Maintenance auxiliary equipment monitoring system and method based on dynamic perception

Abstract

The invention discloses a dynamic perception-based maintenance auxiliary equipment monitoring system and method, and relates to the technical field of auxiliary equipment monitoring, and the system comprises the steps: dividing a monitoring region according to a monitoring range, marking a generator set, collecting historical operation data, auxiliary tool data and fault type data, and building a fault database for the monitoring region; calling the fault information of the monitoring area, analyzing the demand condition of the fault type for the auxiliary tools based on historical data, predicting the demand condition of the auxiliary tools in a monitoring period, analyzing the storage condition of the auxiliary tools in the monitoring range, and prompting management personnel to supplement until the auxiliary tools in the monitoring range are sufficient. According to the method, the fault database is established, maintenance records of effective maintenance personnel are integrated, data interference of invalid personnel is eliminated, and the actual requirements of different fault types for various auxiliary tools are objectively judged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of auxiliary equipment monitoring, in particular to a maintenance auxiliary equipment monitoring system and method based on dynamic perception. BACKGROUND

[0002] In the field of maintenance auxiliary equipment management, the traditional method lacks scientific support for judging the demand for auxiliary tools, and relies more on experience estimation. Since a systematic fault and maintenance correlation database has not been established, and invalid maintenance personnel's interference data has not been excluded, it is difficult to accurately identify the actual demand relationship between different fault types and various auxiliary tools. This makes the prediction of auxiliary tool demand often inconsistent with the actual maintenance scene, either because of excessive reserves causing idle waste, or because of insufficient reserves making it difficult to support maintenance work, thereby affecting the progress of maintenance tasks and failing to ensure the timeliness of equipment maintenance. The existing auxiliary tool resource scheduling and configuration lacks overall planning and regional coordination. When dealing with the tool storage difference of each monitoring area, it is often not first evaluated whether the total storage meets the standard before directly carrying out regional allocation, and the distance factor is not considered in the allocation, often choosing long-distance transportation, increasing time and cost consumption. At the same time, the complementary mechanism between regions is missing, and it is easy to appear the unbalanced situation of part of the region tool accumulation and part of the region tool shortage. Management personnel often rely on subjective judgment when allocating, and the blindness is strong, further increasing the cost of resource management and operation. The traditional auxiliary tool management lacks a continuous and dynamic adjustment mechanism and does not establish a regular evaluation system. After completing the initial configuration, it is difficult to reanalyze the matching of tool reserves and demand in each region within a fixed period, and it is difficult to find the changes in reserves and demand in a timely manner. When the tool is insufficient due to consumption, it is difficult to supplement in a timely manner, which may delay the maintenance and affect the stable operation of the equipment such as the generator set. And the management mode lacks foresight, and relies more on temporary emergency allocation, which not only increases the allocation pressure, but also reduces the stability and reliability of the overall equipment operation and maintenance. SUMMARY

[0003] The purpose of the present application is to provide a maintenance auxiliary equipment monitoring system and method based on dynamic perception to solve the problems raised in the background.

[0004] In order to solve the above technical problems, the present application provides the following technical scheme: a maintenance auxiliary equipment monitoring method based on dynamic perception, comprising the following steps: S1, dividing the monitoring area according to the monitoring range, marking the generator set, collecting historical operation data, auxiliary tool data and fault type data; S2, establishing a fault database for the monitoring area, and calling the fault information of the monitoring area; S3, based on the historical data, analyzing the demand situation of the auxiliary tool for the fault type; S4, for the monitoring area, predict the demand status of the auxiliary tool in a monitoring period; S5, considering the demand status and reserves of the auxiliary tool, analyze the auxiliary tool storage status of the monitoring range, prompt the management personnel to supplement the auxiliary tool, until the auxiliary tool of the monitoring range is sufficient; S6, when monitoring that the auxiliary tool of the monitoring range is sufficient, analyze the auxiliary tool reserves status of the monitoring area, and schedule the auxiliary tool of the monitoring area in the monitoring range.

[0005] Further, in step S1, after authorization, the monitoring range is divided into K monitoring areas, and M groups of generator sets in the monitoring area Z k are marked, wherein Z k represents the kth monitoring area in the monitoring range, and the M groups of generator sets are marked as {A1, A, …, A m , …, A M}, wherein A m represents the mth group of generator sets; collect the historical operation data of the M groups of generator sets, the historical operation data including: historical failure data and historical maintenance data of the generator sets; mark the auxiliary tools in the monitoring area, mark the type and quantity of the auxiliary tools, the number of the type of the auxiliary tools is N, the number of the N kinds of auxiliary tools is {B1, B, …, B n , …, B N}, wherein B n represents the number of the nth kind of auxiliary tool; the number of the failure type of the historical failure of the engine set in the monitoring area Z k is X; through the systematic planning after authorization, a solid foundation is laid for the subsequent maintenance auxiliary equipment monitoring work. The division of monitoring area makes the overall control range clearer, avoiding the confusion that may occur when directly managing the huge monitoring range; marking the generator set can accurately locate each group of equipment, which is convenient for subsequent tracking of its running track and state; collecting historical operation data can provide real and reliable basis for subsequent analysis of failure rules and summary of maintenance experience; clearly defining the type and quantity of auxiliary tools can help real-time grasp of the current tool resource reserves, avoiding delay caused by unclear information in subsequent allocation; counting the failure type can clarify the scope of equipment failure in the area in advance, and make good preparation for subsequent targeted matching of tool demand, so that the monitoring work has clear order and sufficient data support from the starting stage.

[0006] Further, in step S2, a fault database is established for the generator set of the monitoring area Z k , the fault database is used to store the fault information and maintenance information of the engine set, wherein the fault information includes the fault type and the generator set that fails, the maintenance information includes the maintenance personnel information, the auxiliary tool information and the maintenance effect, the maintenance effect includes the maintenance success and the maintenance failure, and then the monitoring area Zk The number of times of the xth type of fault of the engine set in the monitoring area Z is {C1, C, …, C x ,…,C X}, wherein C x represents the number of times of the xth type of fault of the engine set in the monitoring area Z k ; by establishing a fault database for the engine set in the monitoring area, the scattered fault and maintenance information is effectively integrated, and the inconvenience of query and the confusion of management caused by scattered storage of information are avoided. The fault type and the corresponding fault equipment are clearly recorded in the database, which can help subsequent quick positioning of the associated equipment of different faults and reduce the fault troubleshooting time; at the same time, the storage of the maintenance personnel, the auxiliary tool and the maintenance effect information provides a basis for subsequent screening of effective maintenance data and analysis of the correlation between tool use and maintenance effect. In addition, by counting the occurrence of various faults, the occurrence regularity of faults in the area can be preliminarily mastered, which lays a data foundation for subsequent accurate judgment of auxiliary tool demand.

[0007] Further, in step S3, in the monitoring area Z k , for the invalid personnel, the number of times of the xth type of fault after excluding the maintenance information handled by the invalid personnel is Y, the fault database is called to obtain that in Y times of the xth type of fault, the number of times of carrying the nth type of auxiliary tool is D x_n , the maintenance success parameter in D x_n times of maintenance is E x_n , if D x_n =0, E x_n =0; if D x_n ≠0, E x_n is the ratio of the number of times of successful maintenance in D x_n times of maintenance to D x_n , in Y times of the xth type of fault, the number of times of not carrying the nth type of auxiliary tool is d x_n , the maintenance success parameter in d x_n times of maintenance is e x_n , if d x_n =0, e x_n =0; if d x_n ≠0, e x_n is the ratio of the number of times of successful maintenance in d x_n times of maintenance to D x_n , and then the necessary parameter F x_n of the nth type of auxiliary tool for the xth type of fault is obtained, F x_n =D x_n -d x_nF0, F0 is a necessary parameter threshold value, then determine the nth type of auxiliary tool as the necessary carrying auxiliary tool of the xth type of failure; otherwise, determine the nth type of auxiliary tool as the unnecessary carrying auxiliary tool of the xth type of failure, n = 1, 2, …, N, to obtain the necessary carrying auxiliary tool or unnecessary carrying auxiliary tool of the xth type of failure of the N type of auxiliary tool, when the nth type of auxiliary tool is the necessary carrying auxiliary tool of the xth type of failure, the demand value of the xth type of failure for the nth type of auxiliary tool is Q x_n = 1; otherwise, the demand value of the xth type of failure for the nth type of auxiliary tool is Q x_n = 0; The judgment method of the invalid personnel is: for any maintenance personnel α, the maintenance record of the maintenance personnel α is called, if the maintenance success rate of the maintenance personnel α is lower than the set maintenance success rate threshold, the maintenance personnel α is determined as invalid personnel; otherwise, the maintenance personnel α is determined as valid personnel; by excluding the maintenance information of the invalid personnel, the interference of low-quality data on analysis is eliminated, and the data used for judging the necessity of the auxiliary tool is more reliable. By comparing the maintenance situation when carrying and not carrying a certain type of auxiliary tool, and combining the necessary parameter threshold value, the necessary auxiliary tool for various types of failures can be accurately distinguished, and the actual demand correlation of different failures for tools is clear. This targeted analysis makes the tool demand judgment from vague to clear, avoids the inclusion of unnecessary tools in the demand consideration, provides accurate judgment basis for subsequent accurate prediction of auxiliary tool demand, makes the tool management more in line with the actual maintenance demand, and reduces the possibility of resource mismatch.

[0008] Further, in step S4, for the monitoring area Z k , the occurrence frequency of X types of failure types in the previous β monitoring periods with the current time point as the end point is {G 1_n , G 2_n , …, G x_n , …, G X_n}, wherein G x_n represents the occurrence frequency of the xth type of failure in the monitoring area Z k , and β is a reference monitoring period number, and then the predicted demand amount of the nth type of auxiliary tool in one monitoring period in the monitoring area Z k is H k_n : ; n = 1, 2, …, N, to obtain the predicted demand amount of N types of auxiliary tools in one monitoring period in the monitoring area Z k {H k_1 , H k_2 , …, H k_n , …, H k_NBased on the accurate judgment of the fault type and the demand for auxiliary tools in the early stage, and combined with the periodic law of historical fault occurrence, the demand for various types of auxiliary tools in the monitoring area is scientifically predicted. It makes full use of the real fault occurrence frequency data in the fault database, closely links tool demand with actual fault scenarios, and makes the prediction results free from subjective speculation, which is more in line with the actual tool consumption in maintenance. This accurate demand prediction can help managers clearly understand the demand for tools in each area in advance, avoiding both the idle resources caused by excessive tool reserves and the maintenance delays caused by insufficient tool reserves, providing clear quantitative basis for the subsequent overall allocation and regional management of auxiliary tools, and promoting the transformation of auxiliary tool management from passive response to active planning, improving the scientificity and efficiency of tool resource allocation in equipment operation and maintenance.

[0009] Further, in step S5, k=1, 2, …, K is substituted one by one to obtain the predicted demand for the nth auxiliary tool in one monitoring period in the K monitoring areas {H 1_n ,H 2_n ,…,H k_n ,…,H K_n}, and then the total predicted demand J n for the nth auxiliary tool in the monitoring range is obtained, which is the sum of the predicted demand for the nth auxiliary tool in one monitoring period in the K monitoring areas, and then the storage status of the nth auxiliary tool in the monitoring range is judged. If L n ≥(1+P)*J n , it is judged that the storage of the nth auxiliary tool in the monitoring range is sufficient, where L nis the number of the nth kind of auxiliary tool in the kth monitoring area, P is the preset percentage of auxiliary tool reservation; otherwise, it is judged that the storage of the nth kind of auxiliary tool in the monitoring range is insufficient, the management personnel is prompted to supplement the auxiliary tool, until the storage of the nth kind of auxiliary tool in the monitoring range is sufficient, n = 1, 2, …, N is substituted one by one, and the storage conditions of N kinds of auxiliary tools in the monitoring range are judged; by integrating the predicted demand of each kind of auxiliary tool in all monitoring areas, the tool resource status in the monitoring range is controlled from the overall perspective, and the problem of only paying attention to a single area and ignoring the global resource balance is avoided. It first summarizes the total demand of each area to obtain the total demand, and then calculates the reasonable resource bottom line combined with the reservation percentage, so that the storage judgment not only meets the basic demand, but also can cope with possible emergencies, and improves the risk resistance of resource management; when the total amount of a certain tool is found to be insufficient, it is prompted to supplement in time, which can effectively avoid the impact of overall resource shortage on regional maintenance work, and ensure that the tool supply can cover the actual demand of all regions. This demand accounting and storage judgment mode from region to whole lays a solid foundation for subsequent inter-regional tool scheduling, and guarantees the stability and sufficiency of auxiliary tool resource supply in the whole monitoring range.

[0010] Further, in step S6, after the N kinds of auxiliary tools are supplemented, the regional storage of the K monitoring areas is analyzed. In the kth monitoring area, if B n ≥(1+P)*H k_n , it is judged that the nth kind of auxiliary tool in the kth monitoring area is sufficient, and the maximum amount of the nth kind of auxiliary tool dispatched by the kth monitoring area is B n -(1+P)*H k_n ; otherwise, it is judged that the nth kind of auxiliary tool in the kth monitoring area is insufficient, and the demand dispatching amount of the nth kind of auxiliary tool in the kth monitoring area is (1+P)*H k_n -B n , if there is a monitoring area that meets the sufficient nth kind of auxiliary tool and meets the condition that the maximum amount of the nth kind of auxiliary tool dispatched is greater than or equal to (1+P)*H k_n -B n , the monitoring area closest to the kth monitoring area is selected as the dispatch source from the selected monitoring area, and (1+P)*H k_n -B nIf the nth type of auxiliary tool is sufficient, the scheduling maximum of the monitoring area is transported to the kth monitoring area according to the interval distance from small to large; until the nth type of auxiliary tool in the kth monitoring area is sufficient, and then n = 1, 2, …, N is substituted one by one, so that the kth monitoring area is sufficient for N types of auxiliary tools, and then k = 1, 2, …, K is substituted one by one, so that K monitoring areas are sufficient for N types of auxiliary tools, and after judging that K monitoring areas are sufficient for N types of auxiliary tools, the auxiliary tool status of the monitoring area is analyzed again after a monitoring period; through detailed analysis of the auxiliary tool reserves of each monitoring area, the problem of unbalanced distribution of tools between regions is solved. For the region with insufficient tools, the nearest sufficient region is preferentially dispatched, reducing the time and resource consumption in the transportation process, making the scheduling more efficient. When a single region cannot meet the demand, the tools are deployed in order according to the distance to ensure that the shortage region can quickly supplement the tools and avoid the impact of local shortage on maintenance. At the same time, after ensuring that all regions have sufficient tools, the status is analyzed regularly to form a dynamic adjustment mechanism, so that the tool reserves of each region always match the actual demand, preventing resource waste and continuously responding to maintenance needs, providing long-term tool support for stable operation of equipment.

[0011] A maintenance auxiliary equipment monitoring system based on dynamic perception, the system comprising: a data acquisition and region marking module, a fault database establishment module, a tool demand analysis module, a tool demand prediction module, a tool reserve monitoring and replenishment prompting module, and a regional tool scheduling module; The data acquisition and region marking module is used to divide monitoring areas according to a monitoring range, mark a generator set, and collect historical operation data, auxiliary tool data, and fault type data; The fault database establishment module is used to establish a fault database for a monitoring area and call fault information of the monitoring area; The tool demand analysis module is used to analyze the demand status of auxiliary tools based on historical data; The tool demand prediction module is used to predict the demand status of auxiliary tools for a monitoring period for a monitoring area; The tool reserve monitoring and replenishment prompting module is used to consider the demand status and reserves of auxiliary tools, analyze the auxiliary tool storage status of the monitoring range, and prompt the management personnel to replenish the auxiliary tools until the auxiliary tools of the monitoring range are sufficient; The regional tool scheduling module is used to analyze the auxiliary tool reserve status of the monitoring area when it is monitored that the auxiliary tools of the monitoring range are sufficient, and schedule the auxiliary tools of the monitoring area in the monitoring range.

[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] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0017] With reference to Figure 1 and Figure 2 , the present application provides a technical solution: a maintenance auxiliary equipment monitoring method based on dynamic perception, comprising the following steps: S1, dividing the monitoring range into monitoring areas, marking the generator sets, collecting historical operation data, auxiliary tool data and fault type data; S2, establishing a fault database for the monitoring area, calling the fault information of the monitoring area; S3, based on historical data, analyzing the demand status of auxiliary tools for fault types; S4, predicting the demand status of auxiliary tools for a monitoring period for the monitoring area; S5, considering the demand status and reserves of auxiliary tools, analyzing the storage status of auxiliary tools in the monitoring range, prompting the management personnel to supplement the auxiliary tools until the auxiliary tools in the monitoring range are sufficient; S6, when the monitoring range is sufficient, analyzing the storage status of the monitoring area auxiliary tools, and scheduling the monitoring area auxiliary tools in the monitoring range.

[0018] In step S1, after authorization, the monitoring range is divided into K monitoring areas, and M groups of generator sets in the monitoring area Z k are marked, wherein Z k represents the kth monitoring area in the monitoring range, and the M groups of generator sets are denoted as {A1,A,…,A m ,…,A M}, wherein A m represents the mth group of generator sets; collect the historical operation data of the M groups of generator sets, the historical operation data including: historical fault data and historical maintenance data of the generator sets; mark the auxiliary tools in the monitoring area, mark the type and quantity of the auxiliary tools, the number of the types of auxiliary tools is N, and the number of N types of auxiliary tools is {B1,B,…,B n ,…,B N}, wherein B n represents the number of the nth type of auxiliary tool; collect the historical operation data of the M groups of generator sets, the historical operation data including: historical fault data and historical maintenance data of the generator sets; mark the auxiliary tools in the monitoring area, mark the type and quantity of the auxiliary tools, the number of the types of auxiliary tools is N, and the number of N types of auxiliary tools is {B1,B,…,B kThe number of fault types of historical faults of the engine set in the monitoring area Z Through systematic planning after authorization, a solid foundation is laid for subsequent maintenance auxiliary equipment monitoring work. Dividing the monitoring area makes the overall control range clearer and avoids confusion that may occur when directly managing a large monitoring range; marking the generator set can accurately locate each set of equipment and facilitate subsequent tracking of its running track and state; collecting historical running data can provide real and reliable basis for subsequent analysis of fault rules and summary of maintenance experience; clearly defining the types and quantities of auxiliary tools can help real-time grasp of the current tool resource reserve situation and avoid delays caused by unclear information during subsequent allocation; and statistical fault types clarify the scope of equipment faults in the area in advance, making the subsequent targeted matching of tool requirements well-prepared, and making the monitoring work have clear order and sufficient data support from the beginning.

[0019] In step S2, a fault database is established for the generator set in the monitoring area Z k , which is used to store fault information and maintenance information of the engine set, wherein the fault information includes fault types and faulty generator sets, the maintenance information includes maintenance personnel information, auxiliary tool information and maintenance effect, the maintenance effect includes maintenance success and maintenance failure, and then the number of X types of faults of the engine set in the monitoring area Z k is obtained as {C x ,…,C X}, wherein C x represents the number of the xth type of fault of the engine set in the monitoring area Z k ; by establishing a fault database for the generator set in the monitoring area, the scattered fault and maintenance information is effectively integrated, and the inconvenience of information scattered storage and management confusion are avoided. The fault type and corresponding fault equipment are clearly recorded in the database, which can help subsequent quick positioning of associated equipment of different faults and reduce fault troubleshooting time; at the same time, the maintenance personnel, auxiliary tool and maintenance effect information are stored, which provides a basis for subsequent screening of effective maintenance data and analysis of the correlation between tool use and maintenance effect. In addition, by counting the occurrence of various faults, the occurrence regularity of faults in the area can be preliminarily mastered, laying a data foundation for subsequent accurate judgment of auxiliary tool demand.

[0020] In step S3, in the monitoring area Z k , for invalid personnel, the number of occurrences of the xth type of fault after excluding the maintenance information handled by invalid personnel is Y, the fault database is called to obtain that the number of times of carrying the nth type of auxiliary tool in Y times of the xth type of fault is D x_n , the maintenance success parameter in D x_n times of maintenance is E x_n , if D x_n =0, E x_n =0; if Dx_n ≠0, E x_n is D x_n , the ratio of the number of successful repairs in d x_n times of repairs to D x_n , the number of times of carrying no auxiliary tool of the nth type in d x_n times of repairs x_n , the repair success parameter in d x_n times of repairs x_n =0; if d x_n ≠0, e x_n is the ratio of the number of successful repairs in d x_n times of repairs to D x_n , and then the necessary parameter F x_n of the nth auxiliary tool for the xth fault type is obtained x_n =D x_n -d x_n , if F x_n >F0, F0 being a threshold value of the necessary parameter, it is determined that the nth auxiliary tool is a necessary carrying auxiliary tool for the xth fault type; otherwise, it is determined that the nth auxiliary tool is an unnecessary carrying auxiliary tool for the xth fault type, and n=1, 2, …, N is substituted one by one to obtain the necessary carrying auxiliary tool or the unnecessary carrying auxiliary tool of the Nth auxiliary tool for the xth fault type, when the nth auxiliary tool is a necessary carrying auxiliary tool for the xth fault type, the demand value of the xth fault type for the nth auxiliary tool is set as Q x_n =1; otherwise, the demand value of the xth fault type for the nth auxiliary tool is set as Q k =0. The judgment method of the invalid personnel is that: for any repair personnel α, the repair record of the repair personnel α is called, if the repair success rate of the repair personnel α is lower than a threshold value of repair success rate, the repair personnel α is determined to be invalid personnel; otherwise, the repair personnel α is determined to be valid personnel; by excluding the repair information of the invalid personnel, the interference of low-quality data on analysis is eliminated, and the data used for judging the necessity of the auxiliary tool is more reliable. By comparing the repair situations with and without carrying a certain type of auxiliary tool, and combining the threshold value of the necessary parameter, the necessary auxiliary tool for each type of fault can be accurately distinguished, and the actual demand of different faults for the tool is determined. This targeted analysis makes the tool demand judgment clear from fuzzy, avoids the unnecessary tool into the demand consideration, provides accurate judgment basis for subsequent accurate prediction of the demand amount of the auxiliary tool, makes the tool management more in line with the actual repair demand, and reduces the possibility of resource mismatch.

[0021] In step S4, for the monitoring area Z k , the number of occurrences of X types of fault types in the previous β monitoring periods with the current time point as the end point is {G 1_n , G 2_n..., G x_n ..., G X_n}, wherein G x_n represents the number of occurrences of the xth type of fault in the monitoring area Z k , β is the number of reference monitoring periods, and the predicted demand for the nth type of auxiliary tool in the monitoring area Z k in one monitoring period is H k_n : ; Substituting n = 1, 2,..., N, the predicted demand for N types of auxiliary tools in the monitoring area Z k in one monitoring period is {H k_1 , H k_2 ,..., H k_n ,..., H k_N} ; based on the accurate judgment of the fault type and the auxiliary tool demand in the early stage, and combined with the periodic law of historical fault occurrence, the demand for various types of auxiliary tools in the monitoring area is scientifically predicted. It makes full use of the real fault occurrence frequency data in the fault database, closely associates tool demand with actual fault scenarios, makes the prediction result get rid of the limitations of subjective conjecture, and is more in line with the actual tool consumption in maintenance. This accurate demand prediction can help managers clearly understand the demand scale of tools in each area in advance, avoid resource idling caused by excessive tool reserves, and prevent maintenance delays caused by insufficient reserves, providing clear quantitative basis for the overall allocation and regional management of subsequent auxiliary tools, promoting the change of auxiliary tool management from passive response to active planning, and improving the scientificity and efficiency of tool resource allocation in equipment operation and maintenance.

[0022] In step S5, substituting k = 1, 2,..., K, the predicted demand for the nth type of auxiliary tool in the K monitoring areas in one monitoring period is {H 1_n , H 2_n ,..., H k_n ,..., H K_n}, and the predicted total demand for the nth type of auxiliary tool in the monitoring range J n is obtained. The predicted total demand for the nth type of auxiliary tool in the monitoring range is the sum of the predicted demand for the nth type of auxiliary tool in the K monitoring areas in one monitoring period, and then the storage status of the nth type of auxiliary tool in the monitoring range is judged. If L n ≥ (1 + P) * J n , it is judged that the storage of the nth type of auxiliary tool in the monitoring range is sufficient, wherein L nis the number of the nth kind of auxiliary tool in the kth monitoring area, P is the preset percentage of auxiliary tool reservation; otherwise, it is judged that the storage of the nth kind of auxiliary tool in the monitoring range is insufficient, the management personnel is prompted to supplement the auxiliary tool, until the storage of the nth kind of auxiliary tool in the monitoring range is sufficient, n = 1, 2, …, N is substituted one by one, and the storage conditions of N kinds of auxiliary tools in the monitoring range are judged; by integrating the predicted demand of each kind of auxiliary tool in all monitoring areas, the tool resource status in the monitoring range is controlled from the overall perspective, and the problem of only paying attention to a single area and ignoring the global resource balance is avoided. It first summarizes the total demand of each area to obtain the total demand, and then calculates the reasonable resource bottom line combined with the reservation percentage, so that the storage judgment not only meets the basic demand, but also can cope with possible emergencies, and improves the risk resistance ability of resource management; when the total amount of a certain tool is found to be insufficient, it is prompted to supplement in time, which can effectively avoid the influence of the overall resource shortage on the maintenance work in each area, and ensure that the tool supply can cover the actual demand of all areas. This demand accounting and storage judgment mode from region to whole lays a solid foundation for subsequent inter-regional tool scheduling, and guarantees the stability and sufficiency of auxiliary tool resource supply in the whole monitoring range.

[0023] In step S6, after the N kinds of auxiliary tools are supplemented, the regional storage analysis is performed on the K monitoring areas. In the kth monitoring area, if B n ≥(1+P)*H k_n , it is judged that the nth kind of auxiliary tool in the kth monitoring area is sufficient, and the maximum amount of the nth kind of auxiliary tool dispatched by the kth monitoring area is B n -(1+P)*H k_n ; otherwise, it is judged that the nth kind of auxiliary tool in the kth monitoring area is insufficient, and the demand dispatching amount of the nth kind of auxiliary tool in the kth monitoring area is (1+P)*H k_n -B n , if there is a monitoring area that meets the sufficient nth kind of auxiliary tool and meets the condition that the maximum amount of the nth kind of auxiliary tool dispatched is greater than or equal to (1+P)*H k_n -B n , the monitoring area closest to the kth monitoring area is selected as the dispatch source from the selected monitoring area, and (1+P)*H k_n -B nIf the nth type of auxiliary tool is sufficient, the scheduling maximum of the monitoring area is transported to the kth monitoring area in the order of increasing interval distance; if the nth type of auxiliary tool is not sufficient, the scheduling maximum of the monitoring area is transported to the kth monitoring area in the order of increasing interval distance from the monitoring area that meets the requirement of the nth type of auxiliary tool; until the kth monitoring area is sufficient in the nth type of auxiliary tool, and then n = 1, 2,..., N is substituted one by one, so that the kth monitoring area is sufficient in the Nth type of auxiliary tool, and then k = 1, 2,..., K is substituted one by one, so that the K monitoring areas are sufficient in the Nth type of auxiliary tool, and then it is judged whether the K monitoring areas are sufficient in the Nth type of auxiliary tool after a monitoring period, and then the auxiliary tool status of the monitoring area is analyzed again; through detailed analysis of the auxiliary tool reserves of each monitoring area, the problem of unbalanced distribution of tools among regions is solved in a targeted manner. For the region with insufficient tools, the tools are preferentially dispatched from the nearest sufficient region, reducing the time and resource consumption in the transportation process and making the dispatch more efficient. When a single region cannot meet the demand, the tools are sequentially allocated according to the distance to ensure that the shortage region can be quickly supplemented with tools and avoid affecting maintenance due to local shortages. At the same time, after ensuring that all regions have sufficient tools, the status is regularly reanalyzed to form a dynamic adjustment mechanism, so that the tool reserves of each region always match the actual demand, preventing resource waste and continuously responding to maintenance needs, providing long-term tool support for stable operation of equipment.

[0024] A maintenance auxiliary equipment monitoring system based on dynamic perception, the system comprising: a data acquisition and region marking module, a fault database establishment module, a tool demand analysis module, a tool demand prediction module, a tool reserve monitoring and replenishment prompting module, and a regional tool scheduling module; The data acquisition and region marking module is used to divide monitoring areas according to a monitoring range, mark a generator set, and collect historical operation data, auxiliary tool data, and fault type data; The fault database establishment module is used to establish a fault database for a monitoring area and call fault information of the monitoring area; The tool demand analysis module is used to analyze the demand status of auxiliary tools based on historical data; The tool demand prediction module is used to predict the demand status of auxiliary tools for a monitoring period for a monitoring area; The tool reserve monitoring and replenishment prompting module is used to consider the demand status and reserves of auxiliary tools, analyze the storage status of auxiliary tools in the monitoring range, and prompt a management personnel to replenish auxiliary tools until the auxiliary tools in the monitoring range are sufficient; The regional tool scheduling module is used to analyze the reserve status of auxiliary tools of a monitoring area in the monitoring range and schedule the auxiliary tools of the monitoring area when it is monitored that the auxiliary tools in the monitoring range are sufficient.

[0025] In step S1, the monitoring range of the generator sets in the industrial park is divided into three zones after authorization by the park management. The generator sets in each zone are labeled one by one for subsequent tracking. At the same time, the past fault conditions and maintenance records of these generator sets are collected, which will serve as the basis for analysis. In addition, the maintenance auxiliary tools in each zone, such as various wrenches and detection instruments, are clearly labeled by type and existing quantity. The specific types of historical faults of the generator sets in the zone are sorted out to prepare for subsequent analysis.

[0026] In step S2, a fault database is established for the generator sets in each zone. This database stores in detail the fault types of the generator sets, the specific units that have failed, the personnel information during maintenance, the auxiliary tools used, and whether the maintenance results are successful or not. Through this database, the frequency of occurrence of various faults in each zone can be clearly seen, such as the occurrence of a certain type of fault in a zone several times, providing intuitive data support for subsequent analysis.

[0027] In step S3, first, ineffective maintenance personnel are determined. For example, if a maintenance personnel's success rate is much lower than the park's set standard, he is determined to be ineffective, and his maintenance records are excluded. Then, for a certain type of fault, after excluding the maintenance records of ineffective personnel, the number of occurrences of the fault is counted. The fault database is called to analyze the number of times a certain type of auxiliary tool is used when handling the fault and the success of the maintenance, as well as the success of the maintenance without the tool. By comparing, it is determined whether the auxiliary tool is necessary for this type of fault. For example, when handling a certain type of electrical fault, the number of successful maintenance times using a specific detection instrument is much higher than that without the instrument, indicating that the instrument is a necessary tool for handling this type of fault, and thus the demand association of the fault for the tool is determined.

[0028] In step S4, for each zone, the occurrence of various faults in the previous monitoring periods is reviewed. According to these historical patterns, the demand for various auxiliary tools in a monitoring period is predicted. For example, if a certain type of mechanical fault frequently occurs in a certain zone in the past periods and a specific wrench is needed to handle the fault, the periodic demand for this type of wrench in the zone is predicted.

[0029] In step S5, the predicted demand for each type of auxiliary tool in the three zones is summarized to obtain the total demand for the tool in the entire park. Then, the total storage of the tool in the park is compared. If the total storage is sufficient to cover the total demand with a certain reserve, it is determined that the storage is sufficient; if not, the management personnel is prompted to supplement until the storage meets the requirements. 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 dynamic perception-based maintenance assistance device monitoring method, characterized by: The method comprises the following steps: S1, dividing monitoring areas according to a monitoring range, marking generator sets, collecting historical operation data, auxiliary tool data and fault type data; S2, establishing a fault database for the monitoring areas, and calling fault information of the monitoring areas; S3, analyzing demand conditions of the auxiliary tools for the fault types based on the historical data; S4, predicting demand conditions of the auxiliary tools for a monitoring period for the monitoring areas; S5, analyzing storage conditions of the auxiliary tools of the monitoring range by considering the demand conditions and storage of the auxiliary tools, prompting a management personnel to supplement the auxiliary tools until the auxiliary tools of the monitoring range are sufficient; S6, when the auxiliary tools of the monitoring range are sufficient, analyzing storage conditions of the auxiliary tools of the monitoring areas, and scheduling the auxiliary tools of the monitoring areas in the monitoring range.

2. The method of claim 1, wherein: In step S1, after being authorized, the monitoring range is divided into K monitoring areas, and M groups of generating sets in the monitoring area Z k are marked, where Z k represents the kth monitoring area in the monitoring range, and the M groups of generating sets are marked as {A1, A,…, A m ,…,A M}, where A m represents the mth group of generating sets; The historical operation data of M groups of generator sets are collected, and the historical operation data comprises historical fault data and historical maintenance data of the generator sets; Marking the auxiliary tools in the monitoring area, marking the type and quantity of the auxiliary tools, the type of the auxiliary tools is N, the quantity of the N types of auxiliary tools is {B1, B, …, B n ,…,B N}, wherein B n represents the quantity of the nth type of auxiliary tool; collecting the number X of fault types of historical faults of the engine group in the monitoring area Z k .

3. The method of claim 2, wherein: In step S2, a fault database of the generator set in the monitoring area Z k is established, the fault database is used to store fault information and maintenance information of the engine set, the fault information includes fault type and the generator set that has a fault, the maintenance information includes maintenance personnel information, auxiliary tool information and maintenance effect, the maintenance effect includes maintenance success and maintenance failure, and then the number of X types of faults of the engine set in the monitoring area Z k is obtained as {C1, C, …, C x , …, C X}, wherein C x represents the number of the xth type of fault of the engine set in the monitoring area Z k .

4. The method of claim 3, wherein: 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.

5. The method of claim 4, wherein: The judgment method of the invalid personnel is that: for any maintenance personnel α, the maintenance record of the maintenance personnel α is called, if the maintenance success rate of the maintenance personnel α is lower than a set maintenance success rate threshold, the maintenance personnel α is judged as an invalid personnel; otherwise, the maintenance personnel α is judged as a valid personnel.

6. The method of claim 5, wherein: In step S4, the occurrence number of the xth fault type in the monitoring region Z k is obtained, where G 1_n is the occurrence number of the xth fault type in the monitoring region Z 2_n in the previous β monitoring periods with the current time point as the end point, and β is a predetermined reference monitoring period number. x_n X_n , where G x_n represents the occurrence number of the xth fault type in the monitoring region Z k , β is the predetermined reference monitoring period number, and H k is the predicted demand amount of the nth auxiliary tool in one monitoring period in the monitoring region Z k_n .​ ； Substitute n = 1, 2, …, N one by one, get the predicted demand quantity of N types of auxiliary tools in the monitoring area Z k in a monitoring period k_1 , k_2 , k_n , k_N .

7. The method of claim 6, wherein: In step S5, substituting k = 1, 2, …, K one by one, the predicted demand amount of the nth auxiliary tool in one monitoring period in the K monitoring areas is obtained as {H 1_n, H 2_n ,…,H k_n ,…,H K_n}, and then the predicted total demand amount J n of the nth auxiliary tool in the monitoring range is obtained, which is the sum of the predicted demand amount of the nth auxiliary tool in one monitoring period in the K monitoring areas, and then the reserve status of the nth auxiliary tool in the monitoring range is judged. If L n ≥(1+P)*J n , it is judged that the reserve of the nth auxiliary tool in the monitoring range is sufficient, wherein L n is the sum of the nth auxiliary tool in the K monitoring areas, and P is the preset auxiliary tool reservation percentage; otherwise, it is judged that the reserve of the nth auxiliary tool in the monitoring range is insufficient, prompting the management personnel to supplement the auxiliary tool, until it is judged that the reserve of the nth auxiliary tool in the monitoring range is sufficient, substituting n = 1, 2, …, N one by one, and then the reserve status of the N auxiliary tools in the monitoring range is judged.

8. The method of claim 6, wherein: In step S6, after the N types of auxiliary tools are supplemented, the regional reserve analysis is performed on the K monitoring regions. In the kth monitoring region, if B n ≥(1+P)*H k_n , it is determined that the nth type of auxiliary tool in the kth monitoring region is sufficient, and the maximum scheduling amount of the nth type of auxiliary tool in the kth monitoring region is B n -(1+P)*H k_n ; otherwise, it is determined that the nth type of auxiliary tool in the kth monitoring region is insufficient, and the demand scheduling amount of the nth type of auxiliary tool in the kth monitoring region is (1+P)*H k_n -B n . If there is a monitoring region that satisfies the sufficient nth type of auxiliary tool and satisfies the maximum scheduling amount of the nth type of auxiliary tool being greater than or equal to (1+P)*H k_n -B n , the monitoring region closest to the kth monitoring region is selected as the scheduling source from the selected monitoring region, and (1+P)*H k_n -B n nth type of auxiliary tools are transported from the scheduling source to the kth monitoring region; otherwise, the nth type of auxiliary tool corresponding to the maximum scheduling amount of the monitoring region is transported to the kth monitoring region in order of increasing interval distance from the monitoring region that satisfies the sufficient nth type of auxiliary tool, until the nth type of auxiliary tool in the kth monitoring region is sufficient. Then, n=1, 2, …, N is substituted one by one, so that the kth monitoring region is sufficient for the N types of auxiliary tools. Then, k=1, 2, …, K is substituted one by one, so that the K monitoring regions are sufficient for the N types of auxiliary tools. After it is determined that the K monitoring regions are sufficient for the N types of auxiliary tools and after a monitoring period, the auxiliary tool status of the monitoring regions is analyzed again.

9. A dynamic perception based maintenance aid monitoring system, applied to the dynamic perception based maintenance aid monitoring method of any one of claims 1-8, characterized in that: The system comprises a data collection and area marking module, a fault database establishing module, a tool demand analyzing module, a tool demand predicting module, a tool storage monitoring and supplement prompting module and a regional tool scheduling module; The data collection and area marking module is used for dividing monitoring areas according to a monitoring range, marking generator sets, collecting historical operation data, auxiliary tool data and fault type data; The fault database establishing module is used for establishing a fault database for the monitoring areas, and calling fault information of the monitoring areas; The tool demand analyzing module is used for analyzing demand conditions of the auxiliary tools for the fault types based on the historical data; The tool demand predicting module is used for predicting demand conditions of the auxiliary tools for a monitoring period for the monitoring areas; The tool storage monitoring and supplement prompting module is used for analyzing storage conditions of the auxiliary tools of the monitoring range by considering the demand conditions and storage of the auxiliary tools, prompting a management personnel to supplement the auxiliary tools until the auxiliary tools of the monitoring range are sufficient; The regional tool scheduling module is used for analyzing storage conditions of the auxiliary tools of the monitoring areas when the auxiliary tools of the monitoring range are sufficient, and scheduling the auxiliary tools of the monitoring areas in the monitoring range.

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