Electrical equipment-oriented magnetic material type selection optimization method and system

Abstract

The invention discloses an electrical equipment-oriented magnetic material type selection optimization method and system, and the method comprises the steps: carrying out the matching of the type of electrical equipment and the type of a magnetic material according to the information of an application scene, and obtaining material characteristic data; performing nonlinear relation mapping on the electrical equipment type, the magnetic material type and the material characteristic data, and constructing a magnetic material type selection model; and inputting equipment processing requirements into the magnetic material type selection model, and carrying out matching calculation on the application scene characteristic parameters and the intrinsic characteristic parameters to obtain a magnetic material type selection result meeting the equipment processing requirements. According to the method and the device, the information of the magnetic materials for the electrical equipment and the information of the application scenes are integrated, the magnetic material type selection model is constructed, the magnetic materials for the electrical equipment for different application scenes are quickly and accurately selected through the model, and the processing efficiency and the use effect of the electrical equipment are improved.

Description

Technical Field

[0001] This application belongs to the field of power grid equipment design and manufacturing, specifically relating to a method and system for optimizing the selection of magnetic materials for electrical equipment. Background Technology

[0002] Electrical energy is an indispensable energy source for social development. The production, transmission, distribution, conversion, and use of electrical energy require power electronic equipment such as generators, transformers, and motors. Grain-oriented silicon steel and amorphous soft magnetic alloys are key core materials widely used in power electronic equipment. The performance of these magnetic materials determines the energy conversion capability, efficiency, safety, and stability during the manufacturing process and use of electrical equipment, and is a crucial factor in determining the service performance of electrical equipment. Therefore, it is necessary to study the material selection for electrical equipment.

[0003] One existing method for material selection is based on product manuals provided by the materials industry. However, this approach offers limited material sources, and the final selection of materials that meet the manufacturing and usage requirements of electrical equipment largely depends on highly experienced engineers. This results in low selection efficiency and limited accuracy, ultimately affecting the processing progress and product performance of electrical equipment. Another approach is to establish a material selection database based on research. However, existing databases cover limited material data, lack comprehensiveness and timeliness, and lack multi-dimensional correlation data such as application scenarios, manufacturing processes, microstructure, and service performance of typical grades. This fails to meet the requirements for accurate and rapid selection of magnetic materials, limiting the selection of magnetic materials for specific application conditions. Summary of the Invention

[0004] This application proposes a method and system for optimizing the selection of magnetic materials for electrical equipment. It integrates information on magnetic materials used in electrical equipment and application scenarios to construct a magnetic material selection model. Through the model, magnetic materials for electrical equipment in different application scenarios can be selected quickly and accurately, thereby improving the processing efficiency and performance of electrical equipment.

[0005] The first aspect of this application provides a method for optimizing the selection of magnetic materials for electrical equipment, the method comprising:

[0006] Based on the application scenario information, the types of electrical equipment and magnetic materials are matched to obtain material property data; wherein, the material property data includes application scenario characteristic parameters and intrinsic characteristic parameters;

[0007] A nonlinear relationship mapping was performed on the data of electrical equipment type, magnetic material type and material properties to construct a magnetic material selection model;

[0008] The equipment processing requirements are input into the magnetic material selection model, and the application scenario characteristic parameters and intrinsic characteristic parameters are matched and calculated to obtain the magnetic material selection result that meets the equipment processing requirements.

[0009] The above solution combines application scenario information to match electrical equipment types with magnetic material types, obtaining material characteristic data. This integrates information on magnetic materials used in electrical equipment with application scenario information, providing data support for subsequent data to be applicable to different application scenarios. Then, a non-linear relationship mapping is applied to the electrical equipment type, magnetic material type, and material characteristic data to establish connections between the data, and a magnetic material selection model is constructed based on this. Equipment processing requirements are input into the magnetic material selection model for matching. By matching the application scenario characteristic parameters and intrinsic characteristic parameters, accurate magnetic material selection results that meet the equipment processing requirements are quickly obtained. Based on these magnetic material selection results, suitable magnetic material types for electrical equipment processing can be determined, improving the processing efficiency and equipment performance.

[0010] In one possible implementation of the first aspect, the type of electrical equipment and the type of magnetic material are matched based on the application scenario information to obtain material property data, specifically:

[0011] Match the types of electrical equipment with the types of magnetic materials to determine the target users of the magnetic materials;

[0012] Based on the application scenario information and the service object, determine the application scenario characteristic parameters;

[0013] Based on the performance requirements of electrical equipment and the characteristic parameters of the application scenario, the intrinsic characteristic parameters are obtained.

[0014] Material property data are obtained based on the service object, application scenario characteristic parameters, and intrinsic characteristic parameters.

[0015] The above scheme matches the type of electrical equipment with the type of magnetic material, and then combines the application scenario information to obtain the application scenario characteristic parameters, which can be used for subsequent application scenario matching of magnetic materials, so that the magnetic material selection results meet the required application scenarios; then, based on the performance requirements of the electrical equipment and combined with the application scenario characteristic parameters, the intrinsic characteristic parameters are obtained, which are used for subsequent performance requirement matching of magnetic materials, so that the magnetic material selection results meet the performance requirements, thereby improving the performance of the electrical equipment processed with magnetic materials.

[0016] In one possible implementation of the first aspect, the application scenario characteristic parameters are determined based on the application scenario information and the service object, specifically as follows:

[0017] Based on the application scenario information and the service object, the application conditions of the magnetic material are determined, and the machining and heat treatment methods that meet the application conditions are obtained.

[0018] The application conditions, machining methods, and heat treatment methods are used to extract and quantify features to obtain feature values. Then, the feature values ​​are prioritized and classified to obtain application scenario characteristic parameters.

[0019] The above scheme prioritizes the feature values, which is also prioritizing the characteristic parameters of the application scenario. This ensures that the most important features are matched first in subsequent data matching, thus improving the accuracy of the magnetic material selection results.

[0020] In one possible implementation of the first aspect, a nonlinear relationship mapping is performed on the electrical equipment type, magnetic material type, and material property data to construct a magnetic material selection model, specifically:

[0021] Nonlinear relationship mapping is performed on the data of electrical equipment type, magnetic material type and material properties to obtain an equipment-material knowledge graph;

[0022] Based on the equipment-materials knowledge graph, a model architecture is constructed to obtain a magnetic material selection model.

[0023] The above scheme maps electrical equipment type, magnetic material type, and material property data in a nonlinear relationship to ensure that the material property data is matched according to the input electrical equipment type, and then the corresponding magnetic material type is quickly obtained based on the matched material property data, thereby improving the speed of obtaining magnetic material selection results.

[0024] In one possible implementation of the first aspect, the equipment processing requirements are input into the magnetic material selection model, and the application scenario characteristic parameters and intrinsic characteristic parameters are matched and calculated to obtain a magnetic material selection result that meets the equipment processing requirements, specifically as follows:

[0025] The equipment processing requirements are input into the magnetic material selection model for matching, and the first electrical equipment type corresponding to the equipment processing requirements is obtained.

[0026] Based on the first type of electrical equipment, a matching degree index is obtained by matching according to the priority of application scenario characteristic parameters and intrinsic characteristic parameters.

[0027] When the matching degree index is greater than the first threshold, the magnetic material selection result that meets the processing requirements of the equipment is obtained.

[0028] In the above scheme, the first threshold represents the accuracy of the model. When the matching degree index is greater than the first threshold, it means that a magnetic material selection result that meets the equipment processing requirements and the accuracy of the model has been found.

[0029] The second aspect of this application provides a magnetic material selection optimization system for electrical equipment, the system comprising: a data matching module, a magnetic material selection model construction module, and a magnetic material selection result generation module;

[0030] The data matching module is used to match the type of electrical equipment and the type of magnetic material according to the application scenario information to obtain material characteristic data; wherein, the material characteristic data includes application scenario characteristic parameters and intrinsic characteristic parameters;

[0031] The magnetic material selection model construction module is used to perform nonlinear relationship mapping on electrical equipment type, magnetic material type and material property data to construct a magnetic material selection model;

[0032] The magnetic material selection result generation module is used to input the equipment processing requirements into the magnetic material selection model, perform matching calculations on the application scenario characteristic parameters and intrinsic characteristic parameters, and obtain magnetic material selection results that meet the equipment processing requirements.

[0033] In one possible implementation of the second aspect, the data matching module includes: a material property data generation unit;

[0034] The material property data generation unit is used to match electrical equipment types and magnetic material types to determine the service objects of magnetic materials; determine application scenario characteristic parameters based on application scenario information and the service objects; obtain intrinsic characteristic parameters based on the performance requirements of electrical equipment and the application scenario characteristic parameters; and obtain material property data based on the service objects, application scenario characteristic parameters, and intrinsic characteristic parameters.

[0035] In one possible implementation of the second aspect, the material property data generation unit includes: an application scenario property parameter generation unit;

[0036] The application scenario characteristic parameter generation unit is used to determine the application conditions of the magnetic material based on the application scenario information and the service object, and to obtain the machining and heat treatment methods that meet the application conditions; to extract and quantify the application conditions, machining methods and heat treatment methods to obtain feature values, and then to classify the feature values ​​by priority to obtain the application scenario characteristic parameters.

[0037] In one possible implementation of the second aspect, the magnetic material selection model construction module includes: a magnetic material selection model construction unit;

[0038] The magnetic material selection model construction unit is used to perform nonlinear relationship mapping on electrical equipment type, magnetic material type and material property data to obtain equipment-material knowledge graph; based on the equipment-material knowledge graph, a model architecture is constructed to obtain the magnetic material selection model.

[0039] In one possible implementation of the second aspect, the magnetic material selection result generation module includes: a magnetic material selection result generation unit;

[0040] The magnetic material selection result generation unit is used to input the equipment processing requirements into the magnetic material selection model for matching, and obtain the first electrical equipment type corresponding to the equipment processing requirements; according to the first electrical equipment type, the matching calculation is performed according to the priority of application scenario characteristic parameters and intrinsic characteristic parameters to obtain a matching degree index; when the matching degree index is greater than a first threshold, a magnetic material selection result that meets the equipment processing requirements is obtained. Attached Figure Description

[0041] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a schematic flowchart illustrating a method for selecting and optimizing magnetic materials for electrical equipment, provided in a certain embodiment of this application.

[0043] Figure 2 This is a structural diagram of a magnetic material selection and optimization system for electrical equipment provided in a certain embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0045] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0046] like Figure 1 As shown, Figure 1This is a schematic flowchart illustrating a method for optimizing the selection of magnetic materials for electrical equipment according to a certain embodiment of this application. The method for optimizing the selection of magnetic materials for electrical equipment in some embodiments includes steps S1 to S3, detailed below:

[0047] Step S1: Based on the application scenario information, match the type of electrical equipment with the type of magnetic material to obtain material property data;

[0048] In this step, based on the function of electrical equipment in converting electrical signals throughout the power lifecycle, the types of electrical equipment are determined from four directions: power generation, power transformation, power swapping, and power consumption. These electrical equipment types are categorized as follows: large motors (power generation end), power transformers (transmission and distribution end), power electronic transformers (power consumption end), and signal converters (converters, frequency converters, phase shifters, including converters, inverters, voltage regulators, instrument transformers, reactors (inductors, chokes), sensors, magnetic shielding materials, radio frequency identifiers, etc., belonging to the power consumption end). Among these, the electrical equipment is the service target of magnetic materials. The service target can be adjusted according to the functional expansion of the electrical equipment. For example, when the function of the electrical equipment is not limited to electrical signal conversion, the service target of magnetic materials expands, such as wireless power supplies, electromagnetic shielding devices, and magnetic switches.

[0049] After determining the type of electrical equipment, match the type of electrical equipment with the type of magnetic material to determine the target users of the magnetic material.

[0050] Then, based on the application scenario information and the service object, the application conditions of the magnetic material, as well as the machining and heat treatment methods of the magnetic material for preparing the iron core product corresponding to the application conditions, are determined.

[0051] Furthermore, the application conditions are first quantified into four characteristic values: operating capacity (voltage), operating frequency, operating temperature, and surface stress, which the electrical equipment must meet. Then, based on these characteristic values, the prototype core structure for different electrical equipment is determined, thereby determining the corresponding machining and heat treatment methods for the magnetic materials used in manufacturing the core structure. The machining methods include scoring, shearing, and shearing + winding; the heat treatment methods include no heat treatment, conventional heat treatment, and longitudinal magnetic field heat treatment. The mechanical properties of the magnetic structure must meet the requirements of machining and heat treatment of the magnetic materials during core manufacturing.

[0052] For example, the operating capacity of transformers is classified according to the following standards: power transformers (including large transformers of 7.5 to 1500 kV·A and medium transformers of 1000 to 7000 kV·A), distribution transformers (medium transformers of 10 to 500 kV·A), small transformers (voltage below 10 kV and capacity of 1 to 500 kV·A), and special transformers (assigned according to industry-standard values).

[0053] Transformer operating frequency classification: Power frequency transformers operate at 50Hz or 60Hz. Medium frequency transformers operate at 400–1000Hz. High frequency transformers operate at 1000Hz–20kHz. Ultra-high frequency transformers operate at frequencies above 20kHz, generally not exceeding 100kHz.

[0054] The application conditions, machining methods, and thermal processing methods are feature-extracted and quantified to obtain feature values. Then, the feature values ​​are prioritized to obtain application scenario characteristic parameters. The priority is related to feature values ​​such as operating voltage, operating frequency, operating temperature, and surface stress.

[0055] Furthermore, the priority categories of the application scenario characteristic parameters are assigned values. The lower the assigned value, the higher the priority of the attribute during material selection. The priorities, from highest to lowest, are: operating capacity (voltage), operating frequency, operating temperature, surface stress, scoring, machining, and heat treatment. The attributes of the application scenario characteristic parameters are determined according to the application conditions of different commercial electrical equipment and have a fixed value range.

[0056] Based on the performance requirements of electrical equipment and the characteristic parameters of the application scenario, intrinsic characteristic parameters are obtained; wherein, the intrinsic characteristic parameters are set in order of priority from high to low when selecting materials as follows: chemical composition, microstructure, physical properties, magnetic properties, mechanical properties, and grade specifications.

[0057] Finally, based on the service object, application scenario characteristic parameters, and intrinsic characteristic parameters, the material property data is obtained.

[0058] Step S2: Perform nonlinear relationship mapping on the data of electrical equipment type, magnetic material type and material properties to construct a magnetic material selection model;

[0059] In this step, the material property data is first collected, cleaned, and mined to construct a magnetic material characteristic database. The data collection involves obtaining quantified material property data through methods such as magnetic material product standards, manuals, manufacturer information, scientific literature, empirical formula calculations, and laboratory testing. The data cleaning and mining processes involve cleaning experiments on the quantified material property data from different sources, identifying and eliminating suspicious data based on metallurgical, materials science, and mathematical principles, and classifying the data according to attribute characteristics and standardizing the format to obtain a dataset-formatted material property data.

[0060] The material property data is then weighted and cleaned again based on these weights to remove outliers or duplicates, creating a data queue of associated properties with decreasing weights. The material property data is then sorted according to this data queue.

[0061] A nonlinear relationship mapping is performed on the sorted material property data, electrical equipment types, and magnetic material types to construct an equipment-material knowledge graph. The equipment-material knowledge graph is formed by nonlinearly mapping and associating multiple dimensions of characteristic parameters of magnetic materials, such as service objects, electrical equipment types, application scenario characteristic parameters, chemical composition, microstructure, performance, and grade specifications.

[0062] The equipment-materials knowledge graph is written into the magnetic material feature database using machine language tools.

[0063] Then, based on the magnetic material characteristic database, the data architecture, logical architecture, and physical architecture of the model are designed to construct a magnetic material selection model.

[0064] Step S3: Input the equipment processing requirements into the magnetic material selection model, perform matching calculations on the application scenario characteristic parameters and intrinsic characteristic parameters, and obtain the magnetic material selection result that meets the equipment processing requirements.

[0065] In this step, in some embodiments, equipment processing requirements are input into the magnetic material selection model through a human-computer interaction interface. The magnetic material selection model selects electrical equipment types and material characteristic data from the equipment processing requirements for matching, thereby obtaining the first electrical equipment type corresponding to the equipment processing requirements. The first electrical equipment type can also be displayed in the human-computer interaction interface to measure the working efficiency of the material selection software by providing the user with the response time.

[0066] Based on the first type of electrical equipment, a matching degree index is obtained by matching according to the priority of application scenario characteristic parameters and intrinsic characteristic parameters. Further, the matching is performed one by one according to the priority of the application scenario characteristic parameters and intrinsic characteristic parameters from high to low, and the matching degree index is obtained based on the degree of matching.

[0067] When the matching degree index is greater than the first threshold, the magnetic material selection result that meets the processing requirements of the equipment is obtained.

[0068] Furthermore, in order to implement the magnetic material selection and optimization system for electrical equipment corresponding to the above method embodiments, and to achieve the corresponding functional and technical effects, Figure 2 A specific structural diagram of a magnetic material selection and optimization system for electrical equipment is provided. For ease of explanation, only the parts relevant to this embodiment are shown. The magnetic material selection and optimization system for electrical equipment provided in this application embodiment includes:

[0069] The data matching module 201 is used to match the type of electrical equipment and the type of magnetic material according to the application scenario information to obtain material characteristic data; wherein, the material characteristic data includes application scenario characteristic parameters and intrinsic characteristic parameters;

[0070] The magnetic material selection model construction module 202 is used to perform nonlinear relationship mapping on electrical equipment type, magnetic material type and material property data to construct a magnetic material selection model;

[0071] The magnetic material selection result generation module 203 is used to input the equipment processing requirements into the magnetic material selection model, perform matching calculations on the application scenario characteristic parameters and intrinsic characteristic parameters, and obtain magnetic material selection results that meet the equipment processing requirements.

[0072] In some embodiments, the data matching module 201 further includes:

[0073] The material property data generation unit is used to match electrical equipment types and magnetic material types to determine the service objects of magnetic materials; determine application scenario characteristic parameters based on application scenario information and the service objects; obtain intrinsic characteristic parameters based on the performance requirements of electrical equipment and the application scenario characteristic parameters; and obtain material property data based on the service objects, application scenario characteristic parameters, and intrinsic characteristic parameters.

[0074] The application scenario characteristic parameter generation unit is used to determine the application conditions of magnetic materials based on application scenario information and the service object, and obtain the machining and heat treatment methods that meet the application conditions; extract and quantify the application conditions, machining methods and heat treatment methods to obtain feature values, and then classify the feature values ​​by priority to obtain the application scenario characteristic parameters.

[0075] In some embodiments, the magnetic material selection model construction module 202 further includes:

[0076] A magnetic material selection model construction unit is used to perform nonlinear relationship mapping on electrical equipment type, magnetic material type and material property data to obtain an equipment-material knowledge graph; based on the equipment-material knowledge graph, a model architecture is constructed to obtain a magnetic material selection model.

[0077] In some embodiments, the magnetic material selection result generation module 203 further includes:

[0078] The magnetic material selection result generation unit is used to input the equipment processing requirements into the magnetic material selection model for matching, and obtain the first electrical equipment type corresponding to the equipment processing requirements; according to the first electrical equipment type, the matching calculation is performed according to the priority of application scenario characteristic parameters and intrinsic characteristic parameters to obtain a matching degree index; when the matching degree index is greater than a first threshold, the magnetic material selection result that meets the equipment processing requirements is obtained.

[0079] This application proposes a method and system for optimizing the selection of magnetic materials for electrical equipment: Based on application scenario information, the type of electrical equipment and the type of magnetic material are matched to obtain material characteristic data; wherein, the material characteristic data includes application scenario characteristic parameters and intrinsic characteristic parameters; a nonlinear relationship mapping is performed on the electrical equipment type, magnetic material type, and material characteristic data to construct a magnetic material selection model; the equipment processing requirements are input into the magnetic material selection model, and the application scenario characteristic parameters and intrinsic characteristic parameters are matched and calculated to obtain a magnetic material selection result that meets the equipment processing requirements. Its beneficial effects are: integrating information on magnetic materials used in electrical equipment and application scenario information to construct a magnetic material selection model; through this model, magnetic materials for electrical equipment in different application scenarios can be selected quickly and accurately, improving the processing efficiency and performance of electrical equipment.

[0080] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for magnetic material selection optimization for electrical equipment, characterized in that, The application comprises the following steps: According to the application scenario information, the electrical equipment type and the magnetic material type are matched to obtain material characteristic data; wherein the material characteristic data contains application scenario characteristic parameters and intrinsic characteristic parameters; The electrical equipment type, the magnetic material type and the material characteristic data are mapped in a nonlinear relationship to construct a magnetic material selection model; The equipment processing requirements are input into the magnetic material selection model to match and calculate the application scenario characteristic parameters and the intrinsic characteristic parameters, and the magnetic material selection result meeting the equipment processing requirements is obtained.

2. The magnetic material selection optimization method for electrical equipment according to claim 1, characterized in that, The matching of the electrical equipment type and the magnetic material type according to the application scenario information to obtain the material characteristic data is specifically as follows: The service object of the magnetic material is determined by matching the electrical equipment type and the magnetic material type; The application scenario characteristic parameters are determined according to the application scenario information and the service object; The intrinsic characteristic parameters are obtained according to the performance requirements of the electrical equipment and the application scenario characteristic parameters; The material characteristic data is obtained according to the service object, the application scenario characteristic parameters and the intrinsic characteristic parameters.

3. The magnetic material selection optimization method for electrical equipment according to claim 2, characterized in that, The determination of the application scenario characteristic parameters according to the application scenario information and the service object is specifically as follows: The application conditions of the magnetic material are determined according to the application scenario information and the service object, and the mechanical processing mode and the heat treatment mode meeting the application conditions are obtained; The feature values are obtained by feature extraction and quantization of the application conditions, the mechanical processing mode and the heat treatment mode, and then the feature values are classified in priority to obtain the application scenario characteristic parameters.

4. The magnetic material selection optimization method for electrical equipment according to claim 1, characterized in that, The nonlinear relationship mapping of the electrical equipment type, the magnetic material type and the material characteristic data to construct the magnetic material selection model is specifically as follows: The equipment-material knowledge graph is obtained by nonlinear relationship mapping of the electrical equipment type, the magnetic material type and the material characteristic data; The model architecture is constructed based on the equipment-material knowledge graph to obtain the magnetic material selection model.

5. The magnetic material selection optimization method for electrical equipment according to claim 1, characterized in that, The matching and calculation of the application scenario characteristic parameters and the intrinsic characteristic parameters by inputting the equipment processing requirements into the magnetic material selection model to obtain the magnetic material selection result meeting the equipment processing requirements are specifically as follows: The first electrical equipment type corresponding to the equipment processing requirements is obtained by matching the equipment processing requirements input into the magnetic material selection model; The matching degree index is obtained by matching and calculating the application scenario characteristic parameters and the intrinsic characteristic parameters according to the priority of the first electrical equipment type; When the matching degree index is greater than a first threshold value, the magnetic material selection result meeting the equipment processing requirements is obtained.

6. A magnetic material selection optimization system for electrical equipment, characterized in that, The application comprises the following steps: The data matching module, the magnetic material selection model construction module and the magnetic material selection result generation module; The data matching module is used to match the electrical equipment type and the magnetic material type according to the application scenario information to obtain material characteristic data; wherein the material characteristic data contains application scenario characteristic parameters and intrinsic characteristic parameters; The magnetic material selection model construction module is used to map the electrical equipment type, the magnetic material type and the material characteristic data in a nonlinear relationship to construct a magnetic material selection model; The magnetic material selection result generation module is configured to input equipment processing requirements into the magnetic material selection model, and perform matching calculation on the application scene characteristic parameters and the intrinsic characteristic parameters to obtain a magnetic material selection result meeting the equipment processing requirements.

7. The magnetic material selection optimization system for electrical equipment of claim 6, wherein, The data matching module comprises a material characteristic data generation unit. The material characteristic data generation unit is configured to match an electrical equipment type and a magnetic material type to determine a service object of the magnetic material, determine application scene characteristic parameters according to application scene information and the service object, obtain intrinsic characteristic parameters according to performance requirements of the electrical equipment in combination with the application scene characteristic parameters, and obtain material characteristic data according to the service object, the application scene characteristic parameters and the intrinsic characteristic parameters.

8. The magnetic material selection optimization system for electrical equipment of claim 7, wherein, The material characteristic data generation unit comprises an application scene characteristic parameter generation unit. The application scene characteristic parameter generation unit is configured to determine application conditions of the magnetic material according to application scene information and the service object, and obtain a mechanical processing mode and a heat treatment mode meeting the application conditions, perform feature extraction and quantification on the application conditions, the mechanical processing mode and the heat treatment mode to obtain characteristic values, and perform priority classification on the characteristic values to obtain the application scene characteristic parameters.

9. The magnetic material selection optimization system for electrical equipment of claim 6, wherein, The magnetic material selection model construction module comprises a magnetic material selection model construction unit. The magnetic material selection model construction unit is configured to perform nonlinear relationship mapping on an electrical equipment type, a magnetic material type and material characteristic data to obtain an equipment-material knowledge graph, and construct a model architecture based on the equipment-material knowledge graph to obtain a magnetic material selection model.

10. The magnetic material selection optimization system for electrical equipment of claim 6, wherein, The magnetic material selection result generation module comprises a magnetic material selection result generation unit. The magnetic material selection result generation unit is configured to input equipment processing requirements into the magnetic material selection model for matching to obtain a first electrical equipment type corresponding to the equipment processing requirements, perform matching calculation on the application scene characteristic parameters and the intrinsic characteristic parameters according to a priority of the first electrical equipment type to obtain a matching degree index, and obtain a magnetic material selection result meeting the equipment processing requirements when the matching degree index is greater than a first threshold value.

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