Low-voltage transformer area treatment system and method

Abstract

The invention discloses a low-voltage transformer area governance system and method, and relates to the technical field of electric power transformer area governance, a data acquisition module comprehensively obtains five types of data of electricity, equipment, electricity consumption behaviors, weather and maps, and generates a multi-dimensional data set; the data processing module sorts and classifies the multi-dimensional data, calculates core indexes such as a line loss rate and a load increase trend index, and generates an associated data set; the risk reminding module constructs a risk matching rule base, monitors data in real time, automatically triggers risk reminding and governance decision matching, and generates a risk reminding data set; the visual display module integrates the three types of data sets, and visually presents and generates an initial governance interface in multiple forms; the digital twinborn model construction module constructs a target three-dimensional model based on multi-source data, and updates an initial governance interface to generate a target governance interface with a dynamic interaction function. The method achieves the complete-process closed-loop management of the transformer area data, improves the management intelligence and precision level, guarantees the safe and stable operation of the transformer area, and improves the decision scientificity and efficiency.

Description

Technical Field

[0001] This invention relates to the field of power distribution area management technology, and in particular to a low-voltage distribution area management system and method. Background Technology

[0002] With the continuous advancement of smart grid construction, low-voltage distribution areas, as a crucial link in power supply, directly impact power quality and user experience through the sophistication and intelligence of their management. Traditional low-voltage distribution area management relies on manual inspections and simple data statistics, which has many drawbacks: manual inspections are inefficient and have limited frequency, making it difficult to detect equipment anomalies in real time; and data statistics lack in-depth analysis, failing to uncover potential risks.

[0003] Currently, although some power management systems can collect data from low-voltage distribution areas, the data collection dimensions are limited, the data integration capabilities are insufficient, and the visualization display is simple, making it difficult to intuitively present the overall operating status of the distribution area. Summary of the Invention

[0004] This invention provides a low-voltage distribution area management system and method, which solves the technical problems of existing low-voltage distribution area management technologies lacking the ability to deeply integrate and intelligently analyze multi-dimensional data, having weak perception of operational status, and resulting in poor management effects.

[0005] The first aspect of this invention provides a low-voltage distribution area management system, comprising:

[0006] The data acquisition module is used to acquire electrical data, equipment data, electricity consumption behavior data, meteorological data, and map data of the low-voltage distribution area, and generate multidimensional datasets.

[0007] The data processing module is used to sort and classify the data in the multidimensional dataset, and calculate the line loss rate and load growth trend index based on the sorted and classified data to generate a related dataset;

[0008] The risk alert module is used to build a risk matching rule base, and when the data in the associated dataset exceeds the normal range defined by the risk matching rule base, it triggers a risk alert and automatically matches governance decisions to generate a risk alert dataset.

[0009] The visualization module is used to integrate and display the multidimensional dataset, the associated dataset, and the risk alert dataset to generate an initial governance interface;

[0010] The digital twin model construction module is used to construct a target 3D model based on the device data, the map data, the multidimensional dataset, and the associated dataset, and to update the initial governance interface using the target 3D model to generate the target governance interface.

[0011] Optionally, the data acquisition module includes:

[0012] The electrical data acquisition unit is used to collect electrical data, including voltage, current, and power, from low-voltage distribution areas via smart meters.

[0013] The equipment data acquisition unit is used to collect equipment status data in the low-voltage distribution area through status sensors, and retrieve static data including equipment volume and wiring diagram to construct equipment data.

[0014] The electricity consumption behavior data acquisition unit is used to collect electricity consumption behavior data, including electricity consumption time and electricity consumption changes, in the low-voltage distribution area through smart meters and user electricity terminals.

[0015] The meteorological data acquisition unit is used to collect meteorological data, including temperature, humidity, wind speed, and rainfall, in the low-pressure area.

[0016] The map data collection unit is used to collect map data of the low-voltage transformer area through GIS system and drone survey.

[0017] A data integration interface is used to construct a multidimensional dataset by using the electrical data, the equipment data, the electricity consumption behavior data, the meteorological data, and the map data.

[0018] Optionally, the data processing module performs the following steps:

[0019] The electrical data, equipment data, and electricity consumption behavior data in the multidimensional dataset are associated, sorted, and classified according to a preset priority to generate a categorized dataset with dimension labels.

[0020] Extract the total power supply on the low-voltage side of the transformer corresponding to the low-voltage distribution area and the total electricity sales of users in the distribution area, and calculate the line loss rate under each dimension label of the classification dataset according to the preset line loss rate calculation formula to generate line loss rate data;

[0021] Based on the dimensionally labeled classification dataset, load-related data corresponding to the low-voltage transformer area are extracted;

[0022] Select a preset number of historical statistical periods, and filter out the maximum load value within each historical statistical period to generate a historical period maximum load dataset;

[0023] Calculate the load growth rate between two adjacent historical statistical periods in the maximum load data of the historical period, and take the arithmetic mean of the load growth rate of all periods to obtain the average load growth rate;

[0024] Calculate the product between the average load growth rate and the preset load fluctuation coefficient, and standardize it into an index within a preset numerical range to generate a load growth trend index;

[0025] The line loss rate data, the load growth trend index, and the classification dataset are linked and integrated to generate a linked dataset.

[0026] Optionally, the risk alert module performs the following steps:

[0027] A risk knowledge dataset is constructed using risk handling regulations and case data corresponding to the low-voltage distribution area.

[0028] Data for judging the status of equipment and distribution areas is extracted from the risk knowledge dataset and divided into normal value range data and governance decision data to generate two types of basic rule data.

[0029] Based on the two types of basic rule data, a mapping relationship between data types, abnormal numerical ranges and governance decisions is established to generate a risk matching rule base.

[0030] The associated dataset is monitored in real time, and the data in the associated dataset is compared with the normal value range data in the risk matching rule base to generate data comparison results;

[0031] When the data comparison result shows that the data in the associated dataset exceeds the corresponding normal value range, the risk matching mechanism is triggered;

[0032] In response to the risk matching mechanism, the corresponding governance decision is automatically matched from the risk matching rule base to generate a risk governance plan;

[0033] The data exceeding the normal value range and the risk management solution are encapsulated, and a risk alarm command is triggered to generate a risk alert dataset.

[0034] Optionally, the visualization module performs the following steps:

[0035] The data in the associated dataset is rendered graphically to generate data visualization charts;

[0036] Based on the device connection relationships in the device data, different types of devices are labeled with icons of different shapes, and a topology diagram of the entire low-voltage distribution area is drawn.

[0037] Based on the risk status information in the risk alert dataset, a first color identifier is configured for device icons associated with risks and a second color identifier is configured for device icons not associated with risks in the topology diagram, thereby generating a topology status view.

[0038] The map data is overlaid with the associated dataset and the risk warning dataset, and the geographical location of the low-voltage transformer area is marked to generate an initial geographical view of the transformer area;

[0039] Based on the risk status information in the risk alert dataset, a third color identifier is configured for the low-voltage transformer icons associated with risks in the initial transformer geographic view, and a fourth color identifier is configured for the low-voltage transformer icons not associated with risks, thereby generating a target transformer geographic view.

[0040] Configure pop-up interactive functions for the color-coded icons in the topology status view and the target area geographic view. When the user triggers the pop-up, the page will display the corresponding detailed data and related governance decisions in the risk alert dataset.

[0041] Configure menu controls to allow users to select display content for different time ranges and switch between different visualization pages, and configure graphic zoom controls and data file export functions for the data visualization charts, the topology status view and the target area geographic view to generate the initial governance interface.

[0042] Optionally, the digital twin model building module performs the following steps:

[0043] A three-dimensional device model is constructed using the device volume and connection relationship data from the device data.

[0044] Using the dynamic meteorological effects of the map data and the meteorological data in the multidimensional dataset, a three-dimensional environment model including buildings, vegetation, and terrain is constructed.

[0045] The three-dimensional device model and the three-dimensional environment model are combined and spatially aligned to generate an initial three-dimensional model;

[0046] The electrical data in the multidimensional dataset and the associated dataset are mapped to the corresponding components in the initial 3D model. The data status is presented by the color and shape changes of the model components. Data pop-up interactive functions are configured for each component of the model to generate the target 3D model.

[0047] The target 3D model is integrated into the initial governance interface, replacing the static topology diagram as the core visualization carrier, and a dynamic data link is established between the target 3D model and the associated dataset.

[0048] Based on the dynamic data link, a model simulation interface is provided in the initial governance interface. The model simulation interface receives data modification instructions or device operation instructions input by the user through the front-end interactive panel.

[0049] In response to the data modification command or device operation command, the target 3D model is driven by the dynamic data link to dynamically update its shape and data status, and simultaneously trigger the corresponding update of the associated dataset to generate the target governance interface.

[0050] A second aspect of the present invention provides a method for managing low-voltage distribution areas, comprising:

[0051] Acquire electrical data, equipment data, electricity consumption behavior data, meteorological data, and map data from low-voltage distribution areas to generate a multidimensional dataset;

[0052] The data in the multidimensional dataset are sorted and classified, and the line loss rate and load growth trend index are calculated based on the sorted and classified data to generate an associated dataset;

[0053] A risk matching rule base is constructed, and when the data in the associated dataset exceeds the normal range defined by the risk matching rule base, a risk alert is triggered and a governance decision is automatically matched to generate a risk alert dataset.

[0054] The multidimensional dataset, the associated dataset, and the risk alert dataset are integrated and displayed to generate an initial governance interface;

[0055] A target 3D model is constructed based on the device data, the map data, the multidimensional dataset, and the associated dataset, and the initial governance interface is updated using the target 3D model to generate the target governance interface.

[0056] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the low-voltage distribution area management method described above.

[0057] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the low-voltage distribution area management method as described above.

[0058] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the low-voltage distribution area management method as described above.

[0059] As can be seen from the above technical solutions, the present invention has the following advantages:

[0060] This invention provides a low-voltage distribution area management system and method to address the technical problems of existing low-voltage distribution area management technologies, which lack deep integration and intelligent analysis capabilities for multi-dimensional data, have weak awareness of operational status, and thus result in poor management effectiveness. Traditional low-voltage distribution area management technologies suffer from data silos due to the dispersion of data across various monitoring devices and systems, lacking a unified collection and integration mechanism. This leads to incomplete data support and insufficient timeliness. In contrast, the data acquisition module of this invention deploys multiple acquisition terminals, such as smart meters, status sensors, and meteorological monitoring equipment, and establishes data interfaces with GIS systems and UAV survey platforms. It employs the MQTT protocol to ensure transmission stability, comprehensively collects five core data categories—electrical, equipment, electricity consumption behavior, meteorological, and map data—and integrates them to generate a multi-dimensional dataset. This completely breaks down information barriers, providing a precise, comprehensive, and real-time data foundation for subsequent management processes. Traditional technologies lack effective means to process raw data, resulting in low data value conversion rates and difficulty in supporting scientific decision-making. In contrast, the data processing module of this invention sorts and classifies multi-dimensional data according to time, distribution area, and equipment dimensions. Through standardized algorithms, it calculates core indicators such as line loss rate and load growth trend index, transforming scattered raw data into structured, valuable, and correlated datasets. This significantly improves data utilization efficiency and provides quantitative evidence for risk assessment. Traditional technologies often rely on manual inspections to identify risks, leading to delayed responses and a lack of targeted solutions. In contrast, the risk alert module of this invention extracts rules from industry regulations and maintenance cases to construct a risk matching rule base. By comparing correlated monitoring data with the rule base in real time, it automatically triggers risk alerts and matches governance decisions, significantly shortening risk response time, reducing failure rates, and ensuring the safe and stable operation of distribution areas. Traditional visualization methods often present static data listings, making it difficult to intuitively present complex operational situations and risk distributions. In contrast, the visualization module of this invention integrates and displays three types of datasets in multiple forms, including dynamic curves, topology maps, and geographic views. Combined with pop-up interactions, menu switching, zooming, and export functions, it allows users to quickly grasp the operational status and risk situation of distribution areas, improving decision-making efficiency and user experience. Traditional management techniques lack dynamic simulation and predictive capabilities, resulting in insufficiently targeted governance measures. In contrast, the digital twin model construction module of this invention builds three-dimensional equipment and environment models based on multi-source data, achieving dynamic mapping between data and models. It supports users in modifying data and simulating equipment operations through an interactive panel, driving real-time model updates and data synchronization, thus assisting users in conducting refined situational assessments and optimizing solutions. The five modules work in synergy, achieving closed-loop management of the entire process from data collection, processing, risk identification to visualization and dynamic simulation. This significantly improves the intelligence and precision of low-voltage distribution area governance, effectively addressing the pain points of weak situational awareness, delayed decision-making, and inefficient governance inherent in traditional technologies. It perfectly meets the power industry's needs for safe and stable operation and efficient governance of distribution areas, greatly improving the quality and reliability of distribution area management. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 A structural block diagram of a low-voltage distribution area management system provided in an embodiment of the present invention;

[0063] Figure 2 A flowchart illustrating the steps of a low-voltage distribution area management method provided in this embodiment of the invention;

[0064] Figure 3 This is a structural block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0065] This invention provides a low-voltage distribution area management system and method to address the technical problem that existing low-voltage distribution area management technologies lack the ability to deeply integrate and intelligently analyze multi-dimensional data, resulting in weak perception of operational status and poor management effectiveness.

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] It should be noted that, in the optional embodiments of the present invention, the data related to object information, etc., requires the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies. Furthermore, the collection, use, and processing of the relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of the present invention involve data related to an object, it needs to be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0068] Please see Figure 1 , Figure 1 This is a structural block diagram of a low-voltage distribution area management system provided in an embodiment of the present invention.

[0069] The present invention provides a low-voltage distribution area management system, comprising:

[0070] The data acquisition module 101 is used to acquire electrical data, equipment data, electricity consumption behavior data, meteorological data and map data of the low-voltage distribution area, and generate a multidimensional dataset.

[0071] Furthermore, the data acquisition module 101 includes:

[0072] The electrical data acquisition unit is used to collect electrical data, including voltage, current, and power, from low-voltage distribution areas via smart meters.

[0073] In this embodiment of the invention, the electrical data acquisition unit refers to a functional unit specifically designed to collect core electrical operation parameters of low-voltage distribution areas, providing fundamental data support for subsequent line loss calculation and load analysis. Its core acquisition device is a smart meter, which possesses real-time data acquisition, local storage, and remote data transmission capabilities. The acquisition targets are strictly focused on key electrical parameters that best reflect the power supply quality and energy consumption status during the operation of low-voltage distribution areas. Specifically, the electrical data acquisition unit continuously collects three types of core electrical data from the low-voltage side of the transformer, the main line, and key user access nodes within the distribution area through the smart meter at a preset sampling frequency (e.g., 15 minutes / time, which can be flexibly adjusted according to the management needs of the distribution area):

[0074] Voltage data (unit: V): Collects instantaneous and effective values ​​of three-phase voltage to accurately reflect the stability of the power supply voltage in the transformer area, providing a quantitative basis for subsequent judgment of power supply quality problems such as low voltage or excessive voltage fluctuation;

[0075] Current data (unit: A): Collects the operating current values ​​of each line and key equipment, which directly supports the calculation of line loss and the assessment of equipment load status. It is the core data for determining whether the equipment is overloaded.

[0076] Power data (unit: kW): includes two types of parameters: active power and reactive power. Active power is the core basis for calculating power consumption (such as the total power supply on the low-voltage side of the transformer is obtained based on the integration of active power), while reactive power is used to evaluate the power supply efficiency of the distribution area and the power factor optimization requirements.

[0077] After data acquisition is completed, the electrical data acquisition unit transmits the uniformly formatted electrical data to the data integration interface through standardized communication methods such as power line carrier (PLC) and NB-IoT wireless private network, ensuring that the subsequent data processing module can directly extract the data for line loss rate calculation and load growth trend analysis.

[0078] The equipment data acquisition unit is used to collect equipment status data in the low-voltage distribution area through status sensors, and retrieve static data including equipment size and wiring diagram to construct equipment data.

[0079] In this embodiment of the invention, the equipment data acquisition unit refers to a functional unit that integrates dynamic status monitoring and static parameter retrieval to comprehensively acquire all-dimensional information about low-voltage distribution equipment. The data it collects is clearly divided into two categories: equipment status data and static data, which respectively support equipment operation monitoring and 3D model construction. The specific implementation process of the equipment data acquisition unit is as follows:

[0080] Equipment status data acquisition: The equipment data acquisition unit collects dynamic data in real time during equipment operation through status sensors (such as temperature sensors, vibration sensors, and insulation monitoring sensors) deployed on key equipment such as transformers, high-voltage switches, and cable joints. This data includes equipment casing temperature, operating vibration amplitude, insulation resistance value, etc., directly reflecting the real-time health status of the equipment and providing a basis for the subsequent risk warning module to determine whether the equipment has risks such as overload, aging, and insulation deterioration.

[0081] Static data retrieval: The equipment data acquisition unit connects to the equipment file management system and maintenance history database in the distribution area to retrieve static data from the equipment's factory parameters and maintenance records. This data includes equipment volume (providing dimensional basis for building a 3D equipment model for digital twin modeling), equipment wiring diagram (supporting the drawing of topology diagrams and confirmation of equipment connection relationships), equipment rated power, manufacturing date, and historical maintenance records.

[0082] After the two types of data are collected, the equipment data acquisition unit aggregates the data to the data integration interface through the MQTT communication protocol. The dynamic equipment status data supports equipment risk identification and operational status assessment, while the static data supports 3D modeling and topology analysis, together forming a complete data system for the full life cycle management of equipment.

[0083] The electricity consumption behavior data acquisition unit is used to collect electricity consumption behavior data, including electricity consumption time and electricity consumption changes, in low-voltage distribution areas through smart meters and user electricity terminals.

[0084] In this embodiment of the invention, the electricity consumption behavior data acquisition unit refers to a functional unit that collects users' electricity consumption habits and load change patterns to provide core data support for load growth trend prediction. Its core implementation method involves collaborative data collection between smart meters and user electricity terminals to ensure a refined depiction of users' electricity consumption behavior. Specifically, in implementation:

[0085] Smart meters serve as the core data collection device: the electricity consumption behavior data collection unit collects users' electricity consumption (unit: kWh) and electricity consumption change rate through smart meters at preset cycles (such as 1 hour / time), accurately records users' daily and weekly total electricity consumption and time period distribution characteristics, and forms basic electricity consumption behavior data;

[0086] User electricity terminals serve as supplementary data collection devices: the electricity behavior data collection unit collects user-specific electricity usage scenario data through smart sockets, smart home control devices, etc. deployed on the user side. This includes electricity usage time (such as the distribution of electricity usage duration during peak and off-peak hours) and changes in electricity consumption (such as load fluctuations caused by the start and stop of high-power equipment), thereby achieving a deep breakdown of user electricity usage behavior.

[0087] The collected data is processed by the electricity consumption behavior data acquisition unit to standardize the format (e.g., unify the timestamp format to "YYYY-MM-DD HH:MM:SS" and normalize the electricity consumption unit) before being transmitted to the data integration interface. This data provides key basis for calculating the load growth trend index by analyzing the time pattern and load fluctuation characteristics of users' electricity consumption behavior. At the same time, it assists the risk warning module in judging whether there are abnormal electricity consumption behaviors such as electricity theft and illegal electricity use.

[0088] The meteorological data acquisition unit is used to collect meteorological data, including temperature, humidity, wind speed, and rainfall, in the low-pressure area.

[0089] In this embodiment of the invention, the meteorological data acquisition unit refers to a functional unit specifically designed to collect meteorological environmental parameters of the area where the low-pressure transformer station is located, supporting meteorological risk assessment and dynamic visualization. The meteorological data it collects must accurately reflect the direct impact of environmental factors on the operation and load changes of equipment in the transformer station area. In specific implementation, the meteorological data acquisition unit collects the following four types of key meteorological data at a preset frequency (1 hour / time in normal scenarios, which can be increased to 10 minutes / time in special weather conditions such as heavy rain and typhoons) through small automatic weather stations and portable meteorological sensors deployed at preset locations within or around the transformer station area:

[0090] Temperature (unit: °C): Real-time reflection of ambient temperature, used to determine whether high temperature leads to poor heat dissipation or overload operation of equipment, or whether low temperature affects the insulation performance of the line;

[0091] Humidity (unit: %RH): Records the degree of air humidity to provide data support for assessing the risk of equipment moisture damage and potential short circuit faults caused by condensation on lines;

[0092] Wind speed (unit: m / s): Monitors ambient wind speed to determine whether strong winds cause line swaying, foreign objects to become entangled, or the fixed structure of outdoor equipment to become loose;

[0093] Rainfall (unit: mm): Rainfall during the statistical period to assess the risk of outdoor equipment being submerged by heavy rain and the impact of topographic water accumulation on the power supply facilities in the transformer area.

[0094] After being processed by the meteorological data acquisition unit, the collected data is transmitted to the data integration interface via wireless communication. On the one hand, it provides the risk warning module with the basis for judging the risk of meteorological-equipment operation association (such as the risk of high temperature + high load superposition). On the other hand, it provides the digital twin model construction module with the basic data for dynamic meteorological effect simulation (such as wind speed related to vegetation swaying amplitude, and rainfall related to the raindrop rendering effect on the interface).

[0095] The map data collection unit is used to collect map data of low-voltage transformer areas through GIS systems and drone surveys.

[0096] In this embodiment of the invention, the map data collection unit refers to a functional unit that collects geospatial information of low-voltage transformer areas and provides a spatial basis for 3D environment modeling and geographic visualization. Its core functionality lies in combining GIS system retrieval with UAV reconnaissance to ensure the accuracy and completeness of geographic data. The specific implementation process is as follows:

[0097] GIS system data retrieval: The map data collection unit obtains basic geographic data of the area where the substation is located by connecting to the Geographic Information System (GIS), including topographic elevation data (supporting 3D terrain modeling), road distribution, administrative division boundaries, etc., to provide a basis for determining the overall geographical location of the substation and its relationship with the surrounding environment;

[0098] Unmanned aerial vehicle (UAV) survey and data collection: The map data collection unit uses multi-rotor UAVs to conduct low-altitude aerial photography of the low-voltage transformer area to obtain high-definition real-scene image data (resolution ≥ 0.1m). Through image stitching, spatial coordinate calibration and other preprocessing, detailed geographic information such as the actual location coordinates of equipment in the transformer area, building distribution, line direction, and terrain undulation is extracted to make up for the insufficient data accuracy of the GIS system.

[0099] After the two types of data are collected, the map data collection unit performs fusion processing: the detailed data collected by the drone is spatially aligned with the basic geographic data of GIS based on GPS coordinates, redundant data is removed, missing information is filled in, standardized map data is generated and transmitted to the data integration interface. This data directly supports the construction of the three-dimensional environment model in the digital twin model (such as the restoration of the spatial location of terrain, buildings and equipment), and at the same time provides core spatial data for the visualization module to generate the geographic view of the station area.

[0100] The data integration interface is used to construct multidimensional datasets using electrical data, equipment data, electricity consumption behavior data, meteorological data, and map data.

[0101] In this embodiment of the invention, the data integration interface refers to a functional interface that uniformly processes and integrates the data output by the electrical data acquisition unit, equipment data acquisition unit, electricity consumption behavior data acquisition unit, meteorological data acquisition unit, and map data collection unit to generate a multidimensional dataset with standardized structure and consistent data. Its core function is to solve problems such as heterogeneous formats of multi-source data, asynchronous time, and data redundancy, and to provide a unified and usable data source for subsequent data processing modules, risk warning modules, digital twin model construction modules, etc. The data integration interface performs data integration according to the following steps: First, data format standardization: Electrical data, equipment data, electricity consumption behavior data, meteorological data, and map data are converted into preset JSON formats, and data field naming rules are unified (e.g., timestamp fields are uniformly named "time_stamp") and data units are unified (e.g., power is unified to kW, length to m); Second, data time alignment: Using standard UTC timestamps as a benchmark, time synchronization processing is performed on data collected at different frequencies (e.g., aligning meteorological data at 1 hour / time intervals with electrical data at 15 minutes / time intervals by time node to ensure that multiple types of data under the same time dimension can be correlated and analyzed); Third, data deduplication and completion: Duplicate data is removed using a deduplication algorithm based on data similarity, and missing data is completed using linear interpolation to ensure the integrity of the dataset; Fourth, data association and labeling: Dimensional labels (including time dimension, transformer area identification dimension, and equipment identification dimension) are added to each type of data, establishing association relationships between different types of data (e.g., associating and binding electrical data for a certain time period with meteorological data and equipment status data for the corresponding time period). Ultimately, the data integration interface outputs a multidimensional dataset containing information on low-voltage distribution area operation, environment, geography, and other dimensions. This ensures data consistency and availability while providing a structured data foundation for subsequent modules to extract data as needed and conduct correlation analysis.

[0102] The data processing module 102 is used to sort and classify the data in the multidimensional dataset, and calculate the line loss rate and load growth trend index based on the sorted and classified data to generate a related dataset.

[0103] Furthermore, the data processing module 102 can perform the following steps:

[0104] S11. According to the preset priority, the electrical data, equipment data, and electricity consumption behavior data in the multidimensional dataset are associated, sorted, and classified to generate a categorized dataset with dimension labels.

[0105] In this embodiment of the invention, the preset priority refers to the pre-defined sorting rules for the time dimension, transformer area identifier dimension, and equipment identifier dimension. Specifically, data is first sorted chronologically by data acquisition timestamp, then divided into different transformer area data by transformer area identifier, and finally, associated data for each device is distinguished by device identifier. The specific operation of association sorting and classification is based on the association fields such as timestamp, transformer area code, and device number of each data in the multidimensional dataset. Electrical data, equipment data, and electricity consumption behavior data are cross-type associated. For example, the electrical parameters and operating status data of a certain device in a certain transformer area under the same timestamp are bound together. Then, the data is divided into multiple subsets according to the above priority, and each subset is labeled with corresponding time, transformer area, and device dimension labels, ultimately generating a categorized dataset with dimension labels.

[0106] S12. Extract the total power supply on the low-voltage side of the transformer corresponding to the low-voltage distribution area and the total electricity sales of users in the distribution area, and calculate the line loss rate under each dimension label of the classification dataset according to the preset line loss rate calculation formula to generate line loss rate data.

[0107] In this embodiment of the invention, the total power supply on the low-voltage side of the transformer is extracted from the time and distribution area dimension labels of the classification dataset and calculated based on the active power integral in the electrical data, with the unit being kWh; the total electricity sales to users in the distribution area are aggregated from the electricity consumption behavior data under the same dimension label, and are the cumulative value of electricity consumption for each user, with the unit being kWh. The preset line loss rate calculation formula is: Line loss rate = (Total power supply on the low-voltage side of the transformer - Total electricity sales to users in the distribution area) / Total power supply on the low-voltage side of the transformer In the specific calculation, for each dimension label of the classification dataset, the corresponding line loss rate is calculated by substituting it into the above formula, and finally line loss rate data containing each dimension label and the corresponding line loss rate is generated.

[0108] S13. Based on the dimensionally labeled classification dataset, extract the load-related data corresponding to the low-voltage distribution area.

[0109] In this embodiment of the invention, load-related data refers to core data that reflects the magnitude and variation characteristics of the electrical load in a low-voltage distribution area, including real-time load values, cumulative load values ​​for different time periods, and load fluctuation amplitudes. The real-time load values ​​are obtained by converting power parameters from electrical data, and the unit is kW. The extraction operation is implemented based on the dimensional label association of the categorized dataset. Target distribution area data is filtered by distribution area identifier, and then load-related fields for different statistical periods are extracted by time dimension. Irrelevant data such as equipment static parameters are removed to obtain a subset of load-related data for the target distribution area.

[0110] S14. Select historical statistical periods according to the preset number, and filter out the maximum load value in each historical statistical period to generate a historical period maximum load dataset.

[0111] In this embodiment of the invention, the preset number can be set according to actual forecasting needs, such as 30 or 60, preferably 30 historical statistical periods. The historical statistical period is a preset fixed time unit, such as one statistical period per day, i.e., 00:00 to 24:00 per day. The specific operation process is as follows: First, select the preset number of historical statistical periods from the load-related data in reverse chronological order. Then, for each period, iterate through all real-time load values ​​within that period and filter out the maximum value as the characteristic load data of that period. Finally, organize the correspondence between the historical statistical period identifier and the maximum load value into a structured dataset, i.e., the historical period maximum load dataset. By filtering the maximum load value of each period, the peak characteristics of the transformer area load can be focused on, avoiding the interference of load fluctuations within the period on the judgment of the overall growth trend. This dataset provides core sample data for subsequent calculation of the load growth rate, ensuring that the growth trend analysis can reflect the changing pattern of the load peak.

[0112] S15. Calculate the load growth rate between two adjacent historical statistical periods in the maximum load data of the historical period, and take the arithmetic mean of the load growth rate of all periods to obtain the average load growth rate.

[0113] In this embodiment of the invention, the formula for calculating the load growth rate is: Load growth rate = (Maximum load value of the next cycle - Maximum load value of the previous cycle) / Maximum load value of the previous cycle 100%. In specific calculations, the load growth rate of adjacent periods is calculated sequentially according to the time order of historical statistical periods, such as period 2 and period 1, period 3 and period 2, up to period n and period n-1, to obtain n-1 load growth rate data. Then, the arithmetic mean of all load growth rate data is calculated. The arithmetic mean is the sum of all load growth rates divided by the number of load growth rates, and finally the average load growth rate is obtained.

[0114] S16. Calculate the product between the average load growth rate and the preset load fluctuation coefficient, and standardize it into an index within a preset value range to generate a load growth trend index.

[0115] In this embodiment of the invention, the preset load fluctuation coefficient is a correction coefficient based on the statistical analysis of historical load fluctuation amplitudes in the low-voltage distribution area over the past year. Its value ranges from 0.8 to 1.2; the larger the load fluctuation, the closer the coefficient is to 0.8, and the smaller the fluctuation, the closer it is to 1.2. This coefficient is used to correct the average load growth rate, making it more closely reflect the actual load change pattern. The preset numerical range is a standardized range for easy risk assessment, such as 0 to 10; a higher value indicates a more significant load growth trend. The specific operation process is as follows: First, calculate the corrected load growth data, i.e., the average load growth rate multiplied by the preset load fluctuation coefficient; second, use the min-max standardization method to map the corrected load growth data to the preset numerical range. The standardization formula is: Load growth trend index = (corrected load growth data - minimum value) / (maximum value - minimum value) (Preset upper limit - preset lower limit) + preset lower limit, ultimately generating a load growth trend index with values ​​within the preset range. This step transforms abstract growth rate data into an intuitive and comparable quantitative indicator. It improves data reliability through fluctuation coefficient correction and reduces the difficulty of comparing data from different transformer areas through standardization, providing a clear basis for the subsequent risk alert module to determine whether load growth exceeds the safe range.

[0116] S17. Link and integrate the line loss rate data, load growth trend index and classification dataset to generate a linked dataset.

[0117] In this embodiment of the invention, the core of the association integration is the binding operation based on time, transformer area, and equipment dimension labels. The line loss rate data generated in S12 and the load growth trend index generated in S16 are respectively associated with the original data of the corresponding dimension labels in the classification dataset. The original data includes electrical data, equipment data, and electricity consumption behavior data. Two core indicator fields, line loss rate and load growth trend index, are added to form a structured dataset containing original data, core calculated indicators, and dimension labels—that is, the associated dataset. The purpose of this step is to summarize the key results of the data processing stage, achieving unified storage and management of original data and calculated indicators. The subsequent risk alert module can directly extract the corresponding data through the dimension labels for risk assessment, and the visualization module can integrate and display multi-dimensional data based on this dataset, effectively improving the operating efficiency and data usability of subsequent modules.

[0118] The risk alert module 103 is used to build a risk matching rule base, and when the data in the associated dataset exceeds the normal range defined by the risk matching rule base, it triggers a risk alert and automatically matches governance decisions to generate a risk alert dataset.

[0119] Furthermore, the risk alert module 103 can perform the following steps:

[0120] S21. Construct a risk knowledge dataset by adopting risk handling regulations and case data corresponding to low-voltage distribution areas.

[0121] In this embodiment of the invention, based on the application scenarios and operational characteristics of low-voltage distribution areas, corresponding risk handling regulations and case data are selected to construct a risk knowledge dataset. The risk handling regulations are derived from authoritative power industry standards and documents, covering the judgment criteria and handling principles for various risks such as abnormal line losses, overload, and equipment failures. The case data comes from past operation and maintenance records and fault handling archives of low-voltage distribution areas, including specific operational data, impact scope, and successful mitigation measures when risks occur. By constructing a risk knowledge dataset, scattered risk handling criteria can be systematically integrated, avoiding rule bias caused by single data sources. This provides a comprehensive and authoritative data source for subsequent extraction of basic rule data, while reducing data gaps in the subsequent rule construction process, ensuring the accuracy and reliability of risk assessment.

[0122] S22. Extract data from the risk knowledge dataset to determine the status of equipment and distribution areas, and divide it into normal value range data and governance decision data to generate two types of basic rule data.

[0123] In this embodiment of the invention, based on the constructed risk knowledge dataset, key data reflecting the operating status of equipment and distribution areas are extracted in a targeted manner. This data includes both quantitative numerical indicators and qualitative status descriptions. The extracted data is strictly divided into two categories of basic rule data: normal numerical range data, which are parameter thresholds or qualified standards for normal operation of equipment and distribution areas, such as the safe range of line loss rate, the normal range of load growth trend index, and the qualified threshold of equipment operating temperature; and governance decision data, which are specific handling measures corresponding to risk scenarios, such as the line verification process when line loss exceeds the range, the transformer capacity expansion plan when the load is overloaded, and the maintenance steps when equipment is abnormal. By generating two categories of basic rule data through classification, the standards for risk judgment and the basis for risk handling are clarified, providing structured data support for establishing subsequent mapping relationships.

[0124] S23. Based on two types of basic rule data, establish a mapping relationship between data types, abnormal value ranges and governance decisions, and generate a risk matching rule base.

[0125] In this embodiment of the invention, a risk matching rule base is generated by constructing a correlation logic between data type, abnormal value range, and governance decision based on two types of basic rule data. Data type refers to the core indicator type in the associated dataset, such as line loss rate, load growth trend index, and equipment temperature; abnormal value range refers to the specific interval of data exceeding the normal value range, such as a line loss rate greater than 3% or a load growth trend index greater than 8; the mapping relationship is that the abnormal value range under a specific data type corresponds to a unique governance decision, for example, when the line loss rate is between 3% and 5%, it corresponds to the governance decision of line insulation testing and user meter verification. By establishing this precise mapping relationship, scattered rule data is transformed into directly callable structured rules. The generated risk matching rule base supports rapid retrieval and matching, providing efficient rule support for subsequent real-time risk monitoring and automatic decision-making, and significantly improving risk response speed.

[0126] S24. Monitor the associated dataset in real time and compare the data in the associated dataset with the normal value range data in the risk matching rule base to generate data comparison results.

[0127] In this embodiment of the invention, a combination of timed polling and data push is used to continuously monitor the associated dataset in real time. The monitoring cycle can be set according to actual needs, such as once every 5 minutes. Core indicator data corresponding to the risk matching rule base is extracted from the associated dataset. During extraction, association information such as data type, distribution area, and collection time is included to ensure that the data accurately corresponds to the normal value range data in the rule base. The extracted core indicator data is compared one by one with the normal value range data under the corresponding data type in the risk matching rule base to determine whether the indicator data is within the normal range. The comparison results include two types: normal and outside the normal range. For outside the normal range, the specific direction of the deviation must be indicated, such as too high or too low. Through real-time monitoring and comparison, abnormal data in the associated dataset can be detected in a timely manner. The generated structured data comparison results provide a direct basis for subsequent risk assessment, ensuring early identification and warning of risks.

[0128] S25. When the data comparison result shows that the data in the associated dataset exceeds the corresponding normal value range, the risk matching mechanism is triggered.

[0129] In this embodiment of the invention, the data comparison results are verified one by one. When it is confirmed that the data in the associated dataset exceeds the corresponding normal value range, a risk matching mechanism is automatically triggered. During the triggering process, a unique risk event number is generated and associated with the corresponding comparison result record, establishing a full-process traceability link for the risk event, facilitating subsequent querying and tracking by maintenance personnel. Simultaneously, a risk level is set according to the extent to which the data exceeds the normal range; for example, a slight exceedance corresponds to a minor risk level, and a significant exceedance corresponds to a severe risk level. Different risk levels correspond to different response priorities, ensuring that severe risks are addressed first. By triggering the risk matching mechanism, a seamless connection from risk identification to decision initiation is achieved, preventing the escalation of risks due to the omission of abnormal data and providing a guarantee for rapid risk response.

[0130] S26. Response risk matching mechanism, automatically match the corresponding governance decision from the risk matching rule base, and generate risk governance plan.

[0131] In this embodiment of the invention, the risk matching mechanism, triggered by a response, extracts key search conditions such as data type, abnormal value range, and transformer substation type based on the comparison results of risk event associations, and retrieves corresponding governance decision entries from the risk matching rule base. The retrieved governance decision entries are then structured and integrated to clarify the applicable risk scenarios, specific implementation steps, responsible departments, required tools and equipment, and expected completion timelines. For example, for a governance plan with a line loss rate exceeding 3%, the first step is to conduct line insulation testing, with the responsible department being the operations and maintenance department and a completion timeline of 24 hours; the second step is to check user meters, with the responsible department being the marketing department and a completion timeline of 48 hours. The integrated governance decisions are optimized and adjusted based on the risk level and the actual situation of the transformer substation. For minor risks, the implementation process is simplified and the completion timeline is shortened; for severe risks, emergency response steps are added and multi-departmental coordination is required. Through automatic matching and optimization, a targeted and implementable risk governance plan is generated.

[0132] S27. Encapsulate the data and risk management plan that exceed the normal value range, trigger the risk alarm command, and generate a risk reminder dataset.

[0133] In this embodiment of the invention, data exceeding the normal numerical range is associated and encapsulated with the generated risk management solution. The data format is standardized as a structured format to ensure data fields are standardized and relationships are clear. The encapsulated content includes the actual value of the data exceeding the normal range, the normal range, the extent of the exceedance, the collection time, the relevant distribution area, and the complete content of the risk management solution. After encapsulation, a risk alarm command is automatically triggered. Alarm formats include system pop-up alarms, SMS notifications to maintenance personnel, and background voice reminders. The alarm content includes the risk event number, risk level, relevant distribution area, brief information about the exceedance data, and core steps of the management solution, ensuring that maintenance personnel can quickly grasp key risk information. The encapsulated complete data is integrated with the alarm command-related records to generate a risk alert dataset containing basic risk information, exceedance data details, a management solution, and alarm records. This dataset is uniquely identified by the risk event number, stored in the system database, and synchronously transmitted to the visualization module.

[0134] The visualization module 104 is used to integrate and display multidimensional datasets, related datasets, and risk alert datasets to generate the initial governance interface.

[0135] Furthermore, the visualization module 104 can perform the following steps:

[0136] S31. Render the data in the associated dataset graphically to generate data visualization charts.

[0137] In this embodiment of the invention, based on core indicator data such as line loss rate and load growth trend index in the associated dataset, visualization rendering technology is used for graphical processing to generate data visualization charts. The specific graphical rendering method is adapted according to the data type. For example, the time series change of line loss rate is presented using a line chart, the comparison of load growth trend index of different transformer areas is presented using a bar chart, and data distribution characteristics are presented using a pie chart or histogram. Through graphical rendering, abstract numerical data is transformed into intuitive visualization charts, which can clearly show the data change patterns, comparative differences, and distribution characteristics, making it convenient for users to quickly grasp the core information of the associated dataset.

[0138] S32. Based on the device connection relationships in the device data, use icons of different shapes to label different types of devices and draw the overall topology diagram of the low-voltage distribution area.

[0139] In this embodiment of the invention, connection relationship information for various devices such as transformers, switches, and cables is extracted from device data to clarify the wiring logic and hierarchical relationships between devices. Different shapes and icons are used to label different types of devices; for example, transformers are labeled with circular icons, switches with square icons, and cables with line segment icons, ensuring visual differentiation between different types of devices. Based on the labeled device icons and the extracted device connection relationships, an overall topology diagram is drawn according to the actual layout logic of the low-voltage distribution area, clearly showing the distribution location and connection paths of the devices. By drawing the topology diagram, the device composition and network architecture of the low-voltage distribution area can be intuitively displayed.

[0140] S33. Based on the risk status information in the risk alert dataset, configure a first color identifier for device icons associated with risks and a second color identifier for device icons not associated with risks in the topology diagram, and generate a topology status view.

[0141] In this embodiment of the invention, risk status information for each device is extracted from the risk alert dataset to determine whether a device is associated with a risk and the type of risk. Based on the drawn topology diagram, a first color identifier is assigned to the icons of devices associated with risks, and a second color identifier is assigned to the icons of devices not associated with risks. For example, the first color is red, and the second color is green. The color identifier configuration ensures a clear visual contrast. By combining the risk status information with the topology diagram, a topology status view is generated, which can intuitively present the risk distribution of various devices. Users can quickly identify devices associated with risks and devices not associated with risks through icon colors, realizing visualized management and control of device risk status and improving the convenience of risk identification.

[0142] S34. Overlay the map data with the associated dataset and risk alert dataset, and mark the geographical location of the low-voltage transformer area to generate an initial geographical view of the transformer area.

[0143] In this embodiment of the invention, map data is used as the base map. The operational indicator data of the low-voltage substations in the associated dataset and the basic risk information in the risk alert dataset are spatially overlaid on the base map. Simultaneously, the geographical boundaries of each low-voltage substation are precisely marked on the base map. During the overlay process, latitude and longitude coordinate calibration is used to achieve precise matching between the data and geographical locations, ensuring a one-to-one correspondence between the information in the associated dataset and the risk alert dataset and the geographical location of the corresponding substation. The generated initial geographical view of the substations combines abstract data information with specific geographic space, intuitively displaying the geographical distribution of each low-voltage substation and its corresponding basic operational data and risk associations.

[0144] S35. Based on the risk status information in the risk alert dataset, configure a third color identifier for the low-voltage transformer icons associated with risks in the initial transformer geographic view, configure a fourth color identifier for the low-voltage transformer icons not associated with risks, and generate a target transformer geographic view.

[0145] In this embodiment of the invention, the overall risk status information of each low-voltage transformer area is extracted from the risk alert dataset to determine whether any transformer areas are associated with risks. Based on the initial geographical view of the transformer areas, a third color identifier is configured for the icons of low-voltage transformer areas with associated risks, and a fourth color identifier is configured for the icons of low-voltage transformer areas without associated risks. For example, the third color is orange and the fourth color is blue, ensuring clear differentiation of the risk status of the transformer areas. By overlaying the color identifiers, a geographical view of the target transformer areas is generated, which can intuitively present the risk distribution pattern of different low-voltage transformer areas. Users can quickly determine the risk status of each transformer area by the color of the transformer area icon, facilitating a macro-level understanding of the risk distribution of the transformer areas.

[0146] S36. Configure pop-up interactive functionality for color-coded icons in the topology status view and target area geographic view. When triggered by the user, display the corresponding detailed data and associated governance decisions in the risk alert dataset on the page.

[0147] In this embodiment of the invention, a unified pop-up interactive function is configured for device icons with first and second color identifiers in the topology status view, and for substation icons with third and fourth color identifiers in the target substation geographic view. The interaction is triggered by a mouse click. When a user clicks an icon with a color identifier, the system automatically retrieves the corresponding detailed data and associated governance decisions from the risk alert dataset and displays them in a pop-up window. The pop-up window displays core information such as basic information about the device or substation, specific data exceeding the normal range, risk level, implementation steps of the governance decision, and responsible department. By configuring the pop-up interactive function, a layered display of risk information is achieved, presenting both the macro-level risk distribution at the view level and allowing users to obtain detailed risk information and governance solutions through interaction.

[0148] S37. Configure menu controls to allow users to select display content for different time ranges and switch between different visualization pages, and configure graphic zoom controls and data file export functions for data visualization charts, topology status views and target area geographic views to generate the initial governance interface.

[0149] In this embodiment of the invention, a menu control is configured to support flexible user operation. The menu control includes a time range selection option and a page switching option. The time range selection option allows users to customize the display content by selecting different time ranges such as day, week, and month. The page switching option allows users to quickly switch between data visualization charts, topology status views, and target area geographic views. Simultaneously, graphic zoom controls are configured for the data visualization charts, topology status views, and target area geographic views, allowing users to adjust the view size using the mouse wheel or dragging, facilitating the viewing of details or global information. A data file export function is also configured, allowing users to export and store the currently displayed charts and views in formats such as Excel and PDF. Integrating the above control functions and various visualization results generates the initial governance interface.

[0150] The digital twin model building module 105 is used to build a target 3D model based on device data, map data, multidimensional dataset and related dataset, and to update the initial governance interface using the target 3D model to generate the target governance interface.

[0151] Furthermore, the digital twin model building module 105 can perform the following steps:

[0152] S41. Use the equipment volume and connection relationship data in the equipment data to construct a three-dimensional equipment model.

[0153] In this embodiment of the invention, a three-dimensional equipment model is constructed using 3D modeling technology based on the equipment volume parameters and connection relationship information recorded in the equipment data. The equipment volume parameters provide precise dimensional basis for the model, ensuring that the 3D model matches the physical size of the actual equipment. The equipment connection relationship information clarifies the assembly logic and wiring methods between various equipment components, ensuring that the relative positions of the model components correspond to the actual equipment connection relationships. During the modeling process, components are subdivided according to equipment category, such as transformers, switches, and cables, and then assembled according to the connection relationships to form a complete 3D equipment model. This model accurately reproduces the physical structure and connection characteristics of the equipment, providing precise equipment carrier support for subsequent combination with the 3D environment model and data mapping, ensuring the physical consistency of the digital twin model.

[0154] S42. Using dynamic meteorological effects from map data and multidimensional data collection, construct a three-dimensional environmental model that includes buildings, vegetation, and terrain.

[0155] In this embodiment of the invention, a basic geographic framework is established using map data. Geographic information such as terrain elevation, building distribution, and vegetation cover is extracted to construct the basic form of a three-dimensional environment. Simultaneously, dynamic meteorological effects corresponding to meteorological data from a multidimensional dataset are integrated, such as wind speed correlated with vegetation sway, rainfall correlated with raindrop rendering on the interface, and temperature correlated with changes in ambient color temperature, achieving a visualized and dynamic presentation of meteorological factors. During the construction process, geographic coordinate calibration ensures that the spatial positions of terrain and buildings are consistent with reality. Dynamic meteorological effects are linked in real time with meteorological data, generating a three-dimensional environmental model that includes buildings, vegetation, and terrain and possesses dynamic meteorological simulation capabilities.

[0156] S43. Combine and spatially align the 3D device model with the 3D environment model to generate an initial 3D model.

[0157] In this embodiment of the invention, the constructed 3D equipment model and the 3D environment model are combined according to the actual layout logic of the low-voltage distribution area. Specifically, the 3D equipment model is superimposed onto the corresponding geographic coordinates of the 3D environment model according to its actual installation location. Spatial alignment technology is employed during the combination process, based on GPS coordinate calibration or equipment installation reference point positioning, to ensure accurate spatial matching between the 3D equipment model and the 3D environment model, avoiding problems such as equipment floating or positional offset. Through combination and spatial alignment, the physical form of the equipment and the geographical features of the environment are integrated, generating an initial 3D model that can completely reproduce the physical scene of the low-voltage distribution area.

[0158] S44. Map the electrical data and associated datasets in the multidimensional dataset to the corresponding components in the initial 3D model, and present the data status through the color and shape changes of the model components. Configure data pop-up interactive functions for each component of the model to generate the target 3D model.

[0159] In this embodiment of the invention, a mapping relationship is established between electrical data and related datasets in a multidimensional dataset and the components of the initial 3D model. Each model component is bound to corresponding electrical parameters (such as voltage and current) and related indicators (such as line loss rate and load growth trend index). The data status is intuitively presented through color and shape changes of the model components; for example, components display green when electrical data is normal, red when overloaded, and exhibit a slight bulging shape when the load growth trend index exceeds the standard. Simultaneously, a data pop-up interactive function is configured for each model component. When the user triggers the interaction, the pop-up will display detailed electrical data, related indicator values, and relevant risk information bound to the corresponding component. Through data mapping, status visualization, and interactive configuration, the static 3D model is upgraded to a target 3D model with data perception capabilities, achieving deep integration of physical equipment and data.

[0160] S45. Integrate the target 3D model into the initial governance interface, replacing the static topology diagram as the core visualization carrier, and establish a dynamic data link between the target 3D model and the associated dataset.

[0161] In this embodiment of the invention, the generated target 3D model is embedded into the initial governance interface, replacing the original static topology diagram and becoming the core visualization carrier of the interface, allowing users to intuitively grasp the overall status of the transformer area through a 3D scene. Simultaneously, a dynamic data link is established between the target 3D model and the associated dataset. This link can synchronize the updates of the associated dataset in real time, ensuring that the color and shape changes of the model components remain consistent with the data status. For example, when the line loss rate of a certain device in the associated dataset exceeds the standard, the dynamic data link will immediately trigger a color change of the corresponding device component in the model, achieving real-time linkage between data and the model.

[0162] S46. Based on dynamic data linking, a model simulation interface is provided in the initial governance interface. The model simulation interface receives data modification instructions or device operation instructions input by the user through the front-end interactive panel.

[0163] In this embodiment of the invention, relying on the established dynamic data link, a model simulation interface is deployed in the initial governance interface. This interface serves as a bridge for user interaction with the target 3D model, supporting the reception of two types of user commands through the front-end interactive panel. One type is data modification commands, such as adjusting the rated power threshold or load warning value of the equipment; the other type is equipment operation commands, such as simulating switch tripping or transformer capacity expansion. The front-end interactive panel provides intuitive operation entry points, such as input boxes and buttons, facilitating quick input or selection of commands and ensuring the accuracy and convenience of command transmission. The model simulation interface empowers users with the ability to actively intervene and simulate the operating status of the transformer area, providing interactive support for solution verification and risk prediction.

[0164] S47. In response to data modification instructions or device operation instructions, drive the target 3D model through dynamic data links, dynamically update its shape and data status, and synchronously trigger the corresponding update of the associated dataset to generate the target management interface.

[0165] In this embodiment of the invention, after receiving a user instruction, the model simulation interface transmits the instruction to the target 3D model via a dynamic data link, driving the model to dynamically update. If the instruction is for data modification, the color and shape of the model components will be recalculated and updated according to the modified values. For example, after increasing the load warning value, components that previously displayed abnormalities may return to their normal colors. If the instruction is for equipment operation, the model will simulate the equipment's operation process. For example, when a switch is tripped, the corresponding switch component in the model will show a tripped state. Simultaneously, the dynamic data link synchronously triggers the update of corresponding fields in the associated dataset, ensuring the consistency between the data and the model's state. After the update is completed, the initial governance interface is upgraded to the target governance interface, which includes real-time interactive results.

[0166] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a low-voltage distribution area management method provided in an embodiment of the present invention.

[0167] This invention provides a low-voltage distribution area management method, comprising:

[0168] Step 201: Obtain electrical data, equipment data, electricity consumption behavior data, meteorological data, and map data of the low-voltage distribution area to generate a multidimensional dataset.

[0169] In this embodiment of the invention, this step achieves comprehensive acquisition and integration of multi-dimensional data through a data acquisition module. The core objective is to generate a multidimensional dataset covering the operation of the transformer substation, equipment status, and environmental characteristics. Specifically, the data acquisition module deploys various acquisition devices such as smart meters, status sensors, user electricity terminals, and meteorological monitoring equipment. Simultaneously, it establishes data interfaces with GIS systems and UAV survey platforms to collect various core data from the low-voltage transformer substation in real time: electrical data includes operating parameters such as voltage, current, and power; equipment data covers information such as equipment size, connection relationships, and dynamic operating status; electricity consumption behavior data includes data on user electricity consumption time and changes in electricity consumption; meteorological data involves environmental parameters such as temperature, humidity, wind speed, and rainfall; and map data includes terrain, building, vegetation distribution, and the geographical location of the transformer substation. During the acquisition process, a real-time data interface is developed using the MQTT protocol to ensure the stability and timeliness of data transmission, while a data caching mechanism is set up to prevent data loss. Through the unified integration of these multi-source data, a structured multidimensional dataset is generated, providing comprehensive and accurate basic data support for subsequent data processing and model building, avoiding subsequent decision-making biases due to missing or delayed data.

[0170] Step 202: Sort and classify the data in the multidimensional dataset, and calculate the line loss rate and load growth trend index based on the sorted and classified data to generate a related dataset.

[0171] In this embodiment of the invention, this step is the core execution process of the data processing module, aiming to transform raw multidimensional data into structured, correlated data with analytical value. Specifically, the electrical data, equipment data, and electricity consumption behavior data in the multidimensional dataset are first sorted and classified according to time, low-voltage distribution area identification, and equipment number, dividing the data into subsets of different dimensions to ensure data orderliness and relevance. Based on the sorted and classified structured data, two core indicators are calculated using preset formulas: the line loss rate is calculated by extracting the total power supply on the low-voltage side of the transformer and the total electricity sales of users in the distribution area, and then calculating the difference as a percentage, directly reflecting the power supply efficiency of the distribution area. The load growth trend index is generated by selecting the maximum load value of a historical statistical period, calculating the load growth rate of adjacent periods, and standardizing it with a preset load fluctuation coefficient, used to characterize load change patterns. The calculated line loss rate and load growth trend index are then linked and integrated with the classified basic data to generate a correlated dataset containing raw data and core analytical indicators, providing quantitative basis for subsequent risk assessment and governance decisions, significantly improving data utilization efficiency.

[0172] Step 203: Construct a risk matching rule base, and when the data in the associated dataset exceeds the normal range defined by the risk matching rule base, trigger a risk alert and automatically match governance decisions to generate a risk alert dataset.

[0173] In this embodiment of the invention, this step achieves automatic risk identification and decision matching through a risk alert module, with the core being the establishment of a standardized risk assessment and handling system. Specifically, it first extracts key information for judging the status of equipment and distribution areas from industry risk handling regulations and past maintenance case data corresponding to the low-voltage distribution area, dividing this information into two categories of basic rule data: normal value range data and governance decision data. Based on these two types of data, a mapping relationship is established between data types, abnormal value ranges, and governance decisions, generating a risk matching rule base. This rule base clearly defines the normal thresholds for core indicators such as line loss rate and load growth trend index, as well as the corresponding handling solutions when these thresholds are exceeded. By real-time monitoring of the associated dataset, the data is compared with the normal range data in the risk matching rule base. When data is found to exceed the normal range, the risk matching mechanism is automatically triggered, retrieving the corresponding governance decision from the rule base, integrating the out-of-range data with the governance decision, and triggering a risk alarm command to generate a risk alert dataset. This process achieves timely risk detection and automated response, shortens risk handling time, reduces the failure rate, and ensures the safe and stable operation of the distribution area.

[0174] Step 204: Integrate and display the multidimensional dataset, related dataset, and risk alert dataset to generate the initial governance interface.

[0175] In this embodiment of the invention, this step is executed by the visualization module. The core is to intuitively present the core information of the three types of datasets through multi-form visualization and interactive design. Specifically, firstly, indicators such as line loss rate and load growth trend index in the associated dataset are graphically rendered to generate dynamic curves, bar charts, and other data visualization charts. Based on the connection relationships in the equipment data, a low-voltage distribution area topology diagram is drawn. Combined with the risk status information in the risk alert dataset, different colors are used to identify associated and unassociated risk equipment and distribution areas. The map data is overlaid with the three types of datasets to generate a geographic view marking the geographical locations of the distribution areas. Simultaneously, pop-up interactive functions are configured for the icons in the topology diagram and geographic view. Clicking on these icons displays detailed data and associated governance decisions. Menu controls are configured to allow users to select different time ranges for display content and switch visualization pages. Combined with graphics zoom and data export functions, an initial governance interface is formed. This interface transforms complex data into intuitive visualization results, allowing users to quickly grasp the operating status and risk distribution of low-voltage distribution areas, improving user experience and assisting in efficient decision-making.

[0176] Step 205: Construct a target 3D model based on device data, map data, multidimensional dataset, and associated dataset, and update the initial governance interface using the target 3D model to generate the target governance interface.

[0177] In this embodiment of the invention, this step is the core work of the digital twin model construction module, aiming to achieve digital replication and dynamic interaction of the substation area through a 3D model. Specifically, firstly, using equipment volume and connection relationship data from the equipment data, a 3D modeling tool is used to construct a geometric model of the equipment. Related fields such as electrical data and equipment data are set on the model, and a dynamic data display panel is developed. Geographic information from map data is imported to construct a 3D environmental model including buildings, vegetation, and terrain, and a dynamic meteorological effect simulation function based on meteorological data is added. The 3D equipment model and environmental model are combined and spatially aligned to generate an initial 3D model. Then, a mapping relationship is established between electrical data, related datasets, and model components in the multidimensional dataset. Data status is presented through color gradients, shape changes, etc., and pop-up interactive functions are configured for the model components. The target 3D model is integrated into the initial governance interface, replacing the static topology diagram as the core visualization carrier. A dynamic data link is established between the model and related datasets, and the data update frequency is set (real-time data updates every second, non-real-time data updates every minute or day). It also provides a model simulation interface, which receives user data modification commands or equipment operation commands through the front-end interactive panel, drives the model to update dynamically and synchronize with the dataset, and upgrades the initial governance interface to a target governance interface with dynamic simulation and interactive capabilities, helping users to carry out refined management and analysis of low-voltage distribution areas.

[0178] Please see Figure 3 , Figure 3 This is a structural block diagram of a computer device provided in an embodiment of the present invention.

[0179] An electronic device according to an embodiment of the present invention includes: a memory 301 and a processor 302. The memory 301 stores a computer program. When the computer program is executed by the processor 302, the processor 302 performs the low-voltage distribution area management method as described in any of the above embodiments.

[0180] Memory 301 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 301 has storage space 303 for program code 313 for performing any of the method steps described above. For example, storage space 303 for program code may include various program codes 313 for implementing the various steps in the methods described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to perform the various steps in the low-voltage distribution area management method described above.

[0181] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the low-voltage distribution area management method as described in any of the above embodiments.

[0182] This invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the low-voltage distribution area management method as described in any of the above embodiments.

[0183] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0185] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0188] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-voltage distribution area management system, characterized in that, include: The data acquisition module is used to acquire electrical data, equipment data, electricity consumption behavior data, meteorological data, and map data of the low-voltage distribution area, and generate multidimensional datasets. The data processing module is used to sort and classify the data in the multidimensional dataset, and calculate the line loss rate and load growth trend index based on the sorted and classified data to generate a related dataset; The risk alert module is used to build a risk matching rule base, and when the data in the associated dataset exceeds the normal range defined by the risk matching rule base, it triggers a risk alert and automatically matches governance decisions to generate a risk alert dataset. The visualization module is used to integrate and display the multidimensional dataset, the associated dataset, and the risk alert dataset to generate an initial governance interface; The digital twin model construction module is used to construct a target 3D model based on the device data, the map data, the multidimensional dataset, and the associated dataset, and to update the initial governance interface using the target 3D model to generate the target governance interface.

2. The low-voltage distribution area management system according to claim 1, characterized in that, The data acquisition module includes: The electrical data acquisition unit is used to collect electrical data, including voltage, current, and power, from low-voltage distribution areas via smart meters. The equipment data acquisition unit is used to collect equipment status data in the low-voltage distribution area through status sensors, and retrieve static data including equipment volume and wiring diagram to construct equipment data. The electricity consumption behavior data acquisition unit is used to collect electricity consumption behavior data, including electricity consumption time and electricity consumption changes, in the low-voltage distribution area through smart meters and user electricity terminals. The meteorological data acquisition unit is used to collect meteorological data, including temperature, humidity, wind speed, and rainfall, in the low-pressure area. The map data collection unit is used to collect map data of the low-voltage transformer area through GIS system and drone survey. A data integration interface is used to construct a multidimensional dataset by using the electrical data, the equipment data, the electricity consumption behavior data, the meteorological data, and the map data.

3. The low-voltage distribution area management system according to claim 1, characterized in that, The data processing module performs the following steps: The electrical data, equipment data, and electricity consumption behavior data in the multidimensional dataset are associated, sorted, and classified according to a preset priority to generate a categorized dataset with dimension labels. Extract the total power supply on the low-voltage side of the transformer corresponding to the low-voltage distribution area and the total electricity sales of users in the distribution area, and calculate the line loss rate under each dimension label of the classification dataset according to the preset line loss rate calculation formula to generate line loss rate data; Based on the dimensionally labeled classification dataset, load-related data corresponding to the low-voltage transformer area are extracted; Select a preset number of historical statistical periods, and filter out the maximum load value within each historical statistical period to generate a historical period maximum load dataset; Calculate the load growth rate between two adjacent historical statistical periods in the maximum load data of the historical period, and take the arithmetic mean of the load growth rate of all periods to obtain the average load growth rate; Calculate the product between the average load growth rate and the preset load fluctuation coefficient, and standardize it into an index within a preset numerical range to generate a load growth trend index; The line loss rate data, the load growth trend index, and the classification dataset are linked and integrated to generate a linked dataset.

4. The low-voltage distribution area management system according to claim 1, characterized in that, The risk alert module performs the following steps: A risk knowledge dataset is constructed using risk handling regulations and case data corresponding to the low-voltage distribution area. Data for judging the status of equipment and distribution areas is extracted from the risk knowledge dataset and divided into normal value range data and governance decision data to generate two types of basic rule data. Based on the two types of basic rule data, a mapping relationship between data types, abnormal numerical ranges and governance decisions is established to generate a risk matching rule base. The associated dataset is monitored in real time, and the data in the associated dataset is compared with the normal value range data in the risk matching rule base to generate data comparison results; When the data comparison result shows that the data in the associated dataset exceeds the corresponding normal value range, the risk matching mechanism is triggered; In response to the risk matching mechanism, the corresponding governance decision is automatically matched from the risk matching rule base to generate a risk governance plan; The data exceeding the normal value range and the risk management solution are encapsulated, and a risk alarm command is triggered to generate a risk alert dataset.

5. The low-voltage distribution area management system according to claim 1, characterized in that, The visualization module performs the following steps: The data in the associated dataset is rendered graphically to generate data visualization charts; Based on the device connection relationships in the device data, different types of devices are labeled with icons of different shapes, and a topology diagram of the entire low-voltage distribution area is drawn. Based on the risk status information in the risk alert dataset, a first color identifier is configured for device icons associated with risks and a second color identifier is configured for device icons not associated with risks in the topology diagram, thereby generating a topology status view. The map data is overlaid with the associated dataset and the risk warning dataset, and the geographical location of the low-voltage transformer area is marked to generate an initial geographical view of the transformer area; Based on the risk status information in the risk alert dataset, a third color identifier is configured for the low-voltage transformer icons associated with risks in the initial transformer geographic view, and a fourth color identifier is configured for the low-voltage transformer icons not associated with risks, thereby generating a target transformer geographic view. Configure pop-up interactive functions for the color-coded icons in the topology status view and the target area geographic view. When the user triggers the pop-up, the page will display the corresponding detailed data and related governance decisions in the risk alert dataset. Configure menu controls to allow users to select display content for different time ranges and switch between different visualization pages, and configure graphic zoom controls and data file export functions for the data visualization charts, the topology status view and the target area geographic view to generate the initial governance interface.

6. The low-voltage distribution area management system according to claim 1, characterized in that, The digital twin model construction module performs the following steps: A three-dimensional device model is constructed using the device volume and connection relationship data from the device data. Using the dynamic meteorological effects of the map data and the meteorological data in the multidimensional dataset, a three-dimensional environment model including buildings, vegetation, and terrain is constructed. The three-dimensional device model and the three-dimensional environment model are combined and spatially aligned to generate an initial three-dimensional model; The electrical data in the multidimensional dataset and the associated dataset are mapped to the corresponding components in the initial 3D model. The data status is presented by the color and shape changes of the model components. Data pop-up interactive functions are configured for each component of the model to generate the target 3D model. The target 3D model is integrated into the initial governance interface, replacing the static topology diagram as the core visualization carrier, and a dynamic data link is established between the target 3D model and the associated dataset. Based on the dynamic data link, a model simulation interface is provided in the initial governance interface. The model simulation interface receives data modification instructions or device operation instructions input by the user through the front-end interactive panel. In response to the data modification command or device operation command, the target 3D model is driven by the dynamic data link to dynamically update its shape and data status, and simultaneously trigger the corresponding update of the associated dataset to generate the target governance interface.

7. A method for managing low-voltage distribution areas, characterized in that, include: Acquire electrical data, equipment data, electricity consumption behavior data, meteorological data, and map data from low-voltage distribution areas to generate a multidimensional dataset; The data in the multidimensional dataset are sorted and classified, and the line loss rate and load growth trend index are calculated based on the sorted and classified data to generate an associated dataset; A risk matching rule base is constructed, and when the data in the associated dataset exceeds the normal range defined by the risk matching rule base, a risk alert is triggered and a governance decision is automatically matched to generate a risk alert dataset. The multidimensional dataset, the associated dataset, and the risk alert dataset are integrated and displayed to generate an initial governance interface; A target 3D model is constructed based on the device data, the map data, the multidimensional dataset, and the associated dataset, and the initial governance interface is updated using the target 3D model to generate the target governance interface.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the low-voltage distribution area management method as described in claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the low-voltage distribution area management method as described in claim 7.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the low-voltage distribution area management method as described in claim 7.

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