Power grid equipment simulation model construction method, system, terminal and storage medium

Abstract

The application relates to the technical field of power grid operation and maintenance, and specifically provides a power grid equipment simulation model construction method, system, terminal and storage medium, which comprises the following steps: constructing a digital model of a power grid, wherein the digital model comprises virtual equipment corresponding to power grid equipment; obtaining actual operation data of the power grid through the Internet of Things; dividing the actual operation data into multiple data groups according to the types of the power grid equipment to which the actual operation data belongs; setting a basic rule based on the type parameters of the power grid equipment, generating correction parameters of the basic rule based on corresponding data groups by using a deep learning algorithm, and integrating the basic rule and the correction parameters into a data processing rule of corresponding virtual equipment. The application reduces the influence of machine learning bias on the whole and improves the accuracy of the simulation model.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid operation and maintenance, and specifically relates to a method, system, terminal and storage medium for constructing a power grid equipment simulation model. Background Art

[0002] Improving the digitalization of power grids is an inevitable requirement for promoting the deep integration of digital and energy technologies and building a new power system. This process involves not only technological innovation but also a profound transformation of traditional power grid operations. With the rapid development of digital and intelligent technologies, power grid systems are gradually evolving towards greater efficiency, flexibility, and sustainability. However, advancing power grid digitalization also presents numerous challenges.

[0003] Current power grid modeling methods primarily rely on assigning real-world monitoring data to virtual models, hoping to predict the grid's operating status through simulation analysis. However, this approach often suffers from several issues, the most prominent of which is the significant discrepancy between the model's predicted data and the actual monitored data. This discrepancy can arise from a variety of factors, including data acquisition accuracy issues, improper model parameter settings, and the inherent complexity and uncertainty of the power grid system. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method, system, terminal and storage medium for constructing a power grid equipment simulation model to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a method for constructing a power grid equipment simulation model, comprising:

[0006] Constructing a digital model of the power grid, wherein the digital model includes virtual devices corresponding to the power grid devices;

[0007] Obtain actual operation data of the power grid through the Internet of Things;

[0008] Dividing the actual operation data into a plurality of data groups according to the type of the power grid equipment to which it belongs;

[0009] Basic rules are set based on the type parameters of the power grid equipment, and correction parameters of the basic rules are generated based on the corresponding data group using a deep learning algorithm. The basic rules and the correction parameters are integrated into data processing rules for the corresponding virtual equipment.

[0010] In an optional embodiment, constructing a digital model of a power grid includes:

[0011] A digital model of the power grid is constructed using 3D modeling software based on the power grid devices in the power grid and the connection relationships between the power grid devices.

[0012] In an optional embodiment, obtaining actual operation data of the power grid through the Internet of Things includes:

[0013] Establish communication links with sensors deployed on the power grid through the Internet of Things;

[0014] The power data of the power grid equipment collected by the sensor is acquired, wherein the power data includes the input terminal voltage value and current value, the output terminal voltage value and current value and the corresponding working status.

[0015] In an optional embodiment, the actual operation data is divided into multiple data groups according to the type of the power grid equipment to which it belongs, including:

[0016] Analyze the type parameters of the power grid equipment in the power grid, including name, specification parameters, manufacturer, service life and rated life;

[0017] Bind the type parameters of power grid equipment with actual operation data;

[0018] The type parameters of the power grid equipment are clustered, and based on the clustering results, the actual operation data bound to the type parameters are grouped accordingly to obtain multiple data groups.

[0019] In an optional embodiment, basic rules are set based on type parameters of power grid devices, correction parameters of the basic rules are generated based on corresponding data groups using a deep learning algorithm, and the basic rules and correction parameters are integrated into data processing rules for corresponding virtual devices, including:

[0020] Obtain corresponding basic rules based on type parameters of power grid equipment;

[0021] The convolutional neural network is trained using the data set to obtain correction parameters for the set value attributes;

[0022] The standard parameter values ​​in the basic rules are modified using the modification parameters to obtain the data processing rules.

[0023] In a second aspect, the present invention provides a system for constructing a power grid equipment simulation model, comprising:

[0024] A basic construction module, configured to construct a digital model of a power grid, wherein the digital model includes virtual devices corresponding to power grid devices;

[0025] Data acquisition module, used to obtain actual operation data of the power grid through the Internet of Things;

[0026] A data division module, configured to divide the actual operation data into a plurality of data groups according to the type of the power grid equipment to which it belongs;

[0027] A rule correction module is used to set basic rules based on the type parameters of the power grid equipment, generate correction parameters of the basic rules based on the corresponding data group using a deep learning algorithm, and integrate the basic rules and correction parameters into data processing rules for the corresponding virtual equipment.

[0028] In an optional embodiment, the basic building blocks include:

[0029] The three-dimensional modeling unit is used to construct a digital model of the power grid based on the power grid devices in the power grid and the connection relationship between the power grid devices using three-dimensional modeling software.

[0030] In an optional embodiment, the data acquisition module includes:

[0031] a communication establishing unit, configured to establish a communication connection with sensors deployed on the power grid through the Internet of Things;

[0032] The data acquisition unit is used to acquire power data of the power grid equipment collected by the sensor, wherein the power data includes the input terminal voltage value and current value, as well as the output terminal voltage value and current value and the corresponding working status.

[0033] According to a third aspect, a terminal is provided, including:

[0034] processor, memory, wherein

[0035] The memory is used to store computer programs,

[0036] The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0037] In a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the methods described in the above aspects.

[0038] The beneficial effect of the present invention is that the power grid equipment simulation model construction method, system, terminal and storage medium provided by the present invention obtain data processing rules by combining basic rules and correction parameters obtained through data mining, effectively avoiding the problem of non-convergence when relying entirely on machine learning technology to mine data processing rules, reducing the overall impact of machine learning deviations, and improving the accuracy of the simulation model.

[0039] In addition, the present invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0042] Figure 2 FIG. 4 is a schematic block diagram of a system according to an embodiment of the present invention.

[0043] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0046] The power grid equipment simulation model construction method provided by the embodiment of the present invention is executed by a computer device. Accordingly, the power grid equipment simulation model construction system runs in the computer device.

[0047] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject can be a system for building a simulation model of a power grid device. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0048] like Figure 1 As shown, the method includes:

[0049] Step 110: constructing a digital model of the power grid, wherein the digital model includes virtual devices corresponding to the power grid devices;

[0050] Step 120: Acquire actual operation data of the power grid through the Internet of Things;

[0051] Step 130: dividing the actual operation data into multiple data groups according to the type of the power grid equipment to which it belongs;

[0052] Step 140 , setting basic rules based on type parameters of the power grid equipment, generating correction parameters of the basic rules based on the corresponding data group using a deep learning algorithm, and integrating the basic rules and the correction parameters into data processing rules for the corresponding virtual equipment.

[0053] To facilitate understanding of the present invention, the following further describes the method for constructing a power grid equipment simulation model provided by the present invention based on the principle of the method for constructing a power grid equipment simulation model of the present invention and the process of constructing a power grid equipment simulation model in the embodiment.

[0054] Specifically, the method for constructing a power grid equipment simulation model includes:

[0055] S1. Construct a digital model of a power grid, wherein the digital model includes virtual devices corresponding to power grid devices.

[0056] Virtual models include geometric models, physical models, behavioral models and rule models, which can reproduce the geometric shape, attributes, behavior and rules of physical entities. Furthermore, there are mutual data associations, logical associations and rule associations between each discrete virtual model.

[0057] The modeling tools for establishing virtual models include geometric modeling tools, physical modeling tools, behavioral modeling tools, and rule modeling tools. Commonly used modeling techniques include structured light scanning, oblique photography, laser scanning, point cloud acquisition, and manual modeling.

[0058] S2. Obtain the actual operation data of the power grid through the Internet of Things.

[0059] Establish a communication connection with sensors deployed on the power grid through the Internet of Things; obtain power data of the power grid equipment collected by the sensors, the power data including input voltage and current values, as well as output voltage and current values ​​and corresponding working status.

[0060] S3. Divide the actual operation data into multiple data groups according to the type of the power grid equipment to which it belongs.

[0061] The method includes parsing type parameters of grid equipment in a power grid, wherein the type parameters include name, specification parameters, manufacturer, service life, and rated life; binding the type parameters of the grid equipment with actual operation data; clustering the type parameters of the grid equipment, and grouping the actual operation data bound to the type parameters accordingly based on the clustering results to obtain multiple data groups.

[0062] Specifically include:

[0063] 1. Detailed analysis of power grid equipment type parameters

[0064] Name: Clearly identify the name of each type of power grid equipment, such as transformers, circuit breakers, disconnectors, cables, etc., to ensure accurate identification in the database and daily management.

[0065] Specifications: Record the specific specifications of the equipment in detail, including but not limited to rated voltage, rated current, capacity (for transformers), breaking capacity (for circuit breakers), etc. These parameters are important basis for equipment selection, operation and maintenance.

[0066] Manufacturer: Record the manufacturer information of the equipment, including manufacturer name, production batch, etc., which helps to track equipment quality, after-sales service and spare parts procurement.

[0067] Working life: records the cumulative working time or years of the equipment since it was put into operation, which is used to assess the aging degree of the equipment and whether maintenance or replacement is required.

[0068] Rated life: The expected service life of the equipment is set based on the equipment type, material and data provided by the manufacturer, which is used to plan the equipment replacement cycle.

[0069] 2. Binding of type parameters to actual running data

[0070] Data collection: Utilize Internet of Things (IoT) technology, sensor networks, etc. to collect operating status data of power grid equipment in real time or periodically, such as current, voltage, temperature, vibration, etc.

[0071] Data integration: Accurately match and bind the collected actual operating data with the type parameters of the power grid equipment to form a complete equipment file to facilitate subsequent data analysis and processing.

[0072] Data verification: Verify the bound data to ensure its accuracy, completeness and consistency, and reduce analysis deviations caused by data errors.

[0073] 3. Cluster analysis of power grid equipment type parameters

[0074] Feature selection: Select features that have a significant impact on equipment performance and operating status from the type parameters as the basis for cluster analysis, such as rated voltage, rated current, and service life in the specification parameters.

[0075] Clustering algorithm: Use appropriate clustering algorithms (such as K-means, hierarchical clustering, DBSCAN, etc.) to cluster power grid devices based on the selected features and classify devices with similar characteristics into the same category.

[0076] Clustering result evaluation: The effectiveness and rationality of the clustering results are verified by evaluating the similarities of devices within a cluster and the differences between clusters.

[0077] 4. Data grouping based on clustering results

[0078] Data grouping: Based on the clustering results, the power grid devices bound to the actual operation data are divided into multiple data groups. The devices in each data group have high similarity in type parameters.

[0079] S4. Set basic rules based on the type parameters of the power grid equipment, use a deep learning algorithm to generate correction parameters of the basic rules based on the corresponding data group, and integrate the basic rules and the correction parameters into data processing rules for the corresponding virtual equipment.

[0080] The corresponding basic rules are obtained based on the type parameters of the power grid equipment; the convolutional neural network is trained using the data group to obtain the correction parameters of the set value attributes; the standard parameter values ​​in the basic rules are corrected using the correction parameters to obtain the data processing rules.

[0081] Specifically, they include:

[0082] 1. Basic rules for setting type parameters of power grid equipment

[0083] The type parameter settings of power grid equipment are the basis for ensuring the normal operation of the equipment and accurate data collection. These rules generally cover the following aspects:

[0084] Basic electrical parameter attributes: including impedance values ​​of each sequence, rated voltage, power, capacity, etc. These parameters are directly related to the electrical performance and operational stability of the equipment.

[0085] Ledgers and asset attributes: such as equipment model, manufacturer, construction unit, commissioning date, asset number, etc., are used for equipment management and maintenance.

[0086] Status attributes: including the on / off status of the equipment under various operating modes, transformer tap position, etc., reflecting the real-time operating status of the equipment.

[0087] Measurement attributes: Real-time measurement data obtained through collection, such as instantaneous voltage, power, active output, etc., are used for monitoring and data analysis.

[0088] Set value attributes: The output voltage, power, power factor, etc. set by the device are used to control the operating parameters of the device.

[0089] 2. Generate correction parameters using deep learning algorithms

[0090] Deep learning algorithms demonstrate strong capabilities in processing complex data and nonlinear relationships. They can fine-tune basic rules based on large amounts of historical data and generate correction parameters. The specific steps are as follows:

[0091] Data collection and preprocessing: Collect multi-source heterogeneous data such as power grid equipment operating data, fault records, environmental parameters, etc., and perform cleaning, standardization, and feature extraction.

[0092] Model selection and training: Select an appropriate deep learning model (in this implementation, a convolutional neural network (CNN)) and train it using the collected data. The training goal is to minimize the loss function, which is the difference between the model's predicted value and the true value.

[0093] Parameter optimization: During training, optimization algorithms (such as gradient descent, Adam, and RMSprop) are used to adjust model parameters to achieve optimal prediction performance. The selection of optimization algorithms and parameter adjustment are crucial to model performance.

[0094] Generate Correction Parameters: Using a trained deep learning model, we predict and evaluate the type parameter settings of power grid equipment and generate correction parameters. These correction parameters can be optimized for specific equipment or scenarios, improving the accuracy and adaptability of parameter settings.

[0095] 3. Integrate basic rules and correction parameters into data processing rules

[0096] Integrate the basic rules with the correction parameters generated by deep learning to form data processing rules for virtual devices. The specific steps include:

[0097] Rule fusion: The basic rules for setting the type parameters of power grid equipment are integrated with the correction parameters generated by deep learning to form new data processing rules. These rules retain the stability and universality of the basic rules while incorporating the refinement and personalization of the correction parameters.

[0098] Rule Verification: Newly generated data processing rules are verified in a virtual device or test environment to ensure their effectiveness and reliability in real-world applications. The verification process may include various methods such as simulation testing and field trials.

[0099] Rule optimization: Further optimize and adjust data processing rules based on verification results to ensure they can adapt to changes in different scenarios and needs.

[0100] Rule application: Apply optimized data processing rules to the data processing process of virtual devices to achieve efficient and accurate monitoring and management of power grid equipment.

[0101] In some embodiments, the power grid device simulation model construction system may include multiple functional modules composed of computer program segments. The computer program of each program segment in the power grid device simulation model construction system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Function of building a simulation model of power grid equipment.

[0102] In this embodiment, the power grid equipment simulation model construction system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules of system 200 may include: a basic construction module 210, a data acquisition module 220, a data partitioning module 230, and a rule modification module 240. A module, as referred to in the present invention, refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, and is stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0103] A basic construction module, configured to construct a digital model of a power grid, wherein the digital model includes virtual devices corresponding to power grid devices;

[0104] Data acquisition module, used to obtain actual operation data of the power grid through the Internet of Things;

[0105] A data division module, configured to divide the actual operation data into a plurality of data groups according to the type of the power grid equipment to which it belongs;

[0106] A rule correction module is used to set basic rules based on the type parameters of the power grid equipment, generate correction parameters of the basic rules based on the corresponding data group using a deep learning algorithm, and integrate the basic rules and correction parameters into data processing rules for the corresponding virtual equipment.

[0107] Optionally, as an embodiment of the present invention, the basic building block includes:

[0108] The three-dimensional modeling unit is used to construct a digital model of the power grid based on the power grid devices in the power grid and the connection relationship between the power grid devices using three-dimensional modeling software.

[0109] Optionally, as an embodiment of the present invention, the data acquisition module includes:

[0110] a communication establishing unit, configured to establish a communication connection with sensors deployed on the power grid through the Internet of Things;

[0111] The data acquisition unit is used to acquire power data of the power grid equipment collected by the sensor, wherein the power data includes the input terminal voltage value and current value, as well as the output terminal voltage value and current value and the corresponding working status.

[0112] Figure 3 This is a structural diagram of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the power grid equipment simulation model construction method provided in an embodiment of the present invention.

[0113] The terminal 300 may include a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention. The server structure may be a bus structure or a star structure, and may include more or fewer components than shown, or may combine certain components or arrange the components differently.

[0114] Memory 320 can be used to store execution instructions of processor 310. Memory 320 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. When the execution instructions in memory 320 are executed by processor 310, terminal 300 can perform some or all of the steps in the above-described method embodiments.

[0115] The processor 310 is the control center of the storage terminal. It uses various interfaces and lines to connect various parts of the entire electronic terminal. It executes various functions of the electronic terminal and / or processes data by running or executing software programs and / or modules stored in the memory 320, and calling data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0116] The communication unit 330 is configured to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals, or send user data to other terminals.

[0117] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program that, when executed, may include some or all of the steps of each embodiment provided herein. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0118] Therefore, the present invention obtains data processing rules by combining basic rules and correction parameters obtained through data mining, effectively avoiding the problem of non-convergence when relying entirely on machine learning technology to mine data processing rules, reducing the overall impact of machine learning deviations, and improving the accuracy of the simulation model. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.

[0119] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code, and includes instructions for causing a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.

[0120] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0121] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a regular functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, and can be electrical, mechanical or other forms.

[0122] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0123] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0124] Although the present invention has been described in detail with reference to the accompanying drawings and in conjunction with preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, persons of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and such modifications or substitutions shall be within the scope of the present invention. Any changes or substitutions that can be easily conceived by persons skilled in the art within the technical scope disclosed in the present invention shall be within the scope of protection of the present invention.

Claims

1. A method for constructing a power grid equipment simulation model, characterized in that: include: Constructing a digital model of the power grid, wherein the digital model includes virtual devices corresponding to the power grid devices; Obtain actual operation data of the power grid through the Internet of Things; Dividing the actual operation data into a plurality of data groups according to the type of the power grid equipment to which it belongs; Setting basic rules based on type parameters of power grid devices, generating correction parameters for the basic rules based on corresponding data groups using a deep learning algorithm, and integrating the basic rules and the correction parameters into data processing rules for corresponding virtual devices; The actual operation data is divided into multiple data groups according to the type of the power grid equipment to which it belongs, including: Analyze the type parameters of the power grid equipment in the power grid, including name, specification parameters, manufacturer, service life and rated life; Bind the type parameters of power grid equipment with actual operation data; The type parameters of the power grid equipment are clustered, and based on the clustering results, the actual operation data bound to the type parameters are grouped accordingly to obtain multiple data groups.

2. The method according to claim 1, characterized in that Build a digital model of the power grid, including: A digital model of the power grid is constructed using 3D modeling software based on the power grid devices in the power grid and the connection relationships between the power grid devices.

3. The method according to claim 1, characterized in that The actual operation data of the power grid is obtained through the Internet of Things, including: Establish communication links with sensors deployed on the power grid through the Internet of Things; The power data of the power grid equipment collected by the sensor is acquired, wherein the power data includes the input terminal voltage value and current value, the output terminal voltage value and current value and the corresponding working status.

4. The method according to claim 1, wherein The basic rules are set based on the type parameters of the power grid equipment, correction parameters of the basic rules are generated based on the corresponding data group using a deep learning algorithm, and the basic rules and the correction parameters are integrated into the data processing rules of the corresponding virtual equipment, including: Obtain corresponding basic rules based on type parameters of power grid equipment; The convolutional neural network is trained using the data set to obtain correction parameters for the set value attributes; The standard parameter values ​​in the basic rules are modified using the modification parameters to obtain the data processing rules.

5. A power grid equipment simulation model construction system, characterized in that: include: A basic construction module, configured to construct a digital model of a power grid, wherein the digital model includes virtual devices corresponding to power grid devices; Data acquisition module, used to obtain actual operation data of the power grid through the Internet of Things; A data division module, configured to divide the actual operation data into a plurality of data groups according to the type of the power grid equipment to which it belongs; a rule modification module, configured to set basic rules based on type parameters of power grid devices, generate modification parameters of the basic rules based on corresponding data groups using a deep learning algorithm, and integrate the basic rules and modification parameters into data processing rules for corresponding virtual devices; The actual operation data is divided into multiple data groups according to the type of the power grid equipment to which it belongs, including: Analyze the type parameters of the power grid equipment in the power grid, including name, specification parameters, manufacturer, service life and rated life; Bind the type parameters of power grid equipment with actual operation data; The type parameters of the power grid equipment are clustered, and based on the clustering results, the actual operation data bound to the type parameters are grouped accordingly to obtain multiple data groups.

6. The system according to claim 5, characterized in that The basic building blocks include: The three-dimensional modeling unit is used to construct a digital model of the power grid based on the power grid devices in the power grid and the connection relationship between the power grid devices using three-dimensional modeling software.

7. The system according to claim 5, characterized in that The data acquisition module includes: a communication establishing unit, configured to establish a communication connection with sensors deployed on the power grid through the Internet of Things; The data acquisition unit is used to acquire power data of the power grid equipment collected by the sensor, wherein the power data includes the input terminal voltage value and current value, as well as the output terminal voltage value and current value and the corresponding working status.

8. A terminal, characterized in that: include: A memory, used for storing a program for building a simulation model of a power grid device; A processor is configured to implement the steps of the method for constructing a power grid equipment simulation model as described in any one of claims 1 to 4 when executing the power grid equipment simulation model construction program.

9. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a power grid equipment simulation model construction program, and when the power grid equipment simulation model construction program is executed by the processor, the steps of the power grid equipment simulation model construction method according to any one of claims 1 to 4 are implemented.

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