Low-voltage customer installation scheme studying and judging system and method

Abstract

The invention discloses a research and judgment system and method for a low-voltage customer installation scheme, relates to the technical field of electric power installation, and aims to solve the problems that a traditional low-voltage customer installation research and judgment technology is scattered in data, poor in visualization effect, single in research and judgment dimension and incapable of meeting the dual-carbon requirement. A data integration module obtains four types of core data across systems, dynamically matches carbon emission factors through power consumption types and regional characteristics, and constructs an integrated installation business data set; the visual display module is combined with a GIS (Geographic Information System) technology and an intelligent scene adaptation algorithm to realize visual display and convenient interaction of a topological structure and load distribution; the scheme research and judgment module screens adaptive courts through a multi-objective optimization weight model, and carries out technical feasibility, economical efficiency and low-carbon compliance triple evaluation and conflict resolution; and the permission control, data backup and safety protection of the system management module are matched, so that the formulation period of the installation scheme is shortened, the scientific and compliant research and judgment result is ensured, and the adaptability of the installation service quality and the dual-carbon policy is improved.

Description

Technical Field

[0001] This invention relates to the field of power application technology, and in particular to a system and method for evaluating low-voltage customer application schemes. Background Technology

[0002] Currently, with the rapid development of the power industry and the continuous increase in user demand, the volume of low-voltage customer installation applications is growing rapidly. This necessitates efficient application scheme analysis to ensure service quality and efficiency in response to the surge in demand. However, while there have been innovations in the technologies related to low-voltage customer installation schemes, significant common shortcomings still exist.

[0003] Existing technologies related to low-voltage customer installation solutions suffer from several shortcomings. Firstly, at the data integration level, their automated integration capabilities are insufficient, leading to low data acquisition efficiency, easy omission of key information, and difficulty in supporting rapid solution development. Secondly, while they can present some grid information, they lack in-depth visualization of grid topology, load distribution, and other data, resulting in a simplistic approach that fails to intuitively demonstrate the operational status of low-voltage distribution areas and potential problems with the solutions. This impacts the efficiency of staff analysis and the effectiveness of communication with customers, leading to insufficient scientific rigor and accuracy in the evaluation of installation solutions, and making it difficult to meet the evolving needs of current low-voltage customer installation services. Summary of the Invention

[0004] This invention provides a system and method for evaluating low-voltage customer installation plans, which solves the technical problems of existing low-voltage customer installation plan evaluation technologies lacking efficient data integration and in-depth information presentation capabilities, and having insufficient basic support, resulting in poor evaluation results and inability to adapt to business needs.

[0005] The first aspect of this invention provides a low-voltage customer installation application scheme analysis system, comprising:

[0006] The data integration module is used to acquire application data, application cost data, low-voltage distribution area data and carbon emission data, and dynamically match and couple the associated carbon emission factors based on the electricity consumption type in the application data and the regional characteristics in the low-voltage distribution area data to generate an application business dataset.

[0007] The visualization module is used to visualize the low-voltage transformer area data in the application service dataset, and to construct and analyze the visualization scenario through an intelligent scene adaptation algorithm to generate a solution formulation interface.

[0008] The scheme evaluation module is used to screen accessible low-voltage substations based on the application service dataset using a multi-objective optimization weight model, and to evaluate the application schemes generated in the scheme formulation interface from multiple dimensions, thereby generating scheme evaluation results.

[0009] The system management module is used to perform access control, data backup and security management on the data integration module, visualization display module and solution analysis module, and generate system management policies.

[0010] Optionally, the data integration module includes:

[0011] The application data unit is used to connect to the customer application system, obtain application data including customer name, electricity type and electricity address, and assign a unique application number to each application.

[0012] The application cost data unit is used to collect application cost data of different low-voltage distribution areas that have implemented application schemes in the past year. The application cost data includes the purchase cost and transportation cost of different materials.

[0013] The low-voltage distribution area data unit is used to collect basic data and real-time operational data of the low-voltage distribution area and generate low-voltage distribution area data. The basic data includes low-voltage distribution area topology data, geographical range of each low-voltage distribution area, transformer remaining capacity, line parameters, and equipment parameters. The real-time operational data includes power load data within the low-voltage distribution area.

[0014] The carbon emission data unit is used to collect carbon emission data corresponding to the application data. The carbon emission data includes carbon emission factor per unit mileage of transportation vehicles, average carbon emission factor of regional power grid, and carbon emission limit data under dual carbon targets.

[0015] The factor matching unit is used to dynamically match carbon emission factors based on the electricity type in the application data, the regional characteristics in the low-voltage distribution area data, and the estimated length of the wiring path, and couple and associate them with the application data and the low-voltage distribution area data to generate an application business dataset.

[0016] Optionally, the visualization module performs the following steps:

[0017] The network topology is visualized using the basic data of low-voltage distribution areas in the application service dataset, generating a distribution area network topology map containing differentiated device identifiers;

[0018] The load density is calculated using the power load data in the application service dataset, and a heat map is rendered based on the load density to generate a load distribution heat map superimposed on the network topology map of the transformer area.

[0019] The geographic complexity parameters of the application address and the load pressure level of the low-voltage distribution area are extracted by the intelligent scene adaptation algorithm, and combined with the preset scene threshold, a judgment visualization scene adapted to the current application scenario is constructed.

[0020] Based on the installation application identifier entered by the customer, the geographic area is segmented and the electricity address is marked in the analysis and visualization scenario to generate a target area map;

[0021] Call the preset device graphics library, respond to user operation commands to simulate wiring paths on the target area map, and generate an installation plan that includes device model and path information;

[0022] The assessment results corresponding to the application scheme and the application scheme are integrated and displayed in the assessment visualization scenario, and the interface functions are integrated using graphical control commands to generate a scheme formulation interface.

[0023] Optionally, the scheme evaluation module performs the following steps:

[0024] Based on the application service dataset, select low-voltage transformer areas that can be accessed and generate a list of candidate transformer areas.

[0025] Based on the list of candidate substations and the application service dataset, a technical feasibility assessment is performed on the application schemes from the visualization module, and a technical feasibility result is generated.

[0026] Based on the application service dataset, the economic feasibility of the application scheme is evaluated, and economic evaluation results are generated.

[0027] Based on the application service dataset, the low-carbon compliance of the application scheme is assessed, and low-carbon compliance results are generated.

[0028] The proposed solution evaluation results are constructed using the aforementioned technical feasibility results, economic evaluation results, and low-carbon compliance results.

[0029] Optionally, the step of filtering accessible low-voltage distribution areas and generating a list of candidate distribution areas based on the application service dataset includes:

[0030] Extract the geographical range data, transformer remaining capacity data, load density data, and regional characteristic data of each low-voltage distribution area from the application data set;

[0031] Spatial relationship calculations are performed based on the geographical range data and the customer electricity addresses in the application business dataset, and the results are sorted according to the geographical distance weight in the multi-objective optimization weight model to generate a preliminary candidate list.

[0032] Based on the primary candidate list and the remaining transformer capacity data, capacity adequacy is screened, and a secondary candidate list is generated by sorting according to the capacity weights in the multi-objective optimization weight model.

[0033] Based on the secondary candidate list and the load density data, the operational balance is screened, and the load density weights are sorted according to the multi-objective optimization weight model to generate a third-level candidate list.

[0034] The low-carbon adaptability of the transformer substations in the three-level candidate list is verified by combining regional characteristic data. At the same time, the matching degree between the infrastructure and the application requirements is verified. Substations that do not meet the low-carbon adaptability standards or whose infrastructure cannot be matched are eliminated, and a candidate transformer substation list is generated.

[0035] Optionally, the system management module performs the following steps:

[0036] Perform user permission management, assign corresponding data viewing permissions, scheme modification permissions, and system setting permissions to different users, and generate user permission allocation data;

[0037] The full system data generated by the data integration module, the visualization module, and the solution analysis module is periodically backed up to local storage devices and off-site servers to generate system backup data.

[0038] The data integration module collects and stores customer data, and performs encrypted transmission and storage control to generate sensitive encrypted data;

[0039] The system management strategy is constructed using the user permission allocation data, the system backup data, and the sensitive encrypted data.

[0040] The second aspect of this invention provides a method for evaluating low-voltage customer installation plans, comprising:

[0041] Acquire application data, application cost data, low-voltage distribution area data, and carbon emission data. Based on the electricity consumption type in the application data and the regional characteristics in the low-voltage distribution area data, dynamically match and couple the associated carbon emission factors to generate an application business dataset.

[0042] The low-voltage transformer area data in the application service dataset is visualized, and a visualization scenario is constructed and judged through an intelligent scenario adaptation algorithm to generate a solution formulation interface.

[0043] Based on the application service dataset, a multi-objective optimization weight model is used to screen the low-voltage substations that can be accessed, and the application schemes generated in the scheme formulation interface are evaluated from multiple dimensions to generate scheme evaluation results.

[0044] Access control, data backup, and security management are implemented for the control modules corresponding to the application service dataset, the solution formulation interface, and the solution evaluation results, and system management policies are generated.

[0045] The third aspect of the present invention provides an electronic device, including a memory and a processor. 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 customer application scheme judgment method as described above.

[0046] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the low-voltage customer application scheme judgment method as described above.

[0047] 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 customer application scheme judgment method as described above.

[0048] As can be seen from the above technical solutions, the present invention has the following advantages:

[0049] This invention provides a low-voltage customer application scheme evaluation system and method to address the technical problems of existing low-voltage customer application scheme evaluation technologies, which lack efficient data integration and in-depth information presentation capabilities, resulting in insufficient basic support, poor evaluation results, and inability to adapt to business needs. Traditional application evaluation technologies suffer from data silos across multiple systems, lacking unified integration and dynamic correlation capabilities, leading to incomplete data support and inaccurate carbon emission assessments. The data integration module of this invention can cross-system interface to obtain four core data categories: application, cost, transformer area, and carbon emissions. By dynamically matching carbon emission factors with electricity usage type and regional characteristics, it constructs an integrated application business dataset, completely breaking down information barriers and providing a precise and comprehensive data foundation for evaluation. When application scenarios involve complex geographical environments and uneven load distribution, traditional visualization methods lack scenario adaptability, resulting in cluttered information presentation and inefficient operation. The visualization module of this invention uses GIS technology to intuitively present the topology and load heat map, combined with intelligent scenario adaptation algorithms to dynamically adjust the display focus, and interactive functions such as area segmentation and path simulation, making the scheme formulation process efficient and convenient.

[0050] Traditional assessment techniques are often limited to single-dimensional evaluations, making it difficult to balance technical feasibility, economic efficiency, and low-carbon requirements. In contrast, the solution assessment module of this invention uses a multi-objective optimized weight model to screen suitable transformer substations, conducting a comprehensive assessment from three dimensions: technical, economic, and low-carbon compliance. It also possesses conflict resolution capabilities to ensure the scientific rationality of the assessment results. Furthermore, combined with the system management module's hierarchical access control, dual-backup architecture, and sensitive data encryption protection, it not only ensures the standardization of system operations and data security but also significantly improves the efficiency and accuracy of application scheme formulation and assessment, effectively reducing subsequent construction risks and cost waste. It perfectly adapts to the diversified needs of dual-carbon policies and power industry application services, significantly improving the quality and reliability of application services. Attached Figure Description

[0051] 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.

[0052] Figure 1 A structural block diagram of a low-voltage customer application scheme evaluation system provided in an embodiment of the present invention;

[0053] Figure 2 A flowchart illustrating the steps of a low-voltage customer installation scheme evaluation method provided in this embodiment of the invention;

[0054] Figure 3 This is a structural block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0055] This invention provides a low-voltage customer application scheme analysis system and method to solve the technical problems of existing low-voltage customer application scheme analysis technologies lacking efficient data integration and in-depth information presentation capabilities, and insufficient basic support, resulting in poor analysis results and inability to adapt to business needs.

[0056] 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.

[0057] Please see Figure 1 , Figure 1This is a structural block diagram of a low-voltage customer application scheme evaluation system provided in an embodiment of the present invention.

[0058] This invention provides a low-voltage customer application scheme analysis system, comprising:

[0059] The data integration module 101 is used to acquire application data, application cost data, low-voltage distribution area data and carbon emission data, and dynamically match and couple the associated carbon emission factors based on the electricity consumption type in the application data and the regional characteristics in the low-voltage distribution area data to generate an application business dataset.

[0060] Furthermore, the data integration module 101 includes:

[0061] The application data unit is used to connect to the customer application system, obtain application data including customer name, electricity type and electricity address, and assign a unique application number to each application.

[0062] In this embodiment of the invention, the application data unit is the core input unit of the data integration module. It is used to establish the basic identity and core requirement information of the application service, providing accurate source location basis for subsequent data coupling, transformer area selection, and solution analysis. Its specific implementation process is based on the orderly conduct of the application service process:

[0063] 1. Data Interface Integration and Core Data Collection: A standardized RESTful API interface is used to connect with the customer installation application system, ensuring the compatibility and real-time nature of data collection. The collection scope strictly focuses on the core data fields defined in this invention, including the customer name for identity verification, the electricity type providing the basis for subsequent dynamic carbon emission factor matching, and the electricity address supporting geospatial positioning and transformer area selection. The electricity address must be accurate to the house number and latitude and longitude coordinates. Supplementary information such as installation load requirements can also be collected to ensure that the collected data not only conforms to the defined scope but also meets the functional requirements of subsequent modules.

[0064] 2. Unique Application Number Assignment: Strictly adhering to the requirement of assigning a unique identifier to each application, a composite coding rule combining year, area code, and serial number is used to generate the application number. An example of the coding format is: BZ-2024-310105-0001, where BZ is an abbreviation for "application," followed by the year, area code, and annual serial number. The uniqueness of the code is automatically verified during generation to avoid duplicate numbers. A mapping table linking the number to the electricity address and type is established and stored in the system database, providing a quick query basis for subsequent retrieval of application information and location of the application area by number.

[0065] 3. Data association and subsequent support: After collection and encoding, the complete application data is associated with the application number and stored to provide core data such as electricity type and electricity address for the factor matching unit.

[0066] The application data unit replaces manual entry with automated data collection, reducing data error rates; and establishes a data link for the entire application process through a unique application number, enabling traceability and correlation of data at each stage.

[0067] The installation cost data unit is used to collect installation cost data for different low-voltage distribution areas over the past year. The installation cost data includes the purchase cost and transportation cost of different materials.

[0068] In this embodiment of the invention, the installation cost data unit is the core data support unit for the economic evaluation of the scheme. Its function strictly matches the collection of installation cost data for different low-voltage transformer areas over the past year. Furthermore, the installation cost data includes the purchase costs and transportation costs of different materials. It is used to systematically collect, classify, and store historical installation cost data, forming a standardized cost benchmark library, providing a quantitative basis for the economic evaluation of the scheme assessment module. The specific implementation process of this unit is as follows:

[0069] 1. Scope and Type Limitations of Cost Data Collection: Strictly adhering to established requirements, the collection scope focuses on cost data from installation plans implemented in different low-voltage distribution areas over the past year, ensuring the timeliness and relevance of the data. Cost data types are clearly divided into two categories: purchase costs of different materials and transportation costs, with no additional data types added. Specifically, material purchase costs are subdivided according to commonly used material types in the installation plans, recording the unit price, quantity, and total purchase cost for each material. Transportation costs record transportation-related expenses for different installation plans, including transportation mileage, unit price, and transportation losses.

[0070] 2. Standardized Processing and Storage of Cost Data: The collected raw cost data undergoes standardized processing, outliers caused by emergency procurement are removed, and the data is categorized and summarized according to the dimensions corresponding to low-voltage substation areas and installation plans. Standardized cost data tables are generated and stored in the cost benchmark database. The data format is unified into a directly accessible structured format, supporting rapid retrieval by low-voltage substation area, material type, and other dimensions, ensuring that the data retrieval requirements of the economic evaluation module are met.

[0071] 3. Data Support and Functional Integration: When conducting economic evaluation, the scheme analysis module can retrieve corresponding historical cost data from the cost benchmark library based on the equipment model, material specifications, and target low-voltage distribution area information in the application scheme. This data serves as the core basis for calculating the total material procurement cost, total transportation cost, and comparing costs with similar schemes, thus supporting the generation of application business datasets and the realization of the economic evaluation function of the scheme analysis module.

[0072] The low-voltage distribution area data unit is used to collect basic data and real-time operational data of the low-voltage distribution area and generate low-voltage distribution area data. The basic data includes the topology data of the low-voltage distribution area, the geographical range of each low-voltage distribution area, the remaining capacity of the transformer, line parameters and equipment parameters. The real-time operational data includes the power load data within the low-voltage distribution area.

[0073] In this embodiment of the invention, the basic data includes low-voltage distribution area topology data, the geographical range of each low-voltage distribution area, the remaining capacity of transformers, line parameters, and equipment parameters. Real-time operational data includes power load data within the low-voltage distribution area. The low-voltage distribution area data unit is used to comprehensively collect static and dynamic data of the low-voltage distribution area, generate low-voltage distribution area data, and form a complete distribution area data profile, providing core data support for subsequent modules. The specific implementation process is divided into two parts: basic data collection and real-time operational data collection. The specific processing procedure is as follows:

[0074] 1. Basic Data Collection and Integration: The collection scope is limited to five categories of data: low-voltage distribution area topology data, geographical range of each low-voltage distribution area, transformer remaining capacity, line parameters, and equipment parameters. No additional data beyond these limits is collected. The collection method combines system integration with manual data entry. Structured data such as topology and geographical range are obtained through integration with the power GIS system and distribution network equipment management system. Historical data such as equipment parameters are manually entered and improved to ensure data integrity. Data integration links all basic data by low-voltage distribution area number, generating a standardized low-voltage distribution area basic dataset. This ensures that each data set can be uniquely retrieved by distribution area number, meeting the needs of subsequent modules to access data by distribution area dimension.

[0075] 2. Real-time Operational Data Acquisition and Processing: The acquisition scope is strictly limited to power load data within the low-voltage distribution area. Acquisition is conducted through a standardized interface connected to the distribution area's power grid monitoring system, acquiring real-time load-related data such as three-phase current, voltage, and total active power. The acquisition frequency is set to 15 minutes per acquisition as needed. The real-time acquired power load data undergoes preprocessing such as outlier removal and data smoothing to prevent invalid data from affecting subsequent analysis. Historical operational data is also retained for one year, supporting load change trend analysis.

[0076] 3. Data Output and Functional Integration: The integrated low-voltage distribution area data (including basic data and real-time operation data) is transmitted to the factor matching unit for dynamic carbon emission factor matching; the basic data of the low-voltage distribution area is transmitted to the visualization module to support the generation of the distribution area network topology map; and the remaining transformer capacity, power load data, etc. are transmitted to the scheme analysis module to support the screening of accessible distribution areas and the assessment of technical feasibility.

[0077] The carbon emission data unit is used to collect carbon emission data corresponding to the application data. The carbon emission data includes carbon emission factor per unit mileage of transportation vehicles, average carbon emission factor of regional power grid, and carbon emission limit data under dual carbon targets.

[0078] In this embodiment of the invention, carbon emission data includes the setting of carbon emission factors per unit mileage of transportation vehicles, average carbon emission factors of regional power grids, and carbon emission limit data under dual carbon targets. This data is used to specifically collect basic carbon emission data related to vehicle registration applications, providing a quantitative basis for dynamic carbon emission factor matching and low-carbon compliance assessment. The specific implementation process of the carbon emission data unit follows the set data types and collection objectives:

[0079] 1. The scope of carbon emission data collection is strictly limited: only three categories of data are collected: carbon emission factor per unit mileage of transportation vehicles, average carbon emission factor of regional power grid, and carbon emission limits under the dual-carbon target, without any additional expansion. Specifically, the carbon emission factor per unit mileage of transportation vehicles is derived from the provincial greenhouse gas inventory compilation guidelines, collecting relevant data on commonly used transportation vehicles in the implementation of the application scheme; the average carbon emission factor of regional power grid is derived from the National Energy Administration's annual carbon emission report, collecting average carbon emission intensity data for each low-voltage distribution area corresponding to the regional power grid; and the carbon emission limits under the dual-carbon target are determined according to national and local dual-carbon policies, collected categorized by electricity consumption type to ensure accurate correspondence between the data and the application data.

[0080] 2. Data Standardization and Correlation Storage: Standardize the three types of data, unify data units, and ensure that the data format can be directly used for calculations. Establish a correlation mapping table between electricity application type, low-voltage distribution area, and carbon emission data to ensure that data can be quickly retrieved according to the application and low-voltage distribution area dimensions.

[0081] 3. Targeted Data Output: All collected carbon emission data of the three types are transmitted to the factor matching unit for the calculation of dynamic carbon emission factors; carbon emission limit data under dual carbon targets are synchronously transmitted to the scheme analysis module for low-carbon compliance assessment, strictly supporting the generation of application business datasets and the realization of the system's core innovative functions.

[0082] The factor matching unit is used to dynamically match carbon emission factors based on the electricity type in the application data, the regional characteristics in the low-voltage distribution area data, and the estimated length of the wiring path, and couple them with the application data and the low-voltage distribution area data to generate an application business dataset.

[0083] In this embodiment of the invention, firstly, data is retrieved strictly according to three predetermined dimensions: electricity type from the application data unit, regional characteristics from the low-voltage distribution area data unit, and the estimated wiring path length calculated using a GIS system based on the electricity address in the application data and the geographical range in the low-voltage distribution area data. Simultaneously, carbon emission factors per unit mileage of transportation vehicles and the average carbon emission factor of the regional power grid are retrieved from the carbon emission data unit, forming complete data support for factor matching. Secondly, a dynamic matching model is constructed based on the predetermined three-dimensional parameters, with the specific formula as follows:

[0084] ;

[0085] in, As a dynamic carbon emission factor; The basic carbon emission factor is determined based on the type of electricity used in the application data. The regional characteristic adjustment coefficient is determined based on the regional characteristics in the low-voltage distribution area data. This is the path correction value, calculated based on the estimated length of the wiring path:

[0086] .

[0087] Example calculation: An industrial user applies for electricity connection, the electricity type is industrial, the area characteristic in the low-voltage distribution area data is suburban, the estimated wiring path length is 5km, and the carbon emission factor per unit mile of transportation is 0.28. Material weight coefficient =1.0, if the basic carbon emission factor corresponding to industrial electricity consumption is 1.0. =0.6 Regional characteristic adjustment coefficient corresponding to suburbs =1.0, then This intuitively demonstrates the dynamic matching logic.

[0088] Finally, the dynamic carbon emission factor is deeply coupled with the application data and low-voltage distribution area data, and the application cost data from the application cost data unit is integrated to generate an application business dataset. The application business dataset adopts a standardized JSON format and includes four dimensions: application data, low-voltage distribution area data, application cost data, and carbon emission data. Each dimension covers its corresponding core content to ensure data integrity and relevance.

[0089] The visualization module 102 is used to visualize the low-voltage transformer area data in the application business dataset, and to construct and analyze the visualization scenario through the intelligent scenario adaptation algorithm to generate the solution formulation interface.

[0090] Furthermore, the visualization module 102 can perform the following sub-steps:

[0091] S11. Visualize the topology using the basic data of low-voltage distribution areas in the application service dataset, and generate a distribution area network topology map containing differentiated device identifiers.

[0092] In this embodiment of the invention, the distribution network topology map refers to a visual graphic constructed using geographic information technology based on low-voltage distribution area basic data, which intuitively presents the connection relationships and spatial distribution of equipment within the low-voltage distribution area. Differentiated equipment identification refers to using different icon styles and colors to distinguish various core devices within the distribution area, enabling rapid identification of equipment types.

[0093] In the specific implementation process, the basic data of low-voltage distribution areas is first retrieved from the application business dataset. This data is collected and generated by the low-voltage distribution area data unit of the data integration module, including core information such as the topology of the low-voltage distribution area, geographical scope, remaining transformer capacity, line parameters, and equipment parameters. A spatial coordinate system is built based on GIS (Geographic Information System) technology, mapping the geographical scope data of the low-voltage distribution area onto an electronic map base. According to the equipment connection relationships in the topology data, graphic icons for core equipment such as substations, transformers, lines, and distribution boxes are drawn sequentially. The equipment icons adopt a differentiated design: substations are represented by red square icons with the voltage level marked inside; transformers are represented by blue circular icons with rated capacity and remaining capacity marked; lines are represented by line segments of different thicknesses: phase lines are yellow, neutral lines are blue, and ground lines are yellow-green; distribution boxes are represented by gray rectangular icons with the installation location number marked. All equipment icons are associated with corresponding equipment parameters. Users can click on the icons to retrieve detailed information such as equipment model, manufacturing date, years of operation, line resistance, and reactance from the basic data of the low-voltage distribution area, realizing the linkage between topology visualization and equipment parameter query. The generated network topology map of the transformer area must ensure that the geographical coordinates deviate from the actual location by no more than 5 meters, and that the device connection relationship is consistent with the actual topology, so as to provide an accurate spatial carrier for subsequent load distribution display, area segmentation and path simulation.

[0094] S12. Calculate the load density using the power load data in the application service dataset, and render a heat map based on the load density to generate a load distribution heat map overlaid on the transformer network topology map.

[0095] In this embodiment of the invention, load density refers to the total electrical load within a unit power supply area, used to quantify the load concentration degree of low-voltage distribution areas. A load distribution heatmap is a visual image that intuitively presents the spatial distribution of load density through color gradient changes. When overlaid on a distribution area network topology map, it can simultaneously display device connection relationships and load distribution status.

[0096] The specific implementation process is as follows: First, extract the power load data of the low-voltage distribution area from the application data set. This data is obtained by the low-voltage distribution area data unit of the data integration module through the connection to the distribution area power grid monitoring system, and includes real-time power load data of the low-voltage distribution area for nearly 24 hours (collection frequency 15 minutes / time). Second, calculate the load density using the formula: Load density = Total power load of the low-voltage distribution area in 24 hours. The power supply area of ​​the transformer substation is calculated by summing the load data from all data collection points over 24 hours, using the geographic coordinates from the low-voltage transformer substation basic data (using a polygon area calculation formula). The third step involves setting load density grading standards, dividing the load into three levels based on numerical ranges: low load density (0-0.3...). Medium load density (0.3-0.6) High load density (>0.6) The fourth step is to render a heatmap using a linear color mapping rule: low load density corresponds to green, medium load density to yellow, and high load density to red. The color depth gradually increases with the load density value, and the rendering range is strictly limited to the geographical boundary of the low-voltage distribution area. The fifth step is to overlay the rendered load distribution heatmap onto the distribution area network topology map generated in step S11 using the same geographical coordinate system to generate a load distribution heatmap. This ensures that the equipment icons and the heatmap layer do not obscure each other, and that the load distribution and equipment location correspond accurately. This allows users to intuitively identify the equipment and lines corresponding to high-load areas, providing a basis for distribution area selection and path optimization.

[0097] S13. Extract the geographical complexity parameters of the application address and the load pressure level of the low-voltage distribution area through the intelligent scene adaptation algorithm, and combine them with the preset scene threshold to construct a judgment visualization scene that adapts to the current application scenario.

[0098] In this embodiment of the invention, the intelligent scene adaptation algorithm refers to an algorithm that dynamically adjusts the focus of the visualization display based on the core scene characteristics (geographical environment and load status) of the application service. The geographical complexity parameter is an indicator that quantifies the complexity of the geographical environment surrounding the application address. The load pressure level is a tiered indicator reflecting the current operational load tension of the low-voltage distribution area. The preset scene threshold is a critical value used to classify different scene types. The visualization scene analysis refers to a visualization interface optimized for the current application needs, highlighting key information.

[0099] In the specific implementation process, the core parameters are first extracted: one is the geographical complexity parameter, which is calculated based on the application address coordinates in the application business dataset, and the terrain, building, and road data in the electronic map are retrieved. The geographical complexity parameter is calculated using the formula: Geographical Complexity Parameter = Building Density + Road Distribution Density, where Building Density = Building Area within 1 Kilometer of the Application Address. Total area of ​​the region, road distribution density = total length of roads within 1 kilometer of the installation address The first parameter is the total area of ​​the region, with a value range of 0-1; the second is the load pressure level of the low-voltage distribution area, calculated based on the power load data in the application data set, which equals the current load rate equal to the real-time load. The transformer's rated capacity, combined with the load growth rate over the past three months, is calculated based on the load pressure level = load rate. (1 + load growth rate) is calculated, with a value range of 0-100.

[0100] Next, preset scenario thresholds are set: the geographical complexity parameter thresholds are divided into low complexity (<0.3), medium complexity (0.3-0.6), and high complexity (>0.6); the load pressure level thresholds are divided into low pressure (<50), medium pressure (50-80), and high pressure (>80).

[0101] Finally, intelligent scene adaptation is implemented: based on the matching results of parameters and thresholds, the visualization display status is dynamically adjusted: when the geographical complexity and load pressure are high, the scene prioritizes displaying equipment details (labeling equipment model and remaining capacity) and load heatmaps (highlighting areas with load rates > 80%), while hiding the topology structure of non-core areas; when the geographical complexity and load pressure are medium, the topology structure, load distribution, and equipment parameters are displayed in a balanced manner, and a pop-up window showing path optimization suggestions is displayed; when the geographical complexity and load pressure are low, the display of equipment details is simplified, highlighting the geographical range and accessible paths, reducing redundancy in interface information. Through scene adaptation, the visualization interface accurately matches the current application requirements, improving user operation efficiency.

[0102] S14. Based on the application information entered by the customer, perform geographical area segmentation and electricity address labeling in the analysis and visualization scenario to generate a target area map.

[0103] In this embodiment of the invention, geographic region segmentation refers to spatially cropping the visualization scene based on the geographic range associated with the application identification, retaining the core area related to the application service. Electricity address labeling refers to highlighting the specific location corresponding to the application with a specific identifier on the segmented area map. The target area map is a local visualization map focusing on the application address and accessible low-voltage distribution areas, used to simplify irrelevant information and focus on the core business scenario.

[0104] First, after the customer enters the application identifier, the system automatically associates it with the corresponding application data in the application business dataset, extracting the latitude and longitude coordinates of the application address and the geographical range data of the low-voltage distribution areas that can be accessed (provided by the distribution area filtering unit of the solution analysis module). Second, based on GIS spatial clipping technology, a rectangular area covering a 2-kilometer radius around the application address plus the complete range of the low-voltage distribution areas that can be accessed is cut out in the analysis visualization scene. Irrelevant geographical information and topological structures outside this area are clipped to simplify the interface display. Then, the electricity address is marked using a solid red dot with a diameter of 8 pixels. This dot is overlaid on the precise latitude and longitude of the application address, and the customer's name and electricity type are labeled next to the dot. The labeling information is displayed in a floating manner to avoid obscuring the core map content. Finally, a target area map is generated. The map supports zooming (from 1:500 to 1:5000) and panning operations. It retains the equipment identification and load heat map in the analysis and visualization scene, and adds a new area boundary line (dashed line) to clarify the target range, so that users can quickly focus on the relevant area for application and carry out subsequent path simulation operations.

[0105] S15. Call the preset device graphics library, respond to user operation commands to simulate wiring paths on the target area map, and generate an installation plan containing device model and path information.

[0106] In this embodiment of the invention, the preset device graphic library refers to a database storing visual icons of commonly used devices for installation applications. User operation instructions refer to drag-and-drop, selection, and confirmation operations initiated by users through input devices such as mice and touchscreens. Wiring path simulation refers to constructing the power supply line path for the installation application on a target area map by connecting device icons. The installation plan refers to a standardized plan document containing core information such as wiring paths and device configurations.

[0107] First, the system calls upon a pre-defined equipment graphics library, which stores vector icons of commonly used installation equipment such as cables, transformers, distribution boxes, insulators, and poles. Each icon is associated with a list of corresponding equipment models, technical parameters, and cost data, allowing users to select the appropriate model by double-clicking the icon. Second, responding to user commands, the system allows users to simulate wiring paths by dragging equipment icons to target locations, clicking on map nodes to define path nodes, and dragging line segments to connect equipment. The system captures the operation trajectory in real time, automatically generating a path coordinate sequence, and provides a path snapping function (snapping to the nearest equipment node or line) to avoid path intersections and unreasonable detours. Then, during path simulation, the system automatically verifies equipment compatibility (e.g., matching transformer capacity with the installed load) and path feasibility (e.g., avoiding crossing restricted construction areas). If conflicts exist, a warning message will pop up. Finally, the system generates an installation plan, which includes an equipment configuration list (equipment model, quantity, installation location), wiring path details (path length, nodes passed through, laying method), and a path diagram (overlaid on the target area map). All information is organized in a standardized format and synchronously transmitted to the plan evaluation module for subsequent multi-dimensional assessment.

[0108] S16. The assessment results and the application scheme corresponding to the application scheme are integrated and displayed in the assessment visualization scenario, and the interface functions are integrated using graphical control commands to generate the scheme formulation interface.

[0109] In this embodiment of the invention, the assessment result refers to the comprehensive conclusion reached by the scheme assessment module after evaluating the technical feasibility, economic efficiency, and low-carbon compliance of the proposed installation scheme. Graphical control instructions refer to the instruction set used to achieve interface function integration and interactive control. The scheme formulation interface refers to the final operation interface integrating functions such as viewing, modifying, confirming, and exporting schemes.

[0110] The construction process of the solution formulation interface is as follows: First, receive the solution evaluation results, generated by the solution evaluation module. These results include technical feasibility results (such as voltage deviation value, whether the equipment load rate meets the standard), economic evaluation results (such as total cost, cost comparison with similar solutions), low-carbon compliance results (such as total carbon emissions, whether it meets the dual carbon limits), and optimization suggestions. Second, execute the integrated display. On the target area map of the evaluation visualization scenario, a multi-layer overlay method is used to display the results: the wiring path of the proposed solution is highlighted with a solid red line, path segments that do not meet technical feasibility standards are marked with a dashed orange line, and path segments that exceed carbon emission standards are marked with a flashing red effect. The evaluation results are displayed in a floating table on the right, containing columns such as evaluation dimensions, specific indicators, standard thresholds, current values, and result judgments. Users can click on table rows to highlight relevant information about the corresponding path or equipment. Third, integrate interface functions. A function toolbar is set at the top of the interface through graphical control commands, including buttons for saving, modifying, exporting (supporting PDF / Excel format), resimulating the path, and viewing detailed reports. An operation status bar is set at the bottom of the interface, displaying the current operation steps and prompts. The fourth step is to generate a solution formulation interface. After viewing the assessment results, users can directly return to the path simulation stage to modify the solution (such as replacing low-carbon equipment or adjusting the path). After modification, the system will automatically update the assessment results and finally form a standardized application solution that can be submitted directly.

[0111] The solution evaluation module 103 is used to screen accessible low-voltage substations based on the application service dataset using a multi-objective optimization weight model, and to evaluate the application solutions generated in the solution formulation interface from multiple dimensions, generating solution evaluation results.

[0112] Furthermore, the solution analysis module 103 can perform the following steps:

[0113] S21. Based on the application service dataset, filter the low-voltage distribution areas that can be accessed and generate a list of candidate distribution areas.

[0114] In this embodiment of the invention, the candidate transformer area list refers to the set of transformer areas that meet the basic access conditions of the application scheme, selected from all low-voltage transformer areas through a multi-objective optimization weight model. Its core function is to provide accurate evaluation objects for subsequent multi-dimensional evaluation and avoid inefficiency caused by invalid evaluation.

[0115] The process of generating low-carbon compliance results is as follows: First, the core data required for screening is retrieved from the application business dataset, including geographical range data of each low-voltage distribution area, transformer remaining capacity data, load density data, and regional characteristic data. These data are all collected and generated by the corresponding units of the data integration module and coupled to the application business dataset. Screening indicators and dynamic weights are set based on a multi-objective optimization weight model: geographical distance weight 30%-40%, transformer remaining capacity weight 30%-40%, and load density weight 20%-30%. The weights can be flexibly adjusted according to the power grid policy guidance (such as the dual-carbon development period and cost control period).

[0116] The screening process is conducted in a three-tiered progressive manner: First, spatial relationship calculations are performed based on geographical range data and the application address, generating a primary candidate list by weighting geographical distance and prioritizing transformer substations closer to the application address. Second, based on the primary candidate list and transformer remaining capacity data, capacity adequacy screening is performed, retaining only substations with remaining capacity not less than 1.2 times the application load, and generating a secondary candidate list by weighting capacity. Third, based on the secondary candidate list and load density data, operational balance screening is performed, eliminating substations with load densities exceeding 0.8. High-load transformer substations are sorted by load density weight, and their low-carbon adaptability is verified by combining regional characteristic data. Substations that do not meet the dual-carbon target adaptability level or have inadequate infrastructure are eliminated, ultimately generating a candidate substation list. This screening process, through multi-objective weight balancing and a three-level progressive logic, ensures that the selected substations meet basic access requirements in terms of distance, capacity, load, and low-carbon adaptability, providing a high-quality pool of candidates for subsequent evaluation.

[0117] S22. Based on the list of alternative transformer substations and the application service dataset, conduct a technical feasibility assessment of the application schemes from the visualization module and generate technical feasibility results.

[0118] In this embodiment of the invention, the technical feasibility result refers to the conclusive data for assessing whether the application scheme complies with the power grid safety operation specifications, equipment carrying capacity and technical standards, including compliance judgment, key indicator values ​​and optimization suggestions. Its core function is to ensure that the implementation of the application scheme does not affect the safe and stable operation of the low-voltage distribution area.

[0119] The process of generating the technical feasibility results is as follows: First, the data required for the assessment is retrieved: the basic data (line parameters, equipment parameters, transformer remaining capacity) and real-time operating data (power load data) of the target transformer areas in the candidate transformer area list are all from the application business dataset; the core information of the application scheme (wiring path, equipment model, laying method, estimated load) is generated and transmitted by the visualization module. The assessment revolves around three core technical indicators: First, node voltage deviation. Based on the line parameters, transformer parameters and estimated load of the application scheme in the application business dataset, the node voltage of the transformer area after the application scheme is connected is calculated through the circuit power flow calculation model to determine whether it meets the requirement of "voltage deviation ≤" in DL / T1785-2017 "Technical Guidelines for Distribution Network Planning and Design". The evaluation process involves three key aspects: first, the standard of "7%"; second, the line current, which is calculated based on the path length, conductor type, and estimated load of the proposed installation plan to determine if it exceeds the conductor's rated current; and third, the equipment load rate, which is calculated to ensure that the load rate of core equipment such as transformers and distribution boxes does not exceed the safe operating threshold of 85%. Upon completion of the evaluation, a technical feasibility result is generated, clearly indicating whether each indicator meets the standard, its specific value, and the range of deviation. If any non-compliance exists (such as excessive voltage deviation), optimization suggestions are simultaneously output (such as shortening the wiring path or increasing the conductor cross-section). This evaluation process strictly adheres to industry technical standards and is based on precise data calculations to ensure the technical feasibility of the proposed installation plan.

[0120] S23. Based on the application business dataset, conduct an economic evaluation of the application scheme and generate economic evaluation results.

[0121] In this embodiment of the invention, the core data for generating the economic evaluation results comes from the application cost data in the application business dataset. This data is collected by the application cost data unit of the data integration module and includes the material purchase cost, transportation cost, and cost benchmark of similar applications implemented in different low-voltage distribution areas over the past year. First, the estimated total cost of the application plan is calculated: the material purchase cost is determined by retrieving the unit cost data of the corresponding materials (e.g., YJV-1kV-4) according to the equipment model and quantity in the application plan. 25 The total cost is calculated based on the purchase price of the cables. Transportation costs are calculated based on the wiring path length, total material weight, and the unit mileage transportation cost of the corresponding transport vehicles. The estimated total cost = material purchase cost + transportation cost. Next, a cost comparison analysis is conducted: the estimated total cost is compared with the average cost of similar transformer areas and load applications over the past year, calculated using the data from the application service. The cost deviation rate is calculated as follows: Deviation rate = (estimated total cost - average cost) / average cost. 100%). Finally, an economic assessment result is generated, clearly indicating the estimated total cost, cost composition percentage (material cost percentage, transportation cost percentage), cost deviation rate, and assessment grade (deviation rate ≤). 10% is considered excellent. 10%- 20% is considered good, exceeding the standard. A 20% deviation is considered poor. If the cost deviation rate is too high, optimization suggestions are provided (such as replacing materials with more cost-effective ones or optimizing routes to shorten transportation distances). This evaluation process is based on historical cost data and precise calculations to ensure the objectivity and accuracy of cost assessments, providing a cost reference for solution decisions.

[0122] S24. Based on the application business dataset, conduct a low-carbon compliance assessment of the application scheme and generate low-carbon compliance results.

[0123] In this embodiment of the invention, the low-carbon compliance result refers to conclusive data assessing whether the carbon emission level of the application scheme meets the requirements of the dual-carbon policy. This includes total carbon emissions, carbon emission intensity, compliance determination, and low-carbon optimization suggestions. Its core function is to respond to the dual-carbon objectives and ensure the low-carbon and environmentally friendly attributes of the application scheme. During the generation of the low-carbon compliance result, the assessment data comes from carbon emission-related data in the application business dataset, including dynamic carbon emission factors (dynamically calculated by the factor matching unit) and carbon emission limit data under the dual-carbon objectives (collected by the carbon emission data unit). First, the total carbon emissions of the application scheme are calculated using the formula: Total carbon emissions = Application load. Service life Dynamic carbon emission factor + transportation carbon emissions, where transportation carbon emissions = wiring path length Carbon emission factor per unit mileage of transportation vehicles Material weight factor; Carbon emission intensity = Total carbon emissions Loading capacity (unit: Secondly, compliance is assessed: the total carbon emissions are compared with the corresponding carbon emission limits under the dual-carbon targets (set according to electricity consumption type and regional classification), and the carbon emission intensity is compared with the regional power grid average carbon emission factor. If the total carbon emissions are less than or equal to the limit and the carbon emission intensity is less than or equal to the regional power grid average carbon emission factor, it is considered compliant; otherwise, it is considered non-compliant. Finally, a low-carbon compliance result is generated, clearly indicating the total carbon emissions, carbon emission intensity, and compliance assessment conclusion. If non-compliant, targeted optimization suggestions are output (such as replacing with low-carbon equipment, optimizing routes to reduce transportation carbon emissions, and selecting transformer substations with higher low-carbon adaptability levels). S25. Using the technical feasibility results, economic assessment results, and low-carbon compliance results, a solution evaluation result is constructed.

[0124] In this embodiment of the invention, firstly, dynamic weights are assigned to the three major evaluation results based on a multi-objective optimization weight model. The weight allocation rules are linked to power grid policy guidance: the dual-carbon development option is reassigned to technical feasibility (30%), economic efficiency (20%), and low-carbon compliance (50%); the cost control option is reassigned to technical feasibility (30%), economic efficiency (50%), and low-carbon compliance (20%); and the routine operation and maintenance option is reassigned to technical feasibility (40%), economic efficiency (30%), and low-carbon compliance (30%). Secondly, a comprehensive score is calculated, and each dimension is assigned a quantitative score according to the evaluation level (Excellent = 100 points, Good = 80 points, Average = 60 points, Poor = 40 points). The comprehensive score equals the technical feasibility score. Technical weighting + economic score Economic weighting + low-carbon compliance score Low-carbon weighting is applied, and a comprehensive evaluation level is determined based on the overall score (≥90 points for Excellent, 80-89 points for Good, 60-79 points for Pass, and <60 points for Fail). For existing evaluation conflicts (such as excellent economics but unqualified low-carbon compliance, or technical feasibility but excessive cost deviation), conflict resolution logic is invoked. Combining optimization suggestions from various dimensions, 2-3 conflict resolution solutions are generated (such as replacing low-carbon materials to balance cost and low-carbon requirements, or selecting transformer area balancing technology with more capacity and cost considerations). The comprehensive score for each solution is calculated. Finally, a solution evaluation result is constructed, including the comprehensive evaluation level, details of core indicators for each dimension, comparison of conflict resolution solutions, the final recommended solution, and implementation suggestions. This result is simultaneously transmitted to the visualization module for integrated display.

[0125] Furthermore, S21 includes the following steps:

[0126] S211. Extract the geographical range data, transformer remaining capacity data, load density data and regional characteristic data of each low-voltage distribution area from the application data set;

[0127] S212. Based on the geographical range data and the customer electricity address in the application business dataset, perform spatial relationship calculations and sort according to the geographical distance weight in the multi-objective optimization weight model to generate a preliminary candidate list.

[0128] S213. Based on the primary candidate list and transformer remaining capacity data, perform capacity adequacy screening, and sort according to the capacity weight in the multi-objective optimization weight model to generate a secondary candidate list.

[0129] S214. Based on the secondary candidate list and load density data, perform operation balance screening and sort according to the load density weight in the multi-objective optimization weight model to generate a three-level candidate list.

[0130] S215. Combine regional characteristic data to perform low-carbon adaptability verification on the transformer substations in the three-level candidate list, and at the same time verify the matching degree between infrastructure and application requirements. Eliminate transformer substations that do not meet the low-carbon adaptability standards or whose infrastructure cannot be matched, and generate a candidate transformer substation list.

[0131] In this embodiment of the invention, geographical range data, transformer remaining capacity data, load density data, and regional characteristic data of each low-voltage distribution area are extracted from the application business dataset. The extracted data includes core information such as distribution area spatial boundaries, equipment carrying capacity, load distribution intensity, regional low-carbon adaptability attributes, and infrastructure configuration. All data has been standardized, with a unified format and accurate values. Based on the geographical range data and customer electricity addresses in the application business dataset, the shortest straight-line distance between the two is calculated using GIS spatial analysis technology to quantify spatial proximity. According to the geographical distance weight ratio of 30%-40% in the multi-objective optimization weight model, all low-voltage distribution areas are sorted from near to far, retaining the top N distribution areas with the closest distance (N defaults to 20, which can be adjusted through the system management module) to generate a primary candidate list. With the standard that the transformer remaining capacity is not less than 1.2 times the estimated load of the application plan, the primary candidate list is screened for capacity adequacy. After removing distribution areas with insufficient capacity, the remaining capacity is sorted from largest to smallest according to the capacity weight ratio of 30%-40% in the multi-objective optimization weight model to generate a secondary candidate list.

[0132] Load density is calculated by dividing the total real-time load of the transformer area by the power supply area, with a load density ≤ 0.8. To ensure operational balance, a threshold-based screening process was implemented, eliminating transformer substations with excessively concentrated loads and high operational pressure. A three-tiered candidate list was generated, sorted by load density from lowest to highest according to a 20%-30% load density weighting in the multi-objective optimization weighting model, balancing substation operational efficiency and lifespan. Regional characteristic data was used to verify whether the regional power grid carbon emission benchmark for the substation was ≤ the benchmark threshold corresponding to the dynamic carbon emission factor in the application scheme, and whether the dual-carbon target adaptation level was ≥ Level 2, completing the low-carbon adaptability verification. Simultaneously, the line parameters and equipment parameters of the substation were verified to ensure compatibility with the equipment models and laying methods in the application scheme, guaranteeing that the line current carrying capacity meets the application load requirements, completing the infrastructure matching verification. Substations failing both verifications were eliminated, and then weighted according to geographical distance. Distance sorting score + capacity weight Capacity ranking score + load density weight The "load ranking score" is used to calculate a comprehensive score, and a list of candidate transformer substations is generated based on the comprehensive score from high to low to ensure that the selected substations meet the application requirements in multiple dimensions such as space, capacity, load, low carbon, and infrastructure.

[0133] System management module 104 is used to perform access control, data backup and security management of the data integration module, visualization display module and solution analysis module, and generate system management policies.

[0134] Furthermore, the system management module 104 can perform the following steps:

[0135] S31. Perform user permission management, assign corresponding data viewing permissions, scheme modification permissions and system setting permissions to different users, and generate user permission allocation data.

[0136] In this embodiment of the invention, user permission management is a hierarchical control system built on a role-based permission mapping mechanism. Its core objective is to achieve the principle of least privilege through refined permission division, ensuring the controllability of system operations and data access. The process of generating user permission allocation data is as follows: First, users are divided into three core roles according to their business functions: system administrator, application service operator, and data viewing user. System administrators have full permissions, including configuring role permission templates, adjusting multi-objective optimization weight model parameters, and modifying backup cycles, among other system-level settings. Application service operators only have business operation permissions such as data viewing, application scheme modification, and exporting analysis results, without system configuration permissions. Data viewing users can only browse archived application schemes and analysis results, without modification or export permissions. Permission allocation is completed through a visual configuration interface, using a role-based permission set binding method. Each permission set corresponds to a clear operation scope and data access boundary, such as only being able to view application data within its own area. After allocation, structured user permission allocation data is automatically generated, including core fields such as username, role type, permission list, and effective time, and stored in the permission management database.

[0137] S32. Regularly back up all system data generated by the data integration module, visualization module, and solution analysis module to local storage devices and off-site servers to generate system backup data.

[0138] In this embodiment of the invention, data backup is the core means to ensure system data integrity and business continuity. The full system data encompasses all core data, including application business datasets, analysis results, user operation logs, and equipment operation records. The generation process of system backup data adopts a strategy combining incremental and full backups. Incremental backups are performed daily at midnight, synchronizing only data added or modified within the previous 24 hours to reduce backup time and storage usage. Full backups are performed every Sunday at midnight, completely copying all system data to ensure data traceability integrity. The backup data employs a dual-backup architecture: local backups are stored on a high-performance disk array supporting RAID5 redundancy to prevent single-disk failures; off-site backups are deployed on a disaster recovery center server at least 500 kilometers from the main system, transmitted via a dedicated communication link, with data verification mechanisms such as CRC32 ensuring data consistency during transmission. The generated system backup data is named and archived according to backup time, data type, and backup level formats, supporting rapid retrieval and recovery by time point. A backup data retention period is set: 30 days for local backups and 90 days for off-site backups, balancing storage costs and data availability.

[0139] S33. Encrypt and control the transmission and storage of customer data collected and stored by the data integration module, and generate sensitive encrypted data.

[0140] In this embodiment of the invention, customer data includes legally protected sensitive information such as customer name, ID number, contact information, and electricity address. The core objective of encrypted transmission and storage control is to comply with the requirements of laws and regulations such as the Personal Information Protection Law and the Cybersecurity Law, and to prevent the leakage of sensitive data. Specifically, transmission encryption uses the SSL TLS 1.3 protocol to build a secure communication channel. Before data transmission, it is segmented, with each segment not exceeding 1MB, and a unique checksum is added to each segment. The receiving end verifies the data before reassembly, ensuring that the data is not tampered with or stolen during transmission. Storage encryption uses the AES256 GCM algorithm to perform field-level encryption on customer data, that is, only sensitive fields such as ID numbers and mobile phone numbers are encrypted, while non-sensitive fields such as application numbers are kept in plaintext, balancing security and data query efficiency. The encryption key adopts a two-layer management mechanism of master key and data key. The master key is stored in the hardware security module HSM, and the data key is encrypted by the master key and stored in association with the encrypted data. Each encryption and decryption requires verification and authorization through the HSM. The generated sensitive encrypted data includes encrypted customer data, encrypted log records of encryption time and key number, and access audit records, ensuring that sensitive data is traceable throughout its entire lifecycle.

[0141] S34. Build system management policies by using user permission allocation data, system backup data, and sensitive encrypted data.

[0142] In this embodiment of the invention, the system management strategy is a comprehensive management specification that integrates access control, data backup, and sensitive data protection. It serves as the core basis for the secure operation of the system, rather than a simple aggregation of isolated data. The construction process of the system management strategy is as follows: First, based on user permission allocation data, access control rules are formulated, clarifying the operation procedures for different roles, such as requiring double review for scheme modifications, approval for data access scope (e.g., cross-regional data access), and permission change procedures (e.g., permission upgrades require administrator approval and record retention). Second, based on system backup data, backup and recovery procedures are formulated, clarifying the recovery priority after data loss (e.g., priority recovery of application business datasets), recovery operation steps, and post-recovery verification standards (e.g., data integrity not less than 99.99%). Third, based on sensitive encrypted data, encryption management specifications are formulated, clarifying the encryption algorithm update cycle (algorithm security is assessed every 12 months), the key rotation rule (data keys are rotated every 6 months), and the requirement for sensitive data access auditing that all access operations retain logs for at least one year. The system management strategy is presented in a structured document format, including modules such as control objectives, scope of application, specific rules, responsible parties, and performance indicators. At the same time, a dynamic adjustment mechanism is established, and the strategy is optimized and revised every quarter based on system operation logs, security vulnerability scan results, and policy and regulatory updates to ensure the timeliness and applicability of the strategy.

[0143] Please see Figure 2 , Figure 2 A flowchart illustrating the steps of a low-voltage customer application scheme evaluation method provided in this embodiment of the invention.

[0144] This invention provides a method for evaluating low-voltage customer installation plans, comprising:

[0145] Step 201: Obtain application data, application cost data, low-voltage distribution area data and carbon emission data, and based on the electricity consumption type in the application data and the regional characteristics in the low-voltage distribution area data, dynamically match and couple the associated carbon emission factors to generate an application business dataset.

[0146] Step 202: Visualize the low-voltage transformer area data in the application service dataset, and construct and analyze the visualization scenario through the intelligent scenario adaptation algorithm to generate the solution formulation interface;

[0147] Step 203: Based on the application service dataset, use a multi-objective optimization weight model to screen the low-voltage substations that can be accessed, and conduct a multi-dimensional evaluation of the application schemes generated in the scheme formulation interface to generate scheme evaluation results.

[0148] Step 204: Perform access control, data backup and security management on the control modules corresponding to the application business dataset, solution formulation interface and solution analysis results, and generate system management policies.

[0149] In this embodiment of the invention, the customer installation application system, distribution network equipment management system, transformer area power grid monitoring system, and carbon emission data management platform are first linked to synchronously collect four types of key data. Based on this, a basic carbon emission factor is determined according to the electricity consumption type in the installation application data, and an adjustment coefficient is assigned by combining the regional characteristics in the low-voltage transformer area data. A path correction value is calculated with reference to the estimated wiring path length. A dynamic matching algorithm is used to achieve deep coupling between the carbon emission factor and the installation application data and low-voltage transformer area data, ultimately integrating the four types of data to form a unified and complete installation business dataset.

[0150] Based on GIS technology, the basic data of low-voltage distribution areas in the application business dataset is processed to generate a distribution area network topology map with differentiated equipment identifiers. Simultaneously, load density is calculated using power load data, and a heatmap rendering is overlaid on the topology map to clearly present the load distribution status. An intelligent scene adaptation algorithm extracts the geographical complexity parameters of the application address and the load pressure level of the low-voltage distribution area, and dynamically adjusts the display focus based on preset thresholds to achieve precise adaptation between the scene and the application requirements. Based on the application number entered by the customer, geographical areas are segmented and electricity addresses are labeled to generate a target area map. A preset equipment graphics library is called to simulate wiring paths in response to user operations, generating an application plan containing equipment model and path information. Finally, the application plan is integrated with subsequent analysis results, and functions such as saving, modifying, and exporting are integrated to form an intuitive and convenient plan creation interface.

[0151] First, based on the application data set, the geographical scope, remaining transformer capacity, load density, and regional characteristics of each low-voltage distribution area are extracted. Using a multi-objective optimization weighting model with predefined weights, a three-tiered screening process is implemented based on spatial proximity, capacity adequacy, and operational balance. Then, a dual verification of low-carbon adaptability and infrastructure matching is performed to eliminate unqualified distribution areas and generate a candidate list. Subsequently, a multi-dimensional evaluation of the application schemes is conducted: Technical feasibility assessment calculates transformer load rate, total line current, and node voltage deviation rate, comparing them with corresponding limits to determine if the scheme has issues such as overload, overcurrent, or voltage exceeding limits; economic assessment calculates the total cost of material procurement and transportation, comparing it with the annual average cost benchmark of similar application schemes to determine the economic level and identify cost overruns; carbon emission assessment calculates the total carbon emissions and intensity of transportation and customer electricity consumption, comparing them with dual-carbon target limits to obtain the difference results. Finally, the three types of assessment results are integrated, combined with conflict resolution logic, to generate a scheme evaluation result containing comprehensive judgment conclusions and optimization suggestions, assisting staff in making scientific decisions and reducing subsequent construction risks and cost waste.

[0152] The system implements access control, data backup, and security management for the application business dataset, solution formulation interface, and control modules corresponding to solution evaluation results, generating a system management policy. Users are categorized into three roles based on business function: system administrator, application business operator, and data viewer. Corresponding permissions are assigned for data viewing, solution modification, and system settings, achieving least privilege control and preventing unauthorized operations and data leaks. A strategy combining incremental and full backups is adopted, with daily synchronization of newly added or modified data and weekly full data copying. Backup data is stored simultaneously on a local disk array and in an off-site disaster recovery center, ensuring rapid recovery in case of data loss and guaranteeing business continuity. Sensitive customer data is protected with dual encryption during transmission and storage. An encrypted channel is built through a secure communication protocol during transmission, and field-level encryption algorithms are used for protection during storage. Keys are secured through a two-layer management mechanism. Finally, access control data, backup data, and security management data are integrated to formulate a system management policy that includes access control rules, backup and recovery procedures, and encryption management specifications. A dynamic adjustment mechanism is established to ensure the system operates safely and stably while maintaining compliance.

[0153] Please see Figure 3 , Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention.

[0154] 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 executes the low-voltage customer application scheme judgment method as described in any of the above embodiments.

[0155] 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 these codes are run by a computing device, the computing device causes the computing device to perform the various steps in the low-voltage customer application scheme assessment method described above.

[0156] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the low-voltage customer application scheme judgment method as described in the above embodiments.

[0157] 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 customer application scheme judgment method as described in any of the above embodiments.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 customer installation scheme analysis system, characterized in that, include: The data integration module is used to acquire application data, application cost data, low-voltage distribution area data and carbon emission data, and dynamically match and couple the associated carbon emission factors based on the electricity consumption type in the application data and the regional characteristics in the low-voltage distribution area data to generate an application business dataset. The visualization module is used to visualize the low-voltage transformer area data in the application service dataset, and to construct and analyze the visualization scenario through an intelligent scene adaptation algorithm to generate a solution formulation interface. The scheme evaluation module is used to screen accessible low-voltage substations based on the application service dataset using a multi-objective optimization weight model, and to evaluate the application schemes generated in the scheme formulation interface from multiple dimensions, thereby generating scheme evaluation results. The system management module is used to perform access control, data backup and security management on the data integration module, visualization display module and solution analysis module, and generate system management policies.

2. The low-voltage customer installation scheme analysis system according to claim 1, characterized in that, The data integration module includes: The application data unit is used to connect to the customer application system, obtain application data including customer name, electricity type and electricity address, and assign a unique application number to each application. The application cost data unit is used to collect application cost data of different low-voltage distribution areas that have implemented application schemes in the past year. The application cost data includes the purchase cost and transportation cost of different materials. The low-voltage distribution area data unit is used to collect basic data and real-time operational data of the low-voltage distribution area and generate low-voltage distribution area data. The basic data includes low-voltage distribution area topology data, geographical range of each low-voltage distribution area, transformer remaining capacity, line parameters, and equipment parameters. The real-time operational data includes power load data within the low-voltage distribution area. The carbon emission data unit is used to collect carbon emission data corresponding to the application data. The carbon emission data includes carbon emission factor per unit mileage of transportation vehicles, average carbon emission factor of regional power grid, and carbon emission limit data under dual carbon targets. The factor matching unit is used to dynamically match carbon emission factors based on the electricity type in the application data, the regional characteristics in the low-voltage distribution area data, and the estimated length of the wiring path, and couple and associate them with the application data and the low-voltage distribution area data to generate an application business dataset.

3. The low-voltage customer installation scheme analysis system according to claim 1, characterized in that, The visualization module performs the following steps: The network topology is visualized using the basic data of low-voltage distribution areas in the application service dataset, generating a distribution area network topology map containing differentiated device identifiers; The load density is calculated using the power load data in the application service dataset, and a heat map is rendered based on the load density to generate a load distribution heat map superimposed on the network topology map of the transformer area. The geographic complexity parameters of the application address and the load pressure level of the low-voltage distribution area are extracted by the intelligent scene adaptation algorithm, and combined with the preset scene threshold, a judgment visualization scene adapted to the current application scenario is constructed. Based on the installation application identifier entered by the customer, the geographic area is segmented and the electricity address is marked in the analysis and visualization scenario to generate a target area map; Call the preset device graphics library, respond to user operation commands to simulate wiring paths on the target area map, and generate an installation plan that includes device model and path information; The assessment results corresponding to the application scheme and the application scheme are integrated and displayed in the assessment visualization scenario, and the interface functions are integrated using graphical control commands to generate a scheme formulation interface.

4. The low-voltage customer installation scheme analysis system according to claim 1, characterized in that, The scheme evaluation module performs the following steps: Based on the application service dataset, select low-voltage transformer areas that can be accessed and generate a list of candidate transformer areas. Based on the list of candidate substations and the application service dataset, a technical feasibility assessment is performed on the application schemes from the visualization module, and a technical feasibility result is generated. Based on the application service dataset, the economic feasibility of the application scheme is evaluated, and economic evaluation results are generated. Based on the application service dataset, the low-carbon compliance of the application scheme is assessed, and low-carbon compliance results are generated. The proposed solution evaluation results are constructed using the aforementioned technical feasibility results, economic evaluation results, and low-carbon compliance results.

5. The low-voltage customer installation scheme analysis system according to claim 4, characterized in that, The step of filtering accessible low-voltage distribution areas and generating a list of candidate distribution areas based on the application service dataset includes: Extract the geographical range data, transformer remaining capacity data, load density data, and regional characteristic data of each low-voltage distribution area from the application data set; Spatial relationship calculations are performed based on the geographical range data and the customer electricity addresses in the application business dataset, and the results are sorted according to the geographical distance weight in the multi-objective optimization weight model to generate a preliminary candidate list. Based on the primary candidate list and the remaining transformer capacity data, capacity adequacy is screened, and a secondary candidate list is generated by sorting according to the capacity weights in the multi-objective optimization weight model. Based on the secondary candidate list and the load density data, the operational balance is screened, and the load density weights are sorted according to the multi-objective optimization weight model to generate a third-level candidate list. The low-carbon adaptability of the transformer substations in the three-level candidate list is verified by combining regional characteristic data. At the same time, the matching degree between the infrastructure and the application requirements is verified. Substations that do not meet the low-carbon adaptability standards or whose infrastructure cannot be matched are eliminated, and a candidate transformer substation list is generated.

6. The low-voltage customer installation scheme analysis system according to claim 1, characterized in that, The system management module performs the following steps: Perform user permission management, assign corresponding data viewing permissions, scheme modification permissions, and system setting permissions to different users, and generate user permission allocation data; The full system data generated by the data integration module, the visualization module, and the solution analysis module is periodically backed up to local storage devices and off-site servers to generate system backup data. The data integration module collects and stores customer data, and performs encrypted transmission and storage control to generate sensitive encrypted data; The system management strategy is constructed using the user permission allocation data, the system backup data, and the sensitive encrypted data.

7. A method for evaluating low-voltage customer installation plans, characterized in that, include: Acquire application data, application cost data, low-voltage distribution area data, and carbon emission data. Based on the electricity consumption type in the application data and the regional characteristics in the low-voltage distribution area data, dynamically match and couple the associated carbon emission factors to generate an application business dataset. The low-voltage transformer area data in the application service dataset is visualized, and a visualization scenario is constructed and judged through an intelligent scenario adaptation algorithm to generate a solution formulation interface. Based on the application service dataset, a multi-objective optimization weight model is used to screen the low-voltage substations that can be accessed, and the application schemes generated in the scheme formulation interface are evaluated from multiple dimensions to generate scheme evaluation results. The system implements access control, data backup, and security management for the control modules corresponding to the application service dataset, the solution formulation interface, and the solution evaluation results, and generates system management policies.

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 customer application scheme judgment 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 customer installation scheme analysis 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 customer application scheme judgment method as described in claim 7.

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