Visual management and control method and device for power distribution network, storage medium and product

Abstract

The invention discloses a power distribution network visual management and control method and device, a storage medium and a product, and the method comprises the steps: obtaining the multi-dimensional information of a power distribution network in a target region, carrying out the preprocessing of the multi-dimensional information, obtaining a standardized power distribution network data set, carrying out the recognition of a power distribution network problem according to the power distribution network data set, and obtaining a power distribution network fault. The method comprises the following steps: obtaining a structured problem list of a power distribution network, carrying out spatial positioning and attribute mapping processing on the structured problem list in a preset geographic information system to obtain a power distribution network problem spatial distribution representation, and carrying out multi-dimensional analysis and prediction on the operation condition of the power distribution network according to the power distribution network problem spatial distribution representation. And obtaining a management and control strategy of the power distribution network, carrying out resource allocation according to the management and control strategy to obtain a cooperative work flow, and visualizing the problem space distribution representation of the power distribution network and the cooperative work flow in a geographic information system to provide a visual management and control mode. And the management efficiency and safety of the power distribution network are improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of distribution network management and control, and in particular, to a distribution network visualized management and control method, device, storage medium and product. Background Art

[0002] With the continuous expansion and complexity of distribution networks, achieving efficient management and monitoring of distribution networks has become one of the key issues in power system operation.

[0003] Traditional distribution network management and control methods mainly rely on manual inspections, regular maintenance and other means. However, due to the wide coverage and complex structure of the distribution network, the traditional management and control methods are low in timeliness and accuracy, resulting in low efficiency in handling distribution network problems, affecting the management efficiency and safety of the distribution network. Summary of the invention

[0004] The present invention provides a distribution network visualized management and control method, device, storage medium and product, which are used to improve the management efficiency and safety of the distribution network.

[0005] In a first aspect, an embodiment of the present invention provides a method for visual management and control of a distribution network, including:

[0006] Acquire multi-dimensional information of the distribution network in the target area, pre-process the multi-dimensional information to obtain a standardized distribution network data set, and identify distribution network problems based on the distribution network data set to obtain a structured problem list of the distribution network;

[0007] Performing spatial positioning and attribute mapping processing on the structured problem list in a preset geographic information system to obtain a spatial distribution representation of the distribution network problems;

[0008] Performing multi-dimensional analysis and prediction on the operation status of the distribution network according to the spatial distribution representation of the distribution network problem, obtaining a control strategy of the distribution network, and allocating resources according to the control strategy to obtain a collaborative workflow;

[0009] The spatial distribution representation of the distribution network problem and the collaborative workflow are visualized in the geographic information system to provide a visualized management and control method.

[0010] In a second aspect, an embodiment of the present invention further provides a distribution network visualization management and control device, including:

[0011] A data acquisition and classification module is used to acquire multi-dimensional information of the distribution network in the target area, pre-process the multi-dimensional information to obtain a standardized distribution network data set, and identify distribution network problems based on the distribution network data set to obtain a structured problem list of the distribution network;

[0012] A spatial distribution mapping module is used to perform spatial positioning and attribute mapping processing on the structured problem list in a preset geographic information system to obtain a spatial distribution representation of the distribution network problem;

[0013] A control and analysis module, used to perform multi-dimensional analysis and prediction on the operation status of the distribution network according to the spatial distribution representation of the distribution network problem, obtain the control strategy of the distribution network, and allocate resources according to the control strategy to obtain a collaborative workflow;

[0014] A visualization management and control module is used to visualize the spatial distribution representation of the distribution network problem and the collaborative workflow in the geographic information system to provide a visualization management and control method.

[0015] In a third aspect, an embodiment of the present invention further provides a computer device, including:

[0016] one or more processors;

[0017] A storage device for storing one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the distribution network visualization management and control method provided in the first aspect of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the distribution network visualization management and control method provided in the first aspect of the present invention.

[0020] In a fifth aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the distribution network visualization management and control method provided in the first aspect of the present invention.

[0021] In an embodiment of the present invention, multi-dimensional information of the distribution network of the target area is obtained, the multi-dimensional information is pre-processed, a standardized distribution network data set is obtained, and distribution network problems are identified based on the distribution network data set to obtain a structured problem list of the distribution network. The structured problem list is spatially located and attribute mapped in a preset geographic information system to obtain a spatial distribution representation of the distribution network problem. The operation status of the distribution network is multi-dimensionally analyzed and predicted based on the spatial distribution representation of the distribution network problem, the control strategy of the distribution network is obtained, and resources are allocated based on the control strategy to obtain a collaborative workflow. The spatial distribution representation of the distribution network problem and the collaborative workflow are visualized in the geographic information system to provide a visualized control method. Through the application of multi-dimensional information acquisition and deep learning models, the efficiency and accuracy of problem identification are significantly improved. Combined with the geographic information system, the spatial distribution of distribution network problems is intuitively displayed to help optimize resource allocation. At the same time, dynamic monitoring and feedback are realized, collaborative work is promoted, various types of information are integrated, and the management efficiency and safety of the distribution network are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a method for visual control of a distribution network provided in the first embodiment of the present invention;

[0023] Figure 2 A structural block diagram of a distribution network visual control device is provided for the second embodiment of the present invention;

[0024] Figure 3 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] See also Figure 1 , shows a flow chart of a distribution network visualization control method provided by the first embodiment of the present invention, the method can be executed by a distribution network visualization control device, the distribution network visualization control device can be implemented in the form of hardware and / or software, and the distribution network visualization control device can be configured in a computer device. Figure 1 As shown, the method includes:

[0029] Step 101: Acquire multi-dimensional information of the distribution network in the target area, pre-process the multi-dimensional information to obtain a standardized distribution network data set, and identify distribution network problems based on the distribution network data set to obtain a structured problem list of the distribution network.

[0030] The distribution network refers to the power system network that distributes electric energy from the high-voltage transmission network to low-voltage users, including substations, distribution lines and end users. The distribution network is mainly responsible for delivering electricity to various power facilities to ensure the reliability and safety of power supply. The management and control of distribution networks is gradually developing in the direction of digitalization and intelligence. Technologies such as geographic information systems (GIS) have been introduced into distribution network management, realizing the visualization of the location of distribution equipment and the query of equipment attribute information. However, the existing GIS-based distribution network management system is mainly used for the display of static data and simple information query, lacking a comprehensive analysis of distribution network operation problems and intelligent management and control methods. Especially in a complex and changeable operating environment, it is impossible to dynamically identify, spatially locate and intelligently analyze problems, and thus it is impossible to formulate accurate management and control strategies, resulting in lags and inefficiencies in resource allocation and maintenance decisions.

[0031] Multi-dimensional information covers electrical parameters such as voltage, current, and power, equipment operating status information such as transformer oil temperature and switch status, user electricity consumption data such as load curves, and environmental factors such as temperature and humidity.

[0032] In this embodiment, the multi-dimensional information of the distribution network in the target area is obtained, and the multi-dimensional information is pre-processed to improve the data quality, accuracy and consistency of the multi-dimensional information, thereby forming a standardized distribution network data set, and the distribution network problems are identified based on the distribution network data set, and the potential faults and anomalies in the distribution network are systematically analyzed and processed. By generating a structured problem list, various types of problems and their characteristics can be clearly listed, thereby providing a targeted basis for subsequent decision-making and optimization, and improving the management efficiency and service quality of the distribution network.

[0033] In one embodiment of the present invention, step 101 may include the following steps:

[0034] Step 1011: Acquire multi-dimensional information of the distribution network in the target area, and perform data cleaning on the multi-dimensional information according to a preset data quality assessment model.

[0035] In this embodiment, after obtaining the multi-dimensional information of the distribution network in the target area, a preset data quality model is used to identify anomalies and clean the multi-dimensional information; wherein the data quality model identifies outliers based on statistical methods (such as Z-score method, box plot method, etc.), and sets thresholds in combination with domain knowledge. For detected outliers, the system will mark them and process them according to predetermined rules, such as deleting obviously erroneous data, using interpolation to fill missing values, and manually reviewing suspicious data. Data cleaning also includes format unification, converting multi-dimensional information from different sources into a unified format and unit.

[0036] Step 1012: perform spatiotemporal alignment and data fusion processing on the multi-dimensional information after data cleaning to obtain a standardized distribution network data set.

[0037] Spatiotemporal alignment includes time alignment and space alignment. Time alignment converts the multi-dimensional information after data cleaning into a unified time standard (such as Coordinated Universal Time UTC) and uses an interpolation algorithm to process the multi-dimensional information after data cleaning with different sampling frequencies. Spatial alignment associates all multi-dimensional information to a unified spatial reference system through a geographic information system (GIS).

[0038] Data fusion uses multi-source data fusion algorithms, such as Kalman filter to process time series data and Dempster-Shafer evidence theory to process uncertain information. The fusion process considers the reliability of multi-dimensional information sources, assigns different weights, and generates a more comprehensive data set.

[0039] In this embodiment, the multi-dimensional information after data cleaning is subjected to time-space alignment and data fusion processing to eliminate the time and space differences between data sources in different multi-dimensional information and achieve data consistency and integrity. This process can integrate various related information to form a standardized distribution network data set, thereby improving the accuracy of analysis.

[0040] Step 1013: input the distribution network data set into a pre-trained deep learning model to extract abnormal state characteristics of the distribution network and obtain a potential problem feature set of the distribution network.

[0041] In this embodiment, the deep learning model is generally based on a hybrid architecture of a convolutional neural network (CNN) and a long short-term memory network (LSTM). CNN is used to extract spatial features, such as voltage distribution patterns; LSTM is used to capture time series features, such as load change trends. Model training uses historical distribution network operation data, including normal operation data and known fault cases. The standardized electrical parameters such as voltage, current, power factor, and equipment status indicators in the distribution network data set are used as inputs to the deep learning model. The output of the deep learning model is a multidimensional vector that represents the probability and feature description of various potential problems, and a potential problem feature set of the distribution network is obtained.

[0042] Step 1014: Perform cluster analysis and expert rule matching on the potential problem feature set to obtain a structured problem list of the distribution network.

[0043] In this embodiment, cluster analysis generally uses a density clustering algorithm (DBSCAN) or a K-means algorithm (K-means) to preliminarily classify the potential problem feature set, and the number of clusters is determined by the silhouette coefficient or the elbow rule. Then, the clustering results are matched with the preset expert rule base to obtain a structured problem list of the distribution network.

[0044] Exemplarily, the rule base contains problem judgment criteria defined by power system experts, such as "If the voltage deviation exceeds ±7% and lasts for more than 10 minutes, it is judged as a voltage over-limit problem". The rule matching process uses fuzzy logic or decision tree algorithms to consider the combination of multiple conditions. The matching results further refine the problem classification to determine the specific type, severity and scope of impact of the problem. The final structured problem list contains detailed attributes of each problem, such as problem ID, type, location, start time, duration, severity, etc., providing basic data support for subsequent visualization and management decisions.

[0045] Step 102: perform spatial positioning and attribute mapping processing on the structured problem list in a preset geographic information system to obtain a spatial distribution representation of the distribution network problems.

[0046] In this embodiment, the structured problem list is spatially positioned and attribute mapped in a preset geographic information system to intuitively display the spatial distribution characteristics of the distribution network problems, associate the problems with specific geographic locations, help identify problem areas, optimize resource allocation, and improve the operating efficiency and reliability of the distribution network.

[0047] In one embodiment of the present invention, step 102 may include the following steps:

[0048] Step 1021: Perform coordinate conversion processing on the question location information in the structured question list to obtain standardized spatial coordinates consistent with the coordinate system of the geographic information system.

[0049] In this embodiment, coordinate conversion processing of the question location information in the structured question list can be performed using a coordinate conversion algorithm (such as Helmert conversion or Molodensky-Badekas conversion) to convert the location information in the question list from the original coordinate system (such as WGS84) to the coordinate system used by the geographic information system (such as UTM or a specific local coordinate system). The conversion process takes into account the earth ellipsoid parameters, projection mode, and offset of the local coordinate system to ensure that the converted coordinates are consistent with other spatial data in the geographic information system.

[0050] For example, a problem point originally expressed in longitude and latitude (121.5°E, 31.2°N) may be converted to UTM coordinates (340125.5E, 3456789.0N).

[0051] Step 1022: Perform spatial correlation analysis on the power facility data in the geographic information system based on standardized spatial coordinates to obtain topological structure information of the problem-related facilities.

[0052] Spatial association analysis often uses spatial query and topological analysis techniques, such as buffer analysis, nearest neighbor analysis, or spatial join operations.

[0053] In this embodiment, the power facility data in the geographic information system is spatially correlated according to the standardized spatial coordinates, the facility closest to the problem point is found in the power facility layer, and then the associated upstream and downstream facilities are determined through network topology analysis. This process takes into account the connectivity rules of the power network, such as the hierarchical relationship between substations, distribution lines, branch switches, etc. The analysis results generate the topological structure information of the problem-related facilities, including directly related equipment IDs, equipment types, connection relationships, etc.

[0054] Step 1023: The problem attribute information and topological structure information in the structured problem list are integrated to obtain multi-dimensional problem description data.

[0055] In this embodiment, the system combines the problem attribute information (such as problem type, severity, and occurrence time) with the topological structure information of related facilities (such as equipment capacity, operating status, and maintenance records) to form multidimensional problem description data. This multidimensional problem description data not only contains information about the problem itself, but also includes contextual information about the problem in the power network, providing a rich data foundation for subsequent visualization and analysis. The fusion process uses data integration technology, such as feature-level fusion or decision-level fusion methods.

[0056] Step 1024: dynamically configure the preset visualization symbol system according to the multi-dimensional problem description data to obtain visualization representation rules.

[0057] In this embodiment, the visualization symbol system is a predefined visual encoding library that contains various graphic symbols, color schemes, and size mapping rules. The dynamic configuration process uses a rule engine or decision tree algorithm to select appropriate visual elements based on the different attributes of the problem. For example, the system may map voltage problems to red triangle symbols and load problems to blue circle symbols, with the size of the symbol proportional to the severity of the problem. The output of this process is a complete set of visualization representation rules that define how to transform multidimensional problem data into intuitive visual representations.

[0058] Step 1025: perform spatial rendering processing on the multi-dimensional problem description data according to the visualization representation rule to obtain a spatial distribution representation of the distribution network problem containing multiple layers.

[0059] In this embodiment, the rendering engine of the geographic information system is used according to the visualization representation rules to draw the multidimensional problem description data onto the map according to the configured rules. The rendering process takes into account the scale, projection mode and current visible range of the map to ensure that the visual presentation remains clear and informative at different zoom levels. The system creates multiple layers, including base map layers, power facility layers, problem point layers, etc., which can be independently controlled to display and hide. The rendering of problem points not only takes into account the spatial location, but also includes dynamic effects such as flashing or color gradients to highlight important or urgent problems. In addition, the system also generates a heat map layer to intuitively display the density and distribution trend of the problem. The final spatial distribution representation of distribution network problems is a multi-level, information-rich visualization result that can fully display the distribution status and related attributes of distribution network problems in the context of geographic space.

[0060] In one embodiment of the present invention, step 1025 may further include the following steps:

[0061] S10251. Based on the regional division of the target area, the multi-dimensional problem description data is spatially partitioned to obtain problem distribution subsets for each sub-region.

[0062] In this embodiment, according to the administrative or power grid management area division of the target area, spatial indexing technology (such as R-tree or quadtree) is used to perform spatial partitioning processing on the multidimensional problem description data, and the problem description data is quickly distributed to the corresponding area. For each sub-area, the system extracts the frequent power outage lines and problem substation information in the sub-area to form a problem distribution subset. For example, for a municipal target area, the system may divide it into multiple county-level sub-areas, each of which contains specific power outage line and substation problem data.

[0063] S10252. Vectorize the frequent power outage lines and visualization characterization rules in the problem distribution subset to obtain a linear layer characterizing the frequent power outage lines.

[0064] In this embodiment, the vector data model of the geographic information system is used to convert the frequent power outage line data into line segments composed of a series of coordinate points. The processing process takes into account the spatial direction of the line, and uses simplified algorithms such as the Douglas-Peucker algorithm to optimize the number of line segment points to improve rendering efficiency while ensuring accuracy. At the same time, the system assigns specific attributes such as line width, color, and style to each line according to the visualization characterization rules. These attributes reflect the power outage frequency or other important characteristics of the line. For example, a high-frequency power outage line may be assigned a thicker red solid line, while a low-frequency power outage line is represented by a thin yellow dotted line. Such processing results in the formation of a linear layer representing the frequent power outage line.

[0065] S10253. Perform spatial clustering and geometric calculation processing on the substation information in the problem distribution subset to obtain a point layer representing the problem substation in the sub-region and a polygonal layer representing the power supply range of the sub-region.

[0066] In this embodiment, the system performs spatial clustering and geometric calculation processing on the substation information in the problem distribution subset. Spatial clustering uses a density clustering algorithm (DBSCAN) or a K-means algorithm to cluster problem substations with similar geographical locations. The clustering process takes into account the spatial distribution density of the substations and automatically adjusts the clustering parameters to adapt to the characteristics of different regions. For the clustering results, the system calculates the center point and envelope polygon of each cluster. The center point is used to generate a point layer representing the problem substation, and each point represents a clustered set of problem substations. At the same time, the system uses geometry (such as a convex hull algorithm or an alpha shape algorithm, etc.) to calculate the overall distribution range of the problem substations in each sub-region to form a polygonal layer of the power supply range. These polygons not only reflect the spatial distribution of the problem substations, but also provide a basis for subsequent regional statistical analysis.

[0067] S10254. Configure attribute labels for linear layers, point layers, and polygon layers, and integrate them into a spatial distribution representation of distribution network problems containing multiple layers.

[0068] The attribute label configuration is based on the pre-defined label template to add corresponding attribute information to the linear layer, point layer and polygon layer elements. The label of the linear layer contains information such as line number, power outage frequency, number of affected users, etc. The label of the point layer includes the number of substations in the cluster, the main problem type, the average fault duration, etc. The label of the polygon layer may contain summary information such as the area name and the overall power supply reliability index.

[0069] In this embodiment, attribute labels are configured for linear layers, point layers, and polygon layers, and the position and display mode of the labels are optimized to avoid occlusion and overlap, ensuring clear readability at different zoom levels. After the attribute label configuration is completed, the system integrates the linear layers, point layers, and polygon layers into a unified multi-layer structure. The integration process takes into account the display order of the layers to ensure that important information is not obscured. The system also implements a linkage mechanism between layers. For example, when you click on a power outage line on a linear layer, the related problem area points and power supply range polygons will be highlighted. In addition, the system also configures independent display control options for each layer, allowing users to switch the visibility of different layers according to their needs. The final spatial distribution representation of distribution network problems is a dynamic, interactive multi-layer visualization result that can comprehensively and intuitively display the spatial distribution characteristics, correlation relationships, and attribute information of distribution network problems.

[0070] Step 103: Perform multi-dimensional analysis and prediction on the operation status of the distribution network based on the spatial distribution representation of the distribution network problem, obtain the control strategy of the distribution network, and allocate resources based on the control strategy to obtain a collaborative workflow.

[0071] In this embodiment, the multi-dimensional analysis and prediction of the operating status based on the spatial distribution representation of the distribution network problem can fully understand the performance and potential risks of the distribution network. This process helps to formulate effective management and control strategies, ensure the rational allocation of resources, and ultimately form a collaborative workflow. This not only improves the operating efficiency of the distribution network, but also enhances its ability to respond to emergencies, thereby ensuring the stability and reliability of power supply.

[0072] In one embodiment of the present invention, step 103 may include the following steps:

[0073] Step 1031: Perform spatiotemporal clustering analysis on the spatial distribution representation of the distribution network problem to obtain the problem hotspot areas and time series characteristics.

[0074] In this embodiment, a clustering algorithm such as ST-DBSCAN (space-time density clustering algorithm) or OPTICS (sort-based density clustering algorithm) is used to perform space-time clustering analysis on the spatial distribution representation of distribution network problems, while considering the spatial correlation and time correlation of distribution network problems. Clustering algorithm parameters such as neighborhood radius and minimum number of points are optimized by grid search method to adapt to the distribution of problems with different densities. The clustering results identify problem hot spots, which are concentrated in space and similar in time. At the same time, time series features are extracted for each cluster, such as the periodicity, trend and burstiness of the problem, and time series decomposition techniques such as seasonal trend decomposition (STL) are used to complete this task.

[0075] Step 1032: perform association rule mining on the historical operation data of the distribution network based on the problem hotspot areas and time series characteristics to obtain a problem cause association rule set and potential risk assessment indicators.

[0076] Association rule mining is a data mining technique used to discover interesting relationships and patterns between variables in a data set, usually using the Apriori algorithm or the frequent pattern growing algorithm.

[0077] In this embodiment, association rule mining is performed on the historical operation data of the distribution network based on the problem hotspot areas and time series characteristics, and frequent item sets and strong association rules are discovered from a large amount of historical operation data of the distribution network. The association rule mining process considers attributes in multiple dimensions, such as equipment parameters, environmental factors, and operating status. For example, the association rule mining algorithm may find a rule such as "When the ambient temperature exceeds 35°C and the line load rate exceeds 80%, the probability of transformer failure increases by 50%." By setting the minimum support and confidence thresholds, high-quality association rules are screened out to form a problem cause association rule set. At the same time, based on these association rules and the historical operation data of the distribution network, potential risk assessment indicators such as failure probability, impact range, and severity are constructed.

[0078] Step 1033: Perform multi-scenario simulation and deduction processing on the problem cause association rule set and potential risk assessment indicators to obtain the control strategy of the distribution network.

[0079] In this embodiment, multi-scenario simulation and deduction processing of the problem cause association rule set and potential risk assessment indicators can help to fully understand the behavior and potential risks of the distribution network under different conditions, identify key factors and interrelationships, and thus formulate more accurate management and control strategies to improve the reliability and safety of the distribution network.

[0080] In the example, Monte Carlo simulation method or agent-based simulation technology is used to build a digital twin model of distribution network operation, and a multi-scenario simulation process is carried out to consider different operating conditions, weather scenarios and load changes, and evaluate various possible fault conditions and their propagation paths. Through a large number of simulation iterations, the system generates the result distribution under different control strategies, and uses multi-criteria decision analysis (MCDA) methods (such as sequential optimization method or hierarchical analysis method) to comprehensively consider factors such as reliability, economy and implementation difficulty to screen out the optimal set of control strategies.

[0081] Step 1034: Perform multi-objective optimization configuration processing on available resources according to the management and control strategy to obtain a resource allocation plan, and perform task decomposition and priority sorting on the resource allocation plan to obtain a collaborative workflow of the distribution network.

[0082] In this embodiment, based on the obtained management and control strategy, the system uses a genetic algorithm (GA) or a particle swarm optimization algorithm (PSO) to perform multi-objective optimization configuration processing on the available resources, while considering multiple objective functions, such as minimizing overall cost, maximizing problem-solving efficiency, and balancing resource utilization. Various constraints are considered during the optimization process, such as human resource limitations, equipment availability, and time windows. The optimization result obtains a Pareto optimal solution set, which represents the resource allocation scheme under different trade-offs.

[0083] The tasks are decomposed and prioritized based on the resource allocation scheme obtained. Task decomposition uses the work breakdown structure (WBS) technique to decompose large management and control tasks into executable subtasks. For each subtask, the system uses a multi-attribute decision-making method to consider factors such as task urgency, importance, and resource readiness to calculate the priority score of the task. The sorting process also considers the dependencies between tasks and uses the critical path method (CPM) or project evaluation and review technique (PERT) to ensure the rationality of the task sequence. The resulting collaborative workflow includes a detailed task list, execution order, resource allocation, and time schedule, providing a clear execution guide for distribution network management and control work.

[0084] Step 104: Visualize the spatial distribution representation and collaborative workflow of the distribution network problem in the geographic information system to provide a visual management and control method.

[0085] In this embodiment, the spatial distribution representation and collaborative workflow of distribution network problems are visualized in the geographic information system, which can intuitively display the operating status of the distribution network and its related data. This visualization helps identify problem areas and resource distribution, thereby achieving more effective management and optimization, and improving the safety and reliability of the distribution network.

[0086] In one embodiment of the present invention, step 104 may include the following steps:

[0087] Step 1041: visualize the spatial distribution representation of the distribution network problem in the geographic information system, and perform spatiotemporal correlation processing on the spatial distribution representation of the distribution network problem and the collaborative workflow to obtain the correlation relationship between the spatial distribution representation of the distribution network problem and the collaborative workflow.

[0088] In this embodiment, the spatial data processing function of the geographic information system is used to convert the spatial data such as the problem points, lines and regions in the spatial distribution representation of the distribution network problem into a visualization layer, and the spatial distribution representation of the distribution network problem and the collaborative workflow are processed in time and space; the process of time and space association processing uses a spatial association algorithm, such as spatial connection or nearest neighbor analysis, to establish a corresponding relationship between the tasks in the collaborative workflow and the problem points in the spatial distribution representation of the distribution network problem. The association process considers the execution location, impact range and time window of the task, and generates an association matrix to describe the association relationship between each task and the problem point.

[0089] Step 1042: During the collaborative workflow, the spatial distribution representation of the distribution network problem is dynamically updated through the association relationship to obtain a visualization layer that reflects the real-time control progress.

[0090] In this embodiment, during the collaborative workflow, the system dynamically updates the spatial distribution representation of the distribution network problem through the established association relationship. This process involves the processing of real-time data streams and dynamic rendering of layers, reflecting the real-time progress of the management and control work.

[0091] In one embodiment of the present invention, step 1042 may further include the following steps:

[0092] S10421. Collect and parse the task status information in the collaborative workflow in real time to obtain a task progress data set.

[0093] In this embodiment, a distributed data acquisition system is used to collect and parse task status information in a collaborative workflow in real time, and task execution status data is collected through multiple channels such as IoT devices, mobile terminals, and work management systems to form a task progress data set.

[0094] Exemplarily, the collected task status information includes information in multiple dimensions such as task start time, current completion degree, estimated completion time, resource usage, etc. The data collection frequency is dynamically adjusted according to the urgency and importance of the task to ensure that the status of key tasks can be reflected in a timely manner. The collected original task status information is processed through data cleaning and formatting to remove noise and outliers and unify into a standard data format. Then, through the data parsing engine, the formatted data is converted into a structured task progress data set, which contains key information such as the ID, type, current status, and completion percentage of each task.

[0095] S10422. Perform state matching processing on the problem points in the spatial distribution representation of the distribution network problem according to the task progress data set to obtain dynamic marking data reflecting the current processing status of each problem point.

[0096] In this embodiment, the system uses a spatial matching algorithm (such as R-tree index or grid index) to perform state matching processing on the problem points in the spatial distribution representation of the distribution network problem according to the task progress data set, and quickly locates the problem points related to the task. The matching process takes into account the spatial scope and impact area of ​​the task, and accurately associates the task progress information to the corresponding problem points. For tasks across multiple problem points, the system uses a weight distribution algorithm to reasonably allocate task progress to each related problem point based on the severity and impact range of the problem. The matching results generate dynamic marking data that reflects the current processing status of each problem point, including information such as problem ID, associated task ID, current processing stage, and processing progress.

[0097] S10423. Perform time series analysis on the dynamic tag data to obtain a set of trend indicators that describe the speed and efficiency of problem solving.

[0098] In this embodiment, time series analysis techniques (such as moving average, exponential smoothing or autoregressive integrated sliding average model) are used to perform time series analysis on the obtained dynamic marking data to analyze the time pattern of problem solving. The analysis takes into account the historical operation data of the distribution network and the current processing speed, and calculates the processing speed and efficiency indicators of each problem point. For example, the system may calculate an indicator such as "In the past 24 hours, the average voltage problem resolution speed in area A was 5% per hour, an increase of 10% over last week." These analysis results form a set of trend indicators that describe the speed and efficiency of problem solving, including multiple dimensions such as processing speed, efficiency change rate, and expected completion time.

[0099] S10424. Dynamically map the preset visual variable library based on the trend indicator set to obtain a visual coding solution that reflects the progress and efficiency of problem solving.

[0100] In this embodiment, the system dynamically maps the preset visual variable library according to the trend indicator set. The visual variable library contains a variety of visual elements such as color, size, shape, texture and their range of variation. The mapping process uses a visual encoding algorithm, such as linear mapping or logarithmic mapping, to convert numerical trend indicators into visual attributes. For example, processing speed may be mapped to a color variable, from red (slow) to green (fast); the efficiency change rate may be mapped to the symbol size, and an upward trend is represented by a larger symbol. The output of this process is a complete visual encoding scheme that defines how to convert the processing progress and efficiency of each problem point into an intuitive visual representation.

[0101] S10425. Overlay and render the visualization coding scheme and the spatial distribution representation of the distribution network problem to obtain a visualization layer that reflects the real-time control progress.

[0102] In this embodiment, the system performs layer overlay and rendering processing on the generated visualization coding scheme and the original spatial distribution representation of the distribution network problem, and uses the layer management and rendering engine of the geographic information system to overlay the encoded problem processing status information as a new layer on the original problem distribution layer. The rendering process takes into account the transparency, drawing order and symbol priority of the layer to ensure that important information is not obscured. The system also implements a dynamic rendering mechanism that can refresh the layer content in real time according to the frequency of data updates. The rendering result is a dynamic visualization layer that reflects the real-time control progress, which intuitively shows the spatial distribution, processing status and efficiency trend of distribution network problems.

[0103] Step 1043: Perform correlation analysis on the power facility data in the geographic information system based on the visualization layer to obtain a comprehensive information layer including the facility status and problem handling progress.

[0104] In this embodiment, the system performs correlation analysis on the power facility data in the geographic information system based on the updated visualization layer, and associates the problem points and processing progress information with the spatial data of the power facilities by using spatial analysis techniques (such as buffer analysis and topological overlay). The analysis process takes into account the type, capacity and connection relationship of the power facilities, and generates a comprehensive information layer containing the facility status and the progress of problem handling. For example, for a faulty line being processed, the system not only displays the repair progress of the line itself, but also marks the operating status of the affected substations, distribution cabinets and other related facilities.

[0105] Step 1044: Perform multi-dimensional data aggregation and visual coding processing on the comprehensive information layer to obtain a dynamic management and control view of the geographic information system, and provide a visual management and control method through the dynamic management and control view.

[0106] The data aggregation process uses statistical analysis methods (such as density calculation or cluster analysis) to summarize a large amount of discrete problem point and facility information into more understandable macro patterns.

[0107] Visual coding uses visual variable mapping technology (such as color gradients, symbol size changes, and texture differences) to transform multidimensional data into intuitive visual representations. The coding rules are designed based on the principles of human visual perception to ensure that important information can be quickly identified. For example, color depth is used to indicate the severity of the problem, symbol size indicates the scope of impact, and flashing effects are used to highlight urgent tasks.

[0108] In this embodiment, the obtained comprehensive information layer is subjected to multi-dimensional data aggregation and visual coding processing to form a dynamic management and control view of the geographic information system. The view is an interactive multi-level visual interface that supports spatial browsing at different scales and multi-dimensional data query. The core functions of the view include dynamic zooming, layer switching, attribute query, and time series playback. At the macro level, the view shows the problem distribution and processing progress overview of the entire distribution network, presented in the form of a heat map or cluster map. When the user zooms in to a specific area, the view automatically adjusts the display granularity to display more detailed problem points and facility information. The timeline control allows the user to review the historical status or preview future trends to help analyze the problem evolution pattern. In addition, the dynamic management and control view also integrates decision support functions. Through integration with predictive models and optimization algorithms, the view can recommend the optimal resource scheduling plan and processing sequence in real time. For example, when a new emergency problem arises, the system automatically calculates and visualizes the best processing path and required resources. Users can adjust the recommended plan through interactive operations, and the system updates the view in real time to reflect the impact of these adjustments.

[0109] In an embodiment of the present invention, multi-dimensional information of the distribution network of the target area is obtained, the multi-dimensional information is pre-processed, a standardized distribution network data set is obtained, and distribution network problems are identified based on the distribution network data set to obtain a structured problem list of the distribution network. The structured problem list is spatially located and attribute mapped in a preset geographic information system to obtain a spatial distribution representation of the distribution network problem. The operation status of the distribution network is multi-dimensionally analyzed and predicted based on the spatial distribution representation of the distribution network problem, the control strategy of the distribution network is obtained, and resources are allocated based on the control strategy to obtain a collaborative workflow. The spatial distribution representation of the distribution network problem and the collaborative workflow are visualized in the geographic information system to provide a visualized control method. Through the application of multi-dimensional information acquisition and deep learning models, the efficiency and accuracy of problem identification are significantly improved. Combined with the geographic information system, the spatial distribution of distribution network problems is intuitively displayed to help optimize resource allocation. At the same time, dynamic monitoring and feedback are realized, collaborative work is promoted, various types of information are integrated, and the management efficiency and safety of the distribution network are improved.

[0110] Embodiment 2

[0111] Figure 2 A schematic diagram of a visual control device for a distribution network provided in the second embodiment of the present invention is shown in FIG. Figure 2 As shown, the device comprises:

[0112] The data acquisition and classification module 201 is used to acquire multi-dimensional information of the distribution network in the target area, pre-process the multi-dimensional information to obtain a standardized distribution network data set, and identify distribution network problems based on the distribution network data set to obtain a structured problem list of the distribution network;

[0113] A spatial distribution mapping module 202 is used to perform spatial positioning and attribute mapping processing on the structured problem list in a preset geographic information system to obtain a spatial distribution representation of the distribution network problem;

[0114] The control and analysis module 203 is used to perform multi-dimensional analysis and prediction on the operation status of the distribution network according to the spatial distribution representation of the distribution network problem, obtain the control and management strategy of the distribution network, and allocate resources according to the control and management strategy to obtain a collaborative workflow;

[0115] The visualization control module 204 is used to visualize the spatial distribution representation of the distribution network problem and the collaborative workflow in the geographic information system to provide a visualization control method.

[0116] In one embodiment of the present invention, the data acquisition classification module 201 includes:

[0117] A data preprocessing module is used to obtain multi-dimensional information of the distribution network in the target area and perform data cleaning on the multi-dimensional information according to a preset data quality assessment model;

[0118] The standardized data set acquisition module is used to perform spatiotemporal alignment and data fusion processing on the multi-dimensional information after data cleaning to obtain a standardized distribution network data set;

[0119] A potential problem feature set acquisition module, used for inputting the distribution network data set into a pre-trained deep learning model to extract abnormal state features of the distribution network and obtain a potential problem feature set of the distribution network;

[0120] The structured problem list acquisition module is used to perform cluster analysis and expert rule matching processing on the potential problem feature set to obtain the structured problem list of the distribution network.

[0121] In one embodiment of the present invention, the spatial distribution mapping module 202 includes:

[0122] A coordinate conversion module, used for performing coordinate conversion processing on the question location information in the structured question list to obtain standardized spatial coordinates consistent with the coordinate system of the geographic information system;

[0123] A topology information acquisition module, used to perform spatial correlation analysis on the power facility data in the geographic information system according to the standardized spatial coordinates to obtain topology information of problem-related facilities;

[0124] A multi-dimensional problem description data acquisition module, used for fusing the problem attribute information in the structured problem list with the topological structure information to obtain multi-dimensional problem description data;

[0125] A visualization representation rule acquisition module is used to dynamically configure a preset visualization symbol system according to the multi-dimensional problem description data to obtain a visualization representation rule;

[0126] The data rendering module is used to perform spatial rendering processing on the multi-dimensional problem description data according to the visual representation rule to obtain a spatial distribution representation of the power distribution network problem containing multiple layers.

[0127] In one embodiment of the present invention, the data rendering module includes:

[0128] A data partition processing module, used for performing spatial partition processing on the multi-dimensional problem description data according to the regional division of the target area, to obtain problem distribution subsets of each sub-region, wherein the problem distribution subsets include information on frequent power outage lines and problem stations in the sub-region;

[0129] A linear layer acquisition module is used to perform vector processing on the frequent power outage lines in the problem distribution subset and the visualization representation rule to obtain a linear layer representing the frequent power outage lines;

[0130] A point layer and a polygon layer acquisition module, used to perform spatial clustering and geometric calculation processing on the substation information in the problem distribution subset, to obtain a point layer representing the problem substation in the sub-region and a polygon layer representing the power supply range of the sub-region;

[0131] The layer integration module is used to configure attribute labels for the linear layer, the point layer and the polygon layer, and integrate them into a spatial distribution representation of the power distribution network problem containing multiple layers.

[0132] In one embodiment of the present invention, the management and control analysis module 203 includes:

[0133] A problem space distribution representation clustering module is used to perform spatiotemporal clustering analysis on the distribution network problem space distribution representation to obtain problem hotspot areas and time series characteristics;

[0134] An association rule mining module is used to perform association rule mining on the historical operation data of the distribution network according to the problem hotspot area and time series characteristics to obtain a problem cause association rule set and potential risk assessment indicators;

[0135] A control strategy acquisition module is used to perform multi-scenario simulation and deduction processing on the problem cause association rule set and potential risk assessment indicators to obtain the control strategy of the distribution network;

[0136] The collaborative workflow acquisition module is used to perform multi-objective optimization configuration processing on the available resources according to the management and control strategy to obtain a resource allocation plan, and to perform task decomposition and priority sorting on the resource allocation plan to obtain the collaborative workflow of the distribution network.

[0137] In one embodiment of the present invention, the visualization management and control module 204 includes:

[0138] An association relationship acquisition module is used to visualize the spatial distribution representation of the distribution network problem in the geographic information system, and perform spatiotemporal association processing on the spatial distribution representation of the distribution network problem and the collaborative workflow to obtain the association relationship between the spatial distribution representation of the distribution network problem and the collaborative workflow;

[0139] A visualization layer reflection module is used to dynamically update the spatial distribution representation of the distribution network problem through the association relationship during the collaborative workflow process to obtain a visualization layer reflecting the real-time control progress;

[0140] A comprehensive information layer acquisition module is used to perform correlation analysis on the power facility data in the geographic information system according to the visualization layer to obtain a comprehensive information layer including the facility status and the problem handling progress;

[0141] The distribution network management and control module is used to perform multi-dimensional data aggregation and visual coding processing on the comprehensive information layer to obtain a dynamic management and control view of the geographic information system, and provide a visual management and control method through the dynamic management and control view.

[0142] In one embodiment of the present invention, the visualization layer reflection module includes:

[0143] An information collection module is used to collect and analyze the task status information in the collaborative workflow in real time to obtain a task progress data set;

[0144] A state matching module, used to perform state matching processing on the problem points in the spatial distribution representation of the distribution network problem according to the task progress data set, and obtain dynamic marking data reflecting the current processing state of each problem point;

[0145] A data analysis module, used to perform time series analysis on the dynamic marking data to obtain a set of trend indicators describing the speed and efficiency of problem solving;

[0146] A coding scheme acquisition module is used to dynamically map a preset visual variable library according to the trend indicator set to obtain a visual coding scheme that reflects the progress and efficiency of problem processing;

[0147] The layer overlay and rendering module is used to perform layer overlay and rendering processing on the visualization coding scheme and the spatial distribution representation of the distribution network problem to obtain a visualization layer that reflects the real-time control progress.

[0148] The distribution network visualization control device provided in the embodiment of the present invention can execute the distribution network visualization control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the distribution network visualization control method.

[0149] Embodiment 3

[0150] See also Figure 3 , showing a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. The computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The computer device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0151] like Figure 3 As shown, the computer device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the computer device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0152] A number of components in the computer device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the computer device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0153] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the distribution network visualization management and control method.

[0154] In some embodiments, the distribution network visualization management and control method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the computer device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the distribution network visualization management and control method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the distribution network visualization management and control method in any other appropriate manner (e.g., by means of firmware).

[0155] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0156] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0157] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0158] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0159] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0160] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0161] Embodiment 4

[0162] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method for visual management and control of a distribution network as provided in any embodiment of the present invention is implemented.

[0163] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0164] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0165] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distribution network visualization control method, characterized in that: include: Acquire multi-dimensional information of the distribution network in the target area, pre-process the multi-dimensional information to obtain a standardized distribution network data set, and identify distribution network problems based on the distribution network data set to obtain a structured problem list of the distribution network; Performing spatial positioning and attribute mapping processing on the structured problem list in a preset geographic information system to obtain a spatial distribution representation of the distribution network problems; Performing multi-dimensional analysis and prediction on the operation status of the distribution network according to the spatial distribution representation of the distribution network problem, obtaining a control strategy for the distribution network, and allocating resources according to the control strategy to obtain a collaborative workflow; The spatial distribution representation of the distribution network problem and the collaborative workflow are visualized in the geographic information system to provide a visualized management and control method.

2. The method according to claim 1, characterized in that The step of obtaining multi-dimensional information of the distribution network in the target area, preprocessing the multi-dimensional information to obtain a standardized distribution network data set, and identifying distribution network problems based on the distribution network data set to obtain a structured problem list of the distribution network includes: Acquire multi-dimensional information of the distribution network in the target area, and perform data cleaning on the multi-dimensional information according to a preset data quality assessment model; Perform time-space alignment and data fusion processing on the multi-dimensional information after data cleaning to obtain a standardized distribution network data set; Inputting the distribution network data set into a pre-trained deep learning model to extract abnormal state characteristics of the distribution network and obtain a potential problem feature set of the distribution network; Cluster analysis and expert rule matching are performed on the potential problem feature set to obtain a structured problem list of the distribution network.

3. The method according to claim 1, characterized in that The spatial positioning and attribute mapping processing of the structured problem list in the preset geographic information system to obtain the spatial distribution representation of the distribution network problems includes: Performing coordinate conversion processing on the question location information in the structured question list to obtain standardized spatial coordinates consistent with the coordinate system of the geographic information system; Performing spatial correlation analysis on the power facility data in the geographic information system according to the standardized spatial coordinates to obtain topological structure information of problem-related facilities; Fusing the problem attribute information in the structured problem list with the topological structure information to obtain multi-dimensional problem description data; Dynamically configuring a preset visualization symbol system according to the multi-dimensional problem description data to obtain visualization representation rules; The multi-dimensional problem description data is spatially rendered according to the visualization representation rule to obtain a spatial distribution representation of the power distribution network problem containing multiple layers.

4. The method according to claim 3, characterized in that The spatial rendering process is performed on the multi-dimensional problem description data according to the visualization representation rule to obtain a spatial distribution representation of the power distribution network problem containing multiple layers, including: According to the regional division of the target area, the multi-dimensional problem description data is spatially partitioned to obtain problem distribution subsets of each sub-region, wherein the problem distribution subsets include information on frequent power outage lines and problem stations in the sub-region; Vectorizing the frequent power outage lines in the problem distribution subset and the visualization characterization rules to obtain a linear layer characterizing the frequent power outage lines; Performing spatial clustering and geometric calculation processing on the substation information in the problem distribution subset to obtain a point layer representing the problem substation in the sub-region and a polygonal layer representing the power supply range of the sub-region; Attribute labels are configured for the linear layer, the point layer and the polygon layer, and integrated into a spatial distribution representation of the power distribution network problem including multiple layers.

5. The method according to claim 1, characterized in that The multi-dimensional analysis and prediction of the operation status of the distribution network is performed according to the spatial distribution representation of the distribution network problem, a control strategy of the distribution network is obtained, and resources are allocated according to the control strategy to obtain a collaborative workflow, including: Performing spatiotemporal clustering analysis on the spatial distribution representation of the distribution network problem to obtain the problem hotspot area and time series characteristics; Performing association rule mining on the historical operation data of the distribution network according to the problem hotspot area and time series characteristics to obtain a problem cause association rule set and potential risk assessment indicators; Perform multi-scenario simulation and deduction on the problem cause association rule set and potential risk assessment indicators to obtain the control strategy of the distribution network; The available resources are subjected to multi-objective optimization configuration processing according to the management and control strategy to obtain a resource allocation plan, and the resource allocation plan is subjected to task decomposition and priority sorting processing to obtain a collaborative workflow of the distribution network.

6. The method according to any one of claims 1 to 5, characterized in that The spatial distribution representation of the distribution network problem and the collaborative workflow are visualized in the geographic information system to provide a visualized management and control method, including: Visualizing the spatial distribution representation of the distribution network problem in the geographic information system, and performing spatiotemporal correlation processing on the spatial distribution representation of the distribution network problem and the collaborative workflow to obtain a correlation relationship between the spatial distribution representation of the distribution network problem and the collaborative workflow; During the collaborative workflow, the spatial distribution representation of the distribution network problem is dynamically updated through the association relationship to obtain a visualization layer reflecting the real-time control progress; Performing correlation analysis on the power facility data in the geographic information system according to the visualization layer to obtain a comprehensive information layer including the facility status and the progress of problem handling; The comprehensive information layer is subjected to multi-dimensional data aggregation and visual coding processing to obtain a dynamic management and control view of the geographic information system, and a visual management and control method is provided through the dynamic management and control view.

7. The method according to claim 6, characterized in that In the process of the collaborative workflow, the spatial distribution representation of the distribution network problem is dynamically updated through the association relationship to obtain a visualization layer reflecting the real-time control progress, including: Collecting and analyzing the task status information in the collaborative workflow in real time to obtain a task progress data set; Performing state matching processing on the problem points in the spatial distribution representation of the distribution network problem according to the task progress data set to obtain dynamic marking data reflecting the current processing state of each problem point; Performing time series analysis on the dynamic marking data to obtain a set of trend indicators describing the speed and efficiency of problem solving; Dynamically mapping a preset visual variable library according to the trend indicator set to obtain a visual coding scheme that reflects the progress and efficiency of problem handling; The visualization coding scheme and the spatial distribution representation of the distribution network problem are layered and rendered to obtain a visualization layer that reflects the real-time control progress.

8. A computer device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the distribution network visualization management and control method as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the distribution network visualization control method as described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method for visual management and control of a distribution network as described in any one of claims 1 to 7 is implemented.

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