Simulation analysis method, device and equipment of power transmission and transformation equipment and medium

Abstract

The invention relates to the field of industrial intelligence, in particular to a simulation analysis method, device, equipment and medium for power transmission and transformation equipment, and the method comprises the steps: obtaining a simulation instruction inputted by a user, the simulation instruction comprising to-be-simulated target power transmission and transformation equipment and corresponding simulation parameters; determining a reduced-order simulation model corresponding to the simulation instruction, wherein the reduced-order simulation model is pre-constructed based on physical field distribution data corresponding to the target power transmission and transformation equipment by using a preset reduced-order algorithm; and calling the reduced-order simulation model, and performing simulation analysis on the target power transmission and transformation equipment based on the simulation parameters to obtain simulation data corresponding to the target power transmission and transformation equipment. According to the simulation analysis method, the complexity of simulation analysis of the power transmission and transformation equipment is reduced, and the efficiency of simulation analysis is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of industrial intelligence, and in particular to a simulation analysis method, device, equipment and medium for power transmission and transformation equipment. Background Art

[0002] Simulation analysis of power transmission and transformation equipment can evaluate the performance of different design schemes before equipment manufacturing, identify potential risks, and thus optimize the structure and parameters, avoiding the high costs of physical experiments, significantly reducing costs, ensuring safety, and shortening the R&D cycle.

[0003] At present, simulation technology generally uses simulation software to simulate electromagnetic fields, heat, mechanics, power systems and multi-physics field coupling for different design schemes to provide comprehensive and accurate evaluation and optimization basis for the design, manufacture and operation of power transmission and transformation equipment. When traditional simulation software generates simulation data for power transmission and transformation equipment, due to the complexity of the model and large-scale computing requirements, the simulation process may take hours or even days to complete. This long computing wait not only reduces work efficiency, but may also delay the progress of the project. There is a lot of room for improvement in its accuracy and efficiency. In addition, simulation software is mainly foreign software, with complex operation procedures, professional knowledge, and high thresholds. Users need to check a large number of boundary conditions for simulation analysis, and the interaction is complex, making it difficult to meet quasi-real-time requirements.

[0004] There are also many ways to optimize the existing system simulation process. One is to optimize the simulation results according to the scenario to improve the accuracy, such as optimizing the simulation results through some data analysis and machine learning algorithms; another is to establish a reduced-order model at the mechanism level to improve efficiency, such as based on the connection method of the input and output sides of each sub-module in the modular combined DC transformer, the sub-modules of the combined transformer are multi-level decoupled to obtain an equivalent model and then reorganized to improve efficiency. However, the implementation process of these methods is still relatively complicated, and the efficiency of simulation and the accuracy of the obtained simulation data still need to be improved. Summary of the invention

[0005] In view of the above problems in the prior art, the object of the present invention is to provide a simulation analysis method, device, equipment and medium for power transmission and transformation equipment, which can reduce the complexity of simulation analysis of power transmission and transformation equipment and improve the efficiency of simulation analysis.

[0006] In order to solve the above problems, the present invention provides a simulation analysis method for power transmission and transformation equipment, comprising:

[0007] Acquire a simulation instruction input by a user, the simulation instruction including a target power transmission and transformation equipment to be simulated and corresponding simulation parameters;

[0008] Determine a reduced-order simulation model corresponding to the simulation instruction, where the reduced-order simulation model is pre-constructed based on physical field distribution data corresponding to the target power transmission and transformation equipment using a preset reduced-order algorithm;

[0009] The reduced-order simulation model is called to perform simulation analysis on the target power transmission and transformation equipment based on the simulation parameters to obtain the simulation data corresponding to the target power transmission and transformation equipment.

[0010] Further, obtaining the simulation instruction input by the user includes:

[0011] Acquire the voice information input by the user, and recognize the voice information to obtain the voice recognition result;

[0012] The simulation instruction input by the user is determined according to the speech recognition result.

[0013] Further, determining the simulation instruction input by the user according to the speech recognition result includes:

[0014] Matching the speech recognition results with the preset simulation instruction set;

[0015] If the match is successful, the simulation instruction that is successfully matched will be used as the simulation instruction input by the user.

[0016] Further, the simulation parameters include an algorithm type and / or a simulation mode corresponding to the reduced-order simulation model;

[0017] Determine the reduced-order simulation model corresponding to the simulation instruction, including:

[0018] Obtain the equipment type of the target power transmission and transformation equipment;

[0019] A reduced-order simulation model corresponding to the simulation instruction is determined according to the equipment type, algorithm type and / or simulation mode.

[0020] Furthermore, the reduced-order simulation model is called to perform simulation analysis on the target power transmission and transformation equipment based on the simulation parameters to obtain simulation data corresponding to the target power transmission and transformation equipment, including:

[0021] The simulation prediction interface corresponding to the reduced-order simulation model is called to call the reduced-order simulation model, and simulation analysis is performed on the target power transmission and transformation equipment based on the simulation parameters to obtain simulation data corresponding to the target power transmission and transformation equipment.

[0022] Furthermore, the method also includes pre-building a reduced-order simulation model, and the construction process of the reduced-order simulation model includes:

[0023] Acquire a training data set, the training data set including physical field distribution data of power transmission and transformation equipment under different control parameters;

[0024] Constructing an initial reduced-order model based on a preset reduced-order algorithm;

[0025] The initial reduced-order model is trained based on the physical field distribution data, and the parameters and / or structure of the initial reduced-order model are adjusted during the training process to obtain a trained reduced-order simulation model.

[0026] Furthermore, the method further comprises:

[0027] Preprocessing the physical field distribution data to obtain preprocessed physical field distribution data;

[0028] The initial reduced-order model is trained based on the physical field distribution data, and the parameters and / or structure of the initial reduced-order model are adjusted during the training process to obtain a trained reduced-order simulation model, including:

[0029] The initial reduced-order model is trained based on the preprocessed physical field distribution data, and the parameters and / or structure of the initial reduced-order model are adjusted during the training process to obtain a trained reduced-order simulation model.

[0030] Another aspect of the present invention provides a simulation analysis device for power transmission and transformation equipment, comprising:

[0031] An instruction acquisition module is used to acquire a simulation instruction input by a user, wherein the simulation instruction includes a target power transmission and transformation equipment to be simulated and corresponding simulation parameters;

[0032] A model determination module is used to determine a reduced-order simulation model corresponding to a simulation instruction, where the reduced-order simulation model is pre-constructed based on physical field distribution data corresponding to the target power transmission and transformation equipment using a preset reduced-order algorithm;

[0033] The model calling module is used to call the reduced-order simulation model, perform simulation analysis on the target power transmission and transformation equipment based on simulation parameters, and obtain simulation data corresponding to the target power transmission and transformation equipment.

[0034] Another aspect of the present invention provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the simulation analysis method of the power transmission and transformation equipment as described above.

[0035] On the other hand, the present invention provides a computer-readable storage medium, which stores at least one instruction or at least one program. The at least one instruction or the at least one program is loaded and executed by a processor to implement the simulation analysis method of power transmission and transformation equipment as described above.

[0036] Another aspect of the present invention provides a computer program product, including a computer program / instruction, which implements the simulation analysis method of power transmission and transformation equipment as described above when executed by a processor.

[0037] Due to the above technical solution, the present invention has the following beneficial effects:

[0038] According to the simulation and analysis method of the embodiment of the present invention, a dedicated reduced-order simulation model is generated by a data-driven non-intrusive order reduction algorithm and specific power transmission and transformation equipment scenario data. When it is necessary to perform simulation analysis on the power transmission and transformation equipment, the dedicated reduced-order simulation model can be directly called to generate batch simulation data according to the parameters to be analyzed, thereby reducing the complexity of the simulation analysis of the power transmission and transformation equipment. While ensuring the accuracy of the simulation data, the simulation process that takes hours or days to be completed can be accelerated to seconds, thereby greatly improving the efficiency of the simulation analysis and meeting the quasi-real-time requirements of the simulation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments or prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present invention;

[0041] Figure 2 It is a flow chart of a simulation analysis method for power transmission and transformation equipment provided by one embodiment of the present invention;

[0042] Figure 3 is a flow chart of a process of constructing a reduced-order simulation model provided by an embodiment of the present invention;

[0043] Figure 4 is a flowchart of a process for constructing a reduced-order simulation model provided by another embodiment of the present invention;

[0044] Figure 5 It is a flow chart of a simulation analysis method for power transmission and transformation equipment provided by a specific embodiment of the present invention;

[0045] Figure 6 It is a structural schematic diagram of a simulation analysis device for power transmission and transformation equipment provided by an embodiment of the present invention;

[0046] Figure 7 is a block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in 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 creative work are within the scope of protection of the present invention.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0049] The method provided by the embodiment of the present invention can be applied to the power transmission and transformation scenario, and the power transmission and transformation equipment is simulated and analyzed to produce simulation data. The current simulation technology has a lot of room for improvement in accuracy and efficiency due to the complexity of the model and the large scale of computing requirements. On the one hand, the simulation results can be optimized according to the scenario to improve accuracy, such as the patent "A method and device for simulation analysis of power system", which optimizes the simulation results through some data analysis and machine learning algorithms. On the one hand, a reduced-order model at the mechanism level can be established to improve efficiency, such as the patent "A DC transformer simulation modeling method and system based on AC / DC decoupling", based on the connection method of the input and output sides of each sub-module in the modular combined DC transformer, the sub-modules of the combined transformer are multi-level decoupled to obtain an equivalent model and then reorganized to improve efficiency. There is also a way to establish an agent model, using a data-driven reduced-order algorithm, and replacing the original mechanism model with a deep learning model generated after training with input data and output data to improve efficiency. For example, the patent "Data-driven multi-physics field time-domain non-intrusive model reduction method and system" and the paper "Research on digital twin-driven heat exchanger reduced-order modeling and intelligent perception method" both adopt this method, but have not yet been used in related scenarios of power transmission and transformation equipment.

[0050] On the other hand, although it is quite common to obtain control elements for representing user intentions based on voice information, and to generate control instructions based on control elements to control devices, such as the patent "A demand response method, system, device and medium based on voice interaction" provides general steps for combining voice interaction and software technology, and the patent "A smart home appliance control system and control method thereof" provides a voice-combined home appliance control system and control method thereof, which facilitates users to operate home appliances. However, the application of this technology in the relevant scenarios of power transmission and transformation equipment is still in urgent need of development.

[0051] In view of the problem that commonly used simulation software is cumbersome to operate and data simulation usually takes hours or days, the embodiments of the present invention combine the above multiple related technologies to provide a wizard modeling method based on a non-intrusive order reduction algorithm and output a reduced-order simulation model for a special scenario, and then package the model, speech recognition module, speech synthesis module, model calling module and operating environment into an end-side executable program. Users can interact with the program through voice and generate simulation data. This method is highly adapted to the simulation and analysis scenario of power transmission and transformation equipment. Voice calling is simple and fast, and the simulation efficiency is improved to seconds. In actual power transmission and transformation scenarios, it has significant application value.

[0052] The simulation analysis method for power transmission and transformation equipment provided by an embodiment of the present invention is described in detail below.

[0053] Reference Manual Attached Figure 1 , which shows a schematic diagram of the implementation environment of the simulation analysis method of power transmission and transformation equipment provided by an embodiment of the present invention. Figure 1 As shown, the implementation environment may include at least a server 110 and at least one terminal device 120. The at least one terminal device 120 and the server 110 may be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present invention.

[0054] The terminal device 120 may include physical devices such as smart phones, tablet computers, laptop computers, desktop computers, digital assistants, smart speakers, smart wearable devices, vehicle terminals, servers, etc., and may also include software running in physical devices, such as applications, etc., but is not limited thereto. The operating system running on the terminal device 120 may include, but is not limited to, Android system, iOS system, Linux system, Windows system, etc.

[0055] Server 110 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0056] In practical applications, the server 110 can form a simulation reduced-order model by combining data-driven order reduction technology and modeling workflow, and then encapsulate it together with the model, speech recognition module, speech synthesis module, model calling module and operating environment into a program that can be executed offline separately. The terminal device 120 can use the simulation analysis method of power transmission and transformation equipment provided by the embodiment of the present invention to perform simulation analysis on the power transmission and transformation equipment by calling the reduced-order simulation model, and obtain the simulation data corresponding to the power transmission and transformation equipment, so as to improve the convenience and efficiency of the user's simulation interaction, and improve the efficiency of simulation analysis and the accuracy of simulation data.

[0057] It should be noted that Figure 1 It is just an example. Those skilled in the art will understand that although Figure 1 Only two terminal devices 120 are shown, but this does not constitute a limitation on the embodiments of the present invention, and more or fewer terminal devices 120 than shown in the figure may be included.

[0058] Reference Manual Attached Figure 2 , which shows the process of a simulation analysis method for power transmission and transformation equipment provided by an embodiment of the present invention, which can be applied to Figure 1 In the terminal device, the specific Figure 2 As shown, the method may include the following steps:

[0059] S201: Acquire a simulation instruction input by a user, where the simulation instruction includes a target power transmission and transformation equipment to be simulated and corresponding simulation parameters.

[0060] In the embodiment of the present invention, after obtaining the simulation instruction, the terminal device can parse the simulation instruction to determine the target power transmission and transformation equipment to be simulated and its corresponding simulation parameters. The simulation instruction can be used to instruct to simulate the target power transmission and transformation equipment based on the corresponding simulation parameters.

[0061] In the embodiment of the present invention, the target power transmission and transformation equipment may be various types of power transmission and transformation equipment, for example, including but not limited to gas insulated switchgear (GIS), converter valve, transformer and other equipment, and the embodiment of the present invention does not specifically limit this.

[0062] In the embodiment of the present invention, the simulation parameters may include but are not limited to the control parameters such as the current, pressure and / or temperature that need to be predicted, and the algorithm type and / or simulation mode corresponding to the reduced-order simulation model used in the simulation analysis. The algorithm type may include but is not limited to the Proper Orthogonal Decomposition (POD) algorithm, the Galerkin Proper Orthogonal Decomposition (Galerkin-POD) algorithm, the extreme gradient boosting (eXtreme GradientBoosting, XGBoost) algorithm, the random forest algorithm, the autoencoder (Autoencoder, AE) algorithm, the convolutional autoencoder (Convolutional Autoencoder, CAE) algorithm, the U-net algorithm, the algorithm combined with CAE and Transformer, the decision tree regression algorithm and the Transformer and β variant algorithm, etc. The simulation mode may include but is not limited to the electric field mode, the magnetic field mode and the multi-physics field mode (i.e., the mixed mode), etc. The embodiment of the present invention does not specifically limit the type of simulation parameters.

[0063] Among them, for different types of power transmission and transformation equipment, the corresponding simulation parameters may also be different. The simulation parameters may include only a number of parameter values, or may include a parameter document composed of multiple simulation parameters, which is not specifically limited in the embodiment of the present invention. For example, for a converter valve, the corresponding simulation parameters may include parameters such as the current to be predicted and the algorithm type and simulation mode corresponding to the reduced-order simulation model used in the simulation analysis.

[0064] It should be noted that, in some embodiments, when a certain type of parameter is not included in the simulation instruction, the target power transmission and transformation equipment can be simulated and analyzed using default parameters to obtain corresponding simulation data. For example, for a converter valve, assuming that the simulation instruction does not include a simulation mode corresponding to the reduced-order simulation model used in the simulation analysis, the reduced-order simulation model under the multi-physics field mode can be used by default to simulate and analyze the converter valve.

[0065] In some embodiments, the user can input or select the target power transmission and transformation equipment to be simulated and its corresponding simulation parameters through the user interface provided by the terminal device, and click the simulation control in the user interface for instructing simulation analysis, thereby generating the simulation instruction.

[0066] In some embodiments, the user may generate the simulation instruction by voice interaction with the terminal device. Obtaining the simulation instruction input by the user may include: obtaining voice information input by the user, recognizing the voice information to obtain a voice recognition result; and determining the simulation instruction input by the user according to the voice recognition result.

[0067] Specifically, the user may send a voice message for instructing to perform simulation analysis, and the terminal device may collect the voice message through a voice collection device (such as a microphone, etc.) The voice message may include the target power transmission and transformation equipment to be simulated and its corresponding simulation parameters.

[0068] Specifically, the terminal device can use the existing voice recognition method to perform voice recognition on the obtained voice information through the voice recognition module to obtain a voice recognition result. The voice recognition result may include the recognition result of the target power transmission and transformation equipment and the recognition result of the corresponding simulation parameters. The specific process of voice recognition is not described in detail in the embodiment of the present invention.

[0069] Specifically, determining the simulation instruction input by the user according to the speech recognition result may include: matching the speech recognition result with a preset simulation instruction set; if the match is successful, using the successfully matched simulation instruction as the simulation instruction input by the user.

[0070] In an embodiment of the present invention, the terminal device can match from a preset simulation instruction set based on the identification result of the target power transmission and transformation equipment and the identification result of the corresponding simulation parameter. If the match is successful, it can be determined that the correct instruction has been received, and the successfully matched simulation instruction can be used as the simulation instruction input by the user. If the match fails, it can be determined that an erroneous instruction has been received, and the process can be terminated at this time. Among them. The preset simulation instruction set may include simulation instructions for different types of power transmission and transformation equipment under different simulation parameters. The preset simulation instruction set can be pre-set according to actual conditions, and the embodiment of the present invention does not specifically limit this.

[0071] For example, assuming that the target power transmission and transformation equipment identified is a converter valve and the corresponding simulation parameter is 4710A, the corresponding simulation instruction "generate simulation data of the converter valve at a current value of 4710A" can be matched from the preset simulation instruction set.

[0072] It can be understood that by matching the simulation instructions from the simulation instruction set according to the recognition result of the user's voice, the efficiency and accuracy of obtaining the simulation instructions can be improved.

[0073] In some embodiments, the terminal device may also directly generate a simulation instruction based on the identification result of the target power transmission and transformation equipment and the identification result of the corresponding simulation parameter. For example, assuming that the identified target power transmission and transformation equipment is a converter valve and the corresponding simulation parameter is 4710A, a simulation instruction "generate simulation data of the converter valve at a current value of 4710A" may be directly generated.

[0074] It can be understood that by connecting to the internal interface of the model through the voice recognition module and calling the reduced-order simulation model through voice, the efficiency and simplicity of model calling are greatly improved, and simulation analysis by professionals is facilitated.

[0075] S203: Determine a reduced-order simulation model corresponding to the simulation instruction, where the reduced-order simulation model is pre-constructed based on physical field distribution data corresponding to the target power transmission and transformation equipment using a preset reduced-order algorithm.

[0076] In an embodiment of the present invention, one or more reduced-order simulation models may be pre-set. After a simulation instruction is obtained, a reduced-order simulation model for simulating and analyzing the target power transmission and transformation equipment may be determined based on the target power transmission and transformation equipment in the simulation instruction and its corresponding simulation parameters.

[0077] Specifically, determining the reduced-order simulation model corresponding to the simulation instruction may include: obtaining the equipment type of the target power transmission and transformation equipment; and determining the reduced-order simulation model corresponding to the simulation instruction according to the equipment type, algorithm type and / or simulation mode.

[0078] Specifically, for power transmission and transformation equipment of different equipment types, one or more reduced-order simulation models can be pre-built based on the corresponding physical field distribution data, using different types of reduced-order algorithms and / or different simulation modes. After obtaining the simulation instruction, the currently used reduced-order simulation model can be determined according to the equipment type of the target power transmission and transformation equipment, the algorithm type in the simulation parameters, and / or the simulation mode.

[0079] In practical applications, a browser / server (B / S) architecture can be used to generate a dedicated reduced-order simulation model based on the core reduction algorithm and specific power transmission and transformation equipment scenario data through cloud-based wizard modeling, and further packaged and deployed as a portable client / server (C / S) architecture application with the dedicated model, and assigned to different simulation scenarios. The application can exist in the form of an independently run black box. After running the application, command recognition and model call can be performed based on user voice to generate simulation data. Among them, the specific construction method of the reduced-order simulation model will be described in detail later.

[0080] It can be understood that by constructing different reduced-order simulation models based on different equipment types, reduction algorithms and simulation modes, when it is necessary to perform simulation analysis on power transmission and transformation equipment, different models can be selected for calling according to actual needs, which can further improve the accuracy of simulation data.

[0081] S205: calling the reduced-order simulation model, performing simulation analysis on the target power transmission and transformation equipment based on the simulation parameters, and obtaining simulation data corresponding to the target power transmission and transformation equipment.

[0082] In the embodiment of the present invention, the terminal device can directly call the reduced-order simulation model corresponding to the simulation instruction, use the reduced-order simulation model to perform simulation analysis based on simulation parameters, and obtain and output simulation data.

[0083] Specifically, calling the reduced-order simulation model, performing simulation analysis on the target power transmission and transformation equipment based on simulation parameters, and obtaining simulation data corresponding to the target power transmission and transformation equipment may include: calling the simulation prediction interface corresponding to the reduced-order simulation model to call the reduced-order simulation model, performing simulation analysis on the target power transmission and transformation equipment based on simulation parameters, and obtaining simulation data corresponding to the target power transmission and transformation equipment.

[0084] In some embodiments, the terminal device can call the reduced-order simulation model by offline calling. In this case, the terminal device can be provided with a reduced-order simulation model. After determining the reduced-order simulation model corresponding to the simulation instruction, the simulation prediction interface corresponding to the reduced-order simulation model can be called through the model calling module to automatically load and call the reduced-order simulation model, load the simulation parameters, and then perform simulation analysis to obtain the corresponding simulation data.

[0085] In some embodiments, the terminal device may also call the reduced-order simulation model by online calling. In this case, the reduced-order simulation model may be deployed in the server, and the reduced-order simulation model may not be set in the terminal device. After determining the reduced-order simulation model corresponding to the simulation instruction, a model calling request may be sent to the server through the model calling module, so that the server automatically loads the simulation parameters, and then performs simulation analysis to obtain the corresponding simulation data, and returns the simulation data to the terminal device.

[0086] In some embodiments, if the terminal device fails to obtain simulation data, or receives an erroneous instruction, the terminal device may also interact with the user through voice to guide the user to re-instruct or supplement simulation parameters, etc., and then re- / continue simulation analysis to generate simulation data. Specifically, the terminal device may generate a prompt voice through a voice synthesis module and output it through a voice output device (such as a speaker, etc.), so that the user can re-instruct or supplement simulation parameters according to the prompt voice.

[0087] Exemplarily, when the user only instructs to perform simulation analysis on the converter valve, and the current information that needs to be predicted is missing, it can be determined that an erroneous instruction has been received. At this time, the terminal device can generate and output a prompt voice of "Please supplement the current that needs to be predicted" through the voice synthesis module. The user can input "The current that needs to be predicted is 4710A" according to the prompt voice, and the terminal device can recognize through the voice recognition module that the current that needs to be predicted is 4710A, and then continue to perform simulation analysis on the converter valve to obtain the corresponding simulation data. Alternatively, the terminal device can generate and output a prompt voice of "The current that needs to be predicted cannot be determined, please re-instruct" through the voice synthesis module. The user can re-enter the voice information instructing the simulation analysis of the converter valve according to the prompt voice.

[0088] It can be understood that the simulation analysis of power transmission and transformation equipment is realized by voice interaction with users through the voice recognition module and the voice synthesis module, which greatly reduces the complexity of the simulation analysis and enables general engineering personnel to easily perform simulation analysis without having to master complex professional knowledge and skills.

[0089] In actual applications, the reduced-order simulation model, speech recognition module, speech synthesis module, model call module and operating environment can be packaged as an end-side executable program and set in the terminal device. After obtaining the user's voice information, the terminal device can automatically perform simulation analysis of the power transmission and transformation equipment. Among them, the operating environment refers to the Python environment of the dependent library required by the reduced-order algorithm when it is running.

[0090] In summary, according to the simulation and analysis method of an embodiment of the present invention, a dedicated reduced-order simulation model is generated by a data-driven non-intrusive order reduction algorithm and specific power transmission and transformation equipment scenario data. When it is necessary to perform simulation analysis on the power transmission and transformation equipment, the dedicated reduced-order simulation model can be directly called to generate batch simulation data according to the parameters to be analyzed, thereby reducing the complexity of the simulation and analysis of the power transmission and transformation equipment. While ensuring the accuracy of the simulation data, the simulation process that takes hours or days to be completed can be improved to seconds, thereby greatly improving the efficiency of the simulation analysis and meeting the quasi-real-time requirements of the simulation analysis.

[0091] The following is a detailed description of the method for constructing a reduced-order simulation model provided by an embodiment of the present invention.

[0092] Reference Manual Attached Figure 3 , which shows a process of constructing a reduced-order simulation model provided by an embodiment of the present invention. The method can be applied to Figure 1 In the server, the specific Figure 3 As shown, the method may include the following steps:

[0093] S301: Acquire a training data set, where the training data set includes physical field distribution data of power transmission and transformation equipment under different control parameters.

[0094] In an embodiment of the present invention, a batch of original simulation data can be generated by using traditional simulation software as a data source for constructing a reduced-order simulation model. Specifically, a batch of original data sets can be obtained based on the existing mechanism model of the power transmission and transformation equipment as a training data set for the reduced-order simulation model corresponding to the power transmission and transformation equipment. Among them, the training data set may include physical field distribution data in a spatial coordinate system under different control parameters. The control parameters may include, but are not limited to, one or more of the parameters such as current, pressure, and temperature, and the physical field distribution data may be electric field data, magnetic field data, or multi-physical field data, etc., which are not specifically limited in the embodiment of the present invention.

[0095] Exemplarily, for a converter valve, the magnetic field data corresponding to the converter valve under different currents can be obtained as a training data set. The magnetic field data can include 1 index file and 61 spatial coordinate data set files. The index file contains a total of 61 current values. Each data set file contains 18691 spatial coordinates and corresponding magnetic field values. Each current value corresponds to a data set file in sequence, reflecting the physical field conditions corresponding to each spatial coordinate under the current conditions.

[0096] In practical applications, different training data sets can be obtained for different types of power transmission and transformation equipment, and different reduced-order simulation models can be trained. For the same type of power transmission and transformation equipment, different types of physical field distribution data can also be obtained, and multiple reduced-order simulation models with different simulation modes can be trained.

[0097] It should be noted that the physical field distribution data in the training data set may come from the same data source or from multiple different data sources, and the embodiment of the present invention does not specifically limit this.

[0098] It can be understood that by obtaining a batch of original data sets based on the existing mechanism model of power transmission and transformation equipment, and training a reduced-order simulation model for subsequent simulation analysis, the dependence of subsequent simulation analysis on simulation software can be reduced, and the utilization rate of historical data can be improved.

[0099] S303: constructing an initial reduced-order model based on a preset reduced-order algorithm.

[0100] In an embodiment of the present invention, the order reduction algorithm to be used can be predetermined, and then an initial order reduction model based on the order reduction algorithm can be constructed. Among them, the order reduction algorithm can include but is not limited to the POD algorithm, the Galerkin-POD algorithm, the XGBoost algorithm, the random forest algorithm, the AE algorithm, the CAE algorithm, the U-net algorithm, the algorithm combining CAE with Transformer, the decision tree regression algorithm, and the Transformer with β variant algorithm, etc. Based on different order reduction algorithms, an initial order reduction model with different model parameters and / or structures can be constructed, and the specific construction process of the initial order reduction model will not be repeated here.

[0101] In practical applications, for different types of power transmission and transformation equipment, the same or different order reduction algorithms can be used. For the same type of power transmission and transformation equipment, different order reduction algorithms can also be used to train multiple reduced-order simulation models with different simulation modes.

[0102] It can be understood that by constructing different reduced-order simulation models based on different equipment types, reduction algorithms and simulation modes, when it is necessary to perform simulation analysis on power transmission and transformation equipment, different models can be selected for calling according to actual needs, which can further improve the accuracy of simulation data.

[0103] S305: Training the initial reduced-order model based on the physical field distribution data, and adjusting the parameters and / or structure of the initial reduced-order model during the training process to obtain a trained reduced-order simulation model.

[0104] In an embodiment of the present invention, the physical field distribution data can be decomposed and reconstructed based on the initial reduced-order model, that is, the full-order model is converted into several divided-order models, and the top N-order modes are selected for superposition reconstruction, and error analysis is performed. When the model error does not meet the preset requirements, the parameters and / or structure of the initial reduced-order model are adjusted, and the model iterative training is continued until the calculated model error meets the preset requirements.

[0105] Specifically, the number of reconstructed modes N can be pre-set according to actual conditions when constructing the initial reduced-order model, or it can be adjusted based on the model error during the model training process. When performing error analysis, the physical field values ​​under the existing control parameters output by the model can be compared with the physical field values ​​in the training data set under this condition, and the overall error and degree of deviation of the training data set can be calculated as the model error. When the model error is greater than or equal to the preset threshold, it can be determined that the preset requirements are not met. At this time, the parameters and / or structure of the initial reduced-order model can be adjusted, and the adjusted initial reduced-order model can be continued to be trained using the physical field distribution data. When the model error is less than the preset threshold, it can be determined that the preset requirements are met, and the training process can be terminated, and the model at this time is used as the final reduced-order simulation model.

[0106] Among them, the preset threshold can be pre-set according to actual needs, for example, it can be set to 0.001, etc., and the embodiment of the present invention does not specifically limit this. When adjusting parameters, for different initial reduced-order models, different parameters can be adjusted, for example, including but not limited to the number of reconstructed modes, the reconstructed reference mode, the tree depth, the number of leaf nodes, the distance measurement method, the learning rate, the weight decay, the number of iterations, the activation function, etc., one or more of which can be adjusted, and the embodiment of the present invention does not specifically limit this.

[0107] Exemplarily, for a converter valve, the magnetic field data corresponding to the converter valve under different currents can be obtained as a training data set, and the POD algorithm is used to construct an initial reduced-order model to decompose and reconstruct the magnetic field data, and error analysis and parameter adjustment are performed to finally obtain a reduced-order simulation model for the converter valve.

[0108] When performing error analysis, the magnetic field value corresponding to the current value that already exists in the training data set can be selected. Under this current value, the model error is calculated by comparing the result of model reconstruction with the corresponding magnetic field value in the training data set. When adjusting parameters, parameters such as the number of reconstructed modes, the reconstructed reference mode, and / or the error tolerance can be adjusted.

[0109] Assume that after multiple trainings, the error and time can be obtained as shown in the following table:

[0110] Table 1

[0111] Evaluation indicators POD Algorithm time 1.4s error 0.00084

[0112] It can be seen that the model error is 0.00084, which is less than the preset threshold value of 0.001. It can be determined that the preset requirements are met, and the training process can be ended. The model at this time is used as the final reduced-order simulation model.

[0113] It should be noted that other contents of the model training process can refer to the prior art, and the embodiments of the present invention will not be described in detail here.

[0114] In some embodiments, the Figure 4 , the method may further include:

[0115] S302: Preprocessing the physical field distribution data to obtain preprocessed physical field distribution data.

[0116] Accordingly, the initial reduced-order model is trained based on the physical field distribution data, and the parameters and / or structure of the initial reduced-order model are adjusted during the training process to obtain a trained reduced-order simulation model (i.e., S305) as follows:

[0117] S305': training the initial reduced-order model based on the preprocessed physical field distribution data, and adjusting the parameters and / or structure of the initial reduced-order model during the training process to obtain a trained reduced-order simulation model.

[0118] In the embodiment of the present invention, after obtaining the physical field distribution data, the physical field values ​​of each spatial coordinate under different control parameters can be obtained through data preprocessing, and unified format and data alignment and other processing can be performed. Specifically, the preprocessing can include but is not limited to one or more of the processing of grid fitting data reconstruction, non-steady-state outlier removal, specific data screening, unified format and data alignment, etc., and the embodiment of the present invention does not specifically limit this.

[0119] Accordingly, a reduced-order simulation model can be constructed based on the preprocessed physical field distribution data. The specific construction method is similar to step S305. Please refer to the relevant content in step S305, and the embodiment of the present invention will not be repeated here.

[0120] It should be noted that other contents of each step in the embodiment of the present invention can be referred to Figure 2 The relevant contents of the method provided in the illustrated embodiment will not be described in detail in the embodiment of the present invention.

[0121] It can be understood that by preprocessing the physical field distribution data, the problem of physical field distribution being inconsistent due to different data sources or mechanism models can be avoided.

[0122] In practical applications, workflows can be created based on the order reduction algorithm and wizard-style modeling method. After data preprocessing and parameter tuning, the modeling workflow with the best result can be encapsulated into a reduced-order model using Pipeline, including the overall process of data reading, preprocessing, and order reduction. The model can be packaged and output as an application program in combination with the offline speech recognition library Sherpa-Ncnn, the model dependency library, and the operating environment, so as to provide it to users for convenient use offline.

[0123] The following is a reference to the instruction manual attached Figure 5 , the simulation analysis method for power transmission and transformation equipment provided by an embodiment of the present invention is described in detail through a specific embodiment.

[0124] like Figure 5 As shown, a B / S architecture can be used to construct a reduced-order simulation model corresponding to the power transmission and transformation equipment through cloud-based wizard modeling. Specifically, the physical field distribution data corresponding to the power transmission and transformation equipment under different control parameters can be read from one or more data sources. The optional parameters of the preprocessing component are loaded through the data preprocessing component, and the read physical field distribution data are preprocessed to obtain the preprocessed physical field distribution data. The optional parameters of the algorithm component are loaded through the reduced-order algorithm component, and modeling is performed based on the preprocessed physical field distribution data, and the Pipeline is used to encapsulate it into a reduced-order simulation model PKL file.

[0125] Among them, the optional parameters of the preprocessing component may vary according to the different preprocessing methods. According to the different preprocessing methods, the optional parameters of the preprocessing component may include but are not limited to one or more of the fitting method, standardization parameters, filling method parameters, normalization parameters, etc., and the embodiments of the present invention do not make specific limitations on this. The optional parameters of the algorithm component may vary according to the different order reduction algorithms used. According to the different order reduction algorithms used, the optional parameters of the algorithm component may include but are not limited to one or more of the tree depth, the number of leaf nodes, the distance measurement method, the learning rate, the weight decay, the number of iterations, the activation function, etc., and the embodiments of the present invention do not make specific limitations on this.

[0126] It can be understood that by preprocessing the physical field distribution data, the problem of physical field distribution being inconsistent due to different data sources or mechanism models can be avoided.

[0127] like Figure 5 As shown, the reduced-order simulation model can be further packaged and deployed as an application with a portable C / S architecture with the dedicated reduced-order simulation model, and distributed to different simulation scenarios or different terminal devices. Specifically, the output reduced-order simulation model, speech recognition module, speech synthesis module, model loader, model call module and operating environment can be packaged and encapsulated by PyInstaller, and an application (EXE file) that can be executed separately offline can be output, and the application can exist in the form of a black box that can run independently. After running the application, instruction recognition and model call can be performed based on user voice to generate simulation data. Among them, the speech recognition module can include an offline speech recognition library Sherpa-Ncnn, and the operating environment refers to the Python environment of the required dependent library when the reduced-order algorithm is running.

[0128] It can be understood that by adopting the B / S architecture design pattern to output a portable C / S application with a reduced-order simulation model, it can be distributed to different simulation scenarios or different terminal devices, and the application can exist in a black box form, thereby enhancing the security of model parameters and the convenience of distribution.

[0129] like Figure 5 As shown, the reduced-order simulation model can be called by an offline call. Specifically, the terminal device can obtain the user's voice information through a microphone, recognize the voice information through a voice recognition module, and synthesize it through a voice synthesis module and output prompt information through a speaker to achieve voice interaction with the user. The terminal device can match the result of the voice recognition with a preset simulation instruction set to determine whether the user instruction is correct. If the match fails, it can be determined that an erroneous instruction has been received; if the match is successful, it can be determined that a correct instruction has been received, and the simulation instruction that successfully matched can be used as the simulation instruction input by the user. The terminal device can also determine the corresponding reduced-order simulation model in response to the simulation instruction input by the user, and call the simulation prediction interface corresponding to the reduced-order simulation model through the model calling module to automatically load and call the reduced-order simulation model, load the simulation parameters, and then perform simulation analysis to obtain the corresponding simulation data.

[0130] like Figure 5 As shown, the reduced-order simulation model can also be called by online calling. Specifically, the terminal device can determine the corresponding reduced-order simulation model in response to the simulation instruction input by the user, and send a model calling request to the server through the model calling module, so that the server automatically loads the simulation parameters, and then performs simulation analysis to obtain the corresponding simulation data, and returns the simulation data to the terminal device.

[0131] Exemplarily, for the converter valve, the modeling and reconstruction process after parameter optimization can be encapsulated into a reduced-order simulation model PKL format file, and the output reduced-order simulation model, speech recognition module, speech synthesis module, model calling module and operating environment can be further packaged and packaged through PyInstaller, and an application that can be executed offline independently is output. The user can interact with the application through voice. After the application recognizes the current value, it can automatically perform simulation analysis of the converter valve, generate and save simulation data corresponding to the relevant spatial coordinates under the current value.

[0132] It should be noted that other contents of each step in the embodiment of the present invention can be referred to Figures 2 to 4 The relevant contents of the method provided in the illustrated embodiment will not be described in detail in the embodiment of the present invention.

[0133] It can be understood that by outputting a voice-interactive customized model program containing a reduced-order simulation model and an operating environment based on a dedicated scenario for power transmission and transformation, users can interact with the program through voice, and the program automatically simulates and generates the physical field number corresponding to the relevant spatial coordinates under the parameter value after identifying the parameter value. This method has a novel technical link and is very suitable for the simulation design scenario of power transmission and transformation equipment. The voice call is simple and fast, which greatly improves the convenience and efficiency of the user's simulation interaction and reduces the complexity of the simulation analysis of power transmission and transformation equipment. While ensuring the accuracy of the simulation data, the simulation process can be improved to the second level, which greatly improves the efficiency of the simulation analysis and meets the quasi-real-time requirements of the simulation analysis. It has significant application value in the actual power transmission and distribution scenario.

[0134] Reference Manual Attached Figure 6 , which shows the structure of a simulation analysis device 600 for power transmission and transformation equipment provided by an embodiment of the present invention. Figure 6 As shown, the apparatus 600 may include:

[0135] The instruction acquisition module 610 is used to acquire the simulation instruction input by the user, and the simulation instruction includes the target power transmission and transformation equipment to be simulated and its corresponding simulation parameters;

[0136] A model determination module 620 is used to determine a reduced-order simulation model corresponding to the simulation instruction, where the reduced-order simulation model is pre-constructed based on physical field distribution data corresponding to the target power transmission and transformation equipment using a preset reduced-order algorithm;

[0137] The model calling module 630 is used to call the reduced-order simulation model, perform simulation analysis on the target power transmission and transformation equipment based on simulation parameters, and obtain simulation data corresponding to the target power transmission and transformation equipment.

[0138] In some embodiments, the apparatus 600 may further include a model building module for pre-building a reduced-order simulation model;

[0139] Model building modules can include:

[0140] A data set acquisition unit, used to acquire a training data set, the training data set including physical field distribution data of power transmission and transformation equipment under different control parameters;

[0141] An initial model building unit, used for building an initial reduced-order model based on a preset reduced-order algorithm;

[0142] The model training unit is used to train the initial reduced-order model based on the physical field distribution data, and adjust the parameters and / or structure of the initial reduced-order model during the training process to obtain a trained reduced-order simulation model.

[0143] It should be noted that the device provided in the above embodiment, when implementing its functions, only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the corresponding method embodiment belong to the same concept, and the specific implementation process is detailed in the corresponding method embodiment, which will not be repeated here.

[0144] An embodiment of the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the simulation analysis method of power transmission and transformation equipment provided in the above method embodiment.

[0145] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory can also include a memory controller to provide the processor with access to the memory.

[0146] In conjunction with the reference manual Figure 7 , which is a block diagram of an electronic device 700 according to an embodiment of the present invention. The electronic device 700 may include one or more processors 702, a system control logic 708 connected to at least one of the processors 702, a system memory 704 connected to the system control logic 708, a non-volatile memory (NVM) 706 connected to the system control logic 708, and a network interface 710 connected to the system control logic 708.

[0147] The processor 702 may include one or more single-core or multi-core processors. The processor 702 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In the embodiments of the present invention, the processor 702 may be configured to execute the following operations: Figures 2 to 5 One or more of the various embodiments shown.

[0148] In some embodiments, system control logic 708 may include any suitable interface controller to provide any suitable interface to at least one of processors 702 and / or any suitable device or component in communication with system control logic 708 .

[0149] In some embodiments, the system control logic 708 may include one or more memory controllers to provide an interface to the system memory 704. The system memory 704 may be used to load and store data and / or instructions. In some embodiments, the memory 704 of the device 700 may include any suitable volatile memory, such as a suitable dynamic random access memory (DRAM).

[0150] NVM / memory 706 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, NVM / memory 706 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device, such as at least one of a HDD (Hard Disk Drive), a CD (Compact Disc) drive, and a DVD (Digital Versatile Disc) drive.

[0151] NVM / storage 706 may include a portion of storage resources installed on a device of apparatus 700, or it may be accessible to the apparatus but not necessarily a part of the apparatus. For example, NVM / storage 706 may be accessed over a network via network interface 710.

[0152] In particular, system memory 704 and NVM / storage 706 may include, respectively, a temporary copy and a permanent copy of instructions 720. Instructions 720 may include instructions that, when executed by at least one of processors 702, cause device 700 to perform the following operations: Figures 2 to 5 Instructions of the simulation analysis method of power transmission and transformation equipment shown. In some embodiments, instructions 720, hardware, firmware and / or software components thereof may be additionally / alternatively placed in system control logic 708, network interface 710 and / or processor 702.

[0153] The network interface 710 may include a transceiver for providing a radio interface for the device 700 to communicate with any other suitable device (such as a front-end module, an antenna, etc.) through one or more networks. In some embodiments, the network interface 710 may be integrated with other components of the device 700. For example, the network interface 710 may be integrated with at least one of a communication module of the processor 702, a system memory 704, an NVM / storage 706, and a firmware device (not shown) having instructions. When at least one of the processors 702 executes the instructions, the device 700 implements Figures 2 to 5 One or more of the various embodiments shown.

[0154] The network interface 710 may further include any suitable hardware and / or firmware to provide a multiple-input multiple-output radio interface. For example, the network interface 710 may be a network adapter, a wireless network adapter, a telephone modem and / or a wireless modem.

[0155] In one embodiment, at least one of the processors 702 may be packaged together with logic for one or more controllers of the system control logic 708 to form a system in package (SiP). In one embodiment, at least one of the processors 702 may be integrated on the same die with logic for one or more controllers of the system control logic 708 to form a system on a chip (SoC).

[0156] The device 700 may further include an input / output (I / O) device 712. The I / O device 712 may include a user interface to enable a user to interact with the device 700; the design of the peripheral component interface enables the peripheral components to interact with the device 700. In some embodiments, the device 700 further includes a sensor for determining at least one of an environmental condition and location information related to the device 700.

[0157] In some embodiments, the user interface may include, but is not limited to, a display (e.g., an LCD display, a touch screen display, etc.), a speaker, a microphone, one or more cameras (e.g., a still image camera and / or a video camera), a flashlight (e.g., an LED flash), and a keyboard.

[0158] In some embodiments, the peripheral component interface may include, but is not limited to, a non-volatile memory port, an audio jack, and a power interface.

[0159] In some embodiments, the sensors may include, but are not limited to, gyroscope sensors, accelerometers, proximity sensors, ambient light sensors, and positioning units. The positioning unit may also be part of or interact with the network interface 710 to communicate with components of a positioning network (e.g., global positioning system (GPS) satellites).

[0160] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 700. In other embodiments of the present invention, the electronic device 700 may include more or fewer components than shown in the figure, or combine some components, or separate some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0161] An embodiment of the present invention also provides a computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a simulation and analysis method for power transmission and transformation equipment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the simulation and analysis method for power transmission and transformation equipment provided by the above method embodiment.

[0162] Optionally, in an embodiment of the present invention, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0163] An embodiment of the present invention further provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the simulation analysis method of power transmission and transformation equipment provided in the above-mentioned various optional implementation examples.

[0164] It should be noted that the sequence of the embodiments of the present invention described above is for description only and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0165] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0166] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0167] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A simulation analysis method for power transmission and transformation equipment, characterized in that: The method comprises: Acquire a simulation instruction input by a user, wherein the simulation instruction includes a target power transmission and transformation equipment to be simulated and corresponding simulation parameters; Determining a reduced-order simulation model corresponding to the simulation instruction, wherein the reduced-order simulation model is pre-constructed based on physical field distribution data corresponding to the target power transmission and transformation equipment using a preset reduced-order algorithm; The reduced-order simulation model is called, and simulation analysis is performed on the target power transmission and transformation equipment based on the simulation parameters to obtain simulation data corresponding to the target power transmission and transformation equipment.

2. The method according to claim 1, characterized in that: The step of obtaining the simulation instruction input by the user comprises: Acquire voice information input by a user, and recognize the voice information to obtain a voice recognition result; The simulation instruction input by the user is determined according to the speech recognition result.

3. The method according to claim 2, characterized in that The step of determining the simulation instruction input by the user according to the speech recognition result comprises: Matching the speech recognition result with a preset simulation instruction set; If the match is successful, the simulation instruction that is successfully matched is used as the simulation instruction input by the user.

4. The method according to claim 1, characterized in that: The simulation parameters include the algorithm type and / or simulation mode corresponding to the reduced-order simulation model; The determining of the reduced-order simulation model corresponding to the simulation instruction comprises: Acquire the equipment type of the target power transmission and transformation equipment; A reduced-order simulation model corresponding to the simulation instruction is determined according to the equipment type, the algorithm type and / or the simulation mode.

5. The method according to claim 1, characterized in that The calling of the reduced-order simulation model, performing simulation analysis on the target power transmission and transformation equipment based on the simulation parameters, and obtaining simulation data corresponding to the target power transmission and transformation equipment, includes: The simulation prediction interface corresponding to the reduced-order simulation model is called to call the reduced-order simulation model, and simulation analysis is performed on the target power transmission and transformation equipment based on the simulation parameters to obtain simulation data corresponding to the target power transmission and transformation equipment.

6. The method according to claim 1, characterized in that The method further comprises pre-building a reduced-order simulation model, wherein the building process of the reduced-order simulation model comprises: Acquire a training data set, wherein the training data set includes physical field distribution data of power transmission and transformation equipment under different control parameters; Constructing an initial reduced-order model based on a preset reduced-order algorithm; The initial reduced-order model is trained based on the physical field distribution data, and the parameters and / or structure of the initial reduced-order model are adjusted during the training process to obtain the trained reduced-order simulation model.

7. The method according to claim 6, characterized in that The method further comprises: Preprocessing the physical field distribution data to obtain preprocessed physical field distribution data; The initial reduced-order model is trained based on the physical field distribution data, and the parameters and / or structure of the initial reduced-order model are adjusted during the training process to obtain the trained reduced-order simulation model, including: The initial reduced-order model is trained based on the preprocessed physical field distribution data, and the parameters and / or structure of the initial reduced-order model are adjusted during the training process to obtain the trained reduced-order simulation model.

8. A simulation analysis device for power transmission and transformation equipment, characterized in that: include: An instruction acquisition module is used to acquire a simulation instruction input by a user, wherein the simulation instruction includes a target power transmission and transformation equipment to be simulated and its corresponding simulation parameters; A model determination module, used to determine a reduced-order simulation model corresponding to the simulation instruction, wherein the reduced-order simulation model is pre-constructed based on physical field distribution data corresponding to the target power transmission and transformation equipment using a preset reduced-order algorithm; The model calling module is used to call the reduced-order simulation model, perform simulation analysis on the target power transmission and transformation equipment based on the simulation parameters, and obtain simulation data corresponding to the target power transmission and transformation equipment.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the simulation analysis method for power transmission and transformation equipment as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the simulation analysis method for power transmission and transformation equipment as described in any one of claims 1-7.

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