An electromechanical simulation model evaluation method and system based on AI analysis

Abstract

The application belongs to the technical field of electromechanical system simulation, and particularly relates to an electromechanical simulation model evaluation method based on AI analysis, comprising the following steps: data preprocessing, AI evaluation engine construction, dynamic index evaluation and closed-loop optimization; an electromechanical simulation model evaluation system based on AI analysis, comprising the following hierarchical architecture: a data preprocessing layer, an AI evaluation engine layer, a dynamic evaluation layer and a closed-loop optimization layer, one of kernel principal component analysis or variational autoencoder is introduced for nonlinear feature extraction, game theory Nash equilibrium is introduced for dynamic distribution of model weights, and a sliding window anomaly detection is introduced for dynamic adjustment of threshold values. The application initiates the "physical rules + AI reasoning" dual-drive evaluation architecture of electromechanical simulation models; the dynamic weight distribution algorithm improves the evaluation efficiency compared with traditional methods; three-level indexes are gradually converged to realize comprehensive evaluation from local performance to global efficiency.

Description

Technical Field

[0001] This invention belongs to the field of electromechanical system simulation technology, specifically relating to an evaluation method and system for electromechanical simulation models based on AI analysis. Background Technology

[0002] Electrical simulation model evaluation refers to verifying the approximation and credibility of a simulation model to the dynamic characteristics, reliability, and performance of an actual electromechanical system using quantitative or qualitative methods. Its core elements include: Model validation: verifying whether the model accurately reflects system behavior through physical rules (such as differential equations) or data-driven methods (such as Bayesian networks); Parameter influence analysis: assessing the impact of uncertain parameters (such as material properties and load conditions) on simulation results, for example, using non-probabilistic measurement methods; Reliability evaluation: quantifying the model's robustness under extreme conditions based on indicators such as failure time distribution; Dynamic adaptability: dynamically adjusting model parameters using real-time data (such as digital twin technology) to ensure consistency between the evaluation results and the actual system.

[0003] This process aims to ensure the engineering applicability of simulation models in scenarios such as design, optimization, and fault prediction through a combination of mathematical modeling, statistical analysis, and experimental verification.

[0004] Problems with existing technology:

[0005] Traditional electromechanical simulation model evaluation relies heavily on static indicators and human experience, which has problems such as slow response and poor adaptability to complex working conditions. Among the existing technologies, CN112668223A proposes a lightweight model simulation method based on digital twins, but it does not involve an AI-driven dynamic evaluation mechanism. At the same time, although Bayesian networks achieve an accuracy of 98% in fault diagnosis, they lack the ability to evaluate the overall performance of the simulation model. Summary of the Invention

[0006] The purpose of this invention is to provide an evaluation method and system for electromechanical simulation models based on AI analysis. It is the first to create a dual-driven evaluation architecture of "physical rules + AI reasoning" for electromechanical simulation models. The dynamic weight allocation algorithm improves the evaluation efficiency compared with traditional methods. The three-level indicators converge step by step to achieve a comprehensive evaluation from local performance to global effectiveness.

[0007] The specific technical solution adopted by this invention is as follows:

[0008] An AI-based evaluation method for electromechanical simulation models includes the following steps:

[0009] Step 1, Data Preprocessing: Construct a fully parametric geometric model of the electromechanical system using parametric modeling technology, generate a multi-condition simulation dataset using Simulink, and extract key feature parameters and establish a standardized input matrix using principal component analysis (PCA).

[0010] Step 2, AI evaluation engine construction: Integrating physics-based electromechanical system differential equation model, Bayesian network dynamic inference module and lightweight digital twin model to form a hybrid-driven evaluation architecture;

[0011] Step 3, Dynamic Indicator Evaluation: Calculate the comprehensive score through a three-level indicator system, which includes the basic layer (model response speed, computational resource utilization), the enhancement layer (multi-physics coupling accuracy, robustness under extreme conditions), and the intelligent layer (AI prediction deviation rate and self-learning iteration efficiency).

[0012] Step 4, Closed-loop optimization: The model parameters are dynamically adjusted according to the online security assessment algorithm. When the deviation of key parameters exceeds the threshold, the model reconstruction mechanism is triggered, and the dynamic weight allocation of evaluation indicators is realized through the reinforcement learning framework.

[0013] The fault diagnosis accuracy of the Bayesian network dynamic inference module is ≥95%.

[0014] The real-time state update frequency of the lightweight digital twin model is ≤10ms.

[0015] An AI-based electromechanical simulation model evaluation system includes the following layered architecture:

[0016] Data preprocessing layer: configured to construct a fully parametric model of the electromechanical system using parametric geometric modeling technology, generate a multi-condition simulation dataset using Simulink, and extract key feature parameters using principal component analysis to establish a standardized input matrix;

[0017] AI evaluation engine layer: configured to integrate the following modules to form a hybrid driven architecture:

[0018] Physical rule model: Based on the differential equations of electromechanical systems, describing the dynamic characteristics of multi-physics coupling;

[0019] Bayesian network inference module: predicts fault propagation paths using dynamic conditional probability tables, with a diagnostic accuracy of ≥95%;

[0020] Digital twin synchronization unit: supports real-time state update frequency ≤10ms, and achieves model lightweighting through intrinsic orthogonal decomposition;

[0021] Dynamic evaluation layer: configured to calculate a comprehensive score using a three-level indicator system, including:

[0022] Base layer: Model response speed and computational resource utilization;

[0023] Enhancement layer: Multiphysics coupling accuracy and robustness under extreme conditions;

[0024] Intelligent layer: AI prediction bias rate and self-learning iteration efficiency;

[0025] Closed-loop optimization layer: configured to dynamically allocate evaluation index weights through a reinforcement learning framework, and trigger a model reconstruction mechanism when the deviation of key parameters exceeds a threshold.

[0026] The closed-loop optimization module automatically pushes optimization solutions to the digital twin model through the knowledge engineering base.

[0027] The dynamic evaluation module uses fuzzy hierarchical analysis to initialize index weights and dynamically adjusts the weight allocation through a reinforcement learning framework.

[0028] In the data preprocessing layer, either kernel principal component analysis or variational autoencoder is introduced for nonlinear feature extraction to address the limited feature extraction capability of principal component analysis for nonlinear electromechanical systems.

[0029] In the AI ​​evaluation engine layer, game theory Nash equilibrium is introduced to dynamically allocate model weights, which solves the problem that the weight allocation of physical models, Bayesian networks and digital twins all depend on static rules.

[0030] We introduce a cross-modal contrastive learning approach to align the feature space and integrate vibration, thermal imaging, and acoustic signature multimodal data for multimodal data fusion evaluation.

[0031] Generative adversarial networks are introduced to synthesize extreme condition data to address the problem of insufficient measured data.

[0032] In dynamic index evaluation, a sliding window anomaly detection is introduced to dynamically adjust the threshold, which is used to solve the problem that the model reconstruction trigger threshold is a fixed value and therefore cannot adapt to changes in working conditions.

[0033] In closed-loop optimization, constrained reinforcement learning is introduced to improve the reward function;

[0034] A federated learning + model distillation architecture is introduced to alleviate the problem of high pressure on edge computing resources;

[0035] A security monitoring agent layer is introduced to clarify the fault propagation and isolation strategy.

[0036] The technical effects achieved by this invention are as follows:

[0037] (1) In this invention, the linkage between parameterization and simulation directly drives the Simulink dynamic model through geometric parameters, avoiding manual repetitive modeling; the synergy between PCA and standardization, first reducing dimensions and then normalizing, retains the main features and eliminates the influence of dimensions; the introduction of kernel principal component analysis (KPCA) or variational autoencoder (VAE) for nonlinear feature extraction optimizes the limited feature extraction capability of nonlinear electromechanical systems and enhances nonlinear feature extraction.

[0038] (2) This invention pioneers a dual-drive evaluation architecture of "physical rules + AI reasoning" for electromechanical simulation models, which is specifically manifested as follows: multi-physics coupled modeling: the dynamic characteristics of electromechanical systems are accurately described by combining the elastic medium equation and Maxwell's equation; multi-modal data such as vibration, thermal imaging, and acoustic signature are integrated to improve the comprehensiveness of the evaluation; dynamic reasoning and lightweight collaboration: Bayesian networks handle uncertainty, and digital twin models achieve real-time performance through POD order reduction; game theory Nash equilibrium is used to dynamically allocate model weights to compensate for the lack of dynamic collaboration and optimize the multi-model collaboration mechanism; adversarial networks are generated to synthesize extreme working condition data, thereby enhancing data diversity; and the automation, credibility, and adaptability of AI in the evaluation process are significantly improved.

[0039] (3) In this invention, the dynamic weight allocation algorithm improves the evaluation efficiency by 40% compared with the traditional method. Specifically, it is as follows: dynamic and static weight fusion: combining AHP static weights and fuzzy dynamic correction to balance expert experience and real-time data feedback; multi-level scoring mechanism: through the convergence of three-level indicators, a comprehensive evaluation from local performance to global effectiveness is achieved; and the threshold is updated in real time using sliding window statistics to adapt to changes in working conditions.

[0040] (4) This invention combines PID control and reinforcement learning to achieve dual time scale control of "real-time adjustment-long-term optimization"; dynamic weight adaptation: through Q-learning, the weight allocation is directly related to the evaluation index reward, which improves the robustness of the system; at the same time, safety constraints are added, and the fault propagation isolation strategy can be clearly defined. Attached Figure Description

[0041] Figure 1 These are diagrams provided in the embodiments of the present invention. Detailed Implementation

[0042] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0043] like Figure 1 As shown, an AI-based electromechanical simulation model evaluation method includes the following specific steps in data preprocessing:

[0044] Step 1: Parametric geometric modeling

[0045] Parametric modeling techniques are used to define the mathematical relationship between the geometric features and physical parameters of an electromechanical system. For example, geometric parameters include dimensions, and physical parameters include mass and damping coefficients.

[0046] , where M is the parametric geometric model and f(.) is the parameter mapping function.

[0047] Step 2: Simulink Multi-Condition Simulation Dataset Generation

[0048] Configure the dynamic model of the electromechanical system in Simulink, set multiple sets of operating parameters (such as load, speed, and temperature), and run the simulation to generate a time series dataset.

[0049] ,in Let S be the simulation data vector at time t under the s-th working condition, where S is the total number of working conditions.

[0050] Step 3: Principal Component Analysis (PCA) Feature Extraction

[0051] Standardization: Eliminating dimensional differences

[0052] ,in , are the mean and standard deviation of the j-th feature, respectively;

[0053] Covariance matrix calculation:

[0054] , where Z is the standardized data matrix and N is the number of samples;

[0055] Eigenvalue decomposition:

[0056] The eigenvectors corresponding to the top K largest eigenvalues ​​are selected. Constructing the projection matrix ;

[0057] Dimensionality reduction mapping:

[0058] ;

[0059] Based on the aforementioned feature extraction methods, current PCA is a linear dimensionality reduction method, which has limited ability to extract features of nonlinear electromechanical systems (such as friction and contact nonlinearity). Therefore, kernel principal component analysis (KPCA) or variational autoencoder (VAE) is also introduced here for nonlinear feature extraction.

[0060] ;

[0061] , where (VAE loss function).

[0062] Step 4: Construction of the standardized input matrix

[0063] Normalize the data after PCA dimensionality reduction to ensure that all features are of the same magnitude;

[0064] ,

[0065] Final input matrix .

[0066] Based on the above, the linkage between parameterization and simulation directly drives the Simulink dynamic model through geometric parameters, avoiding repetitive manual modeling; the synergy between PCA and standardization, first reducing dimensions and then normalizing, preserves the main features while eliminating the influence of dimensions; the introduction of kernel principal component analysis (KPCA) or variational autoencoder (VAE) for nonlinear feature extraction optimizes the limited feature extraction capability for nonlinear electromechanical systems and enhances nonlinear feature extraction.

[0067] The specific process for building the AI ​​evaluation engine is as follows:

[0068] Step 1: Construction of Physics-Based Differential Equation Model for Electromechanical Systems

[0069] Establish the multiphysics coupling equations for the electromechanical system, including the mechanical dynamics equations, electromagnetic field equations, and control equations:

[0070] Among them, the mechanical dynamics equations (elastic medium equations) are:

[0071] , where ρ is density, u is displacement field, σ is stress tensor, and f is external force;

[0072] Electromagnetic field equations (Maxwell's equations):

[0073] Where E is the electric field strength, B is the magnetic induction intensity, and J is the current density;

[0074] Simultaneous Solution: Solve the system of differential equations using time-domain simulation and then solve for the state variables using numerical integration (such as the Runge-Kutta method). .

[0075] Step 2: Evaluation of Multimodal Data Fusion

[0076] Employing Cross-Modal Contrast Learning (CLIP) to Align Feature Spaces:

[0077] ,in , These are visual and textual features, respectively.

[0078] By integrating multimodal data such as vibration, thermal imaging, and acoustic signatures, the comprehensiveness of the evaluation is improved, and multimodal data fusion evaluation is achieved.

[0079] Step 3: Construction of Bayesian Network Dynamic Inference Module

[0080] Node definition: The state variables of the electromechanical system (such as temperature and vibration) are used as nodes, and the fault modes are used as hidden variables;

[0081] Conditional Probability Table (CPT): Learns the conditional probability relationships between nodes based on historical data;

[0082] The Bayesian network structure is as follows:

[0083] , where Parents(Xi) represents the set of parent nodes of node Xi;

[0084] Dynamic reasoning: Updating posterior probabilities through variational approximation reasoning.

[0085] .

[0086] Step 4: Building a Lightweight Digital Twin Model

[0087] Geometric lightweighting: 3D model data is compressed through mesh simplification and parametric compression;

[0088] Dynamic synchronization: Establishing a data mapping relationship between physical entities and virtual models;

[0089] Among them, model order reduction: dominant modes are extracted using intrinsic orthogonal decomposition (POD).

[0090] ,in As basis functions, These are time-varying coefficients;

[0091] Real-time synchronization equation:

[0092] ,in For mapping functions, This is the synchronization error.

[0093] Step 5: Hybrid Driver Architecture Integration

[0094] Data coupling: The output of the differential equation is used as the input of the Bayesian network, and the digital twin model provides real-time state feedback;

[0095] Joint optimization: Coordinates the outputs of the physical model and the data-driven model through dynamic weight allocation;

[0096] Among them, the hybrid driving equation is:

[0097] ,in For dynamic weighting coefficients, satisfying ;

[0098] Weight update rules (reinforcement learning framework):

[0099] , where η is the learning rate and R is the evaluation metric reward function.

[0100] Based on the above process, since the weight allocation of the physical model, Bayesian network, and digital twin all depend on static rules, game-theoretic Nash equilibrium is also used to dynamically allocate model weights:

[0101] ,in This provides real-time contribution data for each model, thereby compensating for insufficient dynamic collaboration.

[0102] Step 6: Introduce Generative Adversarial Networks

[0103] Constructing a conditional GAN ​​(CGAN) to generate multiphysics coupled data:

[0104] , where c is the working condition vector;

[0105] By using generative adversarial networks to synthesize extreme operating condition data, the problem of insufficient measured data can be solved, thereby enhancing data diversity.

[0106] Based on the above, multi-physics coupled modeling is employed: precise description of the dynamic characteristics of electromechanical systems is achieved by simultaneously applying the elastic medium equations and Maxwell's equations; multimodal data such as vibration, thermal imaging, and acoustic signatures are integrated to enhance the comprehensiveness of the evaluation; dynamic reasoning and lightweight collaboration are implemented: Bayesian networks handle uncertainty, and digital twin models achieve real-time performance through POD order reduction; game theory Nash equilibrium is used to dynamically allocate model weights to compensate for insufficient dynamic collaboration and optimize the multi-model collaboration mechanism; adversarial networks are generated to synthesize extreme working condition data, thereby enhancing data diversity.

[0107] Significantly improves the automation, credibility, and adaptability of AI in the evaluation process.

[0108] The specific process for evaluating dynamic indicators is as follows:

[0109] Step 1: Indicator Standardization

[0110] The third-level indicators are dimensionless to eliminate differences in dimensions and magnitudes.

[0111] Score standardization (applicable to normally distributed data):

[0112] ,in , and are the mean and standard deviation of the j-th indicator, respectively;

[0113] Min-Max standardization (applicable to non-normally distributed data):

[0114] .

[0115] Step 2: Weight Allocation

[0116] The Analytic Hierarchy Process (AHP) determines static weights:

[0117] • Construct the judgment matrix ,in This indicates the relative importance of indicator i compared to indicator j;

[0118] • Calculate the weight vector ,in It is the largest eigenvalue;

[0119] Fuzzy comprehensive evaluation dynamically adjusts weights:

[0120] • Define the membership function Mapping indicator values ​​to fuzzy sets;

[0121] • Calculate the dynamic weight correction coefficient using the fuzzy relation matrix R: .

[0122] Step 3: Calculate the overall score

[0123] The final score is generated by weighting and summing the indicators at each of the three levels.

[0124] Single-layer indicator scoring:

[0125] ,in As weight, Standardized values;

[0126] Overall score:

[0127] ,in For hierarchical weights (base layer ( ), enhancement layer ( ), intelligent layer ( )), ( () is used to score each level.

[0128] Step 4: Dynamic Feedback Optimization

[0129] The model parameter adjustment or reconstruction mechanism is triggered based on the scoring results:

[0130] Parameter adaptive update:

[0131] , where θ are the model parameters and η is the learning rate;

[0132] Model reconstruction triggering conditions:

[0133] ,in Set a preset threshold (e.g., the deviation rate of key parameters ≥5%).

[0134] Based on the above, the model reconstruction trigger threshold is a fixed value. To adapt to changes in operating conditions (such as sudden load changes), the threshold is also dynamically adjusted based on sliding window anomaly detection. , where k is the adjustment coefficient, and the threshold is updated in real time through this sliding window statistic.

[0135] Based on the above, here is an example of the mathematical expression for a third-level indicator.

[0136] Base layer: Model response speed: first token latency; Computational resource utilization: peak memory usage;

[0137] Enhancement layer: Multiphysics coupling accuracy:

[0138] ;

[0139] Robustness under extreme conditions:

[0140] ,in This is a stability indicator function;

[0141] Intelligent Layer: AI Prediction Bias Rate:

[0142] ;

[0143] Self-learning iteration efficiency:

[0144] , where ΔZ is the score improvement and Δt is the iteration time;

[0145] Combining the above technologies, the system integrates dynamic and static weights: combining AHP static weights with fuzzy dynamic correction to balance expert experience and real-time data feedback; a multi-level scoring mechanism: achieving a comprehensive evaluation from local performance to global effectiveness through three levels of indicators converging step by step; and using sliding window statistics to update thresholds in real time to adapt to changes in operating conditions.

[0146] In closed-loop optimization, the specific process is as follows:

[0147] Step 1: Online security assessment and deviation monitoring

[0148] By collecting real-time status data of the electromechanical system (such as temperature, vibration, and current) through sensors and comparing it with the predicted values ​​of the digital twin model, the deviation of key parameters is calculated.

[0149] Deviation calculation: ,in These are measured values. The predicted value for the digital twin model;

[0150] Safety threshold determination: ,in Set a preset safety threshold (e.g., set the insulation aging parameter deviation threshold to 5%).

[0151] Step 2: Dynamic adjustment of model parameters

[0152] Based on deviation feedback, the model parameters are adjusted through control algorithms (such as PID or reinforcement learning) to restore the system to a stable state;

[0153] PID control adjustment: ,in These are the proportional, integral, and differential coefficients;

[0154] Gradient descent optimization: Where η is the learning rate, The loss function (e.g., mean squared error).

[0155] Step 3: Dynamic Weight Allocation in Reinforcement Learning

[0156] The three-level weight allocation is dynamically adjusted based on real-time evaluation metrics using reinforcement learning frameworks (such as Q-learning or PPO algorithm).

[0157] Reward function design: Where α, β, and γ are reward coefficients. It is rated as a three-level scale;

[0158] Weight update rules (taking Q-learning as an example):

[0159] , where s is the state (current weight distribution), a is the action (weight adjustment strategy), and γ is the discount factor;

[0160] Based on the above, since the current reward function does not explicitly include safety constraints (such as overload protection), Constraint Reinforcement Learning (CRL) is also introduced here:

[0161] ,in The safety cost function is defined by ϵ, which represents the upper bound of the constraint, thus improving the reinforcement learning reward function.

[0162] Step 4: Model Reconstruction Mechanism Triggered

[0163] When the deviation of key parameters continues to exceed the threshold, the predefined rules in the knowledge engineering library are invoked to reconstruct the model structure or parameters.

[0164] Reconstruct the decision function: ,in The maximum number of times allowed to exceed the limit;

[0165] Model reconstruction strategies: parameter correction, ; Structural optimization and adjustment of model complexity through robust optimization algorithms.

[0166] Based on the above, the real-time update frequency of the digital twin model is ≤10ms. However, this requirement puts a heavy burden on edge computing resources. Therefore, a federated learning + model distillation architecture is adopted.

[0167] Edge nodes run lightweight student models (such as MobileNet).

[0168] Train the teacher model in the cloud and optimize the edge model through knowledge distillation: This architecture can alleviate the pressure on edge computing resources.

[0169] In addition, the closed-loop optimization did not explicitly define a fault propagation isolation strategy, so a security monitoring agent layer was introduced:

[0170] Predicting fault propagation paths based on LSTM;

[0171] The abnormal subsystem is shut off via the isolation valve:

[0172] .

[0173] Based on the above:

[0174] Closed-loop feedback fusion: Combining PID control and reinforcement learning, it achieves dual-time-scale control of "real-time adjustment - long-term optimization"; Dynamic weight adaptation: By using Q-learning, the weight allocation is directly linked to the evaluation index reward, which improves the robustness of the system; At the same time, it adds safety constraints; It alleviates the pressure on edge computing resources; And it can clearly define the fault propagation isolation strategy.

[0175] An AI-based electromechanical simulation model evaluation system includes the following layered architecture:

[0176] Data preprocessing layer: configured to construct a fully parametric model of the electromechanical system using parametric geometric modeling technology, generate a multi-condition simulation dataset using Simulink, and extract key feature parameters using principal component analysis to establish a standardized input matrix;

[0177] AI evaluation engine layer: configured to integrate the following modules to form a hybrid driven architecture:

[0178] Physical rule model: Based on the differential equations of electromechanical systems, describing the dynamic characteristics of multi-physics coupling;

[0179] Bayesian network inference module: predicts fault propagation paths using dynamic conditional probability tables, with a diagnostic accuracy of ≥95%;

[0180] Digital twin synchronization unit: supports real-time state update frequency ≤10ms, and achieves model lightweighting through intrinsic orthogonal decomposition;

[0181] Dynamic evaluation layer: configured to calculate a comprehensive score using a three-level indicator system, including:

[0182] Base layer: Model response speed and computational resource utilization;

[0183] Enhancement layer: Multiphysics coupling accuracy and robustness under extreme conditions;

[0184] Intelligent layer: AI prediction bias rate and self-learning iteration efficiency;

[0185] Closed-loop optimization layer: configured to dynamically allocate evaluation index weights through a reinforcement learning framework, and trigger a model reconstruction mechanism when the deviation of key parameters exceeds a threshold.

[0186] The closed-loop optimization module automatically pushes optimization solutions to the digital twin model through the knowledge engineering base.

[0187] The dynamic evaluation module uses fuzzy hierarchical analysis to initialize the index weights and dynamically adjusts the weight allocation through a reinforcement learning framework.

[0188] In the data preprocessing layer, either kernel principal component analysis or variational autoencoder is introduced for nonlinear feature extraction to address the limited feature extraction capability of principal component analysis for nonlinear electromechanical systems.

[0189] In the AI ​​evaluation engine layer, game theory Nash equilibrium is introduced to dynamically allocate model weights, which solves the problem that the weight allocation of physical models, Bayesian networks and digital twins all depend on static rules.

[0190] We introduce a cross-modal contrastive learning approach to align the feature space and integrate vibration, thermal imaging, and acoustic signature multimodal data for multimodal data fusion evaluation.

[0191] Generative adversarial networks are introduced to synthesize extreme condition data to address the problem of insufficient measured data.

[0192] In dynamic index evaluation, a sliding window anomaly detection is introduced to dynamically adjust the threshold, which is used to solve the problem that the model reconstruction trigger threshold is a fixed value and therefore cannot adapt to changes in working conditions.

[0193] In closed-loop optimization, constrained reinforcement learning is introduced to improve the reward function;

[0194] A federated learning + model distillation architecture is introduced to alleviate the problem of high pressure on edge computing resources;

[0195] A security monitoring agent layer is introduced to clarify the fault propagation and isolation strategy.

[0196] The working principle of this system is as follows: Please refer to the specific steps of the above-mentioned evaluation method for electromechanical simulation models based on AI analysis.

[0197] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for evaluating electromechanical simulation models based on AI analysis, characterized in that, Includes the following steps: Step 1, Data Preprocessing: Construct a fully parametric geometric model of the electromechanical system using parametric modeling technology, generate a multi-condition simulation dataset using Simulink, extract key feature parameters using principal component analysis, and establish a standardized input matrix; Step 2, AI evaluation engine construction: Integrating physics-based electromechanical system differential equation model, Bayesian network dynamic inference module and lightweight digital twin model to form a hybrid-driven evaluation architecture; Step 3, Dynamic Indicator Evaluation: Calculate a comprehensive score using a three-level indicator system, which includes a basic layer, an enhancement layer, and an intelligent layer; Step 4, Closed-loop optimization: The model parameters are dynamically adjusted according to the online security assessment algorithm. When the deviation of key parameters exceeds the threshold, the model reconstruction mechanism is triggered, and the dynamic weight allocation of evaluation indicators is realized through the reinforcement learning framework.

2. The method according to claim 1, characterized in that, The fault diagnosis accuracy of the Bayesian network dynamic inference module is ≥95%.

3. The method according to claim 1, characterized in that, The real-time state update frequency of the lightweight digital twin model is ≤10ms.

4. An evaluation system for electromechanical simulation models based on AI analysis, characterized in that, Includes the following layered architecture: Data preprocessing layer: configured to construct a fully parametric model of the electromechanical system using parametric geometric modeling technology, generate a multi-condition simulation dataset using Simulink, and extract key feature parameters using principal component analysis to establish a standardized input matrix; AI evaluation engine layer: configured to integrate the following modules to form a hybrid driven architecture: Physical rule model: Based on the differential equations of electromechanical systems, describing the dynamic characteristics of multi-physics coupling; Bayesian network inference module: predicts fault propagation paths using dynamic conditional probability tables, with a diagnostic accuracy of ≥95%; Digital twin synchronization unit: supports real-time state update frequency ≤10ms, and achieves model lightweighting through intrinsic orthogonal decomposition; Dynamic evaluation layer: configured to calculate a comprehensive score using a three-level indicator system, including: Base layer: Model response speed and computational resource utilization; Enhancement layer: Multiphysics coupling accuracy and robustness under extreme conditions; Intelligent layer: AI prediction bias rate and self-learning iteration efficiency; Closed-loop optimization layer: configured to dynamically allocate evaluation index weights through a reinforcement learning framework, and trigger a model reconstruction mechanism when the deviation of key parameters exceeds a threshold.

5. The electromechanical simulation model evaluation system based on AI analysis according to claim 4, characterized in that, It also includes a closed-loop optimization module, which automatically pushes optimization solutions to the digital twin model through a knowledge engineering base.

6. The electromechanical simulation model evaluation system based on AI analysis according to claim 4, characterized in that, It also includes a dynamic evaluation module, which uses fuzzy hierarchical analysis to initialize index weights and dynamically adjusts the weight allocation through a reinforcement learning framework.

7. The electromechanical simulation model evaluation system based on AI analysis according to claim 4, characterized in that, In the data preprocessing layer, either kernel principal component analysis or variational autoencoder is introduced for nonlinear feature extraction to address the limited feature extraction capability of principal component analysis for nonlinear electromechanical systems.

8. The electromechanical simulation model evaluation system based on AI analysis according to claim 4, characterized in that, In the AI ​​evaluation engine layer, game theory Nash equilibrium is introduced to dynamically allocate model weights, which solves the problem that the weight allocation of physical models, Bayesian networks and digital twins all depend on static rules. We introduce a cross-modal contrastive learning approach to align the feature space and integrate vibration, thermal imaging, and acoustic signature multimodal data for multimodal data fusion evaluation. Generative adversarial networks are introduced to synthesize extreme condition data to address the problem of insufficient measured data.

9. The electromechanical simulation model evaluation system based on AI analysis according to claim 4, characterized in that, In dynamic index evaluation, a sliding window anomaly detection is introduced to dynamically adjust the threshold, which is used to solve the problem that the model reconstruction trigger threshold is a fixed value and therefore cannot adapt to changes in working conditions.

10. The electromechanical simulation model evaluation system based on AI analysis according to claim 4, characterized in that, In closed-loop optimization, constrained reinforcement learning is introduced to improve the reward function; A federated learning + model distillation architecture is introduced to alleviate the problem of high pressure on edge computing resources; A security monitoring agent layer is introduced to clarify the fault propagation and isolation strategy.

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