A method and electronic equipment for dispatching low-voltage distribution network maintenance personnel based on a positioning system

Abstract

This application relates to the field of power technology, and provides a method and electronic device for dispatching low-voltage distribution network operation and maintenance personnel based on a positioning system. The method includes: acquiring real-time location data of operation and maintenance personnel terminal devices and generating a dynamic view of personnel distribution; marking work areas where personnel density exceeds or falls below a preset threshold; analyzing the crossover of distribution network lines and equipment sharing in adjacent work areas; assessing resource carrying capacity for work areas where the degree of overlap exceeds a preset overlap threshold; generating personnel dispersal or support instructions; matching the list of distribution network equipment maintenance tasks to be executed with the real-time location data; generating a distribution network task allocation scheme; pushing the scheme to the operation and maintenance personnel terminal devices; receiving execution status feedback; and determining new clustering or vacancy phenomena. If new clustering or vacancy phenomena exist, adjusting personnel distribution and task allocation, updating terminal device instructions, and recording dispatch data.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method and electronic equipment for dispatching low-voltage distribution network maintenance personnel based on a positioning system. Background Technology

[0002] In the operation and maintenance of modern power systems, the operation and maintenance management of low-voltage distribution networks is a crucial area, directly impacting the stability of power supply and the reliability of user experience. With accelerating urbanization and increasing electricity demand, how to dispatch operation and maintenance personnel to meet complex network maintenance needs has become a key issue for the industry. These complex needs are mainly reflected in the diversification of equipment types, the high randomness of fault point distribution, the differentiation of maintenance skill requirements, and the coexistence of emergency repairs and routine inspections. The rational allocation of operation and maintenance personnel not only affects fault response speed but also resource utilization efficiency and overall service quality. However, many current operation and maintenance dispatching methods still have significant limitations. Traditional dispatching methods often rely on static planning or manual coordination, lacking the ability to dynamically perceive and flexibly adjust personnel's real-time location. When facing sudden tasks or complex environments, this approach often leads to uneven personnel allocation, with some areas having concentrated personnel while others suffer from resource scarcity, making efficient teamwork difficult. Focusing on specific challenges, the sharing and coordination of real-time location information becomes the primary difficulty. Most existing operation and maintenance management systems rely on fixed reporting nodes or scheduled reporting to track personnel movements. This approach suffers from information lag and insufficient accuracy, failing to accurately determine the rationality of personnel distribution or detect overlapping or gaps in work areas. This information gap further hinders timely adjustments to scheduling strategies based on actual conditions, such as optimizing task allocation when personnel are concentrated or rapidly organizing support when personnel are dispersed. A deeper problem is that this lack of dynamic adjustment capability directly impacts the flexibility of team collaboration, limiting task execution efficiency and emergency assistance capabilities, and increasing the complexity of operation and maintenance management. Therefore, building a coordination mechanism based on real-time location sharing to dynamically adjust the distance distribution among team members and the degree of overlap in work areas, and improving team collaboration efficiency through intelligent scheduling strategies, has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0003] This invention provides a method for dispatching low-voltage distribution network maintenance personnel based on a positioning system, mainly including:

[0004] The system acquires real-time location data of maintenance personnel's terminal devices, categorizes and organizes the real-time location data according to the work areas of the low-voltage distribution network, displays the current location of maintenance personnel on an electronic map, and generates a dynamic view of personnel distribution.

[0005] The number of personnel in each work area in the dynamic view of personnel distribution is statistically analyzed and compared with the preset standard configuration number of personnel. Work areas with personnel density exceeding or falling below the threshold are marked. The crossover of distribution network lines and sharing of equipment in adjacent work areas are analyzed to determine the degree of overlap of work areas and the phenomenon of personnel gathering or vacancy.

[0006] For work areas where the degree of overlap exceeds the threshold, check the configuration of power distribution network-specific tools and spare parts, assess the resource carrying capacity, and generate personnel dispersal or support instructions based on the skill level of maintenance personnel and the time cost of cross-regional movement.

[0007] Based on the personnel dispersal or support instructions, the list of maintenance tasks to be performed on the distribution network equipment is matched with the real-time location data. Combined with the maintenance task priority and the impact of power outages, a preliminary distribution network maintenance task allocation scheme is generated.

[0008] An optimized distribution network task allocation scheme is generated based on the preliminary distribution network maintenance task allocation scheme.

[0009] The optimized distribution network task allocation scheme is pushed to the terminal equipment of the operation and maintenance personnel, and the execution status feedback is received to determine new clustering or gap phenomena.

[0010] If new clusters or vacancies occur, adjust personnel distribution and task allocation, update terminal device instructions, and record scheduling data.

[0011] An electronic device includes a communication interface, a processor, a memory, and a bus, wherein the communication interface, the processor, and the memory are interconnected via the bus;

[0012] The memory stores machine-readable instructions, and the processor executes the above method by invoking the machine-readable instructions.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0014] This invention discloses a method for scheduling low-voltage distribution network maintenance personnel based on a positioning system. It acquires the location information of maintenance personnel through a real-time positioning and tracking system, dynamically displays personnel distribution on an electronic map, and compares it with standard configurations to identify areas with abnormal personnel density. To address personnel clustering or vacancies, this invention combines distribution network equipment maintenance needs, personnel skill levels, and mobility costs to generate optimized personnel scheduling instructions. Simultaneously, this invention associates and matches maintenance tasks to be executed with personnel locations, considers equipment priority and the impact of power outages, and dynamically generates and optimizes distribution network task allocation schemes, avoiding maintenance conflicts through staggered operations. This invention also monitors task execution status in real time; when new personnel distribution anomalies occur, a dynamic adjustment mechanism is triggered to reallocate tasks and update scheduling instructions. This method can effectively improve the efficiency of low-voltage distribution network operation and maintenance, optimize human resource allocation, reduce equipment maintenance conflicts, and ensure reliable operation of the distribution network. Attached Figure Description

[0015] Figure 1 This is a flowchart of the low-voltage distribution network operation and maintenance personnel scheduling method based on a positioning system according to the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] like Figure 1 The low-voltage distribution network maintenance personnel scheduling method based on the positioning system in this embodiment may specifically include:

[0018] S101. Obtain the real-time location data of the terminal equipment of the maintenance personnel, classify and organize the real-time location data according to the work area of ​​the low-voltage distribution network, display the current location of the maintenance personnel on the electronic map, and generate a dynamic view of personnel distribution.

[0019] Latitude and longitude coordinate data are acquired from positioning terminals worn by maintenance personnel. Location information is collected at preset time intervals via a GPS signal receiving module. Noise is filtered from the received raw coordinate data, and coordinate points with signal strength below a threshold are removed, retaining valid coordinate data to form a personnel location data sequence. Based on a pre-established distribution network equipment ledger database, the geographical boundary coordinates of transformer substations, the start and end point coordinate sequences of distribution line segments, and the center point coordinates of switching stations are extracted. For each coordinate point in the personnel location data sequence, it is determined whether it falls within the boundary polygon of a transformer substation. If the coordinate point is within the boundary of a transformer substation, the corresponding substation affiliation identifier is marked for that coordinate point. Coordinate points with the same substation affiliation identifier are grouped, and the arithmetic mean of all coordinate points in each group is calculated to obtain the personnel distribution center coordinates of that substation. The relative position vector is obtained based on the difference between each personnel coordinate and the distribution center coordinates. Personnel icons for each substation are marked on an electronic map with different colors. The icon positions are refreshed based on real-time updated coordinate data to obtain a dynamic view of personnel distribution.

[0020] Specifically, data acquisition by positioning terminals involves the collaborative work of multiple sensors.

[0021] In one possible implementation, the positioning terminal not only includes a GPS receiver module but also integrates a BeiDou positioning chip and base station positioning functionality. When maintenance personnel enter underground power distribution rooms or densely built-up areas, the GPS signal weakens. At this time, the positioning terminal automatically switches to base station positioning mode, performing triangulation by detecting the signal strength of surrounding base stations. The noise filtering process primarily assesses signal quality. When the horizontal accuracy factor (HDOP) value returned by the GPS module exceeds 2.5, it indicates poor satellite geometry, and the coordinate data is marked as low-quality data. Simultaneously, if the distance between two consecutively collected coordinate points exceeds the distance threshold corresponding to normal walking speed, the system determines this as an abnormal jump and discards the data.

[0022] It should be noted that establishing a database of distribution network equipment requires precise mapping of various types of equipment. The geographical boundaries of transformer substations are obtained through on-site surveys. Surveyors collect the coordinates of key inflection points along the boundaries of the substation's power supply range, forming a closed polygonal area. For distribution line sections, the coordinates of each pole or cable well are recorded and arranged in the order of the line's direction to form a coordinate sequence. The ray method is used to determine whether a coordinate point is inside the polygon. Rays are emitted from the point to be judged in any direction, and the number of intersections between the ray and the polygon boundary is counted. An odd number of intersections indicates the point is inside, and an even number indicates it is outside. This method can accurately handle complex boundary situations such as concave polygons.

[0023] Specifically, the coordinates of the personnel distribution center are calculated using the arithmetic mean method: the longitude values ​​of all personnel within the same distribution area are added together and then divided by the number of personnel to obtain the center longitude; latitude values ​​are calculated similarly. The relative position vector is calculated by subtracting the center coordinates from each personnel's coordinates; the resulting difference vector contains both the personnel's distance from the center and their orientation. When displayed on the electronic map, different distribution areas are distinguished using striking colors such as red, yellow, and blue. The size of personnel icons can be adjusted according to their dwell time; the longer the dwell time, the larger the icon, facilitating quick identification by dispatchers of areas with longer work durations. The dynamic view refresh frequency is consistent with the data acquisition frequency, ensuring real-time map display and enabling dispatchers to promptly grasp the distribution of on-site personnel and rationally allocate human resources.

[0024] S102. Statistically count the number of personnel in each work area in the dynamic view of personnel distribution, compare it with the preset standard configuration number of personnel, mark the work areas where the personnel density exceeds or falls below the preset threshold, analyze the crossover of distribution network lines and the sharing of equipment in adjacent work areas, and determine the degree of overlap of work areas, personnel gathering or vacancy.

[0025] The system extracts the real-time personnel count for each work area from a dynamic view of personnel distribution. It reads the pre-set standard personnel count for each area and calculates the ratio of the actual personnel count to the standard personnel count. If the ratio exceeds a preset upper threshold, the area is marked as having excessive personnel density; if the ratio is below a preset lower threshold, it is marked as having insufficient personnel density. This yields the personnel density status markers and corresponding density ratios for each area. Based on the personnel density status markers and density ratios, it identifies adjacent work area pairs with differences in personnel density. It extracts the equipment list for these adjacent areas from pre-stored power distribution equipment location data, counts the number of power lines crossing area boundaries and the number of switchgear jointly managed by the two areas, and calculates the proportion of shared equipment to the sum of the total equipment in both areas as the equipment sharing degree value. This yields the equipment sharing degree value for each adjacent area pair. For adjacent area pairs where the equipment sharing value exceeds the threshold, and considering the difference in their personnel density ratios, if the personnel density in one area is too high while the personnel density in the adjacent area is too low, it is determined that there is a phenomenon of personnel gathering; if the equipment sharing value of adjacent area pairs exceeds the threshold and there are personnel working in both areas, it is determined that there is an overlap in work areas; if the personnel density in a certain area is too low and the personnel density in all its adjacent areas does not exceed the standard configuration, it is determined that there is a phenomenon of vacancy in that area.

[0026] Specifically, the calculation of the personnel density ratio needs to take into account the actual characteristics of the work area.

[0027] In one possible implementation, the standard number of personnel is determined based on a combination of factors, including the number of devices in the area, historical failure rates, and operational complexity.

[0028] For example, a transformer substation has 10 distribution transformers and 50 branch boxes, with a historical average of 3 failures per month and a standard configuration of 4 maintenance personnel. When real-time monitoring shows 8 personnel in the area, the density ratio is 2.0, exceeding the preset upper limit threshold of 1.5, and the system automatically marks it as an area with excessive personnel density. This marking mechanism can quickly identify abnormal situations in human resource allocation.

[0029] It should be noted that the identification of adjacent work areas relies on the physical connectivity of the power distribution network. The location data of power distribution equipment includes the latitude and longitude coordinates of each device, its corresponding line number, and the area it governs. Two areas are defined as adjacent areas when there is a direct power distribution line connection between them, or when they share the same upstream switch station. The calculation of the equipment sharing degree value takes into account the collaborative work requirements in actual operation and maintenance. Shared equipment includes tie switches at the area boundary, power distribution lines supplying power across areas, and ring main units serving multiple areas.

[0030] Specifically, the logic for judging personnel gathering is based on the principle of supply and demand balance. When the personnel density ratio in area A reaches 2.5 while that in the adjacent area B is only 0.3, it indicates that area A may have experienced an emergency or is undergoing major maintenance, attracting personnel from surrounding areas for support. Determining overlapping work areas focuses on the intersection of actual work scopes. When the equipment sharing ratio between two adjacent areas exceeds 0.3, it means that more than 30% of the equipment requires joint maintenance by personnel from both areas. If personnel are present in both areas at this time, unclear responsibilities and duplicated work are likely to occur. Identifying vacancies is more complex, requiring not only an examination of the current personnel configuration in the area but also an analysis of the support capabilities of surrounding areas.

[0031] For example, the personnel density ratio in area C is only 0.2, while the density ratios in its three adjacent areas are 0.8, 0.6, and 0.7, respectively, all below the standard configuration. This indicates that there is a shortage of personnel throughout the entire area, and the vacancies in area C cannot be resolved by allocating personnel from neighboring areas. This multi-dimensional assessment method can accurately reflect the true distribution of distribution network maintenance personnel, providing a reliable basis for subsequent personnel scheduling decisions.

[0032] S103. For work areas where the degree of overlap exceeds the preset overlap threshold, check the configuration of distribution network special tools and spare parts, assess the resource carrying capacity, and generate personnel dispersal or support instructions based on the skill level of maintenance personnel and the time cost of cross-regional movement.

[0033] When the overlap of work areas exceeds a preset overlap threshold, the inventory data of distribution network-specific tools and spare parts in each overlapping area is obtained. The current quantity of various tools and accessories is counted, and the resource allocation value is obtained by dividing the total number of existing tools by the actual number of workers. If the resource allocation value is lower than the preset standard value, it is determined that the area does not have the resource conditions to accommodate additional personnel, and the resource carrying capacity status of each area is obtained. Based on the resource carrying capacity status, the skill level values ​​of maintenance personnel are read from areas with concentrated personnel and sufficient resources. The current location coordinates of these personnel and the center point coordinates of surrounding areas with insufficient resources are obtained. The straight-line distance between the two points is calculated and divided by the average movement speed to obtain the estimated movement time. The movement time is multiplied by the time cost coefficient and the reciprocal of the skill level to obtain the personnel deployment cost value. The personnel with the lowest deployment cost value are selected to generate a distribution instruction containing the target area number. For the vacant areas identified after the personnel allocation cost value is calculated, the maintenance task type and difficulty level to be performed in the area are extracted. Maintenance personnel with skill level values ​​not lower than the task difficulty level are selected from the adjacent areas. The matching degree is obtained by dividing the personnel skill level value by the task difficulty level. If the matching degree exceeds the preset threshold, a personnel support instruction containing the support personnel number, departure area and target vacant area is generated.

[0034] Specifically, the calculation of resource allocation values ​​needs to take into account the importance of different types of tools.

[0035] In one embodiment, the power distribution network-specific tools include three main categories: safety protection tools, measurement and testing tools, and operation tools, as well as spare parts such as replaceable components like switches and fuses. Safety protection tools, such as insulated gloves and safety helmets, must be provided to each person in a set. Measurement and testing tools, such as multimeters and clamp meters, can be shared by multiple people. Operation tools, such as insulated operating rods, are configured according to the job requirements. Assuming an area has 20 sets of safety protection tools, 8 measuring instruments, and 12 operating levers, and spare parts include 6 sets of critical spare parts (2 sets of transformer spare parts and 4 sets of switch spare parts), 25 commonly used spare parts (15 fuses and 10 contactors), and 50 general spare parts (various basic materials), and 15 actual workers, the resource allocation value needs to be classified and processed when calculating the resource allocation value: the resource allocation value for safety protection is 1.33, the resource allocation value for measurement and testing is 0.53, the resource allocation value for operation is 0.8, and the resource allocation value for spare parts is (6+25+50) pieces ÷ 15 people = 81 ÷ 15 = 5.4. The minimum value of 0.53 is taken as the comprehensive resource allocation value for this area.

[0036] It is important to note that resource availability status directly impacts subsequent personnel allocation decisions. When an area is identified as resource-deficient, no additional personnel should be deployed, even if there are vacancies, to avoid decreased operational efficiency or increased safety risks due to tool shortages. Conversely, areas with sufficient resources can be prioritized for personnel allocation. This resource-based allocation mechanism ensures coordination between personnel distribution and resource allocation.

[0037] Specifically, the calculation of personnel allocation costs integrates two dimensions: time cost and skill matching. The time cost coefficient is usually set according to the urgency of the task, with a higher coefficient for emergency repairs and a lower coefficient for routine inspections. The skill level is based on a five-level system, with level one being a junior worker and level five being a senior technician.

[0038] For example, transferring a Level 3 technician from Area A to Area B, 5 kilometers away, with an average movement speed of 30 kilometers per hour and an estimated travel time of 0.17 hours, and a time cost coefficient of 10, results in a time cost of 1.7. The reciprocal of the skill level is 0.33, and adding these two together yields a transfer cost of 2.03. This calculation method considers both the timeliness of transfer and the rational utilization of personnel skills. The matching degree calculation focuses more on the correspondence between task requirements and personnel capabilities. Maintenance tasks are divided into five difficulty levels based on equipment type and fault complexity, corresponding to personnel skill levels. When a Level 4 technician handles a Level 3 difficulty task, the matching degree is 1.33, indicating sufficient skill capacity. When a Level 2 technician handles a Level 3 task, the matching degree is 0.67, below the threshold of 1.0, making dispatch unsuitable. This quantitative matching mechanism ensures that each maintenance task is undertaken by personnel with appropriate skill levels, avoiding both overqualification and operational risks caused by insufficient capabilities, thus achieving optimal allocation of human resources.

[0039] S104. Based on the personnel dispersal or support instructions, match the list of distribution network equipment maintenance tasks to be executed with the real-time location data, and generate a preliminary distribution network maintenance task allocation scheme by combining the maintenance task priority and the impact of power outage.

[0040] Obtain the target area number and personnel number from the personnel dispersal or support instructions. Determine the personnel's location after deployment based on the center coordinates of the target area in the instructions. Extract the equipment location coordinates and task type for each task from the distribution network equipment maintenance task list. Calculate the straight-line distance from each personnel location to each piece of equipment to be maintained, forming a personnel-task distance matrix. Based on the distance values ​​in the personnel-task distance matrix, read the load level of the distribution network equipment corresponding to each maintenance task. Multiply the load level value by the reciprocal of the distance value to obtain the distance-weighted load importance. Query the number of downstream users for each piece of equipment from the power supply topology data. Add the number of users to the distance-weighted load importance to obtain the comprehensive priority score for each task. Construct a task allocation matrix using the comprehensive priority score, where rows represent personnel, columns represent tasks, and matrix elements are the corresponding comprehensive priority scores. Use the Hungarian algorithm to solve for the optimal matching of the matrix, outputting the maintenance task number and corresponding equipment location assigned to each personnel, generating a preliminary distribution network maintenance task allocation scheme.

[0041] Specifically, the construction of the personnel task distance matrix fully considers the impact of actual geographical conditions on operational efficiency.

[0042] In one possible implementation, when a personnel relocation instruction specifies that a technician be transferred from area A to area B, the coordinates of the target area's center are determined by the arithmetic mean of the coordinates of all equipment in area B. Assuming there is a distribution transformer in area B awaiting maintenance located at 121.5 degrees east longitude and 31.2 degrees north latitude, and the technician's expected location is at the center of area B at 121.48 degrees east longitude and 31.18 degrees north latitude, the straight-line distance can be calculated to be approximately 2.8 kilometers using latitude and longitude conversion. This distance calculation based on actual geographical location provides an objective basis for subsequent task allocation.

[0043] It should be noted that the distance-weighted load importance calculation reflects the principles of proximity service and priority for critical loads. Load levels are typically divided into three levels. Level 1 loads, such as hospitals and important government departments, would have a significant impact if their power outages occurred, and are assigned a value of 3; Level 2 loads, such as commercial centers, are assigned a value of 2; and Level 3 loads, such as ordinary residential electricity, are assigned a value of 1. When a certain level of load equipment is 2 kilometers away from a technician, the reciprocal of the distance is 0.5, which, when multiplied by the load level of 3, yields a distance-weighted load importance of 1.5. This calculation method gives closer and more important equipment a higher priority.

[0044] Specifically, querying power supply topology data involves analyzing the tree structure of the distribution network. Each distribution device has a clear upstream and downstream relationship in the topology, and the number of affected users can be counted by tracing the downstream branches of the device. For example, if a 10kV switch has three downstream branches supplying 500, 300, and 200 households respectively, then a fault in this switch will affect 1000 households. Adding these 1000 households to the previously calculated distance-weighted load importance of 1.5 yields a comprehensive priority score of 1001.5. This score reflects both the power supply importance of the device and the ease of maintenance response. The application of the Hungarian algorithm in task allocation ensures a globally optimal solution. When constructing the task allocation matrix, if there are 3 technicians and 5 maintenance tasks, a 3×5 matrix is ​​formed, where each element represents the comprehensive priority score for a specific technician to complete a specific task. The Hungarian algorithm finds the matching scheme that maximizes the total score through row and column transformations, ensuring that each technician is assigned only one task, and each task is handled by only one technician. The algorithm output might show that technician A is responsible for repairing the hospital's power distribution room, technician B is responsible for replacing switches in the commercial area, and technician C is responsible for handling line faults in the residential area. This allocation scheme maximizes overall maintenance efficiency while satisfying the constraint of one person, one task.

[0045] S105. Generate an optimized distribution network task allocation scheme based on the preliminary distribution network maintenance task allocation scheme.

[0046] Extract the task location and required tool list for each personnel from the initial power distribution network maintenance task allocation plan. Compare the power distribution network-specific tools and spare parts inventory data at each work point, calculate the difference between the required tool quantity and the inventory quantity. If the difference is negative, mark the work point as a resource shortage point. Calculate the proportion of shared equipment between adjacent work points to the total number of equipment. If the proportion exceeds a preset threshold, it is determined that there is overlapping work areas. Based on the resource shortage point marking results and the work area overlap determination results, read the skill level values ​​of relevant personnel and the complexity values ​​of the corresponding maintenance tasks. Divide the inventory quantity at the work point by the required quantity to obtain the resource satisfaction rate. Multiply the ratio of personnel skill level to task complexity by the resource satisfaction rate to obtain the adjusted matching score. Re-execute the task allocation calculation based on the adjusted matching score and output the revised task allocation plan. For the revised task allocation scheme, adjacent work points that still share equipment are identified, and the outage window time data of these work points are extracted from the distribution network operation plan. The data is sorted according to the number of power supply users involved in each work, and the work with more users is allocated to earlier time periods, and the work with fewer users is allocated to later time periods. A preset interval time is set between each time period to generate an optimized distribution network task allocation scheme that includes the time period arrangement.

[0047] Specifically, the inventory comparison of power distribution network-specific tools and spare parts involves a multi-level resource management system.

[0048] In one possible implementation, each work site maintains a tool and spare parts ledger, recording the model and quantity of specialized tools such as insulated operating rods, grounding wire sets, and voltage detectors. If a maintenance task requires two grounding wire sets but the work site only has one set in stock, the difference is -1, and the system automatically marks it as a resource shortage. This negative value marking mechanism can quickly identify resource bottlenecks, preventing maintenance personnel from discovering tool shortages only upon arrival at the site.

[0049] It should be noted that the calculation of the shared equipment ratio reflects the degree of physical overlap between work areas. Adjacent work points often share equipment such as interconnecting switches and T-contacts that require joint maintenance. For example, if work point A has 20 devices and work point B has 15 devices, and 5 of these are interface devices jointly managed by both work points, then the shared equipment ratio is 5 / 35, approximately 14.3%. When this ratio exceeds a preset 10% threshold, it indicates a significant overlap between the two work areas, which can easily lead to unclear maintenance responsibilities or duplicated work.

[0050] Specifically, the introduction of resource fulfillment rate allows for a comprehensive consideration of resource constraints and personnel skills. For example, a work site requires 3 insulated operating rods but only has 2 in stock, resulting in a resource fulfillment rate of 0.67. When a technician with skill level 4 is assigned a task with complexity 3, the initial matching score is 4 / 3 = 1.33. Considering resource constraints, the adjusted matching score becomes 1.33 × 0.67 = 0.89. This adjustment mechanism ensures that even with a high skill matching rate, the priority of the task will decrease due to insufficient resources, prompting the system to find alternative solutions with more abundant resources. The scheduling of power outage windows reflects the continuity requirements of power grid operation. Distribution network operation plans typically divide the day into multiple time periods, each with corresponding load forecasting and power supply reliability requirements.

[0051] For example, during peak industrial and commercial electricity consumption hours from 8:00 AM to 10:00 AM, power outages affecting more than 1,000 households are scheduled; during the relatively low load hours from 2:00 PM to 4:00 PM, maintenance tasks affecting fewer than 500 households are scheduled. A one-hour interval is set between these time periods, ensuring the completion of previous work while allowing time for preparation for subsequent work. This user-impact-based time allocation minimizes the impact of power outages on social production and daily life, while avoiding multiple work teams competing for shared resources during the same time period.

[0052] S106. Push the optimized distribution network task allocation scheme to the terminal equipment of operation and maintenance personnel, receive execution status feedback, and judge new clustering or gap phenomena.

[0053] The task number, target equipment coordinates, and work content for each personnel are extracted from the optimized distribution network task allocation scheme. This data is encapsulated into task instructions and pushed to the terminal devices of maintenance personnel. A weighted path graph is constructed based on the road network of the distribution network area. The Dijkstra algorithm is used to calculate the shortest path from the personnel's current location to the target equipment location, outputting path guidance data including the sequence of nodes passed through and the estimated total distance. The real-time location coordinates of maintenance personnel are received periodically through the terminal devices. The Euclidean distance between the real-time location and the expected location at the corresponding time in the path guidance data is calculated as the location deviation value. If the deviation value exceeds a preset threshold, the actual work location of the personnel is updated. At the same time, the time when each personnel arrives at the target equipment location is recorded, resulting in personnel dynamic data containing the actual work location and arrival time. Based on the actual work location of each personnel in the personnel dynamic data, personnel are statistically assigned according to the work area boundaries. The ratio of the actual number of personnel in each area to the preset standard number of personnel in that area is calculated as the real-time density value. If the real-time density value of a certain area exceeds the upper threshold, it is determined as a new clustering phenomenon; if it is below the lower threshold, it is determined as a new vacancy phenomenon.

[0054] Specifically, constructing a weighted path map of the road network is the foundation for achieving accurate navigation.

[0055] In one embodiment, roads within the distribution network area are abstracted as edges of a graph, with road intersections and device locations serving as nodes. The weight of each edge considers not only physical distance but also road grade factors. Main road segments are weighted at 0.8 times the distance because of higher vehicle speeds; internal roads within residential areas are weighted at 1.5 times the distance, reflecting slower speeds and more turns. Dijkstra's algorithm starts from the starting point and gradually expands to adjacent nodes, selecting the path with the smallest cumulative weight at each step until the target device location is reached. The final output path is the optimal path with the smallest total weight.

[0056] It's important to note that Euclidean distance calculation plays a crucial role in location deviation monitoring. As maintenance personnel travel along the planned route, the system acquires their real-time coordinates at regular intervals. Suppose at a certain moment the expected location is 121.48 degrees East longitude and 31.18 degrees North latitude, while the actual location is 121.485 degrees East longitude and 31.175 degrees North latitude. Through latitude-longitude conversion and planar distance calculation, the deviation is approximately 707 meters. This deviation reflects whether personnel are traveling along the predetermined route; excessive deviation may indicate road construction, traffic congestion, or that personnel have gone to other urgent task locations.

[0057] Specifically, calculating real-time density values ​​requires dynamically updating the personnel distribution within the work area. Each work area has a preset standard number of personnel, determined based on the number of devices within the area, historical failure frequency, and daily inspection workload. For example, a transformer substation has a standard configuration of 4 personnel. When the system detects 7 maintenance personnel in that area, the real-time density value is 1.75. This exceeding of the standard might be due to a sudden failure, with personnel from surrounding areas coming to provide support. Conversely, if only 1 person is detected, the density value is only 0.25, far below the normal level. The density threshold setting reflects the flexible requirements of maintenance management. The upper threshold is usually set at 1.5 to allow temporary personnel gatherings to cope with emergencies; the lower threshold is set at 0.5 to ensure basic maintenance capabilities. When the density value in a certain area reaches 1.8, the system identifies it as a new clustering phenomenon. If this clustering persists for too long, it will create a maintenance vacuum in other areas. By continuously monitoring the real-time density value changes in each area, the system can promptly detect abnormal patterns in personnel distribution, providing real-time basis for scheduling decisions and ensuring that the maintenance resources of the entire distribution network are always in a reasonable configuration.

[0058] S107. If new clusters or vacancies exist, adjust personnel distribution and task allocation, update terminal equipment instructions, and record scheduling data.

[0059] If new clusters or gaps are detected, the distribution network area number and phenomenon type that triggered the adjustment are obtained. The current location and skill level values ​​of all maintenance personnel in these areas are reread. The device numbers and estimated working hours of unfinished maintenance tasks in each area are counted. The remaining workload is obtained by subtracting the used working hours from the estimated working hours. Personnel skill levels are arranged into personnel capability vectors, and the remaining workload of tasks is arranged into task requirement vectors. An allocation matrix is ​​constructed based on the personnel capability vectors and task requirement vectors, with matrix elements representing the personnel's suitability for completing tasks. The Hungarian algorithm is used to solve for the optimal matching scheme, generating an adjustment instruction set containing personnel number, new task number, and target location. The adjustment instruction set is sent to the terminal devices of relevant personnel via push service, and the push time and receipt confirmation status are recorded. Using the personnel-task correspondence in the adjustment instruction set, combined with the push time and receipt confirmation status, the original task number before adjustment and the new task number after adjustment are extracted. The adjustment timestamp, involved personnel numbers, task change details, and distribution network area number are written into the database to generate a distribution network operation and maintenance scheduling log containing complete scheduling process information.

[0060] Specifically, the calculation of the remaining workload needs to be accurately assessed in conjunction with the actual characteristics of the distribution network maintenance task.

[0061] In one embodiment, each maintenance task is assigned an estimated number of man-hours when it is created, which is determined based on the equipment type, fault level, and historical maintenance data.

[0062] For example, the standard work time for replacing a 10kV switch is 4 hours. After 1.5 hours of the task has been completed, the remaining workload is 2.5 hours. For inspection-type tasks, the estimated work time is calculated based on the number of devices to be inspected and the average inspection time per device. This quantitative workload assessment provides a clear benchmark for subsequent personnel reallocation.

[0063] It's important to note that the construction of the personnel capability vector and task requirement vector reflects the concept of supply and demand matching. The personnel capability vector arranges the skill level values ​​of each maintenance personnel in order of their personnel ID, forming a one-dimensional array. For example, if there are 5 maintenance personnel in a certain area with skill levels 3, 4, 2, 5, and 3, the capability vector would be [3, 4, 2, 5, 3]. The task requirement vector arranges the remaining workload of each unfinished task in order of its task ID, such as [2.5, 1.0, 3.5, 0.5] representing the remaining working hours for 4 tasks. This vectorized representation facilitates subsequent matrix operations.

[0064] Specifically, the construction of the assignment matrix and the application of the Hungarian algorithm achieve globally optimal task allocation. Each element of the assignment matrix represents the suitability value for a specific person to complete a specific task. This value comprehensively considers the matching degree between the person's skills and the task complexity, as well as the distance between the person and the task location.

[0065] For example, a skill level 4 person handling a task requiring a skill level 3 has a base fit of 4 / 3 = 1.33. If this person is 2 kilometers away from the task location, with a distance factor of 0.8, the final fit value is 1.06. The Hungarian algorithm finds the allocation scheme that maximizes the total fit through row and column transformations, ensuring that each person is assigned the most suitable task. The generation of the scheduling log not only records the adjustment results but, more importantly, preserves the complete decision-making process. The timestamps in the log are accurate to the second, recording the entire process from detecting the anomaly to completing the adjustment. Task change details include information such as the execution progress of the original task, the reason for interruption, and the urgency of the new task.

[0066] For example, a log entry shows that technician Zhang was reassigned from routine inspection duties in transformer substation A to emergency fault handling in transformer substation B at 14:32:15. The original task completion rate was 60%. The reason for the reassignment was that there was a staff shortage and a level-one load power outage in transformer substation B. This detailed log provides data support for later operation and maintenance efficiency analysis and personnel performance evaluation, and also accumulates experience data for optimizing scheduling strategies.

[0067] This application also provides an electronic device, including a communication interface, a processor, a memory, and a bus. The communication interface, the processor, and the memory are interconnected via the bus. The memory stores machine-readable instructions, and the processor executes the above-described method by calling the machine-readable instructions.

[0068] Based on the embodiments of the present invention described above, and through the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for dispatching low-voltage distribution network maintenance personnel based on a positioning system, characterized in that, The method includes: The system obtains latitude and longitude coordinate data from the terminal equipment of maintenance personnel, filters out coordinate points with signal strength below a preset strength threshold, and retains valid coordinate data to form a personnel location data sequence. Based on the distribution network equipment ledger database, it determines whether the coordinate points in the personnel location data sequence are located within the transformer area boundary and marks the corresponding transformer area affiliation identifier. Coordinate points with the same transformer area affiliation identifier are grouped, the arithmetic mean of the coordinate points within the group is calculated, the coordinates of the personnel distribution center are determined, a relative position vector is generated, personnel icons are marked on the electronic map, the personnel location data sequence is refreshed, and the dynamic view of personnel distribution is updated. The number of personnel in each work area in the dynamic view of personnel distribution is statistically analyzed and compared with the preset standard configuration number of personnel. Work areas with personnel density exceeding or falling below the preset threshold are marked. The crossover of distribution network lines and sharing of equipment in adjacent work areas are analyzed to determine the degree of overlap of work areas and the phenomenon of personnel gathering or vacancy. Obtain inventory data of distribution network-specific tools and spare parts in work areas where the degree of overlap exceeds the preset overlap threshold, calculate the resource allocation ratio of the total number of tools and spare parts to the number of workers, and mark areas with resource allocation values ​​lower than the standard as resource-insufficient areas; Extract the skill level and current location coordinates of maintenance personnel from resource-rich clustered areas, calculate the distance to the center point of resource-insufficient areas, combine the movement time cost and skill level to determine the deployment cost value, and generate a distributed instruction containing the target area number; or for vacant areas, extract the maintenance task type and difficulty level, select maintenance personnel with matching skill levels from adjacent areas, calculate the matching degree between skill level and task difficulty, and generate a support instruction containing personnel number and target area. Extract the personnel number and target area from the personnel dispersion or support instructions, calculate the distance between the location of the maintenance personnel and the location of the equipment to be maintained, and form a distance matrix; based on the distance values ​​of the distance matrix, combined with the load level of the distribution network equipment and the number of downstream users, calculate the comprehensive priority score of the task, construct the task allocation matrix, and generate a preliminary distribution network maintenance task allocation scheme. Extract the task locations and required tool list from the preliminary distribution network maintenance task allocation plan, calculate the difference between tool demand and inventory, and mark resource shortage points; determine the overlap of work areas based on the proportion of shared equipment between adjacent work points to the total number of equipment; adjust the matching weight of personnel skills and task difficulty based on resource shortage points and work area overlap, and regenerate the task allocation plan; allocate off-peak work time for overlapping areas to generate an optimized distribution network task allocation plan; The optimized network task allocation scheme is pushed to the terminal equipment of the operation and maintenance personnel, and the execution status feedback is received to determine new clustering or gap phenomena. If new clusters or vacancies occur, adjust personnel distribution and task allocation, update terminal device instructions, and record scheduling data.

2. The low-voltage distribution network maintenance personnel dispatching method based on a positioning system according to claim 1, characterized in that, The step of statistically analyzing the number of personnel in each work area of ​​the dynamic personnel distribution view and comparing it with the preset standard configuration number of personnel, and marking work areas where the personnel density exceeds or falls below a preset threshold, includes: extracting the real-time number of personnel in each work area of ​​the dynamic personnel distribution view, calculating the ratio with the preset standard configuration number of personnel, and marking areas where the ratio exceeds the upper preset threshold and areas where it falls below the lower preset threshold.

3. The low-voltage distribution network maintenance personnel dispatching method based on a positioning system according to claim 1, characterized in that, The process of pushing the optimized distribution network task allocation scheme to the maintenance personnel's terminal device, guiding the optimal path, receiving execution status feedback, and judging new clustering or gap phenomena includes: encapsulating the task number and target coordinates in the optimized distribution network task allocation scheme, pushing it to the maintenance personnel's terminal device, calculating the shortest path from the current location to the target location; receiving real-time location coordinates, calculating the deviation from the expected location, calculating the population density in the area, and judging new clustering or gap phenomena.

4. The low-voltage distribution network maintenance personnel dispatching method based on a positioning system according to claim 1, characterized in that, If new clusters or vacancies exist, the personnel distribution and task allocation will be adjusted, terminal device instructions will be updated, and scheduling data will be recorded. This includes: obtaining the area number that triggered the adjustment, statistically analyzing real-time location data and unfinished task requirements, constructing a personnel capability and task requirement allocation matrix, generating an adjustment instruction set, pushing it to the terminal device, recording the adjustment time, personnel changes, and task changes, and generating a scheduling log.

5. An electronic device, characterized in that, The method includes a communication interface, a processor, a memory, and a bus, wherein the communication interface, the processor, and the memory are interconnected via the bus; the memory stores machine-readable instructions, and the processor executes the method according to any one of claims 1 to 4 by invoking the machine-readable instructions.

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