necessary auxiliary tool for the xth type of fault. Otherwise, the nth type of auxiliary tool is determined to be an unnecessary auxiliary tool for the xth type of fault. Substituting each value into n=1,2,…,N, we obtain whether the nth type of auxiliary tool is a necessary or unnecessary auxiliary tool for the xth type of fault. When the nth type of auxiliary tool is a necessary auxiliary tool for the xth type of fault, let the requirement value of the nth type of auxiliary tool for the xth type of fault be Q. x_n =1; otherwise, let Q be the demand for the nth type of auxiliary tool for the xth type of fault. x_n =0; The method for determining invalid personnel is as follows: For any maintenance personnel α, their maintenance records are retrieved. If the maintenance success rate of maintenance personnel α is lower than a pre-set maintenance success rate threshold, then maintenance personnel α is determined to be an invalid personnel; otherwise, maintenance personnel α is determined to be a valid personnel. By excluding maintenance information from invalid personnel, the interference of low-quality data on the analysis is eliminated, ensuring that the data used to determine the necessity of auxiliary tools is more reliable. By comparing maintenance situations with and without a certain type of auxiliary tool, combined with necessary parameter thresholds, the necessary auxiliary tools for various types of faults can be accurately distinguished, clarifying the actual need for tools for different faults. This targeted analysis makes tool demand judgment clearer than vague, avoiding the inclusion of unnecessary tools in demand considerations, providing an accurate basis for subsequent prediction of auxiliary tool demand, making tool management more aligned with actual maintenance needs, and reducing the possibility of resource misallocation.
[0008] Furthermore, in step S4, for monitoring area Z... k Within the previous β monitoring periods ending at the current time, the number of occurrences of type X faults is {G}. 1_n G 2_n ,…,G x_n ,…,G X_n}, where G x_n Indicating in monitoring area Z k The number of occurrences of the x-th fault type is given by β, where β is the predetermined reference monitoring cycle number. This leads to the calculation of the number of occurrences of the x-th fault type in the monitoring area Z. k The predicted demand for the nth type of auxiliary tool within one monitoring period is H. k_n : ; Substituting each value into n=1,2,…,N, we obtain the values within the monitoring region Z. k The predicted demand for N types of assistive tools within a monitoring period {H} k_1 H k_2 ,…,H k_n ,…,H k_NBased on accurate prior assessments of fault types and auxiliary tool requirements, combined with historical fault occurrence patterns, the system scientifically predicts the demand for various auxiliary tools within the monitoring area. It fully utilizes real fault frequency data from the fault database, closely linking tool requirements with actual fault scenarios. This allows the prediction results to overcome the limitations of subjective assumptions and better reflect actual tool consumption during maintenance. This precise demand prediction enables managers to clearly define the tool demand scale for each area in advance, avoiding both resource idleness due to excessive tool reserves and maintenance delays caused by insufficient reserves. It provides a clear quantitative basis for subsequent overall allocation and regional management of auxiliary tools, promoting a shift in auxiliary tool management from passive response to proactive planning, and improving the scientific nature and efficiency of tool resource allocation in equipment operation and maintenance.
[0009] Furthermore, in step S5, by substituting k=1,2,…,K one by one, the predicted demand {H} for the nth auxiliary tool in one monitoring period across K monitoring areas is obtained. 1_n H 2_n ,…,H k_n ,…,H K_n}, and thus obtain the total predicted demand J for the nth auxiliary tool within the monitoring range. n The total predicted demand for the nth auxiliary tool within the monitoring range is the sum of the predicted demand for the nth auxiliary tool within one monitoring period in K monitoring areas. This is used to determine the reserve status of the nth auxiliary tool within the monitoring range. If L n ≥(1+P)*J n If L is sufficient, then it is determined that the supply of the nth type of auxiliary tool within the monitoring range is adequate. nThe system calculates the total quantity of the nth type of auxiliary tool across K monitoring areas, where P is the pre-set reserve percentage for auxiliary tools. Otherwise, it determines that the reserve of the nth type of auxiliary tool within the monitoring area is insufficient, prompting management personnel to replenish the auxiliary tools. This process continues until the reserve of the nth type of auxiliary tool within the monitoring area is deemed sufficient. Substituting n=1,2,…,N into the system, the reserve status of the N types of auxiliary tools within the monitoring area is assessed. By integrating the predicted demand for each type of auxiliary tool across all monitoring areas, the system controls the tool resource status within the monitoring area from a holistic perspective, avoiding the problem of focusing only on a single area while neglecting the overall resource balance. It first summarizes the demand from each area to obtain the total demand, then combines this with the reserve percentage to calculate a reasonable resource baseline. This ensures that the reserve assessment not only meets basic needs but also addresses potential emergencies, enhancing the resilience of resource management. When a shortage of a certain tool is detected, timely replenishment is prompted, effectively preventing overall resource shortages from affecting maintenance work in various areas and ensuring that tool supply covers the actual needs of all areas. This demand accounting and reserve assessment model, from regional to overall perspective, lays a solid foundation for subsequent inter-regional tool scheduling, guaranteeing the stability and sufficiency of auxiliary tool resource supply throughout the entire monitoring area.
[0010] Furthermore, in step S6, after supplementing N auxiliary tools, regional reserve analysis is performed on K monitoring areas. In the kth monitoring area, if B n ≥(1+P)*H k_n If the number of auxiliary tools of type n is sufficient in the k-th monitoring area, then the maximum number of auxiliary tools of type n to be scheduled in the k-th monitoring area is B. n -(1+P)*H k_n Otherwise, it is determined that the nth type of auxiliary tools in the kth monitoring area are insufficient, and the demand for the nth type of auxiliary tools in the kth monitoring area is (1+P)*H. k_n -B n If there exists a sufficient number of auxiliary tools of type n and the maximum scheduling quantity of auxiliary tools of type n is greater than or equal to (1+P)*H k_n -B n From the selected monitoring areas, the monitoring area closest to the k-th monitoring area is chosen as the scheduling source, and (1+P)*H is transported from the scheduling source to the k-th monitoring area. k_n -B nIf there is insufficient auxiliary tools of type n, then from the monitoring areas that meet the requirement of sufficient auxiliary tools of type n, transport the maximum number of auxiliary tools of type n corresponding to the monitoring areas according to the interval distance from smallest to largest to the kth monitoring area, until the kth monitoring area has sufficient auxiliary tools of type n. Then substitute n=1,2,…,N one by one to make the kth monitoring area have sufficient auxiliary tools of type n, and then substitute k=1,2,…,K one by one to make the K monitoring areas have sufficient auxiliary tools of type n. After determining that the K monitoring areas have sufficient auxiliary tools of type n, after another monitoring cycle, the auxiliary tool status of the monitoring areas is analyzed again. By conducting a detailed analysis of the auxiliary tool reserves of each monitoring area, the problem of tool distribution imbalance between areas is addressed in a targeted manner. For areas with insufficient tools, priority is given to dispatching from the nearest sufficient area, reducing the time and resource consumption during transportation and making dispatching more efficient. When a single area cannot meet the demand, tools are allocated in an orderly manner according to distance to ensure that the shortage area can quickly replenish tools and avoid maintenance being affected by local shortages. Meanwhile, after ensuring sufficient tools in all areas, the situation is regularly re-analyzed to form a dynamic adjustment mechanism, so that the tool reserves in each area always match the actual needs. This not only prevents the waste of resources but also enables continuous response to maintenance needs, providing long-term tool support for the stable operation of equipment.
[0011] A maintenance auxiliary equipment monitoring system based on dynamic perception, comprising: a data acquisition and area marking module, a fault database establishment module, a tool demand analysis module, a tool demand prediction module, a tool inventory monitoring and replenishment prompt module, and an area tool scheduling module; The data acquisition and area marking module is used to divide the monitoring area according to the monitoring range, mark the generator set, and collect historical operating data, auxiliary tool data and fault type data. The fault database establishment module is used to establish a fault database for the monitoring area and call up fault information of the monitoring area. The tool requirements analysis module is used to analyze the demand for auxiliary tools based on historical data. The tool demand forecasting module is used to forecast the demand for auxiliary tools for a monitoring period for the monitoring area. The tool inventory monitoring and replenishment reminder module is used to consider the demand and inventory of auxiliary tools, analyze the storage status of auxiliary tools within the monitoring range, and remind managers to replenish auxiliary tools until there are sufficient auxiliary tools within the monitoring range. The regional tool scheduling module is used to analyze the availability of auxiliary tools in the monitoring area and schedule auxiliary tools within the monitoring area when sufficient auxiliary tools are detected.
[0012] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: Firstly, it improves the accuracy of auxiliary tool demand prediction, ensuring smooth maintenance. By establishing a fault database, integrating maintenance records from qualified personnel, and eliminating interference from invalid personnel data, it can more objectively determine the actual demand for various auxiliary tools for different fault types. Based on the correlation between historical fault occurrence patterns and tool usage, the predicted auxiliary tool demand is more closely aligned with actual maintenance scenarios, avoiding the problem of excessive or insufficient tool reserves due to biased demand assessments. This ensures that the necessary auxiliary tools can be promptly deployed during maintenance, providing a fundamental guarantee for the efficient advancement of maintenance work.
[0013] On the one hand, optimizing the scheduling and allocation of auxiliary tools reduces management costs. After determining the auxiliary tool needs of each monitoring area, the overall sufficiency of total reserves is assessed first, and then scheduling is carried out based on the differences in reserves between regions. Priority is given to allocating resources to nearby areas with sufficient reserves, reducing the time and cost of long-distance transportation and achieving resource complementarity between regions. Through scientific scheduling, the distribution of auxiliary tool resources across the entire monitoring area becomes more balanced, avoiding waste caused by tool backlogs in some areas and tool shortages in others. This also reduces the blind spots in tool allocation by management personnel, lowering resource management and operating costs.
[0014] On the other hand, enhancing the continuity and stability of auxiliary tool management ensures long-term equipment operation. This method periodically re-analyzes the status of auxiliary tools, reassesses the matching of tool reserves and demand in each region after a monitoring cycle, and promptly identifies changes in reserves and fluctuations in demand. By replenishing insufficient tools in advance and adjusting configurations between regions, maintenance delays caused by tool problems can be effectively avoided, ensuring the continuous and stable operation of equipment such as generator sets. Simultaneously, this dynamic management model makes auxiliary tool management more planned and forward-looking, reduces the pressure of temporary emergency deployment, and improves the overall stability and reliability of equipment operation and maintenance management. Attached Figure Description
[0015] 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 a maintenance auxiliary equipment monitoring system based on dynamic sensing according to the present invention; Figure 2 This is a flowchart of a maintenance auxiliary equipment monitoring method based on dynamic perception according to the present invention. Detailed Implementation
[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0017] Please see Figure 1 and Figure 2 The present invention provides a technical solution: a method for monitoring maintenance auxiliary equipment based on dynamic sensing, comprising the following steps: S1. Divide the monitoring area according to the monitoring range, mark the generator sets, and collect historical operating data, auxiliary tool data and fault type data; S2. Establish a fault database for the monitoring area and retrieve fault information from the monitoring area; S3. Based on historical data, analyze the demand for auxiliary tools according to different fault types; S4. For the monitoring area, predict the demand for auxiliary tools for one monitoring cycle; S5. Considering the demand and reserves of auxiliary tools, analyze the storage status of auxiliary tools within the monitoring range, and remind managers to replenish auxiliary tools until there are sufficient auxiliary tools within the monitoring range; S6. When it is found that there are sufficient auxiliary tools within the monitoring range, analyze the reserve status of auxiliary tools in the monitoring area and schedule the auxiliary tools within the monitoring range.
[0018] In step S1, after authorization, the monitoring range is divided into K monitoring areas, and monitoring area Z is... k The M generator sets within the area are marked, where Z k This represents the k-th monitoring area within the monitoring range. The M generator sets are denoted as {A1, A2, ..., A3}. m ,…,A M}, where A m This represents the m-th generator set; historical operating data of the M-th generator set is collected, including historical fault data and historical maintenance data; auxiliary tools within the monitoring area are marked, specifying their type and quantity. The number of auxiliary tool types is N, and the total number of N types of auxiliary tools is {B1, B2, ..., B3}. n ,…,B N}, where B n Indicates the quantity of the nth type of auxiliary tool; the monitoring area Z. kThe number of historical fault types of the engine sets within the area is X. Through authorized systematic planning, a solid foundation is laid for subsequent monitoring of auxiliary maintenance equipment. Dividing the monitoring area clarifies the overall control scope, avoiding the confusion that can easily arise from directly managing a large monitoring area; marking generator sets allows for precise location of each set of equipment, facilitating subsequent tracking of its operating trajectory and status; collecting historical operating data provides a reliable basis for subsequent analysis of fault patterns and summarizing maintenance experience; clarifying the types and quantities of auxiliary tools allows for real-time monitoring of current tool resource reserves, avoiding delays caused by unclear information during subsequent deployment; statistically analyzing fault types clarifies the scope of equipment faults within the area in advance, laying the groundwork for targeted matching of tool needs, ensuring that the monitoring work has a clear structure and sufficient data support from the outset.
[0019] In step S2, the monitoring area Z is... k A fault database is established for the generator sets. This database stores fault information and maintenance information for the generator sets. The fault information includes the fault type and the generator set where the fault occurred. The maintenance information includes maintenance personnel information, auxiliary tool information, and maintenance results. Maintenance results are categorized as successful or unsuccessful, thereby obtaining the monitoring area Z. k The number of X types of faults in the engine unit is {C1, C, ..., C}. x ,…,C X}, where C x Indicates monitoring area Z k The database records the number of times the xth type of fault occurs in the generator sets within the monitoring area. By establishing a fault database for generator sets in the monitoring area, scattered fault and maintenance information is effectively integrated, avoiding the inconvenience of querying and management chaos caused by fragmented information storage. The database clearly records fault types and corresponding faulty equipment, which helps to quickly locate related equipment for different faults and reduce fault investigation time. At the same time, the stored information on maintenance personnel, auxiliary tools, and maintenance results provides a basis for subsequent screening of effective maintenance data and analysis of the correlation between tool use and maintenance results. In addition, by statistically analyzing the occurrence of various faults, the occurrence patterns of faults in the area can be preliminarily grasped, laying a data foundation for accurate judgment of the need for auxiliary tools.
[0020] In step S3, in monitoring area Z k In the process, after excluding maintenance information handled by invalid personnel, the number of occurrences of type x fault is Y. The fault database is then accessed to determine the number of times type x faults occurred in the Y occurrences, where type n auxiliary tools were carried. x_n In D x_n The success parameter for this repair was E. x_n If D x_n =0, let E x_n =0; if Dx_n ≠0, E x_n D x_n The number of successful repairs in this repair and D x_n The ratio, in Y types of faults, is d, the number of times the nth type of auxiliary tool was not carried. x_n , in d x_n The success parameter for this repair is e. x_n If d x_n =0, let e x_n =0; if d x_n ≠0, e x_n For d x_n The number of successful repairs in this repair and D x_n The ratio of the two values is used to obtain the necessary parameter F for the nth auxiliary tool for the xth type of fault. x_n =D x_n -d x_n If F x_n If the value of the auxiliary tool is greater than or equal to F0, where F0 is a pre-defined threshold for necessary parameters, then the nth type of auxiliary tool is determined to be a necessary auxiliary tool for the xth type of fault. Otherwise, the nth type of auxiliary tool is determined to be an unnecessary auxiliary tool for the xth type of fault. Substituting each value into n=1,2,…,N, we obtain whether the nth type of auxiliary tool is a necessary or unnecessary auxiliary tool for the xth type of fault. When the nth type of auxiliary tool is a necessary auxiliary tool for the xth type of fault, let the requirement value of the nth type of auxiliary tool for the xth type of fault be Q. x_n =1; otherwise, let Q be the demand for the nth type of auxiliary tool for the xth type of fault. x_n =0; The method for determining invalid personnel is as follows: For any maintenance personnel α, their maintenance records are retrieved. If the maintenance success rate of maintenance personnel α is lower than a pre-set maintenance success rate threshold, then maintenance personnel α is determined to be an invalid personnel; otherwise, maintenance personnel α is determined to be a valid personnel. By excluding maintenance information from invalid personnel, the interference of low-quality data on the analysis is eliminated, ensuring that the data used to determine the necessity of auxiliary tools is more reliable. By comparing maintenance situations with and without a certain type of auxiliary tool, combined with necessary parameter thresholds, the necessary auxiliary tools for various types of faults can be accurately distinguished, clarifying the actual need for tools for different faults. This targeted analysis makes tool demand judgment clearer than vague, avoiding the inclusion of unnecessary tools in demand considerations, providing an accurate basis for subsequent prediction of auxiliary tool demand, making tool management more aligned with actual maintenance needs, and reducing the possibility of resource misallocation.
[0021] In step S4, for monitoring area Z k Within the previous β monitoring periods ending at the current time, the number of occurrences of type X faults is {G}. 1_n G 2_n,…,G x_n ,…,G X_n}, where G x_n Indicating in monitoring area Z k The number of occurrences of the x-th fault type is given by β, where β is the predetermined reference monitoring cycle number. This leads to the calculation of the number of occurrences of the x-th fault type in the monitoring area Z. k The predicted demand for the nth type of auxiliary tool within one monitoring period is H. k_n : ; Substituting each value into n=1,2,…,N, we obtain the values within the monitoring region Z. k The predicted demand for N types of assistive tools within a monitoring period {H} k_1 H k_2 ,…,H k_n ,…,H k_N Based on accurate prior assessments of fault types and auxiliary tool requirements, combined with historical fault occurrence patterns, the system scientifically predicts the demand for various auxiliary tools within the monitoring area. It fully utilizes real fault frequency data from the fault database, closely linking tool requirements with actual fault scenarios. This allows the prediction results to overcome the limitations of subjective assumptions and better reflect actual tool consumption during maintenance. This precise demand prediction enables managers to clearly define the tool demand scale for each area in advance, avoiding both resource idleness due to excessive tool reserves and maintenance delays caused by insufficient reserves. It provides a clear quantitative basis for subsequent overall allocation and regional management of auxiliary tools, promoting a shift in auxiliary tool management from passive response to proactive planning, and improving the scientific nature and efficiency of tool resource allocation in equipment operation and maintenance.
[0022] In step S5, substituting k=1,2,…,K one by one, we obtain the predicted demand {H} for the nth auxiliary tool in one monitoring period across K monitoring areas. 1_n H 2_n ,…,H k_n ,…,H K_n}, and thus obtain the total predicted demand J for the nth auxiliary tool within the monitoring range. n The total predicted demand for the nth auxiliary tool within the monitoring range is the sum of the predicted demand for the nth auxiliary tool within one monitoring period in K monitoring areas. This is used to determine the reserve status of the nth auxiliary tool within the monitoring range. If L n ≥(1+P)*J n If L is sufficient, then it is determined that the supply of the nth type of auxiliary tool within the monitoring range is adequate. nThe system calculates the total quantity of the nth type of auxiliary tool across K monitoring areas, where P is the pre-set reserve percentage for auxiliary tools. Otherwise, it determines that the reserve of the nth type of auxiliary tool within the monitoring area is insufficient, prompting management personnel to replenish the auxiliary tools. This process continues until the reserve of the nth type of auxiliary tool within the monitoring area is deemed sufficient. Substituting n=1,2,…,N into the system, the reserve status of the N types of auxiliary tools within the monitoring area is assessed. By integrating the predicted demand for each type of auxiliary tool across all monitoring areas, the system controls the tool resource status within the monitoring area from a holistic perspective, avoiding the problem of focusing only on a single area while neglecting the overall resource balance. It first summarizes the demand from each area to obtain the total demand, then combines this with the reserve percentage to calculate a reasonable resource baseline. This ensures that the reserve assessment not only meets basic needs but also addresses potential emergencies, enhancing the resilience of resource management. When a shortage of a certain tool is detected, timely replenishment is prompted, effectively preventing overall resource shortages from affecting maintenance work in various areas and ensuring that tool supply covers the actual needs of all areas. This demand accounting and reserve assessment model, from regional to overall perspective, lays a solid foundation for subsequent inter-regional tool scheduling, guaranteeing the stability and sufficiency of auxiliary tool resource supply throughout the entire monitoring area.
[0023] In step S6, after supplementing N auxiliary tools, regional reserve analysis is performed on K monitoring areas. In the kth monitoring area, if B n ≥(1+P)*H k_n If the number of auxiliary tools of type n is sufficient in the k-th monitoring area, then the maximum number of auxiliary tools of type n to be scheduled in the k-th monitoring area is B. n -(1+P)*H k_n Otherwise, it is determined that the nth type of auxiliary tools in the kth monitoring area are insufficient, and the demand for the nth type of auxiliary tools in the kth monitoring area is (1+P)*H. k_n -B n If there exists a sufficient number of auxiliary tools of type n and the maximum scheduling quantity of auxiliary tools of type n is greater than or equal to (1+P)*H k_n -B n From the selected monitoring areas, the monitoring area closest to the k-th monitoring area is chosen as the scheduling source, and (1+P)*H is transported from the scheduling source to the k-th monitoring area. k_n -B nIf there is insufficient auxiliary tools of type n, then from the monitoring areas that meet the requirement of sufficient auxiliary tools of type n, transport the maximum number of auxiliary tools of type n corresponding to the monitoring areas according to the interval distance from smallest to largest to the kth monitoring area, until the kth monitoring area has sufficient auxiliary tools of type n. Then substitute n=1,2,…,N one by one to make the kth monitoring area have sufficient auxiliary tools of type n, and then substitute k=1,2,…,K one by one to make the K monitoring areas have sufficient auxiliary tools of type n. After determining that the K monitoring areas have sufficient auxiliary tools of type n, after another monitoring cycle, the auxiliary tool status of the monitoring areas is analyzed again. By conducting a detailed analysis of the auxiliary tool reserves of each monitoring area, the problem of tool distribution imbalance between areas is addressed in a targeted manner. For areas with insufficient tools, priority is given to dispatching from the nearest sufficient area, reducing the time and resource consumption during transportation and making dispatching more efficient. When a single area cannot meet the demand, tools are allocated in an orderly manner according to distance to ensure that the shortage area can quickly replenish tools and avoid maintenance being affected by local shortages. Meanwhile, after ensuring sufficient tools in all areas, the situation is regularly re-analyzed to form a dynamic adjustment mechanism, so that the tool reserves in each area always match the actual needs. This not only prevents the waste of resources but also enables continuous response to maintenance needs, providing long-term tool support for the stable operation of equipment.
[0024] A maintenance auxiliary equipment monitoring system based on dynamic perception, the system comprising: a data acquisition and area marking module, a fault database establishment module, a tool demand analysis module, a tool demand prediction module, a tool inventory monitoring and replenishment prompt module, and an area tool scheduling module; The data acquisition and area marking module is used to divide the monitoring area according to the monitoring range, mark the generator set, and collect historical operating data, auxiliary tool data, and fault type data. The fault database establishment module is used to establish a fault database for the monitoring area and retrieve fault information from the monitoring area. The tool requirements analysis module is used to analyze the demand for auxiliary tools based on historical data. The tool demand forecasting module is used to forecast the demand for auxiliary tools for a monitoring period for a specific monitoring area. The tool inventory monitoring and replenishment reminder module is used to consider the demand and inventory of auxiliary tools, analyze the storage status of auxiliary tools within the monitoring range, and remind managers to replenish auxiliary tools until there are sufficient auxiliary tools within the monitoring range. The regional tool scheduling module is used to analyze the availability of auxiliary tools in the monitoring area and schedule auxiliary tools within the monitoring area when sufficient auxiliary tools are detected.
[0025] Example 1: Taking the monitoring of auxiliary equipment for generator set maintenance in an industrial park as an example, in step S1, after authorization from the park management, the monitoring scope of the entire park's generator sets is divided into three zones. Each generator set in each zone is marked for easy tracking; simultaneously, past fault information and maintenance records of these generator sets are collected, and this data will serve as the basis for analysis. Furthermore, the types and current quantities of maintenance auxiliary tools in each zone, such as various wrenches and testing instruments, are clearly marked; and the specific types of historical faults of the generator sets in each zone are identified to prepare for subsequent analysis.
[0026] In step S2, a fault database is established for the generator sets in each area. This database stores detailed information about the fault types of the generator sets, the specific generator set that experienced the fault, as well as personnel information, auxiliary tools used, and whether the repair was successful or unsuccessful. This database clearly shows the frequency of various faults within each area; for example, it shows if a certain type of fault occurred multiple times in a particular area, providing intuitive data support for subsequent analysis.
[0027] In step S3, invalid repair personnel are first identified. For example, if a repair personnel's repair records show a success rate far below the park's set standard, they are deemed invalid, and their repair information is excluded. Then, for a specific type of fault, after excluding invalid personnel's repair records, the frequency of that fault is counted. The fault database is accessed to analyze the frequency and success rate of repairs using a particular auxiliary tool when handling the fault, as well as the success rate when not using it. By comparing these, the necessity of the auxiliary tool for this type of fault is determined. For example, when handling a certain type of electrical fault, the number of successful repairs using a specific testing instrument is far higher than when it is not used, indicating that the testing instrument is a necessary tool for handling this type of fault, thus determining the demand correlation between the fault and the tool.
[0028] In step S4, for each area, the occurrence of various faults in the previous monitoring cycles is reviewed. Based on these historical patterns, the demand for various auxiliary tools in that area within a monitoring cycle is predicted. For example, if a certain type of mechanical fault frequently occurs in a certain area in past cycles, and handling this fault requires a specific wrench, the cycle demand for this type of wrench in that area is predicted.
[0029] In step S5, the predicted demand for each type of auxiliary tool in the three areas is summarized to obtain the total demand for the tool in the entire park. Then, the total reserves of the tool in the park are compared. If the total reserves are sufficient to cover the total demand and have a certain reserve, the reserves are deemed sufficient; if they are insufficient, management personnel are prompted to replenish the reserves until the requirements are met. This judgment is made for each type of auxiliary tool.
[0030] In step S6, after replenishing the auxiliary tools, the reserves in each area are analyzed. If the reserves of a certain type of tool in a certain area meet its cycle requirements and have reserves, it is determined that the tool is sufficient in that area, and any surplus can be allocated; if it is insufficient, the quantity to be allocated is calculated. Priority is given to allocating from the nearest area with sufficient reserves of that tool. If the nearest area cannot meet the requirements, allocation is made from other areas with sufficient reserves in order of distance, until the tool is sufficient in that area. This allocation is performed for each type of tool and each area to ensure that all areas have sufficient auxiliary tools. After all areas are satisfied, a monitoring cycle is completed, and the status of auxiliary tools in each area is analyzed again to enter the next round of dynamic monitoring and allocation.
[0031] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary sensing device embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
Claims
1. A method for monitoring maintenance auxiliary equipment based on dynamic sensing, characterized in that: The method includes the following steps: S1. Divide the monitoring area according to the monitoring range, mark the generator sets, and collect historical operating data, auxiliary tool data and fault type data; S2. Establish a fault database for the monitoring area and retrieve fault information from the monitoring area; S3. Based on historical data, analyze the demand for auxiliary tools according to different fault types; S4. For the monitoring area, predict the demand for auxiliary tools for one monitoring cycle; S5. Considering the demand and reserves of auxiliary tools, analyze the storage status of auxiliary tools within the monitoring range, and remind managers to replenish auxiliary tools until there are sufficient auxiliary tools within the monitoring range; S6. When it is found that there are enough auxiliary tools in the monitoring range, analyze the storage status of auxiliary tools in the monitoring area and schedule the auxiliary tools in the monitoring area within the monitoring range. In step S3, in monitoring area Z k In the process, after excluding maintenance information handled by invalid personnel, the number of occurrences of type x fault is Y. The fault database is then accessed to determine the number of times type x faults occurred in the Y occurrences, where type n auxiliary tools were carried. x_n In D x_n The success parameter for this repair was E. x_n If D x_n =0, let E x_n =0; if D x_n ≠0, E x_n D x_n The number of successful repairs in this repair and D x_n The ratio, in Y types of faults, is d, the number of times the nth type of auxiliary tool was not carried. x_n , in d x_n The success parameter for this repair is e. x_n If d x_n =0, let e x_n =0; if d x_n ≠0, e x_n For d x_n The number of successful repairs in this repair and D x_n The ratio of the two values is used to obtain the necessary parameter F for the nth auxiliary tool for the xth type of fault. x_n =D x_n -d x_n If F x_n If the value of the auxiliary tool is greater than or equal to F0, where F0 is a pre-defined threshold for necessary parameters, then the nth type of auxiliary tool is determined to be a necessary auxiliary tool for the xth type of fault. Otherwise, the nth type of auxiliary tool is determined to be an unnecessary auxiliary tool for the xth type of fault. Substituting each value into n=1,2,…,N, we obtain whether the nth type of auxiliary tool is a necessary or unnecessary auxiliary tool for the xth type of fault. When the nth type of auxiliary tool is a necessary auxiliary tool for the xth type of fault, let the requirement value of the nth type of auxiliary tool for the xth type of fault be Q. x_n =1; otherwise, let Q be the demand for the nth type of auxiliary tool for the xth type of fault. x_n =0; In step S4, for monitoring area Z k Within the previous β monitoring periods ending at the current time, the number of occurrences of type X faults is {G}. 1_n G 2_n ,…,G x_n ,…,G X_n }, where G x_n Indicating in monitoring area Z k The number of occurrences of the x-th fault type is given by β, where β is the predetermined reference monitoring cycle number. This leads to the calculation of the number of occurrences of the x-th fault type in the monitoring area Z. k The predicted demand for the nth type of auxiliary tool within one monitoring period is H. k_n : ； Substituting each value into n=1,2,…,N, we obtain the values within the monitoring region Z. k The predicted demand for N types of assistive tools within a monitoring period {H} k_1 H k_2 ,…,H k_n ,…,H k_N } 2. The method for monitoring maintenance auxiliary equipment based on dynamic perception according to claim 1, characterized in that: In step S1, after authorization, the monitoring range is divided into K monitoring areas, and monitoring area Z is... k The M generator sets within the area are marked, where Z k This represents the k-th monitoring area within the monitoring range. The M generator sets are denoted as {A1, A2, ..., A3}. m ,…,A M }, where A m This represents the m-th generator set; Collect historical operating data of generator sets M, including historical fault data and historical maintenance data of the generator sets; The auxiliary tools within the monitoring area are marked, specifying their type and quantity. The number of auxiliary tool types is N, and the total number of N types of auxiliary tools is {B1, B2, ..., B3}. n ,…,B N }, where B n Indicates the quantity of the nth type of auxiliary tool; the monitoring area Z. k The number of fault types in the historical faults of the engine unit is X.
3. The method for monitoring maintenance auxiliary equipment based on dynamic perception according to claim 2, characterized in that: In step S2, the monitoring area Z is... k A fault database is established for the generator sets. This database stores fault information and maintenance information for the generator sets. The fault information includes the fault type and the generator set where the fault occurred. The maintenance information includes maintenance personnel information, auxiliary tool information, and maintenance results. Maintenance results are categorized as successful or unsuccessful, thereby obtaining the monitoring area Z. k The number of X types of faults in the engine unit is {C1, C, ..., C}. x ,…,C X }, where C x Indicates monitoring area Z k The number of times the xth type of failure occurs in the engine unit.
4. The method for monitoring maintenance auxiliary equipment based on dynamic sensing according to claim 3, characterized in that: The method for determining invalid personnel is as follows: for any maintenance personnel α, retrieve the maintenance records of maintenance personnel α. If the maintenance success rate of maintenance personnel α is lower than the preset maintenance success rate threshold, then maintenance personnel α is determined to be an invalid personnel; otherwise, maintenance personnel α is determined to be a valid personnel.
5. The method for monitoring maintenance auxiliary equipment based on dynamic perception according to claim 4, characterized in that: In step S5, substituting k=1,2,…,K one by one, we obtain the predicted demand {H} for the nth auxiliary tool in one monitoring period across K monitoring areas. 1_n, H 2_n ,…,H k_n ,…,H K_n }, and thus obtain the total predicted demand J for the nth auxiliary tool within the monitoring range. n The total predicted demand for the nth auxiliary tool within the monitoring range is the sum of the predicted demand for the nth auxiliary tool within one monitoring period in K monitoring areas. This is used to determine the reserve status of the nth auxiliary tool within the monitoring range. If L n ≥(1+P)*J n If L is sufficient, then it is determined that the supply of the nth type of auxiliary tool within the monitoring range is adequate. n Let P be the sum of the number of the nth type of auxiliary tools in the K monitoring areas, and P be the pre-set reserve percentage of auxiliary tools; otherwise, it is determined that the reserves of the nth type of auxiliary tools in the monitoring area are insufficient, and the management personnel are prompted to replenish the auxiliary tools, until it is determined that the reserves of the nth type of auxiliary tools in the monitoring area are sufficient. Substitute n=1,2,…,N one by one to determine the reserve status of the N types of auxiliary tools in the monitoring area.
6. The method for monitoring maintenance auxiliary equipment based on dynamic perception according to claim 5, characterized in that: In step S6, after supplementing N auxiliary tools, regional reserve analysis is performed on K monitoring areas. In the kth monitoring area, if B n ≥(1+P)*H k_n If the number of auxiliary tools of type n is sufficient in the k-th monitoring area, then the maximum number of auxiliary tools of type n to be scheduled in the k-th monitoring area is B. n -(1+P)*H k_n Otherwise, it is determined that the nth type of auxiliary tools in the kth monitoring area are insufficient, and the demand for the nth type of auxiliary tools in the kth monitoring area is (1+P)*H. k_n -B n If there exists a sufficient number of auxiliary tools of type n and the maximum scheduling quantity of auxiliary tools of type n is greater than or equal to (1+P)*H k_n -B n From the selected monitoring areas, the monitoring area closest to the k-th monitoring area is chosen as the scheduling source, and (1+P)*H is transported from the scheduling source to the k-th monitoring area. k_n -B n If there is no sufficient type n auxiliary tool, then from the monitoring area where type n auxiliary tools are sufficient, transport the maximum number of type n auxiliary tools corresponding to the scheduling monitoring area in ascending order of interval distance to the kth monitoring area, until type n auxiliary tools are sufficient in the kth monitoring area. Then substitute n=1,2,…,N one by one to make type n auxiliary tools sufficient in the kth monitoring area. Then substitute k=1,2,…,K one by one to make type n auxiliary tools sufficient in the K monitoring areas. After determining that type n auxiliary tools are sufficient in the K monitoring areas and after another monitoring cycle, analyze the auxiliary tool status of the monitoring area again.
7. A maintenance auxiliary equipment monitoring system based on dynamic perception, wherein the system is applied to the maintenance auxiliary equipment monitoring method based on dynamic perception as described in any one of claims 1-6, characterized in that: The system includes: a data acquisition and area marking module, a fault database establishment module, a tool demand analysis module, a tool demand prediction module, a tool reserve monitoring and replenishment prompt module, and an area tool scheduling module; The data acquisition and area marking module is used to divide the monitoring area according to the monitoring range, mark the generator set, and collect historical operating data, auxiliary tool data and fault type data. The fault database establishment module is used to establish a fault database for the monitoring area and call up fault information of the monitoring area. The tool requirements analysis module is used to analyze the demand for auxiliary tools based on historical data. The tool demand forecasting module is used to forecast the demand for auxiliary tools for a monitoring period for the monitoring area. The tool inventory monitoring and replenishment reminder module is used to consider the demand and inventory of auxiliary tools, analyze the storage status of auxiliary tools within the monitoring range, and remind managers to replenish auxiliary tools until there are sufficient auxiliary tools within the monitoring range. The regional tool scheduling module is used to analyze the availability of auxiliary tools in the monitoring area and schedule auxiliary tools within the monitoring area when sufficient auxiliary tools are detected.