An artificial intelligence-based electrical configuration automatic generation method and system

Abstract

The application belongs to the technical field of electrical configuration automatic generation, and discloses an electrical configuration automatic generation method and system based on artificial intelligence; the system comprises a knowledge base management module, an expert reasoning module, a configuration file generation module, an electrical rule editing module, a Web rendering engine module and a user interaction interface module, obtains a device knowledge data report, reasons the electrical design based on the device knowledge data report, obtains a reasoning result report, converts the reasoning result report into a configuration file recognizable by the rendering engine, obtains an electrical configuration configuration report, obtains a rendering state feedback report, integrates all functions of the system, and provides a unified user interface, supports device model establishment, knowledge rule editing and configuration data binding, and overall, the application has the remarkable advantages of high design efficiency and quality improvement capability, good configuration standard unification effect and flexible system rule adjustment.

Description

Technical Field

[0001] This invention relates to the field of automatic electrical configuration generation technology, and more specifically, to an automatic electrical configuration generation method and system based on artificial intelligence. Background Technology

[0002] An automatic electrical configuration generation system is an advanced technology platform that utilizes computer technology and artificial intelligence algorithms to transform electrical engineering design from a traditional manual drafting method to an automated and intelligent generation method. By integrating electrical engineering knowledge, design specifications, equipment parameters, and layout rules, this system can automatically generate electrical single-line diagrams, system topology diagrams, and monitoring configuration interfaces that conform to engineering standards. In modern industrial digitalization and smart grid construction, this type of system has become a core tool for improving the efficiency of power monitoring system implementation, ensuring design quality, and reducing engineering costs.

[0003] Traditional electrical configuration design primarily relies on manual work by engineers, involving multiple steps such as equipment selection, drawing preparation, data point binding, and interface configuration. This process is time-consuming, labor-intensive, and prone to errors. With the increasing scale and complexity of power systems, especially the emergence of new power systems such as renewable energy integration and microgrid construction, traditional design methods are no longer sufficient to meet the demands for rapid deployment, flexible adjustment, and standardized management. Therefore, developing a system capable of automating and intelligently generating electrical configurations has become an urgent industry need.

[0004] In view of this, the present invention proposes an automatic electrical configuration generation method and system based on artificial intelligence to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution, including:

[0006] The knowledge base management module is used to collect electrical equipment parameters based on the user interface, process them, and obtain equipment knowledge data reports.

[0007] Furthermore, the steps for collecting and processing electrical equipment parameters based on the user interface include:

[0008] S1.1: Collect electrical equipment parameters input by staff based on the user interface to obtain the equipment parameter dataset;

[0009] S1.2: Verify the data integrity of the equipment parameter dataset. When the verification result is that a parameter is missing, fill it with the default value and unify the units of all values ​​to obtain a standardized dataset.

[0010] S1.3: Based on a standardized dataset, and according to the device ID, the device parameters are converted into fact records, all fact records are packaged, and a device knowledge data report is obtained;

[0011] S1.4: Store the equipment knowledge data report in the database and output it to the expert reasoning module;

[0012] The expert reasoning module is used to perform reasoning on electrical designs based on equipment knowledge data reports and generate reasoning result reports.

[0013] Furthermore, the steps for reasoning about electrical designs based on equipment knowledge data reports include:

[0014] S2.1: Obtain the equipment knowledge data report, retrieve the electrical design rule table based on the database, and initialize the inference environment of the CLIIPS expert system;

[0015] S2.2: Input the equipment knowledge data report and electrical design rule table into the CLIIPS expert system, perform reasoning, and output a rule reasoning report;

[0016] S2.3: Calculate the initial position coordinates of each device based on the rule-based reasoning report to obtain the device layout report;

[0017] S2.4: Pack the rule reasoning report and device layout report, obtain the reasoning result report, and record the rule conflicts in the reasoning process of step S2.2 to obtain the conflict detection report;

[0018] S2.5: Output the reasoning result report to the configuration file generation module and the conflict detection report to the knowledge base management module;

[0019] The configuration file generation module is used to convert the inference result report into a configuration file that the rendering engine can recognize, thereby obtaining an electrical configuration report;

[0020] Furthermore, the steps to convert the inference results report into a configuration file that the rendering engine can recognize include:

[0021] S3.1: Read the inference result report, retrieve the corresponding SVG template from the database based on the device type of the device ID, apply the device location coordinates to the SVG template, and obtain a device graphic element set report;

[0022] S3.2: Calculate the SVG path for each connection relationship and obtain a connection graph set report;

[0023] S3.3: Generate an electrical configuration report based on the device graphical element set report and the connection graphical set report;

[0024] S3.4: Verify the completeness and consistency of the inference result report to obtain the configuration file verification report;

[0025] S3.5: Output the electrical configuration report to the Web rendering engine module and the configuration file verification report to the expert inference module;

[0026] The electrical rule editing module provides a visual rule editing interface, manages design rules, and generates rule definition data reports.

[0027] Furthermore, a visual rule editing interface is provided, and the steps for managing design rules include:

[0028] S4.1: Supports staff to create new rules or edit existing rules through the rule management interface of the user interface, and provides a rule editor that allows users to input rule conditions and conclusions through forms or scripts;

[0029] S4.2: Supports the function of converting natural language to rule-based language;

[0030] S4.3: Supports adding data point binding and display rules to knowledge rules and combining them with the existing SCADA system's device data points to complete the automatic binding of data points in the group map system;

[0031] S4.4: Support staff to test the rules using simulated fact sets. Once the staff confirms the test results, proceed to step S4.5.

[0032] S4.5: Save the rules tested in step S4.4 to obtain the rule definition data report;

[0033] S4.6: Output the rule definition data report to the knowledge base management module;

[0034] The Web rendering engine module is used to parse the electrical configuration report, render interactive SVG graphics, and obtain a rendering status feedback report.

[0035] Furthermore, the steps of parsing the electrical configuration report and rendering interactive SVG graphics include:

[0036] S5.1: Parse the electrical configuration report to obtain the parsed configuration report;

[0037] S5.2: Create an SVG canvas and insert the device graphic description list and connection path list from the parsed configuration report into the SVG canvas to obtain the SVG canvas rendering report;

[0038] S5.3: Based on the binding information in the parsed configuration report, a data listener is created for each data point binding. When a data update is received, the corresponding SVG graphic element is found according to the device ID and data point type in the binding, and the attributes of the SVG image element are updated.

[0039] S5.4: Record the status and errors during the rendering process, and package them together with the SVG canvas rendering report to obtain a rendering status feedback report;

[0040] S5.5: Output the rendering status feedback report to the user interface module;

[0041] The user interface module is used to integrate all system functions and provide a unified user interface to support user operations, knowledge management and data operations.

[0042] Furthermore, the steps to integrate all system functions and provide a unified user interface to support user operations, knowledge management, and data manipulation include:

[0043] S6.1: When staff create a new project, they enter the equipment parameter configuration interface, which supports staff to add equipment item by item or import equipment parameters in batches.

[0044] After the user completes the parameter input, the system outputs a chart generation command to the expert reasoning module.

[0045] S6.2: Supports staff to switch to the rule management interface and call the editor component of the electrical rule editing module to be embedded in the page;

[0046] S6.3: After the Web rendering engine module finishes rendering, the user interface module will embed the SVG canvas rendering report into the page for display and provide staff with adjustment tools;

[0047] S6.4: After staff make adjustments to the SVG canvas rendering report, support staff to save the adjustments and learn from them, extract the differences after adjustment, package them with the current device parameters, and obtain the user adjustment feedback report;

[0048] S6.5: Output user adjustment feedback reports to the knowledge base management module for optimization of subsequent reasoning projects;

[0049] Furthermore, an artificial intelligence-based method for automatically generating electrical configurations includes:

[0050] S1: Collect electrical equipment parameters based on the user interface, process them, and obtain equipment knowledge data reports;

[0051] S2: Based on the equipment knowledge data report, perform reasoning on the electrical design and obtain a reasoning result report;

[0052] S3: Convert the inference result report into a configuration file that the rendering engine can recognize, and obtain the electrical configuration report;

[0053] S4: Provides a visual rule editing interface, manages design rules, and generates rule definition data reports;

[0054] S5: Based on the electrical configuration report, parse it and render an interactive SVG graphic to obtain a rendering status feedback report;

[0055] S6: Integrates all system functions and provides a unified user interface to support user operation, system learning, and data manipulation.

[0056] The technical effects and advantages of the automatic electrical configuration generation method and system based on artificial intelligence of the present invention are as follows:

[0057] This invention collects and processes electrical equipment parameters through a user interface to obtain an equipment knowledge data report. Based on this report, it performs reasoning on electrical designs, generating a reasoning result report. This report is then converted into a configuration file recognizable by the rendering engine, resulting in an electrical configuration report. A visual rule editing interface is provided to manage design rules, generating a rule definition data report. The electrical configuration report is then parsed, and interactive SVG graphics are rendered, producing a rendering status feedback report. This invention integrates all system functions and provides a unified user interface, supporting user operation, system learning, and data manipulation. The system, through its knowledge base management module and expert reasoning module, integrates equipment parameters, connection rules, and... This invention systematically organizes knowledge such as layout specifications to maximize design efficiency. Furthermore, it establishes a standardized electrical configuration file through a configuration file generation module and a Web rendering engine module, reducing the need for multiple data entries and ensuring data consistency throughout the entire process. Finally, through an electrical rule editing module and a user interface module, it establishes a visual editing, testing, and optimization mechanism for design rules. This allows the system to not only apply existing knowledge but also continuously learn new rules and optimize existing ones through user interaction, forming a self-evolving knowledge system. Overall, this invention has significant advantages in improving design efficiency and quality, achieving good configuration standardization, and allowing for flexible adjustment of system rules. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of an automatic electrical configuration generation system based on artificial intelligence according to the present invention;

[0059] Figure 2 This is a schematic diagram of an automatic electrical configuration generation method based on artificial intelligence according to the present invention;

[0060] Figure 3 This is a schematic diagram of the result of an automatic electrical configuration generation system based on artificial intelligence according to the present invention. Detailed Implementation

[0061] 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, and 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.

[0062] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0063] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0064] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0065] In practice, the server-side equipment deployed by the AI-based automatic electrical configuration generation system may consist of one or more devices. This AI-based automatic electrical configuration generation system can be implemented as: a business instance, a virtual machine, or hardware devices. For example, this AI-based automatic electrical configuration generation system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this AI-based automatic electrical configuration generation system can be understood as software deployed on a cloud node, used to provide AI-based automatic electrical configuration generation to various user terminals. Alternatively, this AI-based automatic electrical configuration generation system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Or, this AI-based automatic electrical configuration generation system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide AI-based automatic electrical configuration generation to various user terminals.

[0066] In terms of implementation, the AI-based automatic electrical configuration generation system and the user terminal are mutually compatible. That is, if the AI-based automatic electrical configuration generation system is implemented as an application installed on a cloud service platform, the user terminal is implemented as a client that establishes a communication connection with the application; or if the AI-based automatic electrical configuration generation system is implemented as a website, the user terminal is implemented as a webpage; or if the AI-based automatic electrical configuration generation system is implemented as a cloud service platform, the user terminal is implemented as a mini-program in an instant messaging application.

[0067] like Figure 1 The figure shown is a system architecture diagram of an artificial intelligence-based automatic electrical configuration generation system provided in an embodiment of the present invention.

[0068] The AI-based automatic electrical configuration generation system described in this invention can be hosted on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server or server cluster), or it can be developed into a website. Depending on the functions implemented, the AI-based automatic electrical configuration generation system may include a knowledge base management module, an expert reasoning module, a configuration file generation module, an electrical rule editing module, a web rendering engine module, and a user interface module. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0069] In this embodiment of the invention, in the AI-based automatic electrical configuration generation system, each of the above modules can be implemented independently and can call other modules. Here, "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. For example, the user interface module can call the same information acquisition module to obtain information collected by that module. Based on the above characteristics, in the AI-based automatic electrical configuration generation system provided in this embodiment of the invention, without modifying the program code, the applicability of the AI-based automatic electrical configuration generation system architecture can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion to quickly and flexibly expand the AI-based automatic electrical configuration generation system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0070] Example 1

[0071] Please see Figure 1 As shown in this embodiment, an automatic electrical configuration generation system based on artificial intelligence is described. The system includes:

[0072] The knowledge base management module is used to collect electrical equipment parameters based on the user interface, process them, and obtain equipment knowledge data reports.

[0073] Furthermore, the steps for collecting and processing electrical equipment parameters based on the user interface include:

[0074] S1.1: Collect electrical equipment parameters input by staff based on the user interface to obtain the equipment parameter dataset;

[0075] S1.2: Verify the data integrity of the equipment parameter dataset. When the verification result is that a parameter is missing, fill it with the default value and unify the units of all values ​​to obtain a standardized dataset.

[0076] It needs to be explained that unifying the units of all values ​​means, for example, unifying the unit of voltage values ​​to kilovolts and the unit of size values ​​to millimeters.

[0077] S1.3: Based on a standardized dataset, and according to the device ID, the device parameters are converted into fact records, all fact records are packaged, and a device knowledge data report is obtained;

[0078] It should be explained that fact records refer to, for example, including but not limited to, device ID, device type, parameter set, and collection timestamp, where the parameter set consists of parameter names and the corresponding parameter values;

[0079] S1.4: Store the equipment knowledge data report in the database and output it to the expert reasoning module;

[0080] The expert reasoning module is used to reason about the electrical design based on the equipment knowledge data report and obtain a reasoning result report.

[0081] Furthermore, the steps for reasoning about electrical designs based on equipment knowledge data reports include:

[0082] S2.1: Obtain the equipment knowledge data report, retrieve the electrical design rule table based on the database, and initialize the inference environment of the CLIIPS expert system;

[0083] S2.2: Input the equipment knowledge data report and electrical design rule table into the CLIIPS expert system, perform reasoning, and output a rule reasoning report;

[0084] It should be explained that the rule reasoning report includes, but is not limited to, a set of connection relationships between devices, a sequence of device layouts, and a set of data point binding suggestions;

[0085] S2.3: Calculate the initial position coordinates of each device based on the rule-based reasoning report to obtain the device layout report;

[0086] S2.4: Pack the rule reasoning report and device layout report, obtain the reasoning result report, and record the rule conflicts in the reasoning process of step S2.2 to obtain the conflict detection report;

[0087] S2.5: Output the reasoning result report to the configuration file generation module and the conflict detection report to the knowledge base management module;

[0088] It needs to be explained that building a web-based clips expert system involves encapsulating the expert system's knowledge management interface and providing the system's knowledge injection management interface.

[0089] The configuration file generation module is used to convert the inference result report into a configuration file that the rendering engine can recognize, thereby obtaining an electrical configuration report;

[0090] Furthermore, the steps to convert the inference results report into a configuration file that the rendering engine can recognize include:

[0091] S3.1: Read the inference result report, retrieve the corresponding SVG template from the database based on the device type of the device ID, apply the device location coordinates to the SVG template, and obtain a device graphic element set report;

[0092] It needs to be explained that applying device position coordinates to an SVG template means performing a translation transformation on the SVG template;

[0093] S3.2: Calculate the SVG path for each connection relationship and obtain a connection graph set report;

[0094] S3.3: Generate an electrical configuration report based on the device graphical element set report and the connection graphical set report;

[0095] It should be explained that the electrical configuration report includes a list of device graphical descriptions, a list of connection paths, a list of data point bindings, and layout information. Taking the graphical description list as an example, the image description list includes the device ID, SVG element, position, and size.

[0096] S3.4: Verify the completeness and consistency of the inference result report to obtain the configuration file verification report;

[0097] S3.5: Output the electrical configuration to the Web rendering engine module and output the configuration file verification report to the expert inference module;

[0098] It needs to be explained that, based on the characteristics of electrical single-line diagrams in electrical systems, the drawing rules for electrical single-line diagrams and the data point binding and combination methods are sorted out, and corresponding knowledge rules are compiled for the configuration file used to automatically generate configuration diagrams;

[0099] The electrical rule editing module is used to provide a visual rule editing interface, manage design rules, and obtain rule definition data reports;

[0100] Furthermore, a visual rule editing interface is provided, and the steps for managing design rules include:

[0101] S4.1: Supports staff to create new rules or edit existing rules through the rule management interface of the user interface, and provides a rule editor that allows users to input rule conditions and conclusions through forms or scripts;

[0102] It should be explained that the rules include layout rules, connection rules, and display rules;

[0103] S4.2: Supports the function of converting natural language to rule-based language;

[0104] S4.3: Supports adding data point binding and display rules to knowledge rules and combining them with the existing SCADA system's device data points to complete the automatic binding of data points in the group map system;

[0105] It should be explained that step S4.3 is used to automate the previously cumbersome process of configuring data and binding points, thereby improving engineering efficiency and saving time and costs.

[0106] S4.4: Support staff to test the rules using simulated fact sets. Once the staff confirms the test results, proceed to step S4.5.

[0107] It should be explained that "testing" refers to retrieving the CLIIPS expert system to perform the test and returning the matching results;

[0108] S4.5: Save the rules tested in step S4.4 to obtain the rule definition data report;

[0109] S4.6: Output the rule definition data report to the knowledge base management module;

[0110] The Web rendering engine module is used to parse the electrical configuration report, render interactive SVG graphics, and obtain a rendering status feedback report.

[0111] Furthermore, the steps of parsing the electrical configuration report and rendering interactive SVG graphics include:

[0112] S5.1: Parse the electrical configuration report to obtain the parsed configuration report;

[0113] It should be explained that the parsing in step S5.1 refers to extracting data such as the device graphical description, connection path, and binding information from the electrical configuration report;

[0114] S5.2: Create an SVG canvas and insert the device graphic description list and connection path list from the parsed configuration report into the SVG canvas to obtain the SVG canvas rendering report;

[0115] S5.3: Based on the binding information in the parsed configuration report, a data listener is created for each data point binding. When a data update is received, the corresponding SVG graphic element is found according to the device ID and data point type in the binding, and the attributes of the SVG image element are updated.

[0116] It should be explained that the data listener is used to monitor real-time data sources;

[0117] S5.4: Record the status and errors during the rendering process, and package them together with the SVG canvas rendering report to obtain a rendering status feedback report;

[0118] S5.5: Output the rendering status feedback report to the user interface module;

[0119] It needs to be explained that a rendering engine is developed in the web front-end page to generate the corresponding SVG screen from the electrical single-line diagram configuration file generated by the expert system and combine it with relevant data points and animation effects;

[0120] The user interface module is used to integrate all system functions and provide a unified user interface to support user operations, knowledge management and data operations.

[0121] Furthermore, the steps to integrate all system functions and provide a unified user interface to support user operations, knowledge management, and data manipulation include:

[0122] S6.1: When staff create a new project, they enter the equipment parameter configuration interface, which supports staff to add equipment item by item or import equipment parameters in batches.

[0123] After the user completes the parameter input, the system outputs a chart generation command to the expert reasoning module.

[0124] It needs to be explained that a dedicated web-based human-computer interaction interface is developed, in which manual input is required for the electrical system to input information such as the attributes of the equipment, distribution cabinets, transformers and other electrical parameters, and this information is converted into facts for the expert system and saved.

[0125] S6.2: Supports staff to switch to the rule management interface and call the editor component of the electrical rule editing module to be embedded in the page;

[0126] S6.3: After the Web rendering engine module finishes rendering, the user interface module will embed the SVG canvas rendering report into the page for display and provide staff with adjustment tools;

[0127] It needs to be explained that a database suitable for CLIPS expert system knowledge storage is constructed for the storage, editing, and management of expert system knowledge;

[0128] S6.4: After staff make adjustments to the SVG canvas rendering report, support staff to save the adjustments and learn from them, extract the differences after adjustment, package them with the current device parameters, and obtain the user adjustment feedback report;

[0129] S6.5: Output user adjustment feedback reports to the knowledge base management module for optimization of subsequent reasoning projects;

[0130] The beneficial effects of this embodiment are as follows: Electrical equipment parameters are collected and processed through a user interface to obtain an equipment knowledge data report. Based on this report, electrical design is inferred to produce a reasoning result report. This report is then converted into a configuration file recognizable by the rendering engine to obtain an electrical configuration report. A visual rule editing interface is provided to manage design rules and obtain a rule definition data report. The electrical configuration report is parsed, and interactive SVG graphics are rendered to produce a rendering status feedback report. This integrates all system functions and provides a unified user interface, supporting user operation, rule knowledge editing, and data manipulation. This allows the system to integrate equipment parameters and connection details in electrical engineering design through the knowledge base management module and expert reasoning module. This embodiment systematically organizes knowledge such as rules and layout specifications to maximize design efficiency. Furthermore, it establishes a standardized electrical configuration file through a configuration file generation module and a Web rendering engine module, reducing the need for multiple data entries and ensuring data consistency throughout the entire process. Finally, through an electrical rule editing module and a user interface module, it establishes a visual editing, testing, and optimization mechanism for design rules. This allows the system to not only apply existing knowledge but also continuously learn new rules and optimize existing ones through user interaction, forming a self-evolving knowledge system. Overall, this embodiment has significant advantages in improving design efficiency and quality, achieving good data standardization, and demonstrating strong adaptive learning capabilities.

[0131] Example 2

[0132] Please see Figure 2As shown, the parts not described in detail in this embodiment are described in Embodiment 1. An automatic electrical configuration generation method based on artificial intelligence is provided. The method includes: S1: collecting electrical equipment parameters based on a user interface and processing them to obtain an equipment knowledge data report;

[0133] S2: Based on the equipment knowledge data report, perform reasoning on the electrical design and obtain a reasoning result report;

[0134] S3: Convert the inference result report into a configuration file that the rendering engine can recognize, and obtain the electrical configuration report;

[0135] S4: Provides a visual rule editing interface, manages design rules, and generates rule definition data reports;

[0136] S5: Based on the electrical configuration report, parse it and render an interactive SVG graphic to obtain a rendering status feedback report;

[0137] S6: Integrates all system functions and provides a unified user interface to support user operation, system learning, and data manipulation.

[0138] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the present invention.

Claims

1. An automatic electrical configuration generation system based on artificial intelligence, characterized in that, The system includes: a knowledge base management module, an expert reasoning module, a configuration file generation module, an electrical rule editing module, a web rendering engine module, and a user interface module, wherein: The knowledge base management module is used to collect electrical equipment parameters based on the user interface, process them, and obtain equipment knowledge data reports. The expert reasoning module is used to reason about the electrical design based on the equipment knowledge data report and obtain a reasoning result report. The configuration file generation module is used to convert the inference result report into a configuration file that the rendering engine can recognize, thereby obtaining an electrical configuration report; The electrical rule editing module is used to provide a visual rule editing interface, manage design rules, and obtain rule definition data reports; The Web rendering engine module is used to parse the electrical configuration report, render interactive SVG graphics, and obtain a rendering status feedback report. The user interface module is used to integrate all system functions and provide a unified user interface to support user operations, knowledge management, and data operations.

2. The automatic electrical configuration generation system based on artificial intelligence according to claim 1, characterized in that, The steps for collecting and processing electrical equipment parameters based on a user interface include: S1.1: Collect electrical equipment parameters input by staff based on the user interface to obtain the equipment parameter dataset; S1.2: Verify the data integrity of the equipment parameter dataset. When the verification result is that a parameter is missing, fill it with the default value and unify the units of all values ​​to obtain a standardized dataset. S1.3: Based on a standardized dataset, and according to the device ID, the device parameters are converted into fact records, all fact records are packaged, and a device knowledge data report is obtained; S1.4: Store the equipment knowledge data report in the database and output it to the expert reasoning module.

3. The automatic electrical configuration generation system based on artificial intelligence according to claim 2, characterized in that, The steps involved in reasoning about electrical designs based on equipment knowledge data reports include: S2.1: Obtain the equipment knowledge data report, retrieve the electrical design rule table based on the database, and initialize the inference environment of the CLIIPS expert system; S2.2: Input the equipment knowledge data report and electrical design rule table into the CLIIPS expert system, perform reasoning, and output a rule reasoning report; S2.3: Calculate the initial position coordinates of each device based on the rule-based reasoning report to obtain the device layout report; S2.4: Pack the rule reasoning report and device layout report, obtain the reasoning result report, and record the rule conflicts in the reasoning process of step S2.2 to obtain the conflict detection report; S2.5: Output the reasoning result report to the configuration file generation module and the conflict detection report to the knowledge base management module.

4. The automatic electrical configuration generation system based on artificial intelligence according to claim 3, characterized in that, The steps to convert the inference results report into a configuration file that the rendering engine can recognize include: S3.1: Read the inference result report, retrieve the corresponding SVG template from the database based on the device type of the device ID, apply the device location coordinates to the SVG template, and obtain a device graphic element set report; S3.2: Calculate the SVG path for each connection relationship and obtain a connection graph set report; S3.3: Generate an electrical configuration report based on the device graphical element set report and the connection graphical set report; S3.4: Verify the completeness and consistency of the inference result report to obtain the configuration file verification report; S3.5: Output the electrical configuration report to the Web rendering engine module and the configuration file verification report to the expert inference module.

5. The automatic electrical configuration generation system based on artificial intelligence according to claim 1, characterized in that, Providing a visual rule editing interface and managing design rules involves the following steps: S4.1: Supports staff to create new rules or edit existing rules through the rule management interface of the user interface, and provides a rule editor that allows users to input rule conditions and conclusions through forms or scripts; S4.2: Supports the function of converting natural language to rule-based language; S4.3: Supports adding data point binding and display rules to knowledge rules and combining them with the existing SCADA system's device data points to complete the automatic binding of data points in the group map system; S4.4: Support staff to test the rules using simulated fact sets. Once the staff confirms the test results, proceed to step S4.

5. S4.5: Save the rules tested in step S4.4 to obtain the rule definition data report; S4.6: Output the rule definition data report to the knowledge base management module.

6. The automatic electrical configuration generation system based on artificial intelligence according to claim 4, characterized in that, The steps for parsing the electrical configuration report and rendering interactive SVG graphics include: S5.1: Parse the electrical configuration report to obtain the parsed configuration report; S5.2: Create an SVG canvas and insert the device graphic description list and connection path list from the parsed configuration report into the SVG canvas to obtain the SVG canvas rendering report; S5.3: Based on the binding information in the parsed configuration report, a data listener is created for each data point binding. When a data update is received, the corresponding SVG graphic element is found according to the device ID and data point type in the binding, and the attributes of the SVG image element are updated. S5.4: Record the status and errors during the rendering process, and package them together with the SVG canvas rendering report to obtain a rendering status feedback report; S5.5: Output the rendering status feedback report to the user interface module.

7. The automatic electrical configuration generation system based on artificial intelligence according to claim 1, characterized in that, The steps for integrating all system functions and providing a unified user interface to support user operations, knowledge management, and data manipulation include: S6.1: When staff create a new project, they enter the equipment parameter configuration interface, which supports staff to add equipment item by item or import equipment parameters in batches. After the user completes the parameter input, the system outputs a chart generation command to the expert reasoning module. S6.2: Supports staff to switch to the rule management interface and call the editor component of the electrical rule editing module to be embedded in the page; S6.3: After the Web rendering engine module finishes rendering, the user interface module will embed the SVG canvas rendering report into the page for display and provide staff with adjustment tools; S6.4: After staff make adjustments to the SVG canvas rendering report, support staff to save the adjustments and learn from them, extract the differences after adjustment, package them with the current device parameters, and obtain the user adjustment feedback report; S6.5: Output user adjustment feedback reports to the knowledge base management module for optimization of subsequent reasoning projects.

8. An automatic electrical configuration generation method based on artificial intelligence, implemented according to any one of claims 1-7, characterized in that, The work includes the following steps: S1: Collect electrical equipment parameters based on the user interface, process them, and obtain equipment knowledge data reports; S2: Based on the equipment knowledge data report, perform reasoning on the electrical design and obtain a reasoning result report; S3: Convert the inference result report into a configuration file that the rendering engine can recognize, and obtain the electrical configuration report; S4: Provides a visual rule editing interface, manages design rules, and generates rule definition data reports; S5: Based on the electrical configuration report, parse it and render an interactive SVG graphic to obtain a rendering status feedback report; S6: Integrates all system functions and provides a unified user interface to support user operation, system learning, and data manipulation.

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