A metrology attachment path optimization method

Abstract

The present application provides a metering and connecting path optimization method to solve the problem of insufficient technical efficiency in the prior art, comprising the following steps: obtaining full amount of special transformer user data from a power utilization information collection system, updating the latitude and longitude information of the special transformer user in full amount, querying the distance and time information between all locations in the external network in batches every month, importing the results into the internal network local database, and establishing a metering and connecting work plan compilation model. The present application not only optimizes the path selection process, but also improves the automation and intelligent level of plan compilation, thereby providing effective support and improvement strategies for the operation and management of the power system.

Description

Technical Field

[0001] This invention relates to the field of power analysis technology, and in particular to a method for optimizing metering connection paths. Background Technology

[0002] In today's power system operations, effective path planning is key to improving service efficiency and customer satisfaction. Especially in metering installation and connection operations, accurate path planning can significantly reduce maintenance time and costs, thereby improving overall economic efficiency and system reliability. With technological advancements, particularly in information technology and intelligent algorithms, traditional manual path planning methods can no longer meet the demands for both efficiency and accuracy.

[0003] Current research primarily focuses on optimizing path planning problems under specific conditions using various algorithms. However, in the specific scenario of power system metering and connection, several unique challenges remain. These challenges include handling large amounts of geographic location data, updating work point information in real time, and optimizing multi-objective path decisions. Furthermore, existing research rarely combines Robotic Process Automation (RPA) technology with intelligent path planning to achieve more efficient data processing and task execution.

[0004] To address the above issues, Chinese invention patent CN202411190567.X proposes a parallel power supply path search method and system based on a graph database. The method includes: creating a power grid power supply association graph model based on structured model connection files of all substations, transmission lines, feeders, loads, and distributed power sources within a region, as well as equipment coordinate information; updating the power grid power supply association graph model based on the latest minute-level cross-sectional data; calculating the optimized power supply path using mixed-integer linear programming based on the updated power grid power supply association graph model; quickly finding alternative paths bypassing the faulty area using Dijkstra's algorithm based on the optimized power supply path; refining the power supply path through in-depth analysis using a power source tracing algorithm based on the alternative paths bypassing the faulty area; distributing the computational tasks across multiple nodes for parallel execution using graph parallel computing technology to obtain path search results; confirming the power supply path from the source side to the target power station based on the path search results and power supply status, and highlighting the power supply path on geographical coordinates using a graph database visualization component, while simultaneously performing minute-level dynamic updates to obtain intuitive and complete path information. However, this invention aims to provide an accurate power supply path and lacks efficiency optimization, so the computing speed will inevitably decrease when a large amount of data comes in. Summary of the Invention

[0005] The present invention mainly addresses the problem of insufficient efficiency in existing technologies by providing a method for optimizing metering assembly paths.

[0006] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions: A method for optimizing metering assembly paths includes the following steps: S1. The system uses RPA technology to periodically obtain full data of dedicated transformer users from the electricity information collection system. The system automatically handles data inconsistency issues by comparing the local database with the source system data. S2. Utilize RPA technology to periodically and automatically access PMS3.0 and fully update the latitude and longitude information of dedicated transformer users; S3. By accessing the automatic query interface of the external navigation website API, query the distance and time information between all locations in batches on the external network every month, and import the results into the local database on the internal network; S4. Establish a model for compiling a metering and assembly work plan.

[0007] As a preferred option, in step S2, for newly added special transformer user information in the database, the PMS3.0 system is automatically accessed after being triggered by RPA technology to retrieve the missing latitude and longitude information and supplement it to the local database.

[0008] As a preferred option, in step S3, after the latitude and longitude information of the newly added dedicated transformer user is completed, the route query function of the PMS3.0 system is triggered using RPA technology to obtain the distance and time information from the newly added dedicated transformer user to all dedicated transformer users.

[0009] As a preferred embodiment, step S4 includes the following steps: S4.1 Optimize the annual metering installation work plan compilation model for the annual electricity meter replacement work plan. Objective function: In the formula: D ij Let P be a two-point travel time matrix, representing the travel time from measurement work location i to measurement work location j, where P is the set of measurement work locations. Constraints: In the formula: T i To measure working hours at the work site, C max The maximum time allotted for the work plan; S4.2 Optimize the temporary metering connection task processing model for temporary metering connection work plan preparation. Objective function: Maximize|S| In the formula: S is the set of locations for the final service, which is a combination of locations selected from the set of temporary task locations and the set of optional service locations.

[0010] Constraints: In the formula: T max Given the maximum time window, E is the set of temporary task locations.

[0011] As a preferred embodiment, step S4.1 includes the following steps: S4.1.1 Initialize the metering operation plan and start building a new metering operation plan from the central dispatch point; S4.1.2 Build a time-saving schedule and use the CW time-saving algorithm to calculate the time-saving for each pair of metered work locations; S4.1.3 Update the metering operation plan; S4.1.4 For each work location being considered for inclusion in the plan, calculate the new cumulative time when attempting to add it to the current plan; S4.1.5. Check the planned time capacity.

[0012] As a preferred embodiment, in step S4.1.5, if the new cumulative time is not greater than the maximum time of the work plan, the work locations that are being considered for inclusion in the plan are added to the current metering work plan, and the cumulative time of the current plan is updated to the new cumulative time. If the new cumulative time is greater than the maximum time of the work plan, the current metering work plan is terminated, the current metering work plan is saved to the metering work plan set, and all locations included in the current metering work plan are removed from the set of unserved metering work locations.

[0013] As a preferred option, if the set of unserved metered work locations is empty, create an independent round-trip plan for each of the remaining work locations that are being considered for inclusion in the plan.

[0014] As a preferred option, if the set of unserved metered work locations cannot further optimize the current activity plan, create an independent round-trip plan for each remaining work location that is being considered for inclusion in the plan.

[0015] As a preferred embodiment, step S4.2 includes the following steps: S4.2.1 Initialize temporary task locations and optional service locations, calculate the time savings of all possible locations for the metered operation location, and store them in the time-saving schedule; S4.2.2 Use a time-saving schedule, prioritize the path combination that saves the most time to start the build, and ensure that all temporary task locations are included; S4.2.3. Try adding each optional service location from the time-saving schedule in descending order of time savings. For each location, calculate the new cumulative time after the addition and check if it exceeds the given maximum time window. S4.2.4. Stop adding locations if adding any new location would cause the total time to exceed the given maximum time window. S4.2.5 Verify whether the final path meets the timely processing requirements of all temporary tasks, and select the path with the most service locations as the optimal solution.

[0016] As a preferred approach, in step S4.2.4, if there are still locations that have not been added, but no more locations can be added without violating the time window limit, then the algorithm terminates.

[0017] Therefore, the advantages of the present invention are: First, latitude and longitude coordinates are extracted from the same maintenance system using automation technology and interfaced with map software APIs to establish a dynamically updated database, ensuring the real-time nature and accuracy of the data. Second, an innovative approach combining the CW saving algorithm with the vehicle delivery time window (CVRP) model is applied to intelligently plan an efficient annual metering and installation operation schedule. Finally, this study also considers the priority adjustment of temporary tasks to ensure that high-priority tasks can be processed promptly when necessary. These innovations not only optimize the route selection process but also improve the automation and intelligence level of planning, providing effective support and improvement strategies for the operation and maintenance management of power systems. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0020] Example: In today's power system operations, effective path planning is key to improving service efficiency and customer satisfaction. Especially in metering installation and connection operations, accurate path planning can significantly reduce maintenance time and costs, thereby improving overall economic efficiency and system reliability. With technological advancements, particularly in information technology and intelligent algorithms, traditional manual path planning methods can no longer meet the demands for both efficiency and accuracy.

[0021] Route planning, as a core technology for optimizing logistics and enhancing system efficiency, plays a crucial role in smart grids and modern logistics. With the rapid development of intelligent algorithms and computing technologies, route planning methods have evolved from simple heuristic algorithms to complex intelligent algorithms and the application of big data processing technologies.

[0022] In smart grids, key challenges in route planning include effectively managing and optimizing maintenance and inspection routes for grid equipment to reduce operating costs and improve response speed. In the logistics sector, particularly in power system metering and installation, efficiently arranging routes to serve a wide geographical area is crucial for improving service quality and reducing costs.

[0023] Current research primarily focuses on optimizing path planning problems under specific conditions using various algorithms. However, in the specific scenario of power system metering and connection, several unique challenges remain. These challenges include handling large amounts of geographic location data, updating work point information in real time, and optimizing multi-objective path decisions. Furthermore, existing research rarely combines Robotic Process Automation (RPA) technology with intelligent path planning to achieve more efficient data processing and task execution.

[0024] This application proposes a method for optimizing metering connection routes. In power systems, accurate data is the cornerstone of effective route planning and operation and maintenance management. This application utilizes RPA technology to automate the data acquisition, verification, and updating processes, ensuring that the metering connection route optimization model operates based on the latest and most accurate data. The method includes the following steps: S1. Utilizing RPA technology, the system periodically acquires full data on dedicated transformer users from the electricity information collection system. The application of RPA technology significantly improves the efficiency and accuracy of data acquisition, avoiding errors and omissions that may occur with manual operations. The acquired data includes key information such as the dedicated transformer user's electricity number, account name, address, electricity category, and capacity. This data is crucial for subsequent business analysis and decision-making. After data acquisition, the system automatically handles data inconsistencies by comparing the local database with the source system data. Through comparison, the system can quickly identify differences between the two sets of data, including redundant records in the local database, missing user information, and inconsistent data fields. Specifically, redundant records in the local database are deleted, and missing user information is automatically added, ensuring the integrity and consistency of the local user basic information data.

[0025] S2. After the dedicated transformer user data is synchronized, for newly added dedicated transformer user information in the database, after the dedicated transformer user data synchronization process is completed, RPA technology is used to periodically and automatically access PMS3.0 to fully update the latitude and longitude information of the dedicated transformer users to ensure the accuracy of subsequent route planning.

[0026] After synchronizing the dedicated transformer user data, the system further processes the newly added dedicated transformer user information in the database to ensure the comprehensiveness and application value of the data. Especially for newly added dedicated transformer users, their geographical location information (i.e., latitude and longitude information) is critical data in business scenarios such as route planning, equipment inspection, and fault repair. Therefore, after the dedicated transformer user data synchronization process is completed, the system will initiate another automated process based on RPA technology to periodically access PMS3.0 (Power Management System version 3.0) and fully update the latitude and longitude information of all dedicated transformer users.

[0027] The specific operation process is as follows: The RPA robot will simulate manual operation, log into the PMS3.0 system, and extract the geographical location information of newly added dedicated transformer users one by one according to the preset query conditions. This information is usually stored in the form of latitude and longitude coordinates, which can accurately reflect the actual location of the user's equipment. After the extraction is completed, the RPA robot will automatically populate this data back into the local database and associate it with the corresponding dedicated transformer user information for storage.

[0028] To ensure the accuracy of latitude and longitude information, the system also performs data verification during the update process. For example, it compares the new user's latitude and longitude data with the existing user's geographic location information to determine if the data is reasonable, avoiding deviations caused by system errors or data entry problems. Furthermore, for users whose latitude and longitude information cannot be obtained from PMS3.0, the system generates an anomaly report, which is submitted for manual review and supplementation to ensure data integrity.

[0029] S3. Full-scale travel time data update: In view of the slow query time on the intranet, the automatic query interface of the external navigation website API is used to query the travel and time information between all locations in batches on the external network every month. This process can effectively avoid the bottleneck of slow intranet query speed, while ensuring the comprehensiveness and reliability of the data, and import the results into the local intranet database.

[0030] To ensure the accuracy of imported data, the imported data will be validated to ensure consistency with the results of external queries. Furthermore, to further improve data management efficiency, indexes will be created in the database and query logic will be optimized, enabling quick access and use of travel time data when developing work plans. Through these operations, not only can the efficiency of data updates be effectively improved, but more reliable data support will also be provided for subsequent work plan development.

[0031] S4. Establish a work plan preparation model for metering and assembly. The goal of this stage is to build a scientific, reasonable, and efficient work plan preparation model based on the updated full-trip time data from the previous steps and in conjunction with actual business needs. During the model design process, we will employ advanced algorithms and data analysis techniques, such as graph theory algorithms and optimization algorithms, to ensure the model can operate efficiently in complex real-world scenarios. Simultaneously, to improve the model's practicality and adaptability, we will conduct multiple tests and iterative optimizations to ensure it meets the needs of different business scenarios. Through the establishment and application of this model, we can significantly improve the efficiency of metering and assembly work planning, reduce the cost of manual intervention, and provide strong technical support for the optimization of the overall workflow.

[0032] The main contributions include: First, by extracting latitude and longitude coordinates from the same maintenance system through automation technology and interfacing with map software APIs, a dynamically updated database was established, ensuring the real-time performance and accuracy of the data. Second, an innovative approach combining the CW saving algorithm with the time window vehicle delivery model (CVRP) was applied to intelligently plan efficient annual metering and installation operation schedules. Finally, this study also considered the priority adjustment of temporary tasks, ensuring that high-priority tasks can be processed promptly when necessary. Through these innovations, this paper not only optimizes the route selection process but also improves the automation and intelligence level of planning, providing effective support and improvement strategies for the operation and maintenance management of power systems.

[0033] In step S2, for newly added special transformer user information in the database, the PMS3.0 system is automatically accessed after being triggered by RPA technology to retrieve the missing latitude and longitude information and supplement it to the local database.

[0034] In step S3, after the latitude and longitude information of the newly added dedicated transformer user is completed, the route query function of the PMS3.0 system is triggered using RPA technology to obtain the distance and time information from the newly added dedicated transformer user to all dedicated transformer users.

[0035] Step S4 includes the following steps: S4.1 Optimize the annual metering and installation work plan preparation model for the annual electricity meter replacement work plan. The annual metering and installation work plan preparation model is mainly used to optimize the annual electricity meter replacement work plan. It is applied to the annual replacement list issued at the beginning of the year. By preparing a unified plan, the work efficiency can be significantly improved.

[0036] Variable and function definitions: P: The set of metering locations, which includes all locations that require services.

[0037] F: The set of metered operation locations that have been served, including locations that have been included in at least one program.

[0038] U: The set of unserved metering locations, which includes locations that have not yet been served.

[0039] C max : Maximum time for work plan, limiting the maximum total time that can be included in a single plan.

[0040] R: Set of metering operation plans, initialized to an empty set.

[0041] D ij : Two-point travel time matrix, representing the travel time from measurement work location i to measurement work location j.

[0042] T i : Working time at each measurement work site, the time required to complete the work at each measurement work site.

[0043] r: The metering operation plan currently being built.

[0044] p: The work location is being considered for inclusion in plan r.

[0045] T r The cumulative time of the current plan r tracks the total time of work scheduled in r, used to determine whether it exceeds C. max .

[0046] T new : Try adding a new metering point p to the projected cumulative time after the plan r, to decide whether to add p to r.

[0047] p last The last job location visited in plan r.

[0048] Objective function: In the formula: D ij Let P be the two-point travel time matrix, representing the travel time from metering operation location i to metering operation location j. P is the set of metering operation locations. The objective function aims to minimize the total travel time between all locations that must be visited in the path planning. The optimization objective is to reduce the total travel time, thereby improving the overall efficiency of metering and assembly operations.

[0049] Constraints: In the formula: T i To measure working hours at the work site, C max The constraint of this optimization problem, which is to ensure that the total time of any metered work plan (including travel time and work time) does not exceed the preset maximum allowable time C, is... max.

[0050] S4.2 Optimize the Temporary Metering Connection Task Processing Temporary metering connection work plan generation model. This model optimizes the processing of temporary metering connection tasks by generating temporary work plans quickly and effectively through flexible path planning and emergency response strategies. The model utilizes advanced algorithms to optimize the access order of temporary and optional service locations to maximize the number of locations served within a limited time, while ensuring that all emergency tasks are processed promptly and accurately.

[0051] Variable and function definitions: E: Temporary mission location assembly.

[0052] G: Optional service location set, which refers to additional locations that may be served as a side service while completing temporary tasks.

[0053] T max : The maximum time window given for completing all tasks.

[0054] S: The final set of service locations is a combination of locations selected from E and G, used to optimize the total number of service locations.

[0055] P: The set of all considered location pairs.

[0056] S ij : Savings schedule, calculates the time saved for each pair of metering work locations (i,j).

[0057] e: The specific location of the temporary mission, which is in set E.

[0058] Objective function: Maximize|S| In the formula: S is the set of locations for the final service, which is a combination of locations selected from the set of temporary task locations and the set of optional service locations.

[0059] Constraints: Ensure that the total time for all tasks to complete does not exceed T. max : Temporary tasks must be prioritized to ensure that all e∈E are in S: In the formula: T max Given the maximum time window, E is the set of temporary task locations.

[0060] Step S4.1 includes the following steps: S4.1.1 Initialize the metering operation plan. Start building a new metering operation plan from the central dispatch point (power supply company address).

[0061] S4.1.2 Construct a time-saving schedule S, and use the CW time-saving algorithm to calculate the time-saving S for each pair of metered work locations (i,j). ij The formula for calculating time savings is: S ij =D [0][i] +D [0][j] -D [i][j] +T i +T l The purpose of the formula is to reduce the extra time spent traveling from the center point to both locations and back, compared to traveling directly from one point to another, as well as the working time at those two locations. The time saved is S. ij Sort the locations from largest to smallest. This sorting will be used for subsequent route planning, prioritizing combinations of locations that save the most time.

[0062] S4.1.3 Update the metering operation plan, traverse the set of metering operation locations P to be served, define the set of unserved metering operation locations U as the set P that initially contains all locations, and gradually exclude the served locations during the construction of the plan r.

[0063] Application of the Savings Schedule S ij When selecting the next location p to add to plan r, prioritize saving time S. ij The largest location pair, i.e., sorting the unserved locations in U according to their time savings S relative to the last location in the current schedule. lastp Prioritize selections from largest to smallest.

[0064] For each location p∈U, calculate the new cumulative time T after adding r. new Each plan has an independent time-cumulative variable T. r , specifically tracking the cumulative time of the program.

[0065] S4.1.4 For each work location p being considered for inclusion in the plan, when attempting to add it to the current plan r, calculate the new cumulative time T. new p last Indicates the last location to be visited in the plan: T new =T r +D [plast][p] +D [p][0] -D [plast][0] +T p .

[0066] S4.1.5. Check the time capacity of the plan.

[0067] In step S4.1.5, if the new cumulative time is not greater than the maximum planned work time T... new ≤C max Then, the work location p that is being considered for inclusion in the plan will be added to the current metered work plan r, and the cumulative time T of the current plan will be updated. r For the new cumulative time T new If the new accumulated time is greater than the maximum time of the work plan, the current metering operation plan r is terminated, the current metering operation plan r is saved to the metering operation plan set R, and all locations contained in the current metering operation plan r are removed from the unserved metering operation location set U.

[0068] If the set of unserved metered work locations U is empty, create an independent round-trip plan for each remaining work location p∈U that is being considered for inclusion in the plan.

[0069] If the set of unserved metered work locations U cannot further optimize the current activity plan, create an independent round-trip plan for each remaining work location p∈U that is being considered for inclusion in the plan.

[0070] Step S4.2 includes the following steps: S4.2.1 Initialize the temporary task location E and the optional service location G, calculate the time savings of all possible locations (i,j) for the metered task location, and store them in the time-saving schedule S. ij .

[0071] S4.2.2, Use the Savings Schedule S ij Prioritize the path combination that saves the most time to start building, and ensure that all temporary mission locations E are included.

[0072] S4.2.3. Try adding each optional service location g∈G from the savings schedule in descending order of savings time. For each location g, calculate the new cumulative time after the addition and check if it exceeds the given maximum time window T. max .

[0073] S4.2.4 Adding any new location will cause the total time to exceed the given maximum time window T. max Stop adding when the time is right.

[0074] S4.2.5 Verify whether the final path meets the timely processing requirements of all temporary tasks, and select the path with the most service locations as the optimal solution.

[0075] In step S4.2.4, if there are still locations that have not been added, but no more locations can be added without violating the time window limit, then the algorithm terminates.

[0076] This study experimentally verified the effectiveness of a path optimization model integrating RPA technology and intelligent algorithms in power system metering and connection work. This model not only improved the efficiency of work planning but also optimized the accuracy and response speed of work execution.

[0077] By integrating RPA technology, automated data acquisition and updating significantly improve the speed and accuracy of data processing, providing reliable input for intelligent algorithms. Simultaneously, the application of the CW cost-saving algorithm and the time-window vehicle delivery model (CVRP) makes route planning more scientific, considering not only cost minimization but also ensuring service quality. The system's dynamic update capability allows route planning to respond quickly and re-optimize routes when ad-hoc tasks arise, ensuring the timely completion of high-priority tasks.

[0078] Furthermore, the path optimization model developed in this study possesses strong dynamic update capabilities, which is particularly outstanding when handling temporary tasks or unexpected situations. In power system metering and connection work, the occurrence of temporary tasks is unavoidable, such as sudden equipment failures or urgent customer demands. Traditional path planning methods often lack flexibility, and once the path is determined, it is difficult to cope with sudden changes. However, this model, through real-time data updates and dynamic path optimization algorithms, can respond quickly when temporary tasks occur, recalculating and optimizing the path to ensure that high-priority tasks are allocated and completed in a timely manner. For example, when the system detects a new high-priority task, the model quickly adjusts the original path planning based on the current vehicle location, task distribution, and time window constraints, and generates a new optimal path in the shortest possible time. This dynamic adjustment capability not only improves the system's adaptability and robustness but also significantly reduces operational delays and resource waste caused by unexpected tasks in practical applications.

[0079] To further validate the model's effectiveness, this study simulated various real-world scenarios in its experimental design, including peak-hour task density, geographically unevenly distributed task locations, and combinations of tasks with different priorities. Experimental results show that, compared to traditional manual planning methods or single path optimization algorithms, the path optimization model integrating RPA technology and intelligent algorithms demonstrates significant advantages in several key metrics.

[0080] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for optimizing metering assembly paths, characterized in that, Includes the following steps: S1. The system uses RPA technology to periodically obtain full data of dedicated transformer users from the electricity information collection system. The system automatically handles data inconsistency issues by comparing the local database with the source system data. S2. Utilize RPA technology to periodically and automatically access PMS3.0 and fully update the latitude and longitude information of dedicated transformer users; S3. By accessing the automatic query interface of the external navigation website API, query the distance and time information between all locations in batches on the external network every month, and import the results into the local database on the internal network; S4. Establish a model for compiling a metering and assembly work plan.

2. The metering assembly path optimization method according to claim 1, characterized in that, In step S2, for newly added special transformer user information in the database, the PMS3.0 system is automatically accessed after being triggered by RPA technology to retrieve the missing latitude and longitude information and supplement it to the local database.

3. The metering assembly path optimization method according to claim 2, characterized in that, In step S3, after the latitude and longitude information of the newly added dedicated transformer user is completed, the route query function of the PMS3.0 system is triggered using RPA technology to obtain the distance and time information from the newly added dedicated transformer user to all dedicated transformer users.

4. The metering assembly path optimization method according to claim 1, characterized in that, Step S4 includes the following steps: S4.1 Optimize the annual metering installation work plan compilation model for the annual electricity meter replacement work plan; S4.2 Optimize the temporary metering connection task processing model for temporary metering connection work plan preparation.

5. The metering assembly path optimization method according to claim 4, characterized in that, Step S4.1 includes the following steps: S4.1.1 Initialize the metering operation plan and start building a new metering operation plan from the central dispatch point; S4.1.2 Build a time-saving schedule and use the CW time-saving algorithm to calculate the time-saving for each pair of metered work locations; S4.1.3 Update the metering operation plan; S4.1.4 For each work location being considered for inclusion in the plan, calculate the new cumulative time when attempting to add it to the current plan; S4.1.

5. Check the planned time capacity.

6. The metering assembly path optimization method according to claim 5, characterized in that, In step S4.1.5, if the new cumulative time is not greater than the maximum time of the work plan, the work locations that are being considered for inclusion in the plan are added to the current metering work plan, and the cumulative time of the current plan is updated to the new cumulative time. If the new cumulative time is greater than the maximum time of the work plan, the current metering work plan is terminated, the current metering work plan is saved to the metering work plan set, and all locations included in the current metering work plan are removed from the set of unserved metering work locations.

7. The metering assembly path optimization method according to claim 6, characterized in that, If the set of unserved metered work locations is empty, create an independent round-trip plan for each of the remaining work locations that are being considered for inclusion in the plan.

8. A metering assembly path optimization method according to claim 6 or 7, characterized in that, If the set of unserved metered work locations cannot further optimize the current activity plan, create an independent round-trip plan for each remaining work location that is being considered for inclusion in the plan.

9. The metering assembly path optimization method according to claim 4, characterized in that, Step S4.2 includes the following steps: S4.2.1 Initialize temporary task locations and optional service locations, calculate the time savings of all possible locations for the metered operation location, and store them in the time-saving schedule; S4.2.2 Use a time-saving schedule, prioritize the path combination that saves the most time to start the build, and ensure that all temporary task locations are included; S4.2.

3. Try adding each optional service location from the time-saving schedule in descending order of time savings. For each location, calculate the new cumulative time after the addition and check if it exceeds the given maximum time window. S4.2.

4. Stop adding locations if adding any new location would cause the total time to exceed the given maximum time window. S4.2.5 Verify whether the final path meets the timely processing requirements of all temporary tasks, and select the path with the most service locations as the optimal solution.

10. The metering assembly path optimization method according to claim 9, characterized in that, In step S4.2.4, if there are still locations that have not been added, but no more locations can be added without violating the time window limit, the algorithm terminates.

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