Power distribution network intelligent mapping method based on dot matrix layout and dynamic weight

Abstract

The invention discloses a power distribution network intelligent mapping method based on dot matrix layout and dynamic weight, and relates to the technical field of distribution network mapping, and the method comprises the steps: constructing a dot matrix layout system corresponding to a power distribution network; the dot matrix layout system is divided into a plurality of uniformly distributed dot matrix units through regional gridding topographic factor correction equipment density feedback three-level calculation by taking geographic coordinates of a region where the power distribution network is located as a reference; the method specifically comprises the following steps: after gridding preliminary division, correcting the side length in combination with topographic factors, and then performing iterative adjustment according to equipment density; the self-adaptive smoothing processing is based on GIS vector topographic data, and a boundary is fitted through a Bezier curve; according to the method, the real-time performance and the accuracy of the mapping in a complex environment and an emergency scene are ensured by combining precision verification, on the whole, the intelligent, high-precision and efficient dynamic updating of the mapping of the power distribution network is realized, and the operation and maintenance efficiency, the emergency response speed and the fault processing capability of the distribution network are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of distribution network mapping technology, and more specifically, to a smart distribution network mapping method based on matrix layout and dynamic weights. Background Technology

[0002] In power distribution network management, visualization graphics are crucial tools for operation and maintenance, planning, and emergency response. Traditional mapping methods, often based on fixed grids or simple geographic coordinate systems, struggle to adapt to complex terrains (such as mountainous and riverine areas) and high-density urban environments, leading to inaccurate equipment layout and unintuitive topology connections. Existing data acquisition relies heavily on manual input or single systems, lacking multi-source fusion and traceability mechanisms, resulting in inconsistent data quality and issues such as duplication, missing data, or anomalies. Preprocessing often ignores terrain factors and dynamic adjustments to equipment density, leading to unreasonable grid division and low mapping accuracy. Topology identification algorithms typically use static weights, failing to dynamically optimize based on real-time equipment failure risks, resulting in slow response in emergency scenarios. Furthermore, mapping engines lack scene-based rendering capabilities, hindering rapid view switching to meet different needs such as normal operation and maintenance, emergency repairs, and fault diagnosis; the imperfect dynamic update mechanism impacts repair efficiency and decision-making accuracy. Therefore, an intelligent mapping method is urgently needed, combining grid layout, multi-source data fusion, and adaptive algorithms to improve the accuracy, real-time performance, and scene adaptability of distribution network mapping. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides a method for intelligent mapping of distribution networks based on lattice layout and dynamic weights.

[0004] This invention provides a smart mapping method for distribution networks based on lattice layout and dynamic weights, comprising the following steps: S1: Construct a grid layout system corresponding to the power distribution network; the grid layout system is based on the geographical coordinates of the area where the power distribution network is located, and forms several uniformly distributed grid units through a three-level calculation and division using regional gridded terrain factors to correct equipment density feedback; specifically, after initial gridding, the side length is corrected by combining terrain factors, and then iteratively adjusted according to equipment density; each grid unit corresponds to a unique coordinate identifier, and the grid unit boundary adopts adaptive smoothing processing to avoid terrain obstacles; the adaptive smoothing processing is based on GIS vector terrain data, and the boundary is fitted by Bézier curves; S2: Collect basic data of the power distribution network; the basic data includes at least distribution network equipment parameter data, equipment topology connection data and equipment geographical location data; during the collection process, establish data traceability tags, and associate each data entry with the collection device ID, collection time and data credibility level; S3: Preprocess the collected basic data; the preprocessing includes data cleaning, data standardization and outlier removal; after preprocessing, a data quality report is generated, including data integrity rate, outlier removal rate and standardization pass rate; S4: Based on the preprocessed basic data and the dot matrix layout system, the corresponding position of each distribution network device in the dot matrix layout is determined by an improved topology relationship identification algorithm, and the mapping relationship of the device dot matrix unit is established; the improved topology relationship identification algorithm is a minimum spanning tree algorithm that integrates the dynamic weight of device priority; the device priority weight is dynamically adjusted according to the real-time fault risk of the device, and the real-time fault risk of the device is calculated based on the device operating parameters; S5: Based on the mapping relationship and the topology connection data between devices, call the intelligent mapping engine to generate a visual graphic of the power distribution network; the visual graphic includes at least a device distribution layer, a topology connection layer, and a dot matrix coordinate annotation layer; the intelligent mapping engine has a built-in scene-based rendering template, which can automatically switch rendering rules according to the distribution network scene; the distribution network scene includes normal operation and maintenance, emergency repair, and fault diagnosis.

[0005] Preferably, in step S1, the terrain obstacles include rivers, mountains, and large buildings; the specific logic of the regional gridded terrain factor correction calculation is to first calculate the initial side length, and then correct the initial side length in combination with the terrain factor.

[0006] Preferably, in step S2, the power distribution equipment parameter data includes equipment model, rated parameters, and operating status; rated parameters include rated voltage, rated current, and rated power; operating status includes running, out of service, and under maintenance; the equipment topology connection data includes conductor parameters, connection nodes, and line routing; conductor parameters include conductor model, cross-sectional area, and impedance; connection nodes include node number and node type; line routing includes routing angle and the area traversed; and the equipment geographical location data includes latitude and longitude and installation elevation. For basic data collection in high-density urban power distribution networks, a multi-source data fusion collection mode is also adopted. High-density urban power distribution networks are characterized by dense equipment and severe building obstruction. In addition to collecting real-time equipment parameter data through the power distribution network SCADA system and updating equipment geographical location data through the GIS system, additional data on equipment appearance and surrounding environment are obtained through drone aerial photography, and the location data of buried lines are obtained through underground pipeline detectors. When manually supplementing data, it is necessary to perform double verification by combining drone aerial images and underground pipeline detection data. The verification logic is that the deviation between the manually entered equipment location data and the equipment location identified by the drone aerial images meets the preset terrain adaptation deviation requirements, and the deviation between the equipment location data and the route deviation of the underground pipeline detection data meets the corresponding angle requirements, before it can be included in the basic database. The collection cycle is dynamically adjusted according to the equipment type.

[0007] Preferably, in step S3, the data cleaning removes duplicate data and repairs missing data through device ID uniqueness verification and parameter logical consistency verification; device ID uniqueness verification deletes duplicate entries by comparing the unique device code; parameter logical consistency verification is based on the rated parameter range of the device, and if the parameter exceeds the rated range, it is marked as needing repair, and repair is performed by interpolating data from adjacent time periods; the data standardization unifies the device parameter units to international standard units and the geographic location data coordinate system to the WGS84 coordinate system, and converts the device installation elevation to absolute elevation through the elevation datum conversion formula; the abnormal data removal identifies abnormal data through the 3σ criterion and device operating status correlation analysis; after calculating the mean μ and standard deviation σ of the parameter data, data exceeding the range of [μ3σ, μ+3σ] are removed, and then a second screening is performed in combination with the real-time operating status of the device; The elevation correction for data standardization also includes combining regional digital elevation model data, calculating the benchmark elevation of the equipment location through elevation interpolation, and then correcting the equipment installation height using the equipment installation height correction formula; the preprocessed basic data is encrypted and stored using blockchain technology, adopting a consortium blockchain architecture, and the hash value of each data block is calculated and generated by the unique device identifier, collection timestamp, and data content.

[0008] Preferably, in step S4, the equipment priority weight is calculated using an equipment importance assessment model. The input parameters of the equipment importance assessment model include power supply radiation range, fault impact coefficient, and operating years. The power supply radiation range is reflected by the user or area covered by the equipment power supply. The fault impact coefficient is reflected by the degree of impact of equipment faults on the distribution network. The operating years are reflected by the operating time of the equipment. The specific execution logic of the improved topology relationship identification algorithm is to construct the equipment topology association graph using the Kruskal algorithm, calculate the initial weight of each topology path, and then adjust the priority of the topology connection path by combining the dynamic weight of equipment priority. When determining the corresponding position of equipment in the dot matrix layout, a hierarchical mapping + topology index logic is also adopted for the mapping unit determination of large distribution network equipment. Large distribution network equipment includes substations and large switching stations. After calculating the circumscribed rectangle of the equipment's geometric contour, the coverage range of dot matrix units is determined according to the ratio of the length and width of the circumscribed rectangle to the side length of the dot matrix unit. The equipment is then divided into core functional areas and auxiliary functional areas. The core functional area corresponds to one core dot matrix unit, and the auxiliary functional area corresponds to multiple associated dot matrix units. The selection of associated dot matrix units is determined by topology distance calculation. After the mapping relationship is established, equipment position conflict detection is required. A topology association index is established between the core dot matrix unit and the associated dot matrix units.

[0009] Preferably, in step S5, the device distribution layer labels the device type and operating status; the device type is distinguished by icons; the operating status is distinguished by color; the topology connection layer labels the line parameters and connection relationships; the line parameters include conductor type and voltage level; the connection relationships are distinguished by line shape; the dot matrix coordinate labeling layer labels the dot matrix unit ID and corresponding geographical area. For the visualization and graphic generation of emergency power supply scenarios, the intelligent mapping engine also adopts a fast mapping optimization logic; emergency power supply scenarios include temporary power distribution networks after natural disasters; temporary emergency equipment is configured with exclusive graphic identifiers, and the identifier size differs from that of regular equipment; emergency equipment includes emergency generators and temporary lines; when generating graphics, core power supply lines are rendered first, and the rendering priority is calculated based on the importance of emergency power supply; it supports dynamically adjusting the layer display content according to user permissions; users with different permissions can view different ranges of layer information. The system features a visual graphical interaction function for troubleshooting distribution network faults, and also supports interactive fault tracing analysis. When a user clicks on a faulty device icon, detailed device parameter data, topology connection relationships, and historical operating data are displayed. The system can also call fault propagation path calculation algorithms. When simulating topology change trends under different operating scenarios, the system additionally outputs a fault impact range prediction for the fault scenario. The interactive results are displayed in the form of graphical annotations and data tables.

[0010] Preferably, in step S1, the construction of the dot matrix layout system for complex terrain also includes a calculation logic for iterative verification of partition side length adaptation based on terrain zoning; complex terrain includes mountains and areas where multiple rivers intersect; the region is divided into mountain sub-regions, plain sub-regions, and water sub-regions using GIS terrain data; the GIS terrain data includes vector information such as slope, rivers, and elevation; the side length of the mountain sub-region is adjusted based on the slope value; the water sub-region is divided only for the dot matrix units corresponding to the lines crossing the water, and the side length has a proportional relationship with the side length of the dot matrix units in the adjacent plain sub-region; after the side length is adjusted, the matching degree of the device dot matrix unit is verified; if the matching degree does not meet the preset adaptation requirements, the side length is iteratively adjusted again.

[0011] Preferably, step S6 is also included: for the dynamic updating of visualization graphics in emergency distribution network scenarios, an emergency priority update mechanism is adopted; emergency distribution network scenarios include temporary distribution networks after natural disasters; in addition to the dual dynamic updates of trigger-based and timed updates, an emergency update mode is triggered when a natural disaster occurs; at this time, the data change monitoring frequency is adjusted; edge computing nodes prioritize processing data changes of emergency equipment; edge computing nodes are deployed in substations and emergency command vehicles in the distribution network area; a local preprocessing incremental upload mode is adopted; the core line priority update logic is adopted during incremental updates; the update response time of the matrix unit and associated topology corresponding to the core line meets the preset emergency response time requirements; the update response time of non-core lines can be appropriately extended; timed full updates are automatically paused in emergency scenarios; until the emergency state is lifted; the lifting of the emergency state is determined based on the relevant notices issued by the emergency management department.

[0012] Preferably, in step S6, the collaborative capture logic of the data change monitoring module and the edge computing node also includes: when the edge computing node performs local preprocessing on the collected change data, it adopts emergency data priority sorting; calculates data priority based on the emergency importance of the data-related device and the urgency of the data change; data with higher priority is uploaded first; the bidirectional data interaction interface between the data change monitoring module and the distribution network SCADA system and GIS system adopts a redundant channel design; wired channels and wireless channels are established simultaneously; when one channel fails, it automatically switches to the other channel; the judgment logic for valid change data is that the data format conforms to the preset standard, the data content matches the current operating status of the device, and the data source is traceable.

[0013] Preferably, step S7 is also included: For accuracy verification in different scenarios, a scenario-based verification standard is adopted; the verification cycle is synchronized with the data acquisition cycle; for location accuracy verification in complex terrain scenarios, the deviation between the device location and the actual GPS measurement data meets the preset complex terrain deviation requirements; for high-density urban scenarios, the deviation value needs to be corrected in conjunction with the building obstruction coefficient; for topology accuracy verification in emergency scenarios, the topology matching degree verification adopts core line priority verification, first verifying the topology connection relationship of the core power supply line, then verifying the ordinary lines, and the topology matching degree of the core line meets the preset emergency topology matching degree requirements; for parameter accuracy verification, for parameters with high real-time requirements, the parameter error rate meets the preset real-time parameter error rate requirements, and for static parameters, the parameter error rate meets the preset static parameter error rate requirements.

[0014] Beneficial effects: By introducing a dot matrix layout system, combined with regional gridding, terrain factor correction, and iterative adjustment of equipment density, adaptive partitioning and boundary smoothing of dot matrix units are achieved, effectively avoiding terrain obstacles and improving layout uniformity and mapping accuracy. Data acquisition adopts a multi-source fusion mode, with the addition of traceability tags and credibility levels to ensure data reliability and integrity. The preprocessing stage generates high-quality data reports through data cleaning, standardization, and anomaly removal, laying the foundation for subsequent topology identification. An improved topology relationship identification algorithm integrates dynamic weights of equipment priorities and adjusts mapping relationships according to real-time fault risks, improving the accuracy of equipment location. The intelligent mapping engine has built-in scene-based rendering templates, supporting multi-scene view switching such as normal operation and maintenance, emergency repair, and fault diagnosis, and integrates rapid mapping optimization and fault tracing interaction functions to enhance user experience and decision support. In addition, dynamic updates adopt an emergency priority mechanism and edge computing, combined with accuracy verification, to ensure the real-time performance and accuracy of mapping in complex environments and emergency scenarios. Overall, this method achieves intelligent, high-precision, and efficient dynamic updates for power distribution network mapping, significantly improving distribution network operation and maintenance efficiency, emergency response speed, and fault handling capabilities. Attached Figure Description

[0015] Figure 1 This is a flowchart of the mapping method of the present invention; Detailed Implementation

[0016] like Figure 1 As shown: A smart mapping method for distribution networks based on lattice layout and dynamic weights includes the following steps: S1: Construct a grid layout system corresponding to the power distribution network; the grid layout system is based on the geographical coordinates of the area where the power distribution network is located, and forms several uniformly distributed grid units through a three-level calculation and division of equipment density feedback by regional gridded terrain factor correction. Specifically, after initial gridding, the side length is corrected by combining terrain factors, and then iteratively adjusted according to equipment density; each grid unit corresponds to a unique coordinate identifier, and the grid unit boundary adopts adaptive smoothing processing to avoid terrain obstacles; the adaptive smoothing processing is based on GIS vector terrain data and the boundary is fitted by Bézier curves. It should be noted that, firstly, a uniform grid is initially divided based on regional geographic coordinates; secondly, the grid side length is adaptively corrected by incorporating GIS terrain data (such as slope and water area); finally, iterative adjustments are made based on the equipment density distribution within the region, ultimately forming an optimized lattice unit layout. The lattice unit boundaries are adaptively smoothed using Bézier curves to avoid terrain obstacles. S2: Collect basic data of the power distribution network; the basic data includes at least distribution network equipment parameter data, equipment topology connection data and equipment geographical location data; during the collection process, establish data traceability tags, and associate each data entry with the collection device ID, collection time and data credibility level; S3: Preprocess the collected basic data; the preprocessing includes data cleaning, data standardization and outlier removal; after preprocessing, a data quality report is generated, including data integrity rate, outlier removal rate and standardization pass rate; S4: Based on the preprocessed basic data and the dot matrix layout system, the corresponding position of each distribution network device in the dot matrix layout is determined by an improved topology relationship identification algorithm, and the mapping relationship of the device dot matrix unit is established; the improved topology relationship identification algorithm is a minimum spanning tree algorithm that integrates the dynamic weight of device priority; the device priority weight is dynamically adjusted according to the real-time fault risk of the device, and the real-time fault risk of the device is calculated based on the device operating parameters; S5: Based on the mapping relationship and the topology connection data between devices, call the intelligent mapping engine to generate a visual graphic of the power distribution network; the visual graphic includes at least a device distribution layer, a topology connection layer, and a dot matrix coordinate annotation layer; the intelligent mapping engine has a built-in scene-based rendering template, which can automatically switch rendering rules according to the distribution network scene; the distribution network scene includes normal operation and maintenance, emergency repair, and fault diagnosis.

[0017] It should be noted that the coordinate identifier format is XY, where X is the horizontal coordinate number and Y is the vertical coordinate number; the equipment density iterative adjustment logic in the three-level calculation is as follows: if the number of devices in a dot matrix unit exceeds the reasonable range calculated based on the average equipment footprint and the dot matrix unit area, then the unit is split into 4 equal-area sub-units; if the number of devices is less than this range, then 4 adjacent units are merged into 1 unit; the data reliability level is divided according to the reliability of the data acquisition method, and the reliability level of SCADA real-time data is higher than that of manually supplemented data; the real-time equipment fault risk calculation is associated with the ratio of actual current to rated current, the ratio of operating time to design life, and the ambient temperature deviation, where the ambient temperature deviation is the difference between the actual ambient temperature and the design operating temperature; the data quality report is used to determine whether to proceed to the next step, and the data integrity rate and standardization pass rate must meet the basic requirements of the subsequent topology recognition algorithm for data integrity; the switching logic of the scene rendering template is set based on the functional requirements of the distribution network scenario, displaying all layers in the normal operation and maintenance scenario, and highlighting emergency-related layers in the emergency repair scenario; As an optional embodiment, in step S1, the terrain obstacles include rivers, mountains, and large buildings; the specific logic of the regional gridded terrain factor correction calculation is to first calculate the initial side length, and then correct the initial side length in combination with the terrain factor.

[0018] It should be noted that rivers are natural or man-made water channels; mountains are mountains with significant elevation differences; large buildings are buildings with a certain building area or height; the initial side length is calculated as initial side length = √(total area of ​​the region / number of preset grid units), and the number of preset grid units is determined based on the total number of distribution network devices in the region; the terrain factor is calculated as terrain factor = 1 + α × slope + β × elevation difference, where α and β are terrain influence coefficients, assigned values ​​based on the actual influence of terrain classification in power system communication design; the slope is calculated as slope = arctan(elevation difference / horizontal distance), and the horizontal distance is obtained through the straight-line distance between two points in GIS terrain data; the terrain influence coefficient α is adjusted according to the terrain type, with higher values ​​for mountainous terrain than for plains, and values ​​for hilly terrain falling in between; the terrain influence coefficient β is adjusted according to the elevation difference range, with higher values ​​for larger elevation differences. As an optional embodiment, in step S2, the power distribution equipment parameter data includes equipment model, rated parameters, and operating status; rated parameters include rated voltage, rated current, and rated power; operating status includes running, out of service, and under maintenance; the equipment topology connection data includes conductor parameters, connection nodes, and line routing; conductor parameters include conductor model, cross-sectional area, and impedance; connection nodes include node number and node type; line routing includes routing angle and the area traversed; and the equipment geographical location data includes latitude and longitude and installation elevation. For basic data collection in high-density urban power distribution networks, a multi-source data fusion collection mode is also adopted. High-density urban power distribution networks are characterized by dense equipment and severe building obstruction. In addition to collecting real-time equipment parameter data through the power distribution network SCADA system and updating equipment geographical location data through the GIS system, additional data on equipment appearance and surrounding environment are obtained through drone aerial photography, and the location data of buried lines are obtained through underground pipeline detectors. When manually supplementing data, it is necessary to perform double verification by combining drone aerial images and underground pipeline detection data. The verification logic is that the deviation between the manually entered equipment location data and the equipment location identified by the drone aerial images meets the preset terrain adaptation deviation requirements, and the deviation between the equipment location data and the route deviation of the underground pipeline detection data meets the corresponding angle requirements, before it can be included in the basic database. The collection cycle is dynamically adjusted according to the equipment type.

[0019] It should be noted that the equipment density of high-density urban power distribution networks is determined based on the distribution density characteristics of urban power distribution network equipment; the sampling frequency of the power distribution network SCADA system is set according to the real-time parameter monitoring requirements; the update frequency of the GIS system is set according to the frequency of equipment location changes; the flight altitude and image resolution of UAV aerial photography are set according to the detection range and accuracy requirements; the detection depth and positioning accuracy of underground pipeline detectors are set according to the underground pipeline burial depth requirements; the preset terrain adaptation deviation is determined based on the resolution of UAV aerial images, and the higher the resolution, the smaller the allowable deviation value; the angle requirement for line routing deviation is set according to the underground pipeline detection accuracy, and the smaller the angle deviation, the higher the topology connection accuracy; the adjustment standard for the collection cycle is set according to the importance of the equipment, and the collection cycle for core equipment such as transformers and switch stations is shorter than that for ordinary distribution boxes; the credibility level in the data traceability label is set according to the reliability of the data collection method, and the credibility of automatically collected data is higher than that of manually collected data, and the credibility of directly detected data is higher than that of indirectly inferred data; As an optional embodiment, in step S3, the data cleaning removes duplicate data and repairs missing data through device ID uniqueness verification and parameter logical consistency verification; device ID uniqueness verification deletes duplicate entries by comparing the unique device code; parameter logical consistency verification is based on the rated parameter range of the device, and if the parameter exceeds the rated range, it is marked as needing repair, and repair is performed by interpolating data from adjacent time periods; the data standardization unifies the device parameter units to international standard units and the geographic location data coordinate system to the WGS84 coordinate system, and converts the device installation elevation to absolute elevation through the elevation datum conversion formula; the abnormal data removal identifies abnormal data through the 3σ criterion and device operating status correlation analysis; after calculating the mean μ and standard deviation σ of the parameter data, data exceeding the range of [μ3σ, μ+3σ] are removed, and then a second screening is performed in combination with the real-time operating status of the device; The elevation correction for data standardization also includes combining regional digital elevation model data, calculating the benchmark elevation of the equipment location through elevation interpolation, and then correcting the equipment installation height using the equipment installation height correction formula; the preprocessed basic data is encrypted and stored using blockchain technology, adopting a consortium blockchain architecture, and the hash value of each data block is calculated and generated by the unique device identifier, collection timestamp, and data content.

[0020] It should be noted that the unique equipment code includes the asset number; the international standard units for voltage are kV, current is A, and power is kW; latitude and longitude accuracy and installation elevation accuracy are set according to geographical location positioning requirements; the specific judgment criteria for parameter logical consistency verification are that the current, voltage, and power parameters are within the reasonable fluctuation range of the corresponding equipment's rated parameters; missing data repair uses linear interpolation, and the number of interpolation data points is set according to the duration of the missing data; the regional datum in the elevation datum conversion formula adopts the 1985 National Elevation Datum; the secondary screening of the equipment's real-time operating status is used to exclude zero-value parameters of normally shut-down equipment; 0 current for shut-down equipment and 0 power for maintenance equipment are reasonable data under normal operating conditions, while 0 current for operating equipment is abnormal data; regional digital elevation model data. The resolution is set according to the elevation accuracy requirements; the higher the resolution, the more detailed the elevation data. The specific method for elevation interpolation is to use the elevation values ​​of three known digital elevation model points around the device if there is no digital elevation model data at the device's location. The reference elevation deviation in the device installation height correction formula is the difference between the measured elevation at the device's location and the reference elevation of the digital elevation model. The nodes of the consortium blockchain architecture include the distribution network operation and maintenance center, data acquisition terminal, and monitoring platform. The hash value is calculated as hash value = SHA256 (device ID + timestamp + data content + acquisition device signature). The pass criteria for the data quality report are data integrity rate, anomaly removal rate, and standardization pass rate. If these criteria are not met, data acquisition and preprocessing will be re-executed. As an optional embodiment, in step S4, the equipment priority weight is calculated through an equipment importance assessment model. The input parameters of the equipment importance assessment model include power supply radiation range, fault impact coefficient, and operating years. The power supply radiation range is reflected by the range of users or areas covered by the equipment power supply. The fault impact coefficient is reflected by the degree of impact of equipment faults on the distribution network. The operating years are reflected by the time the equipment has been in operation. The specific execution logic of the improved topology relationship identification algorithm is to construct the equipment topology association graph through the Kruskal algorithm, calculate the initial weight of each topology path, and then adjust the priority of the topology connection path in combination with the dynamic weight of equipment priority. When determining the corresponding position of equipment in the dot matrix layout, a hierarchical mapping + topology index logic is also adopted for the mapping unit determination of large distribution network equipment. Large distribution network equipment includes substations and large switching stations. After calculating the circumscribed rectangle of the equipment's geometric contour, the coverage range of dot matrix units is determined according to the ratio of the length and width of the circumscribed rectangle to the side length of the dot matrix unit. The equipment is then divided into core functional areas and auxiliary functional areas. The core functional area corresponds to one core dot matrix unit, and the auxiliary functional area corresponds to multiple associated dot matrix units. The selection of associated dot matrix units is determined by topology distance calculation. After the mapping relationship is established, equipment position conflict detection is required. A topology association index is established between the core dot matrix unit and the associated dot matrix units.

[0021] It should be noted that the initial weight of the topology path is calculated based on the line length; the longer the line, the higher the initial weight. The line length is related to the line impedance coefficient, which is determined according to the conductor type. In the calculation expression of the equipment importance assessment model, the power supply radiation range, fault impact coefficient, and service life correspond to different weight coefficients, which are set according to the actual impact of each parameter in the distribution network planning and design on the equipment importance. The number of covered users is quantified by the actual number of users covered by the equipment. The outage area is quantified by the area of ​​the outage area that may be caused by equipment failure. The equipment aging degree is quantified by the ratio of the equipment's operating time to its design life. The criteria for determining large distribution network equipment are the size characteristics of large equipment that needs to be identified separately in the distribution network field. The vertices of the circumscribed rectangle correspond to the coordinates of the lattice unit. The core functional area includes the main transformer area of ​​the substation and the circuit breaker area of ​​the switch station. The auxiliary functional area includes the auxiliary facilities area of ​​the substation and the control area of ​​the switch station. The expression for calculating the topology distance is: Topology distance = √[(x1x2)] 2 +(y1y2) 2 x1 and y1 are the coordinates of the core lattice unit, and x2 and y2 are the coordinates of the lattice unit to be screened. The screening criterion for associated lattice units is that the topological distance meets the preset topological distance requirement, which is set based on the actual positional relationship between the core area and the auxiliary area. The logic for detecting device position conflicts is to count the number of similar devices mapped to the same lattice unit. If the number exceeds the preset conflict threshold, the lattice unit side length in step S1 is readjusted, and the adjustment range is a fixed proportion of the current side length. The preset conflict threshold is calculated based on the lattice unit area and the device footprint. As an optional embodiment, in step S5, the device distribution layer labels the device type and operating status; the device type is distinguished by icons; the operating status is distinguished by color; the topology connection layer labels the line parameters and connection relationships; the line parameters include conductor type and voltage level; the connection relationships are distinguished by line type; the dot matrix coordinate labeling layer labels the dot matrix unit ID and corresponding geographical area. For the visualization and graphic generation of emergency power supply scenarios, the intelligent mapping engine also adopts a fast mapping optimization logic; emergency power supply scenarios include temporary power distribution networks after natural disasters; temporary emergency equipment is configured with exclusive graphic identifiers, and the identifier size differs from that of regular equipment; emergency equipment includes emergency generators and temporary lines; when generating graphics, core power supply lines are rendered first, and the rendering priority is calculated based on the importance of emergency power supply; it supports dynamically adjusting the layer display content according to user permissions; users with different permissions can view different ranges of layer information. The system features a visual graphical interaction function for troubleshooting distribution network faults, and also supports interactive fault tracing analysis. When a user clicks on a faulty device icon, detailed device parameter data, topology connection relationships, and historical operating data are displayed. The system can also call fault propagation path calculation algorithms. When simulating topology change trends under different operating scenarios, the system additionally outputs a fault impact range prediction for the fault scenario. The interactive results are displayed in the form of graphical annotations and data tables.

[0022] It should be noted that the color-coding standard for operational status is green for operational status, gray for shutdown status, yellow for maintenance status, and red for fault status; the line type distinction standard for connection relationships is solid lines for operating lines and dashed lines for backup lines; the format of the dot matrix unit ID is XY; the corresponding geographical areas include streets, communities, etc.; natural disasters include earthquakes and floods; the color of the special graphic identifier for emergency equipment is different from that of conventional equipment; the emergency generator icon is a triangle, and the temporary line shape is a dashed line; the difference in identifier size is represented by the visually identifiable difference that the emergency equipment identifier is larger than that of conventional equipment; core power supply lines include lines connecting hospitals, schools, and government agencies; the calculation of the importance of emergency power supply is related to the priority of the power supply object and the line load rate; the priority of the power supply object is set according to the relevant requirements of national emergency response to public emergencies; the line load rate = real-time load / rated load; different user permissions include emergency command personnel, maintenance personnel, and ordinary users; emergency command personnel can view the emergency power supply range prediction layer and the fault location layer; maintenance personnel... Personnel can view the equipment parameter details layer and the topology connection layer; ordinary users can only view the equipment distribution overview layer; detailed equipment parameter data includes real-time parameter curves for a recent period; topology connection relationships include upstream and downstream equipment association diagrams; the implementation logic of the fault propagation path calculation algorithm is based on the equipment topology connection relationship and fault type, traversing possible fault propagation paths using a breadth-first search method; short-circuit faults trace line nodes; overload faults trace load-side equipment; fault impact range prediction labels the affected user areas and the estimated power outage duration; graphical annotations include fault propagation paths and impact ranges; data tables include a list of affected equipment, the number of users, and the estimated recovery time; the time range of historical operation data is the past few months; data granularity is set according to the importance of the equipment, with finer granularity for core equipment and slightly coarser granularity for ordinary equipment; the scene-based rendering template parameters of the intelligent mapping engine are set according to the distribution network scenario; the transparency of layers in normal operation and maintenance scenarios is fully clear; the transparency of non-core layers is reduced in emergency repair scenarios; non-fault layers are displayed in grayscale in fault diagnosis scenarios; As an optional embodiment, step S1, the construction of the dot matrix layout system for complex terrain, also includes the logic of iterative verification calculation for dividing the partition side lengths of the terrain into sub-regions based on terrain zoning. The complex terrain includes mountains and areas where multiple rivers intersect. The region is divided into mountain sub-regions, plain sub-regions, and water sub-regions using GIS terrain data. The GIS terrain data includes vector information such as slope, rivers, and elevation. The side lengths of the mountain sub-regions are adjusted based on the slope values. The water sub-regions are divided only for the dot matrix units corresponding to the lines crossing the water, and the side lengths are proportional to the side lengths of the dot matrix units in the adjacent plain sub-regions. After the side lengths are adjusted, the matching degree of the device dot matrix units is verified. If the matching degree does not meet the preset adaptation requirements, the side lengths are iteratively adjusted again.

[0023] It should be noted that mountainous areas are defined as regions with slopes that can affect the coverage angle of lattice units; multi-river confluence areas are defined as regions where two or more rivers meet the conditions for forming intersecting terrain; the slope value is calculated as slope = arctan(elevation difference / horizontal distance); the horizontal distance is calculated through the projected distance between two points in GIS; the standard for adjusting the side length of mountain sub-regions is that the greater the slope, the smaller the side length, to ensure that the lattice unit can completely cover the terrain unit where the equipment is located, and to avoid the equipment crossing terrain units with specific slopes; the side length of the lattice unit in the water sub-region is... The fixed ratio of the side length of the lattice unit in the adjacent plain sub-region; the side length of the plain sub-region is determined according to the equipment density, and different equipment density intervals correspond to different side lengths; the calculation expression for the matching degree of the equipment lattice unit is matching degree = number of lattice units covering the equipment / total number of lattice units; the preset adaptation requirement is that the matching degree meets the requirement of complete coverage of the equipment by the lattice unit; the iterative verification logic is that if the matching degree does not meet the requirement after the first adjustment, the side length adjustment range is adjusted, and the matching degree is recalculated until the matching degree meets the standard or the number of iterations reaches a certain number; if the requirement is still not met after the number of iterations reaches the standard, manual intervention is performed; As an optional embodiment, step S6 is also included: for the dynamic updating of visualization graphics in emergency distribution network scenarios, an emergency priority update mechanism is adopted; emergency distribution network scenarios include temporary distribution networks after natural disasters; in addition to the dual dynamic updates of trigger-based and timed updates, an emergency update mode is triggered when a natural disaster occurs; at this time, the data change monitoring frequency is adjusted; edge computing nodes prioritize processing data changes of emergency equipment; edge computing nodes are deployed in substations and emergency command vehicles in the distribution network area; a local preprocessing incremental upload mode is adopted; the core line priority update logic is adopted during incremental updates; the update response time of the matrix unit and associated topology corresponding to the core line meets the preset emergency response time requirements; the update response time of non-core lines can be appropriately extended; timed full updates are automatically paused in emergency scenarios; until the emergency state is lifted; the lifting of the emergency state is determined based on the relevant notices issued by the emergency management department.

[0024] Natural disasters include earthquakes and floods; the determination of natural disasters is based on disaster warnings issued by the National Meteorological Administration and the China Earthquake Administration; the standard for adjusting the data change monitoring frequency is that the frequency is higher in emergency update mode than in normal scenarios; emergency equipment includes emergency generators, temporary switches, and mobile transformer substations; local preprocessing logic includes filtering invalid data, standardizing valid data, and marking the urgency level of data; standardization of valid data includes unifying units and coordinates; the criteria for determining core lines are lines connecting emergency power supply points and important loads, as well as distribution network backbone lines; emergency power supply points include emergency generator access points; important loads include hospitals and schools; the determination of distribution network backbone lines is based on the cross-sectional area characteristics of lines in the distribution network that undertake the main power supply tasks; the preset emergency response time is determined according to the disaster level, with different disaster levels corresponding to different response times; the normal cycle of timed full updates is set according to data update requirements; during recovery, incremental updates are performed first, followed by full updates; As an optional embodiment, in step S6, the collaborative capture logic of the data change monitoring module and the edge computing node also includes: when the edge computing node performs local preprocessing on the collected change data, it adopts emergency data priority sorting; calculates data priority based on the emergency importance of the data-associated device and the urgency of the data change; data with higher priority is uploaded first; the bidirectional data interaction interface between the data change monitoring module and the distribution network SCADA system and GIS system adopts a redundant channel design; wired channels and wireless channels are established simultaneously; when one channel fails, it automatically switches to the other channel; the judgment logic for valid change data is that the data format conforms to the preset standard, the data content matches the current operating status of the device, and the data source is traceable.

[0025] The calculation formula for prioritizing emergency data is: Emergency Data Priority = Emergency Importance of Data-Associated Device + Urgency of Data Change. The emergency importance of data-associated devices is set according to device type, with different device types corresponding to different importance scores. The urgency of data changes is set according to data type, with different data types corresponding to different urgency scores. Wired channels use fiber optics; wireless channels use 4G / 5G. The bandwidth of wired and wireless channels is set according to data transmission requirements. Channel failure is determined by a packet loss rate reaching a proportion that indicates a channel anomaly. Channel switching time meets real-time transmission requirements. The preset data format is JSON, including fields such as device ID, acquisition time, parameter name, parameter value, and reliability level. An example of data content matching the current operating status of the device is that the emergency generator's operating speed is within the normal operating range, and the temporary line's current is within the reasonable fluctuation range of the rated current. The basis for determining data source traceability is the inclusion of the acquisition device ID and signature information. The signature information is generated using the SHA1 encryption algorithm. The bandwidth allocation of redundant channels is set according to the scenario. In emergency update mode, wired channels prioritize transmitting emergency data; in normal mode, wired channels transmit full data, and wireless channels transmit incremental data. As an optional embodiment, step S7 is also included: For accuracy verification in different scenarios, a scenario-based verification standard is adopted; the verification cycle is synchronized with the data acquisition cycle; for location accuracy verification in complex terrain scenarios, the deviation between the device location and the actual GPS measurement data meets the preset complex terrain deviation requirements; for high-density urban scenarios, the deviation value needs to be corrected in conjunction with the building obstruction coefficient; for topology accuracy verification in emergency scenarios, the topology matching degree verification adopts core line priority verification, first verifying the topology connection relationship of the core power supply line, and then verifying the ordinary lines, and the topology matching degree of the core line meets the preset emergency topology matching degree requirements; for parameter accuracy verification, for parameters with high real-time requirements, the parameter error rate meets the preset real-time parameter error rate requirements, and for static parameters, the parameter error rate meets the preset static parameter error rate requirements.

[0026] If the verification result of any dimension fails to meet the preset requirements, return to step S4 to re-optimize the mapping relationship of the equipment dot matrix unit; if the position deviation is found, adjust the terrain adaptation coefficient in the equipment dot matrix unit mapping algorithm; if the topology deviation is found, recalculate the equipment priority weight; if the parameter deviation is found, return to step S3 to re-standardize the data; the verification result generates an accuracy verification report, which includes the deviation values ​​of each dimension, the pass rate, and optimization suggestions. The verification cycle is set according to the equipment type, with shorter verification cycles for core equipment and longer verification cycles for ordinary equipment; the positioning accuracy of the on-site GPS measured data meets the requirements for high-precision positioning; the preset complex terrain deviation is set based on terrain complexity, with a higher deviation threshold for mountainous terrain than for hilly terrain, and higher for hilly terrain than for plains; the building occlusion coefficient is calculated as occlusion coefficient = 1 (building height / horizontal distance between equipment and building); the conditions for the building height and horizontal distance between equipment and building are that the building height is greater than the equipment height and the horizontal distance is less than the distance that can cause significant occlusion, in which case the occlusion coefficient takes a specific range of values; the deviation correction formula is corrected deviation value = actual deviation value × occlusion coefficient; the topology connection relationship of the core power supply line is confirmed through on-site manual inspection; the preset emergency topology matching degree is set according to the disaster level, with higher matching degree requirements for more severe disasters; parameters with high real-time requirements include Current, voltage, and power; static parameters include equipment model, rated power, and installation location; preset real-time parameter error rate and preset static parameter error rate have different requirements depending on the parameter type, with stricter requirements for real-time parameter error rate and slightly more lenient requirements for static parameter error rate; the parameter error rate is calculated as: Error Rate = |(Graphical Display Value / On-site Measured Value) / On-site Measured Value| × 100%; the adjustment range of the terrain adaptation coefficient is a reasonable proportion that increases the adaptation coefficient for complex terrain to improve matching accuracy; the adjustment range of the equipment priority weight is a reasonable proportion that increases the weight of core line equipment to improve topology accuracy; the parameter deviation is adjusted by increasing the number of parameter verifications; the pass standard for the accuracy verification report is that the pass rate of location accuracy, topology accuracy, and parameter accuracy meet the basic requirements of distribution network operation and maintenance for mapping accuracy, and if any pass rate fails to meet the standard, the corresponding step is triggered to be re-executed.

[0027] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of this template.

[0028] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A power distribution network intelligent mapping method based on dot matrix layout and dynamic weight, characterized in that, The method comprises the following steps: S1: Constructing a dot-matrix layout system corresponding to the power distribution network; the dot-matrix layout system is based on geographic coordinates of a region where the power distribution network is located, and is formed by regionally grid-terrain factor correction equipment density feedback three-level calculation division to form a plurality of uniformly distributed dot-matrix units; Specifically, after preliminary division by grid, the length is corrected in combination with the terrain factor, and then adjusted iteratively according to the equipment density; each dot-matrix unit corresponds to a unique coordinate identifier, and the dot-matrix unit boundary is adaptively smoothed to avoid terrain obstacles; the adaptive smoothing is based on GIS vector terrain data, and the boundary is fitted by a Bezier curve; S2: Collecting basic data of the power distribution network; the basic data at least includes distribution network equipment parameter data, equipment inter-topological connection data and equipment geographic position data; during the collection process, a data traceability label is established, and each data entry is associated with a collection equipment ID, a collection time, a data credibility level; S3: Preprocessing the collected basic data; the preprocessing includes data cleaning, data standardization and abnormal data elimination; After preprocessing, a data quality report is generated, including data integrity rate, abnormal elimination rate and standardization qualification rate; S4: Based on the preprocessed basic data and the dot-matrix layout system, the corresponding positions of each distribution network equipment in the dot-matrix layout are determined by an improved topological relationship identification algorithm, and a mapping relationship of the equipment dot-matrix unit is established; The improved topological relationship identification algorithm is a minimum spanning tree algorithm fused with dynamic weights of equipment priority; the equipment priority weight is dynamically adjusted according to the real-time fault risk of the equipment, and the real-time fault risk of the equipment is calculated based on equipment operating parameters; S5: According to the mapping relationship and the topological connection data between the equipment, an intelligent mapping engine is called to generate a visual graph of the power distribution network; the visual graph at least includes an equipment distribution layer, a topological connection layer and a dot-matrix coordinate annotation layer; The intelligent mapping engine has a scenario-based rendering template, which can automatically switch rendering rules according to the distribution network scenario; the distribution network scenario includes normal operation and maintenance, emergency repair and fault troubleshooting.

2. The power grid intelligent mapping method based on dot matrix layout and dynamic weight according to claim 1, characterized in that, In step S1, the terrain obstacles include rivers, mountains and large buildings; the specific logic of the regionally grid-terrain factor correction calculation is to first calculate the initial length, and then correct the initial length in combination with the terrain factor.

3. The power grid intelligent mapping method based on dot matrix layout and dynamic weight according to claim 1, characterized in that, In step S2, the distribution network equipment parameter data includes equipment model, rated parameter and operating state; the rated parameter includes rated voltage, rated current and rated power; the operating state includes operation, shutdown and maintenance; the topological connection data between the equipment includes conductor parameter, connection node and line direction; The conductor parameter includes conductor model, cross-sectional area and impedance; the connection node includes node number and node type; The line direction includes direction angle and passing region; the equipment geographic position data includes latitude and longitude and installation elevation; For the basic data collection of high-density urban distribution network, a multi-source data fusion collection mode is also adopted. The high-density urban distribution network is characterized by dense equipment and serious building obstruction. In addition to collecting real-time equipment parameter data through the distribution network SCADA system and updating equipment geographic location data through the GIS system, the appearance of the equipment and the surrounding environment data are obtained through unmanned aerial vehicle aerial photography, and the position data of the buried line are obtained through the underground pipeline detector. When manually supplementing and recording data, the unmanned aerial vehicle aerial image and the underground pipeline detection data need to be double-checked. The checking logic is that the deviation of the manually entered equipment position data and the equipment position identified by the unmanned aerial vehicle aerial image meets the preset topographic adaptation deviation requirement, and the deviation of the line direction of the underground pipeline detection data meets the corresponding angle requirement, so as to be included in the basic database. The collection period is dynamically adjusted according to the type of equipment.

4. The method of claim 1, wherein, In step S3, the data cleaning removes duplicate data and repairs missing data through device ID uniqueness verification and parameter logic consistency verification. The device ID uniqueness verification removes duplicate entries by comparing the unique codes of the devices. The parameter logic consistency verification is based on the judgment of the rated parameter range of the device. If the parameter exceeds the rated range, it is marked as to be repaired and repaired by interpolation of adjacent period data. The data standardization unifies the units of device parameters to international standard units, unifies the coordinate system of geographic location data to WGS84 coordinate system, and converts the installation elevation of the device to absolute elevation through an elevation reference conversion formula. The abnormal data elimination identifies abnormal data through the 3σ criterion and device operation state correlation analysis. After the mean μ and standard deviation σ of the parameter data are calculated, the data outside the range [μ3σ, μ+3σ] are removed, and then secondary screening is performed in combination with the real-time operation state of the device. The elevation correction of the data standardization also includes obtaining the reference elevation of the position of the device through elevation interpolation calculation in combination with regional digital elevation model data, and then correcting the installation height of the device through a device installation height correction formula. The preprocessed basic data is encrypted and stored by using the blockchain technology. The alliance chain architecture is adopted, and the hash value of each data block is calculated and generated by the device unique identifier, the collection timestamp, and the data content.

5. The method of claim 1, wherein, In step S4, the device priority weight is calculated by a device importance evaluation model. The input parameters of the device importance evaluation model include the power supply radiation range, the fault influence coefficient, and the operation life. The power supply radiation range is reflected by the user or regional range covered by the power supply of the device. The fault influence coefficient is reflected by the influence degree of the device fault on the distribution network. The operation life is reflected by the running time of the device. The specific execution logic of the improved topological relationship identification algorithm is to construct a device topological correlation graph through the Kruskal algorithm, calculate the initial weight of each topological path, and then adjust the priority of the topological connection path in combination with the dynamic weight of the device priority. In determining the corresponding position of the device in the dot matrix layout, the mapping unit for large distribution network equipment also adopts hierarchical mapping + topology index logic; the large distribution network equipment includes substations and large switch stations; after calculating the circumscribed rectangle of the device geometric contour, the range of the covered dot matrix unit is determined according to the proportional relationship between the length and width of the circumscribed rectangle and the length of the dot matrix unit; then the device is divided into a core functional area and an auxiliary functional area, the core functional area corresponds to one core dot matrix unit, and the auxiliary functional area corresponds to multiple associated dot matrix units; The screening of the associated dot matrix unit is determined by topology distance calculation; after the mapping relationship is established, the device position conflict detection is required; the topology association index is established between the core dot matrix unit and the associated dot matrix unit.

6. The method of claim 1, wherein, In step S5, the device distribution layer labels the device type and the running state; the device type is distinguished by icons; the running state is distinguished by colors; the topology connection layer labels the line parameters and the connection relationship; the line parameters include the conductor type and the voltage grade; The connection relationship is distinguished by line shapes; the dot matrix coordinate labeling layer labels the dot matrix unit ID and the corresponding geographic area; For the visualization of the emergency power supply scene, the intelligent mapping engine also adopts fast mapping optimization logic; the emergency power supply scene includes temporary distribution network after natural disasters; the temporary access emergency equipment is configured with a special graphic identifier, and the identifier size is different from that of the conventional equipment; the emergency equipment includes emergency generators and temporary lines; when generating the graph, the core power supply line is preferentially rendered, and the rendering priority is calculated by the emergency power supply importance; the display content of the layer can be dynamically adjusted according to the user authority; different authority users view different ranges of layer information; For the visualization of the emergency power supply scene, the intelligent mapping engine also adopts fast mapping optimization logic; the emergency power supply scene includes temporary distribution network after natural disasters; the temporary access emergency equipment is configured with a special graphic identifier, and the identifier size is different from that of the conventional equipment; the emergency equipment includes emergency generators and temporary lines; when generating the graph, the core power supply line is preferentially rendered, and the rendering priority is calculated by the emergency power supply importance; the display content of the layer can be dynamically adjusted according to the user authority; different authority users view different ranges of layer information; For the visualization of the emergency power supply scene, the intelligent mapping engine also adopts fast mapping optimization logic; the emergency power supply scene includes temporary distribution network after natural disasters; the temporary access emergency equipment is configured with a special graphic identifier, and the identifier size is different from that of the conventional equipment; the emergency equipment includes emergency generators and temporary lines; when generating the graph, the core power supply line is preferentially rendered, and the rendering priority is calculated by the emergency power supply importance; the display content of the layer can be dynamically adjusted according to the user authority; different authority users view different ranges of layer information; 7. The method of claim 1, wherein: The interactive results are displayed in the form of graphic annotation + data table. In step S1, for the construction of the dot matrix layout system of complex terrain, it also includes adopting terrain partition division, partition edge length adaptation iteration verification calculation logic; the complex terrain includes mountainous areas and multiple river crossing areas; The region is divided into mountain sub-regions, plain sub-regions and water sub-regions through GIS terrain data; the GIS terrain data includes slope, river and elevation vector information; the mountain sub-region adjusts the edge length based on the slope value; the water sub-region only divides the dot matrix unit corresponding to the line crossing the water area, and the edge length has a proportional relationship with the edge length of the adjacent plain sub-region dot matrix unit; after the edge length is adjusted, the device dot matrix unit matching degree verification is performed; 8. The method of claim 1, wherein, If the matching degree does not meet the preset adaptation requirement, the edge length is re-iterated and adjusted. It also includes step S6: dynamic update of the visualization of the emergency distribution network scene; an emergency priority update mechanism is adopted; The emergency distribution network scene includes temporary distribution network after natural disasters; in addition to the double dynamic update of the trigger type + timing type, the emergency update mode is triggered when a natural disaster occurs; The data change monitoring frequency is adjusted at this time; the edge computing node preferentially processes data changes of emergency equipment; and the edge computing node is deployed in a distribution network area substation and an emergency command vehicle. A local pre-processing incremental uploading mode is adopted; and a core line preferential updating logic is adopted during incremental updating. The response time of point array units and associated topologies corresponding to the core line meets the preset emergency response time requirement; and the non-core line updating response time can be appropriately extended. Timed full updating is automatically suspended in an emergency scenario; until the emergency state is released; and the emergency state is released according to relevant notifications issued by an emergency management department.

9. The power grid intelligent mapping method based on dot matrix layout and dynamic weight according to claim 8, characterized in that, In step S6, the cooperative capture logic of the data change monitoring module and the edge computing node further includes that, when the edge computing node performs local pre-processing on the collected change data, an emergency data priority is adopted. The data priority is calculated based on the emergency importance of the data associated equipment and the emergency degree of the data change. Data with high priority is preferentially uploaded. A redundant channel design is adopted for the bidirectional data interaction interface of the data change monitoring module and the distribution network SCADA system and the GIS system. A wired channel and a wireless channel are simultaneously established. When one of the channels fails, the system automatically switches to the other channel. The judgment logic for valid change data includes that the data format meets the preset standard, the data content matches the current operating state of the equipment, and the data source is traceable.

10. The method of claim 1, wherein, In step S7, scene-based verification standards are adopted for precision verification in different scenarios; and the verification period is synchronized with the data collection period. In a complex terrain scenario, the position accuracy verification is that the deviation value of the equipment position and the field GPS measured data meets the preset complex terrain deviation requirement; and in a high-density urban area scenario, the deviation value needs to be corrected in combination with a building shielding coefficient. In an emergency scenario, the topology accuracy verification is that the topology matching degree verification adopts a core line preferential verification, the topology connection relationship of the core power supply line is verified first, and then the ordinary line is verified; and the topology matching degree of the core line meets the preset emergency topology matching degree requirement. The parameter accuracy verification is that, for real-time parameters, the parameter error rate meets the preset real-time parameter error rate requirement; and for static parameters, the parameter error rate meets the preset static parameter error rate requirement.

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