Integrated management method for work order data of multiple maintenance sites and electronic equipment

Abstract

The invention discloses a work order data integrated management method for multiple maintenance sites and electronic equipment, and relates to the technical field of work order data processing. The comprehensive management method for the work order data of the multiple maintenance sites comprises the following steps of performing work order accuracy evaluation based on historical work order accuracy parameters of the maintenance sites, and performing system self-optimization processing on the maintenance sites according to historical work order data of the maintenance sites based on obtained work order accuracy evaluation results; performing work order primary screening based on the to-be-allocated work order data of each maintenance network and the basic data of each engineer to obtain an allocatable engineer corresponding to each to-be-allocated work order; according to the method, the engineer priority evaluation is carried out based on the work order demand parameter of each allocatable engineer, and then the work order allocation management is carried out based on the obtained engineer priority evaluation result, so that the improvement of the work order allocation accuracy is realized, and the work order data scheduling efficiency and the quality control closed loop of the maintenance network are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of work order data processing, and in particular to a comprehensive management method for work order data of multiple maintenance sites and electronic equipment. Background Art

[0002] In today's service industry, with its widespread deployment and decentralized operations across multiple maintenance locations, a vast amount of work order data is constantly generated across these locations, covering diverse information such as equipment failures, maintenance progress, and customer feedback. Due to differences in location and systems, this data presents fragmentation, inconsistent formats, and asynchronous updates, severely impacting maintenance efficiency and service quality. Therefore, achieving comprehensive management of work order data from multiple maintenance locations, integrating, standardizing, and deeply mining this data's value, has become a key requirement for improving overall operational management and competitiveness.

[0003] The existing comprehensive work order data management method uses a digital system to automatically generate and assign work orders based on customer needs, while shortening response time through GIS positioning. During the execution phase, maintenance personnel use mobile devices to update records such as maintenance progress and replacement parts in real time, and the system automatically synchronizes the status. After the work order is completed, the system conducts multi-dimensional analysis of the work order data, such as fault type distribution, average repair time, spare parts consumption, etc., and generates visual reports to assist in formulating preventive maintenance plans, optimize spare parts inventory, and continuously improve service quality through a closed-loop customer feedback loop.

[0004] For example, the invention patent with publication number CN117829507A discloses a method, device and equipment for automatic allocation of IoT resources for work order management, including: obtaining resource information of the IP address segment to be allocated for the resource work order; determining a resource allocation plan based on the resource information; and allocating resources to the resource work order based on the resource allocation plan.

[0005] For example, the invention patent with publication number CN119338162A discloses a work order allocation and tracking management system and method based on the Internet of Things, which includes: a work order information acquisition module, a work order weight determination module, a work order allocation module, a work order tracking module and a work order analysis module. The work order information acquisition module is used to obtain work order information automatically uploaded by the Internet of Things device and work order information submitted by the user. The work order weight determination module is used to extract key feature factors from the obtained work order information, and perform weighted calculation on the key feature factors through a weight algorithm to determine the weight value of the work order.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the existing technology, in the process of work order allocation management, due to the timing difference between the knowledge base update cycle and the real-time work order generation, when the knowledge base does not include the latest technical solutions, the generated work order may contain an outdated parts list, resulting in low accuracy of the work order data generated based on the knowledge base information. The accuracy of work order allocation management based on the work order data is also reduced, resulting in poor efficiency in the comprehensive management of work order data. Summary of the Invention

[0007] The embodiments of the present application provide a method and electronic equipment for comprehensive management of work order data of multiple maintenance outlets, thereby solving the problem in the prior art that, in the process of work order allocation management, due to the timing difference between the knowledge base update cycle and the real-time work order generation, when the knowledge base does not include the latest technical solutions, the generated work order may contain an outdated parts list, resulting in low accuracy of the work order data generated based on the knowledge base information, reduced accuracy of work order allocation management based on the work order data, and poor efficiency of comprehensive management of work order data, thereby achieving precision and efficiency improvement in work order data scheduling.

[0008] The embodiment of the present application provides a comprehensive management method for work order data of multiple maintenance outlets, including the following steps: performing work order accuracy evaluation based on historical work order accuracy parameters of each maintenance outlet, and then performing system self-optimization processing on each maintenance outlet based on the historical work order data of each maintenance outlet based on the obtained work order accuracy evaluation results, and the system self-optimization processing includes knowledge base dynamic processing and abnormal time-consuming identification processing; performing initial screening of work orders based on the to-be-assigned work order data of each maintenance outlet and the basic data of each engineer to obtain the assignable engineers corresponding to each to-be-assigned work order, and the initial screening of work orders means matching the information of the to-be-assigned work order data with the basic data information of the engineer to screen a set of candidate engineers that meet the work order processing conditions; based on the The engineer priority is evaluated based on the work order demand parameters, and then the work order allocation management is performed based on the obtained engineer priority evaluation results. The work order allocation management means dynamically allocating work orders based on the engineer priority evaluation results, and self-optimizing and adjusting the estimated work order time of similar subsequent work orders according to the actual work order processing time to improve the accuracy of work order generation in the maintenance outlet work order management system; dynamic processing of the knowledge base means dynamically adjusting the knowledge base update frequency of the maintenance outlet work order management system to improve the accuracy of work order generation; abnormal time-consuming identification processing means issuing time warnings based on the work order time parameters of the maintenance outlet to improve the operating efficiency of the maintenance outlet work order management system. The maintenance outlet work order management system is a digital management platform for maintenance outlets to automatically generate work orders based on customer needs.

[0009] An embodiment of the present application provides an electronic device for comprehensive management of work order data of multiple maintenance outlets, including: a processor; a memory, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, any one of the methods for comprehensive management of work order data of multiple maintenance outlets is implemented.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. A work order accuracy calculation model is constructed based on historical work order data of maintenance outlets, triggering dynamic processing of the knowledge base and abnormal time-consuming identification processing, thereby improving the knowledge support accuracy and abnormal situation detection capabilities of problem handling; based on the work order requirement characteristics and the basic characteristics of the engineer, an optimal set of candidate engineers is generated to match the processing personnel for the work order; priority dispatch is executed based on the comprehensive analysis results of the work order requirement parameters of the optimal candidate set, and the estimated time parameters of similar work orders are continuously iterated and optimized based on the actual processing time, thereby ensuring the rationality and timeliness of work order processing, thereby achieving high efficiency, accuracy and scientificity in work order allocation management.

[0011] 2. By comprehensively evaluating the accuracy of work orders based on the historical accuracy parameters of each maintenance outlet and comparing them with the work order accuracy threshold, it is possible to determine whether the accuracy of the work order data generated by the maintenance outlet meets the standards. If it does not meet the standards, the knowledge base update frequency is dynamically adjusted according to the deviation of the work order accuracy, achieving accurate positioning and flexible response to work order accuracy issues; if it meets the standards, abnormal time consumption is identified and processed based on the average maintenance time parameters, thereby ensuring the effectiveness of maintenance time management, and further achieving efficient monitoring and intelligent optimization of the work order processing process.

[0012] 3. Comprehensively assessing work order demand based on each engineer's work order demand parameters allows for objective and clear differentiation between engineer categories with different levels of demand, including primary and secondary engineers, and preliminary classification management based on engineer category. Based on descending sorting, work orders with urgent or complex demands are assigned to primary engineers, while routine work orders are assigned to secondary engineers. This ensures rational and efficient work order allocation and optimal distribution and management of work order data. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Flowchart of the comprehensive management method of work order data for multiple maintenance sites provided in an embodiment of the present application.

[0014] Figure 2 A flowchart of the system self-optimization process provided in an embodiment of the present application.

[0015] Figure 3 A flowchart of the work order allocation management process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The present application provides a method and electronic device for comprehensive work order data management at multiple maintenance sites. This solves the problem in the prior art that, in the work order allocation and management process, due to the timing difference between the knowledge base update cycle and the real-time work order generation, when the knowledge base does not include the latest technical solutions, the generated work order may contain an outdated parts list. This results in low accuracy of the work order data generated based on the knowledge base information, reduced accuracy of work order allocation management based on the work order data, and poor efficiency of comprehensive work order data management. The overall concept is as follows: First, a work order accuracy calculation model is constructed based on the historical work order data of maintenance outlets, and the work order accuracy calculation model is used to trigger dynamic knowledge base processing and abnormal time-consuming identification processing; second, the optimal set of candidate engineers is generated through the work order demand characteristics and the basic characteristics of engineers; finally, priority dispatch is executed based on the comprehensive analysis results of the work order demand parameters of the optimal set of candidate engineers, and the estimated time parameters of similar work orders are continuously iterated and optimized based on the actual processing time, thereby improving the accuracy of work order allocation management.

[0017] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0018] like Figure 1 FIG. 1 is a flow chart of a method for comprehensive management of work order data of multiple maintenance sites provided in an embodiment of the present application, and the method includes the following steps: Based on the historical work order accuracy parameters of each maintenance network, the work order accuracy is evaluated. Then, based on the work order accuracy evaluation results, the system self-optimization processing of each maintenance network is carried out according to the historical work order data of each maintenance network. The system self-optimization processing includes dynamic processing of the knowledge base and abnormal time-consuming identification processing; based on the work order data to be assigned of each maintenance network and the basic data of each engineer, the work order is initially screened to obtain the assignable engineers corresponding to each work order to be assigned. The initial screening of the work order means matching the information of the work order data to be assigned with the basic data information of the engineer to select a set of candidate engineers that meet the work order processing conditions; based on the work order demand parameters of each assignable engineer, the engineer priority is evaluated. Then, work order allocation management is performed based on the obtained engineer priority evaluation results. Work order allocation management means dynamically allocating work orders based on the engineer priority evaluation results, and self-optimizing and adjusting the estimated work order time of similar subsequent work orders according to the actual work order processing time to improve the accuracy of work order generation in the maintenance outlet work order management system; knowledge base dynamic processing means dynamically adjusting the knowledge base update frequency of the maintenance outlet work order management system to improve the accuracy of work order generation; abnormal time-consuming identification processing means issuing time warnings based on the work order time parameters of the maintenance outlet to improve the operating efficiency of the maintenance outlet work order management system. The maintenance outlet work order management system is a digital management platform for maintenance outlets to automatically generate work orders based on customer needs.

[0019] In this embodiment, the maintenance outlet work order management system builds a work order accuracy calculation model based on the historical work order data of the maintenance outlet, triggers dynamic processing of the knowledge base and abnormal time-consuming identification processing, can timely update the knowledge base and accurately locate abnormalities, and improve problem handling efficiency and quality; generates the optimal set of candidate engineers through work order demand characteristics and engineer basic characteristics, can accurately match work orders with suitable engineers, and give full play to the advantages of engineers; executes priority dispatching based on the comprehensive analysis results of the work order demand parameters of the optimal set, can ensure that urgent and important work orders are handled first, and assigns work orders with high repetitiveness, low risk, and low cost to low-demand engineers, ensuring the balance between work orders and engineers; continuously iterates and optimizes the estimated time parameters of similar work orders based on the actual processing time, making the estimation and generation of work order data more accurate, effectively improving the accuracy of work order allocation management.

[0020] In addition, the work order management database is used to store relevant data of the comprehensive management method of work order data based on multiple maintenance outlets, including: historical work order accuracy parameter reference data, historical work order accuracy parameter influence ratio data and work order accuracy deviation threshold, etc. The data in the work order management database can be directly integrated through other internal systems of the enterprise, such as ERP system, CRM system, etc., or it can be obtained by connecting with third-party systems such as supply chain partners and logistics systems to obtain data such as raw material delivery information and inventory levels.

[0021] Furthermore, the historical work order accuracy parameters include: work order diagnosis accuracy, parts prediction accuracy and maintenance time estimation accuracy; the steps of evaluating the accuracy of work orders based on the historical work order accuracy parameters of each maintenance outlet include: obtaining historical work order accuracy parameter reference data and historical work order accuracy parameter influence ratio data from a preset work order management database; historical work order accuracy parameter reference data include: critical work order diagnosis accuracy, critical parts prediction accuracy and critical maintenance time estimation accuracy; historical work order accuracy parameter influence ratio data include: work order diagnosis accuracy influence ratio, parts prediction accuracy and critical maintenance time estimation accuracy. The accuracy impact ratio and the maintenance man-hour estimation accuracy impact ratio are compared; the historical work order accuracy parameters of each historical work order of each maintenance outlet are compared with the historical work order accuracy parameter reference data to obtain the work order accuracy evaluation parameters of each maintenance outlet; the work order accuracy evaluation parameters of each maintenance outlet are weighted using the historical work order accuracy parameter influence ratio data, and the weighted processing results are coupled to obtain the work order accuracy measurement index of each maintenance outlet; the work order accuracy measurement index is a comprehensive indicator that quantitatively measures the accuracy of work orders generated by maintenance outlets based on historical work order accuracy parameters.

[0022] The method for obtaining the work order accuracy calculation index of each maintenance outlet is as follows: ; Where, represents the work order accuracy calculation index of the m-th maintenance network, Indicates the impact ratio of work order diagnosis accuracy, Indicates the impact ratio of accessories prediction accuracy, Indicates the impact ratio of the maintenance man-hour estimation accuracy.

[0023] The diagnostic accuracy of the work order of the mth maintenance network is the percentage of the work orders whose diagnosis results are consistent with the actual fault in the total number of historical work orders of the maintenance network. represents the critical work order diagnosis accuracy, The accuracy rate of parts prediction for the mth maintenance outlet is the percentage of work orders in the maintenance outlet whose parts prediction results are consistent with the actual parts demand, out of the total number of historical work orders. Indicates the accuracy of critical parts prediction, represents the maintenance man-hour estimation accuracy of the m-th maintenance network point, The critical maintenance man-hour estimation accuracy is defined as the percentage of work orders in the maintenance network whose estimated maintenance man-hours are consistent with the actual maintenance man-hours, out of the total number of historical work orders. m is the maintenance network number, and m = 1, 2, 3, ..., M, where M is the total number of maintenance networks.

[0024] In the work order management database, an accuracy assessment of work orders generated by the maintenance outlet work order management system is performed. The corresponding influence ratios of the three parameters, work order diagnosis accuracy, parts prediction accuracy, and maintenance time estimation accuracy, are used to quantitatively measure their contribution to the work order accuracy measurement indicators. A mapping table is configured for each parameter separately. The table contains a one-to-one or many-to-one correspondence, which records in detail the possible historical work order accuracy parameter values ​​and their corresponding influence ratios. In actual application, the three parameter values ​​obtained by real-time measurement are entered into the corresponding mapping table respectively, and the corresponding influence ratio can be automatically matched, and the value of the influence ratio is in the range of 0 to 1.

[0025] In this embodiment, work order diagnosis is the foundation for work order accuracy. If the work order diagnosis is incorrect, parts prediction and work time estimates are likely to be off. Inaccurate parts predictions can lead to parts shortages and secondary purchases, extending the repair cycle and impacting repair efficiency. Inaccurate work time estimates can lead to unreasonable engineer scheduling, impacting overall service efficiency. Therefore, the accuracy of work order diagnosis, parts prediction, and repair work time estimates jointly determine the quality of work order generation. For businesses, a higher work order accuracy metric allows for more precise matching of engineer skills, parts inventory, and scheduling, reducing customer complaints due to misdiagnosis, missing parts, or delayed repairs, while also reducing additional costs such as rework, emergency stocking, and overtime. For engineers, a higher work order accuracy metric reduces ineffective work caused by incorrect predictions and improves work efficiency.

[0026] like Figure 2 As shown, this is a flowchart of the system self-optimization processing flow provided in an embodiment of the present application. In the diagram, the accuracy index represents the work order accuracy deviation index, the threshold represents the work order accuracy threshold, the deviation index represents the work order accuracy deviation index, and the deviation threshold represents the work order accuracy deviation threshold.

[0027] Specifically, the steps of performing system self-optimization processing on each maintenance outlet according to the historical work order data of each maintenance outlet based on the obtained work order accuracy evaluation result include: obtaining a work order accuracy threshold from a preset work order management database; comparing the work order accuracy calculation index of each maintenance outlet with the work order accuracy threshold respectively; if the work order accuracy calculation index of a maintenance outlet is lower than the work order accuracy threshold, then the difference between the work order accuracy threshold and the work order accuracy calculation index of the maintenance outlet is recorded as the work order accuracy deviation index, and the knowledge base update frequency of the maintenance outlet is dynamically processed based on the work order accuracy deviation index; if the work order accuracy calculation index of a maintenance outlet is not lower than the work order accuracy threshold, then the average maintenance time parameter of the maintenance outlet is obtained based on the work order time parameter of each historical work order data of the maintenance outlet, and abnormal time consumption is identified based on the average maintenance time parameter of the maintenance outlet, and the work order time parameters include work order generation time, work order allocation time and work order receipt time.

[0028] Among them, the step of dynamically processing the knowledge base update frequency of the maintenance network based on the work order accuracy deviation index includes: obtaining the work order accuracy deviation threshold from the preset work order management database; comparing the work order accuracy deviation index with the work order accuracy deviation threshold; if the work order accuracy deviation index is lower than the work order accuracy deviation threshold, matching the work order accuracy deviation index with the update frequency adjustment value corresponding to each work order accuracy deviation range preset in the work order management database; summing the matched update frequency adjustment value with the knowledge base update frequency to obtain the adjusted knowledge base update frequency; the work order management database establishes an association mapping table between the work order accuracy deviation range and the update frequency adjustment value, A fast matching query between the two is achieved. The mapping table adopts a key-value pair structure design, in which the load type is used as the query key and the corresponding knowledge base update frequency is used as the return value. It supports one-to-one or many-to-one association relationships. When the update frequency adjustment value needs to be obtained, it is only necessary to retrieve the mapping table with the work order accuracy deviation index as the query condition. The work order management database can efficiently complete the matching search and return the accurate corresponding knowledge base update frequency set. The same is true for the work order time adjustment value; if the work order accuracy deviation index is not lower than the work order accuracy deviation threshold, an abnormal prompt of the maintenance site work order management system will be issued, notifying the preset personnel to perform maintenance, triggering the work order review process during the maintenance period, and ensuring the accuracy of work order creation.

[0029] In this embodiment, the knowledge base within the maintenance site work order management system is the system's core intelligent support module. It utilizes a relational database architecture to digitally accumulate and structure the storage of maintenance knowledge. This knowledge base primarily consists of six core data modules: a failure mode library records the characteristics and probability of various equipment failures; a solution library stores standard maintenance procedures and methods; a parts database maintains part codes and compatibility information; a work hour standard library contains time parameters for various maintenance operations; a historical case library accumulates typical maintenance examples; and a standards and specifications library ensures that all operations comply with industry regulations and safety requirements.

[0030] A close digital collaboration mechanism has been formed between the maintenance outlet work order management system and the knowledge base. When a maintenance outlet receives a repair report from a customer, the system first intelligently identifies the most likely cause of the fault from the knowledge base's fault mode library based on the equipment model and fault phenomenon description, using a semantic analysis algorithm or fault code matching. It then automatically associates the corresponding standardized solution in the knowledge base, including detailed repair steps, a list of required special tools, safety precautions, and quick links to relevant technical documentation. In the parts preparation phase, the system automatically retrieves parts data from the knowledge base based on the solution, intelligently recommends standard parts codes and names, and queries the spare parts status of the maintenance outlet's local inventory and central warehouse in real time online, including information on available replacement parts.

[0031] It's worth noting that the diagnostic accuracy of a maintenance site work order management system is highly dependent on the accuracy and timeliness of the failure mode and solution data in the knowledge base. Similarly, the accuracy of the estimated work hours automatically calculated by the system based on the solution ID, equipment model, and environmental factors directly depends on the formula settings and coefficient calibration of the work hour standard library in the knowledge base. For difficult faults with a low degree of matching, the system intelligently searches the historical case library in the knowledge base to provide engineers with reference solutions for similar cases. The entire work order generation process is subject to real-time verification of the standards and specifications library in the knowledge base to ensure that every link complies with the latest safety standards, quality requirements, and industry regulations.

[0032] After the maintenance task is completed, the maintenance site work order management system will transmit key data such as the actual fault code and root cause analysis finally confirmed by the engineer back to the knowledge base for dynamic updating of the probability statistics in the fault mode library. If any data issues are found in the knowledge base during the maintenance process, including inaccurate fault descriptions, missing solution steps, incorrect parts coding, deviations in labor time estimates, or outdated standards and specifications, engineers can submit knowledge correction suggestions directly in the system. This feedback will be used for continuous optimization of the knowledge base after review. This two-way empowerment design enables the knowledge base and maintenance site work order management system to continuously improve the level of intelligent service through continuous iteration.

[0033] Furthermore, the average maintenance time parameters include the average work order generation time, the average work order allocation time and the average work order reception time; the steps for identifying and processing abnormal time consumption based on the average maintenance time parameters of the maintenance outlet include: obtaining maintenance time parameter reference data from a preset work order management database, specifically including: work order generation time threshold, work order allocation time threshold and work order reception time threshold; comparing the average maintenance time parameters of the historical work order data of the maintenance outlet with the maintenance time parameter reference data respectively, if the average maintenance time parameters of the maintenance outlet do not exceed the corresponding maintenance time parameter reference data, no processing is performed; if the average work order generation time of the maintenance outlet exceeds the work order generation time threshold, a work order creation abnormality prompt is issued; if the average work order allocation time of the maintenance outlet exceeds the work order allocation time threshold, a work order allocation abnormality prompt is issued; if the average work order reception time of the maintenance outlet exceeds the work order reception time threshold, a work order reception abnormality prompt is issued.

[0034] In this embodiment, the present invention monitors the work order accuracy measurement index of each maintenance network in real time, and performs system self-optimization processing based on the index comparison result. When the work order accuracy deviation index exceeds the work order accuracy threshold, it indicates that the quality of the work order automatically generated by the maintenance network work order management system meets the system requirements, and it is necessary to further evaluate the operating efficiency of the maintenance network work order management system based on the maintenance time parameters of the maintenance network. When it is found that a certain maintenance time parameter exceeds its preset threshold, an abnormal warning is issued to notify the preset system maintenance personnel to handle it; the knowledge base update mechanism is automatically triggered based on the difference between the work order accuracy measurement index and the work order accuracy threshold, and the knowledge base update frequency is dynamically adjusted to give priority to the latest solutions and replacement parts for similar faults in the entire network. The relationship and working time correction data are pushed to the target outlets to quickly make up for the lag of their knowledge base and avoid repeated errors caused by outdated experience data; and when the work order accuracy deviation index exceeds the work order accuracy deviation threshold, it means that the work order accuracy of the maintenance outlet is seriously deviated. The system immediately generates an abnormal alarm and freezes the outlet's autonomous creation authority, and simultaneously triggers the work order review process. During the system repair period, the outlet's knowledge base enters "read-only mode", prohibits local data upload, and prevents pollution of the global knowledge base. The headquarters expert team cross-validates the newly created work orders based on the real-time updated global knowledge base, which not only blocks the flow of erroneous work orders into the execution link, but also reversely optimizes the knowledge base through review data, prevents the spread of local errors, and maintains the authority of the global knowledge base. The present invention transforms the work order accuracy measurement index into the core driving force for knowledge base iteration, realizes the intelligent iterative optimization of the knowledge base, ensures the timeliness of maintenance services, and maintains the accuracy and authority of the knowledge base, thereby continuously improving service quality while reducing operating costs.

[0035] Furthermore, based on the work order data to be assigned of each maintenance outlet and the basic data of each engineer, the work orders are initially screened, and the steps of obtaining the assignable engineers corresponding to each work order to be assigned include: the work order data to be assigned includes the location of the work order to be assigned, the time of the work order to be assigned and the type of the work order to be assigned; the basic data includes location data, time data and skill type data, and the time data includes first time data and second time data; in step one, if the skill type data of an engineer meets the type of a work order to be assigned, then directly proceed to step two, otherwise no additional processing is performed; in step two, if the first time data of the engineer contains the time period of the work order to be assigned, then directly proceed to step three, otherwise no additional processing is performed; in step three, determine whether the difference between the location data of the engineer and the location of the work order to be assigned exceeds a preset distance threshold. If so, no additional processing is performed, otherwise the engineer is recorded as the assignable engineer corresponding to the work order to be assigned.

[0036] In this embodiment, the first time data represents the engineer's idle time period, and the second time data represents the engineer's occupied time period. The maintenance network work order management system categorizes all pending work orders, clearly defining the type of each work order, such as electrical fault repair, mechanical fault repair, and software system debugging. A skill profile is also established for each engineer, detailing the types of skills they possess. For example, Engineer C possesses skills in electrical fault repair and electronic equipment debugging. By screening engineers whose location, time, and type meet the work order assignment criteria, engineers can complete work order tasks more efficiently, reducing the work order processing cycle and waiting time, thereby improving the overall work efficiency of the entire team or enterprise. This also helps establish a comprehensive data management system to support subsequent data analysis and decision-making.

[0037] Furthermore, the work order requirement parameters include historical maintenance quality, the number of assigned work orders, and historical maintenance time; the steps of evaluating the priority of engineers based on the work order requirement parameters of each assignable engineer include: obtaining work order requirement parameter reference data and work order requirement parameter impact ratio from a preset work order management database; work order requirement parameter reference data include: critical maintenance quality, critical number of work orders, and critical maintenance time; work order requirement parameter impact ratio data include: historical maintenance quality impact ratio, assigned work order number impact ratio, and historical maintenance time impact ratio; the historical maintenance quality of each assignable engineer is assigned to the work order management database. , the critical number of work orders and the critical maintenance time are compared with the critical maintenance quality, the number of assigned work orders and the historical maintenance time of each assignable engineer respectively, to obtain the work order demand influencing parameters of the assignable engineers corresponding to each work order to be assigned; the work order demand influencing parameters of each assignable engineer are weighted using the work order demand parameter influence ratio data, and then the weighted processing results are coupled to obtain the work order demand calculation index of the assignable engineers corresponding to each work order to be assigned; the work order demand calculation index is a comprehensive indicator that quantitatively measures the work order demand degree of the assignable engineers based on the work order demand parameters.

[0038] The method for obtaining the work order demand calculation indicators of each assignable engineer is as follows: ; Where, It represents the work order demand calculation index of the e-th assignable engineer. Indicates the impact ratio of historical maintenance quality, Indicates the impact ratio of the number of assigned work orders. Indicates the impact ratio of historical maintenance time.

[0039] It represents the historical maintenance quality of the e-th assignable engineer, which is the ratio of the number of repair work orders completed in one go to the total number of repair work orders in the engineer's history. Q represents the critical maintenance quality. Indicates the number of assigned work orders of the e-th assignable engineer, which is the number of work orders that the engineer currently receives and has not completed in the maintenance network work order management system. N represents the critical number of work orders. represents the historical maintenance time of the e-th assignable engineer, is the average time taken to complete the same type of work orders in the engineer's historical maintenance work orders. The three indicators together reveal the boundary of the engineer's comprehensive service capability. L represents the critical maintenance time, where e is the number of each assignable engineer, e = 1, 2, 3, ..., and E, E is the total number of assignable engineers.

[0040] In the work order management database, the impact ratios of the three parameters—historical maintenance quality, the number of assigned work orders, and historical maintenance duration—are used to quantitatively measure their contribution to the work order demand measurement indicators. Each parameter is individually configured with a mapping table, which contains a one-to-one or many-to-one correspondence, detailing each possible work order demand value and its corresponding impact ratio. In actual application, the three parameter values ​​obtained from real-time measurements are entered into the corresponding mapping table, and the corresponding impact ratio is automatically matched. The impact ratio value ranges from 0 to 1.

[0041] like Figure 3 As shown, it is a flowchart of the work order allocation management process provided by an embodiment of the present application. In the diagram, the demand index represents the work order demand calculation index, and the threshold represents the work order demand calculation threshold. Specifically, the steps of work order allocation management based on the obtained engineer priority evaluation results include: obtaining the work order demand calculation threshold from a preset work order management database; comparing the work order demand calculation index of each assignable engineer with the work order demand calculation threshold; if the work order demand calculation index of an assignable engineer reaches the work order demand calculation threshold, the assignable engineer is recorded as the first engineer; if the work order demand calculation index of an assignable engineer does not reach the work order demand calculation threshold, the assignable engineer is recorded as the second engineer; sorting the first engineers in descending order based on the work order demand calculation index, and executing the work order allocation operation in sequence; if an engineer does not respond to the work order, the allocation process of the second engineer is triggered until the work order allocation is completed; assigning routine work orders with high batch and repetitiveness, such as unified parameter calibration and parts replacement for a batch of equipment of the same model, to the second engineer, which is suitable for engineers to complete in a relatively relaxed state and helps to maintain the work enthusiasm and concentration of the second engineer.

[0042] Work order allocation management is performed based on the obtained engineer priority evaluation results. It also includes, after the work order allocation is completed, when the maintenance time reaches the work order time preset in the work order management database, determining whether the completed workload exceeds the preset critical workload. If so, a timeout warning is issued; otherwise, the difference between the critical workload and the completed workload is recorded as the deviation workload, and the deviation workload is matched with the work order time adjustment value corresponding to each deviation work range preset in the work order management database. The work order time of subsequent work orders of the same type is dynamically adjusted according to the matched work order time adjustment value.

[0043] In this embodiment, the present invention sorts work orders from large to small according to the demand for work order allocation, so that resources are tilted towards high-efficiency and high-load engineers, improving the overall resource utilization efficiency. At the same time, a mechanism is set up for triggering secondary allocation when there is no response, avoiding the situation where work orders are piled up due to a certain engineer's failure to respond in time. It can adapt to various actual situations, such as engineer leave, equipment failure, etc., so that the work order allocation system has stronger adaptability and ensures the smooth progress of work order allocation. Regular work orders are allocated to engineers whose work order demand measurement indicators do not meet the work order demand measurement threshold, which not only improves the quality of work orders, but also reduces the time loss of dispatching repetitive work orders. The present invention balances the workload of engineers and allocates work orders according to the actual needs and skill levels of engineers, so that resources are fully utilized. At the same time, through mechanisms such as timeout prompts and estimated time adjustments, it strengthens the management and supervision of engineers and improves the overall execution and collaboration of the team.

[0044] Among them, an embodiment of the present application also provides an electronic device for comprehensive management of work order data of multiple maintenance outlets, including: a processor; a memory, the memory storing computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the comprehensive management methods of work order data of multiple maintenance outlets is implemented.

[0045] The technical solution in the above-mentioned embodiment of the present application, first, constructs a work order accuracy calculation model based on the historical work order data of the maintenance outlets, and triggers knowledge base dynamic processing and abnormal time-consuming identification processing based on the work order accuracy calculation model; secondly, generates an optimal set of candidate engineers through work order demand characteristics and engineer basic characteristics; finally, executes priority dispatching based on the comprehensive analysis results of the work order demand parameters of the optimal set of candidate engineers, and continuously iterates and optimizes the estimated time parameters of similar work orders based on the actual processing time, thereby improving the accuracy of work order allocation management.

[0046] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0047] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0050] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0051] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

Claims

1. A comprehensive management method for work order data of multiple maintenance outlets, characterized in that: The following steps are involved: Perform work order accuracy assessment based on historical work order accuracy parameters of each maintenance network, and then perform system self-optimization processing on each maintenance network based on the obtained work order accuracy assessment results and the historical work order data of each maintenance network. The system self-optimization processing includes dynamic knowledge base processing and abnormal time-consuming identification processing; Performing a preliminary screening of work orders based on the work order data to be assigned at each maintenance site and the basic data of each engineer to obtain the assignable engineers corresponding to each work order to be assigned. The preliminary screening of work orders involves matching the information of the work order data to be assigned with the basic data of the engineers to select a set of candidate engineers that meet the work order processing conditions. Performing an engineer priority assessment based on the work order requirement parameters of each assignable engineer, and then performing work order allocation management based on the obtained engineer priority assessment results. The work order allocation management means dynamically allocating work orders based on the engineer priority assessment results, and self-optimizing and adjusting the estimated work order time of similar subsequent work orders based on the actual work order processing time to improve the accuracy of work order generation in the maintenance network work order management system; The dynamic processing of the knowledge base means dynamically adjusting the knowledge base update frequency of the maintenance outlet work order management system to improve the accuracy of work order generation. The abnormal time-consuming identification processing means issuing time warnings based on the work order time parameters of the maintenance outlet to improve the operating efficiency of the maintenance outlet work order management system. The maintenance outlet work order management system is a digital management platform for maintenance outlets to automatically generate work orders based on customer needs.

2. The method for comprehensive management of work order data of multiple maintenance sites according to claim 1, characterized in that: The historical work order accuracy parameters include: work order diagnosis accuracy, parts prediction accuracy, and maintenance time estimation accuracy; The steps for evaluating work order accuracy based on the historical work order accuracy parameters of each maintenance site include: Obtain historical work order accuracy parameter reference data and historical work order accuracy parameter impact ratio data from the preset work order management database; The historical work order accuracy parameter reference data includes: critical work order diagnosis accuracy, critical parts prediction accuracy, and critical maintenance man-hour estimation accuracy; The historical work order accuracy parameter impact ratio data includes: the work order diagnosis accuracy impact ratio, the parts prediction accuracy impact ratio, and the maintenance time estimation accuracy impact ratio; Compare the historical work order accuracy parameters of each historical work order at each maintenance outlet with the historical work order accuracy parameter reference data to obtain the work order accuracy evaluation parameters of each maintenance outlet; The work order accuracy evaluation parameters of each maintenance network are weighted using the historical work order accuracy parameter influence ratio data. The weighted processing results are then coupled to obtain the work order accuracy measurement indicators of each maintenance network. The work order accuracy calculation index is a comprehensive index that quantitatively measures the accuracy of work orders generated by maintenance outlets based on historical work order accuracy parameters.

3. The method for comprehensive management of work order data of multiple maintenance sites as claimed in claim 2, characterized in that: The step of performing system self-optimization processing on each maintenance network point based on the obtained work order accuracy evaluation result and the historical work order data of each maintenance network point includes: Obtain work order accuracy thresholds from a pre-set work order management database; The work order accuracy measurement index of each maintenance network is compared with the work order accuracy threshold. If the work order accuracy measurement index of a maintenance network is lower than the work order accuracy threshold, the difference between the work order accuracy threshold and the work order accuracy measurement index of the maintenance network is recorded as the work order accuracy deviation index. The knowledge base update frequency of the maintenance network is dynamically processed based on the work order accuracy deviation index. If the work order accuracy calculation index of a maintenance outlet is not lower than the work order accuracy threshold, the average maintenance time parameter of the maintenance outlet is obtained based on the work order time parameters of each historical work order data of the maintenance outlet, and abnormal time consumption is identified and processed based on the average maintenance time parameter of the maintenance outlet. The work order time parameters include the work order generation time, the work order allocation time and the work order reception time.

4. The method for comprehensive management of work order data of multiple maintenance sites as claimed in claim 3, characterized in that: The step of dynamically processing the knowledge base update frequency of the maintenance network based on the work order accuracy deviation index includes: Obtain the work order accuracy deviation threshold from the preset work order management database; Compare the work order accuracy deviation index with the work order accuracy deviation threshold. If the work order accuracy deviation index is lower than the work order accuracy deviation threshold, match the work order accuracy deviation index with the update frequency adjustment value corresponding to each work order accuracy deviation range preset in the work order management database, and dynamically adjust the knowledge base update frequency based on the matched update frequency adjustment value. If the work order accuracy deviation index is not lower than the work order accuracy deviation threshold, an abnormal prompt of the maintenance site work order management system will be issued.

5. The method for comprehensive management of work order data of multiple maintenance sites as claimed in claim 3, characterized in that: The average maintenance time parameters include average work order generation time, average work order allocation time, and average work order reception time; The step of performing abnormal time-consuming identification processing based on the average maintenance time parameter of the maintenance network includes: Obtain maintenance time parameter reference data from a preset work order management database, including: work order generation time threshold, work order allocation time threshold, and work order receipt time threshold; Compare the average maintenance time parameters of the historical work order data of the maintenance network with the maintenance time parameter reference data respectively. If the average maintenance time parameters of the maintenance network do not exceed the corresponding maintenance time parameter reference data, no processing will be performed; If the average work order generation time of the maintenance outlet exceeds the work order generation time threshold, a work order creation exception prompt will be issued; If the average work order allocation time of the maintenance network exceeds the work order allocation time threshold, a work order allocation abnormality prompt will be issued; If the average work order receiving time of the maintenance outlet exceeds the work order receiving time threshold, a work order receiving abnormality prompt will be issued.

6. The method for comprehensive management of work order data of multiple maintenance sites as claimed in claim 1, characterized in that: The step of performing a preliminary screening of work orders based on the work order data to be assigned at each maintenance site and the basic data of each engineer to obtain the assignable engineers corresponding to each work order to be assigned includes: The work order data to be assigned includes the work order location to be assigned, the work order time to be assigned, and the work order type to be assigned; The basic data includes location data, time data and skill type data, and the time data includes first time data and second time data; In step 1, if the skill type data of an engineer matches the type of work order to be assigned, the process goes directly to step 2; otherwise, no additional processing is performed. In step 2, if the engineer's first-time data contains the time period of the work order to be assigned, then directly proceed to step 3; otherwise, no additional processing is performed; Step three: determine whether the difference between the engineer's location data and the location of the work order to be assigned exceeds a preset distance threshold. If so, no additional processing is performed; otherwise, the engineer is recorded as an assignable engineer corresponding to the work order to be assigned.

7. The method for comprehensive management of work order data of multiple maintenance sites as claimed in claim 1, characterized in that: The work order requirement parameters include historical maintenance quality, number of assigned work orders, and historical maintenance duration; The steps for evaluating engineer priority based on the work order requirement parameters of each assignable engineer include: Obtain work order requirement parameter reference data and work order requirement parameter impact ratio from the preset work order management database; The work order requirement parameter reference data includes: critical maintenance quality, critical work order quantity and critical maintenance time; The work order demand parameter impact ratio data includes: historical maintenance quality impact ratio, assigned work order quantity impact ratio, and historical maintenance duration impact ratio; Compare the historical maintenance quality, critical number of work orders, and critical maintenance time of each assignable engineer with the critical maintenance quality, the number of assigned work orders, and historical maintenance time of each assignable engineer, respectively, to obtain the work order demand influencing parameters of the assignable engineer corresponding to each work order to be assigned; The work order demand parameter impact ratio data is used to weight the work order demand impact parameters of each assignable engineer. The weighted processing results are then coupled to obtain the work order demand measurement indicators of the assignable engineers corresponding to each work order to be assigned. The work order demand calculation index is a comprehensive index that quantitatively measures the degree of work order demand of assignable engineers based on work order demand parameters.

8. The method for comprehensive management of work order data of multiple maintenance sites as claimed in claim 7, characterized in that: The step of performing work order allocation management based on the obtained engineer priority evaluation result includes: Obtain the work order demand calculation threshold from the preset work order management database; Compare the work order demand calculation index of each assignable engineer with the work order demand calculation threshold. If the work order demand calculation index of an assignable engineer reaches the work order demand calculation threshold, the assignable engineer will be recorded as the first engineer; If the work order demand calculation indicator of an assignable engineer does not reach the work order demand calculation threshold, the assignable engineer will be recorded as the second engineer; Based on the work order demand calculation index, the first engineers are sorted in descending order and the work order allocation operation is performed in sequence. If an engineer does not respond to the work order, the allocation process of the second engineer is triggered until the work order allocation is completed.

9. The method for comprehensive management of work order data of multiple maintenance sites as claimed in claim 7, characterized in that: The work order allocation management based on the obtained engineer priority evaluation results also includes, after the work order allocation is completed, when the maintenance time reaches the work order time preset in the work order management database, determining whether the completed workload exceeds the preset critical workload, and if so, issuing a timeout warning, otherwise recording the difference between the critical workload and the completed workload as the deviation workload, matching the deviation workload with the work order time adjustment value corresponding to each deviation work range preset in the work order management database, and dynamically adjusting the work order time of subsequent work orders of the same type according to the matched work order time adjustment value.

10. An electronic device for comprehensive management of work order data at multiple maintenance sites, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for comprehensive management of work order data of multiple maintenance sites as claimed in any one of claims 1 to 9 is implemented.

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