A battery SOC and SOH comprehensive evaluation system and predictive maintenance method thereof

Abstract

The present invention discloses a battery SOC and SOH comprehensive evaluation system and a predictive maintenance method thereof, the system comprising: a multi-source data acquisition module, a data preprocessing module, a multi-source data fusion module, a deep learning SOC estimation module, a transfer learning SOH evaluation module, a graph neural network battery pack state evaluation module, a reinforcement learning predictive maintenance decision module, a big data analysis platform, an edge computing node, an explainable AI module, an adaptive online learning module, a digital twin module, a quantum machine learning module, a federated learning framework, a self-supervised learning anomaly detection module, a multimodal deep learning module, an adaptive sampling strategy module, a causal reasoning fault diagnosis module, etc. The present invention realizes a comprehensive and accurate evaluation of the battery state by integrating a variety of advanced artificial intelligence and big data technologies, and formulates a predictive maintenance strategy based on the evaluation results to extend the battery life and improve the system reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and in particular relates to a battery SOC and SOH comprehensive evaluation system and a predictive maintenance method thereof. Background Art

[0002] With the rapid development of electric vehicles, renewable energy storage systems and other fields, the evaluation of battery performance and safety is particularly important. However, the existing battery status evaluation methods and technologies have many shortcomings and are difficult to meet the needs of practical applications.

[0003] 1. Limitations of traditional battery status assessment methods:

[0004] Open circuit voltage method (OCV): SOC is estimated by measuring the voltage of the battery in a static state, but its accuracy is greatly affected by factors such as temperature and aging, and it is difficult to provide accurate assessment under dynamic operating conditions.

[0005] Coulomb counting method: SOC is calculated by integrating the charge and discharge current of the battery, but it requires precise initial values ​​and cumulative error correction. Long-term use will lead to error accumulation and affect the accuracy of the evaluation.

[0006] Internal resistance method: SOH is evaluated by measuring the change in the internal resistance of the battery. However, the internal resistance is greatly affected by changes in temperature and current, making it difficult to provide stable and reliable evaluation results.

[0007] 2. Deficiencies of existing machine learning methods:

[0008] Support vector machine (SVM): used for classification and regression of SOC and SOH, but requires a large amount of labeled data for training, and has strict assumptions on data distribution, which affects its generalization ability.

[0009] Random Forest: It integrates multiple decision trees for prediction and has good generalization ability, but its computational complexity is high and it is difficult to run efficiently in a resource-constrained environment.

[0010] Neural Network (NN): Especially deep learning models (such as convolutional neural network CNN and recurrent neural network RNN), which perform well in processing complex nonlinear relationships and time series data, but the model training time is long and has high requirements on data quantity and quality.

[0011] 3. Disadvantages of big data and edge computing technology:

[0012] Big data analysis: Through distributed computing frameworks (such as Hadoop and Spark), massive amounts of battery data are stored, processed and analyzed to mine potential rules and patterns in the data. However, traditional big data processing methods have problems with data transmission delays and lack of real-time performance.

[0013] Edge computing: Introducing edge computing nodes into the battery management system can realize local preprocessing and preliminary analysis of data, reduce data transmission delays, and improve system response speed. However, the computing power of edge computing nodes is limited and it is difficult to process complex deep learning models.

[0014] 4. Shortcomings of predictive maintenance technology:

[0015] Statistical analysis: Statistical analysis methods based on historical data, such as regression analysis and time series analysis, are used to predict equipment failure trends, but they are highly dependent on data and have difficulty handling complex nonlinear relationships.

[0016] Machine learning: Although machine learning algorithms (such as decision trees, random forests, and neural networks) are used to build fault prediction models, the prediction accuracy has been improved to a certain extent. However, there are still problems such as long model training time and high requirements on data volume and quality.

[0017] Deep learning and reinforcement learning: In recent years, deep learning and reinforcement learning methods have been widely used in predictive maintenance. They can handle complex nonlinear relationships and dynamic environments, but their computational complexity is high and the model interpretability is poor.

[0018] 5. Limitations of existing technologies:

[0019] Insufficient accuracy and reliability: Traditional methods and some machine learning methods still lack accuracy and reliability when dealing with complex battery state changes, and it is difficult to meet high precision requirements.

[0020] Limited data processing capabilities: As the amount of battery data continues to increase, existing systems have bottlenecks in data processing and analysis capabilities, making it difficult to efficiently process and utilize this data.

[0021] Poor model adaptability: The existing models have poor adaptability and generalization capabilities under different types of batteries and usage environments, and are difficult to cope with diverse application scenarios.

[0022] Lack of intelligent decision-making support: Existing systems mostly rely on preset maintenance strategies and lack intelligent predictive maintenance decisions, making it difficult to dynamically adjust maintenance plans based on changes in battery status.

[0023] Lack of explainability and transparency: Although some machine learning models (such as deep neural networks) have good performance, they lack explainability, making it difficult for users to understand the decision-making process of the model, which reduces the credibility of the system.

[0024] Data security and privacy protection issues: Centralized data storage and processing methods have the risk of data leakage and tampering, making it difficult to ensure data security and privacy protection.

[0025] Computational efficiency and system performance limitations: Some advanced machine learning and deep learning models have high computational complexity, long training and inference times, and are difficult to run efficiently in resource-constrained environments.

[0026] Insufficient anomaly detection and long-term prediction capabilities: The existing system has limited capabilities in detecting abnormal battery conditions and potential faults, which are prone to missed reports and false alarms, affecting the reliability of the system; it also has insufficient capabilities in predicting long-term trends in battery conditions, making it difficult to provide accurate long-term prediction results.

[0027] In summary, although the existing technology has made certain progress in battery status assessment and predictive maintenance, it still has many shortcomings. Summary of the invention

[0028] In order to address the shortcomings of the prior art, the present invention aims to provide a battery SOC (State of Charge) and SOH (State of Health) comprehensive evaluation system, which integrates a variety of advanced artificial intelligence and big data technologies to achieve a comprehensive and accurate evaluation of the battery status, and formulate a predictive maintenance strategy based on the evaluation results to extend the battery life and improve system reliability.

[0029] In order to achieve the above object, the technical solution adopted by the present invention is:

[0030] A battery SOC and SOH comprehensive evaluation system, comprising:

[0031] Multi-source data acquisition module, used to collect battery operation data using multiple sensors, including voltage, current, temperature, and pressure parameters;

[0032] Data preprocessing module, used to clean, standardize and extract features from the collected raw data;

[0033] Multi-source data fusion module, which uses data fusion algorithms to integrate multi-dimensional and multi-scale battery operation data, historical usage data, and environmental data;

[0034] Deep learning SOC estimation module, which uses historical charge and discharge data and real-time operation data to accurately estimate the battery's SOC;

[0035] The transfer learning SOH evaluation module is used to transfer the SOH evaluation model trained on a large number of similar batteries to the target battery using transfer learning technology, and quickly build a personalized SOH evaluation model;

[0036] The graph neural network battery pack status assessment module is used to model the battery pack as a graph structure, use the graph neural network GNN to capture the mutual influence between batteries, and evaluate the status of the entire battery pack;

[0037] Reinforcement learning predictive maintenance decision module: A predictive maintenance decision system based on deep reinforcement learning that automatically generates the optimal maintenance strategy based on battery status assessment results and historical maintenance data;

[0038] Big data analysis platform for storing, processing and analyzing massive battery data;

[0039] Edge computing nodes are used to introduce edge computing nodes into the battery management system for local preprocessing and preliminary analysis of data;

[0040] Explainable AI module, which uses explainable AI technology to explain the battery status assessment and predictive maintenance decision-making process;

[0041] Adaptive online learning module, which is used to design adaptive online learning algorithms so that the system can continuously update and optimize the model based on newly collected data;

[0042] The digital twin module is used to build a digital twin model of the battery system, combining real-time data and AI models to perform virtual-real analysis and prediction of the battery status;

[0043] Quantum machine learning module, which is used to utilize the parallel computing power of quantum computers and combine quantum machine learning algorithms to build an efficient battery state prediction model;

[0044] Federated learning framework, which is used to design a distributed model training framework based on federated learning, allowing different battery users to jointly train and optimize the battery status assessment model while protecting data privacy;

[0045] A self-supervised learning anomaly detection module is used to use self-supervised learning technology to automatically learn the characteristic representation of normal battery operation using a large amount of unlabeled battery operation data, thereby detecting abnormal conditions;

[0046] Multimodal deep learning module, which is used to integrate multimodal data of batteries, design multimodal deep learning models, and comprehensively evaluate battery status;

[0047] Adaptive sampling strategy module, which is used to design an adaptive sampling strategy based on reinforcement learning, and dynamically adjust the data collection frequency and accuracy according to the current state of the battery and historical data;

[0048] The causal reasoning fault diagnosis module is used to introduce the causal reasoning model, combine domain knowledge and data-driven methods, build a causal relationship diagram of battery failure, and perform accurate fault diagnosis and root cause analysis;

[0049] The meta-learning rapid adaptation module is used to use meta-learning technology to enable the system to quickly adapt to new types of batteries or new usage environments;

[0050] The attention mechanism time series prediction module is used to introduce a multi-head attention mechanism into the time series prediction model to capture the key time points and features in the battery state change to improve the accuracy of long-term prediction;

[0051] The knowledge graph maintenance knowledge management module is used to build a knowledge graph in the field of battery maintenance and integrate relevant information to provide knowledge support for predictive maintenance decisions;

[0052] Blockchain data credibility assurance module, which uses blockchain technology to record key battery status data and maintenance operations to ensure data immutability and traceability;

[0053] Generative Adversarial Network Data Augmentation Module, which is used to generate simulated battery degradation data using GAN to enhance the diversity of training datasets;

[0054] Automatic machine learning model optimization module, used to automatically search and optimize neural network structure and hyperparameters, and continuously improve the performance of battery state assessment models.

[0055] Furthermore, the multi-source data acquisition module includes a voltage sensor, a current sensor, a temperature sensor and a pressure sensor.

[0056] The deep learning SOC estimation module uses a deep learning model of a long short-term memory network (LSTM) to estimate the SOC of a battery.

[0057] The big data analysis platform adopts the Spark distributed computing framework.

[0058] The multimodal deep learning module integrates multimodal data of the battery including electrochemical data, thermal images, and acoustic signals.

[0059] The relevant information integrated by the knowledge graph maintenance knowledge management module includes expert experience, historical maintenance records and equipment manuals.

[0060] The automatic machine learning model optimization module adopts AutoML technology.

[0061] The present invention also proposes a battery predictive maintenance method based on the above-mentioned battery SOC and SOH comprehensive evaluation system, which specifically includes the following steps:

[0062] 1) Data acquisition and preprocessing: Use the multi-source data acquisition module to collect battery operation data, and use the data preprocessing module to perform data cleaning, standardization and feature extraction;

[0063] 2) Data fusion and analysis: Use the multi-source data fusion module to integrate the processed data and conduct preliminary analysis on the big data analysis platform;

[0064] 3) SOC estimation: Using the deep learning SOC estimation module, the battery SOC is accurately estimated based on the LSTM model;

[0065] 4) SOH evaluation: Through transfer learning of the SOH evaluation module, a personalized SOH evaluation model can be quickly built and applied;

[0066] 5) Battery pack status assessment: Use the graph neural network battery pack status assessment module to comprehensively assess the status of the entire battery pack;

[0067] 6) Anomaly detection and fault diagnosis: Combine the self-supervised learning anomaly detection module and the causal reasoning fault diagnosis module to perform early detection and accurate diagnosis of battery abnormal conditions;

[0068] 7) Predictive maintenance decision-making: Generate the optimal maintenance strategy based on the reinforcement learning predictive maintenance decision-making module;

[0069] 8) Decision explanation and knowledge support: Use the explainable AI module to explain the decision-making process, and provide knowledge support through the knowledge graph maintenance knowledge management module;

[0070] 9) Model update and optimization: Continuously update and optimize the system model through adaptive online learning module and automatic machine learning model optimization module;

[0071] 10) Data security and privacy protection: Adopting the federated learning framework and blockchain data credibility assurance module to ensure data security and user privacy;

[0072] 11) Advanced analysis and prediction: Combine the digital twin module, quantum machine learning module and multimodal deep learning module to conduct more in-depth status analysis and long-term trend prediction;

[0073] 12) System adaptability optimization: Improve the system's adaptability to different types of batteries and usage environments through meta-learning fast adaptation modules and adaptive sampling strategy modules;

[0074] 13) Data enhancement and model generalization: Generate adversarial network data enhancement module to improve the model’s recognition ability and generalization performance of various abnormal situations.

[0075] Furthermore, data cleaning, standardization and feature extraction in step 1) specifically include the following:

[0076] 1-1) Data cleaning includes: removing duplicate data; handling missing values, including interpolation, deletion or estimation; correcting erroneous data; identifying and handling outliers;

[0077] 1-2) Data standardization includes: normalization, including Min-Max scaling; standardization; logarithmic transformation; one-hot encoding, including categorical variables;

[0078] 1-3) The extracted features include: time domain features, including mean, variance, and peak; frequency domain features, including FFT and power spectral density; time-frequency domain features, including wavelet transform; statistical features, including skewness and kurtosis.

[0079] Furthermore, the data integration process in step 2) specifically includes:

[0080] 2-1) Data fusion: integration of multi-source data, including sensors, historical records, maintenance logs; time synchronization; spatial alignment;

[0081] 2-2) Data dimensionality reduction: PCA principal component analysis; t-SNE dimensionality reduction algorithm; autoencoder;

[0082] 2-3) Data enhancement: oversampling technology; data synthesis; noise injection.

[0083] Furthermore, the estimation methods in the estimation and evaluation performed in step 3) and step 4) specifically include: regression analysis; time series forecasting; machine learning model analysis;

[0084] Evaluation indicators include: MSE mean square error; MAE mean absolute error; R2 score; accuracy, precision, and recall;

[0085] Cross-validation: K-fold cross-validation; time series cross-validation.

[0086] Step 5) When using a graph neural network (GNN) to evaluate the battery pack status, the battery pack is modeled as a graph structure, and the graph neural network is used to evaluate the mutual influence between batteries:

[0087] Let the graph be G = (V, E), where V is the node set (batteries) and E is the edge set (connection relationship between batteries);

[0088] The node feature update rule is:

[0089]

[0090] In the formula, is the feature representation of node i in round t+1; is the feature representation of node i in round t; is the feature representation of node j in round t; σ is the activation function (such as ReLU); N (i) is the set of neighbor nodes of node i; W is the weight matrix; W0 represents the self-connection weight matrix, which is used to adjust the influence of the node's own characteristics; deg(i) represents the degree of node i, that is, the number of edges connected to node i; deg(j) represents the degree of node j, that is, the number of edges connected to node j.

[0091] Furthermore, the abnormality and fault maintenance strategies in step 6) and step 7) include:

[0092] Preventive maintenance: time-based regular inspections; condition-based maintenance plans;

[0093] Predictive maintenance: Failure prediction based on machine learning; Remaining useful life RUL estimation;

[0094] Fault diagnosis: root cause analysis; fault tree analysis;

[0095] Maintenance optimization: maintenance scheduling optimization; spare parts inventory management.

[0096] Furthermore, step 7) generates an optimal maintenance strategy based on a deep reinforcement learning method, combining the battery status assessment results and the maintenance history data, specifically including:

[0097] The state transition model is:

[0098] s t+1 =f(s t ,a t );

[0099] Reward function:

[0100] r t =R(s t ,a t );

[0101] Strategy optimization algorithm:

[0102]

[0103] Among them, s t is the current state; a t is the current action; r t is the current reward; α is the learning rate, which controls the influence of new information on the Q value update; γ is the discount factor, which is used to balance the immediate reward and long-term benefits; s′ is the new state after the action is executed; a′ represents the learning rate after the action is executed; Q(s,a) is the Q value of taking action a in state s, which represents the estimate of long-term benefits; Represents the maximum Q value of all possible actions in the new state s'.

[0104] Furthermore, the model updating and optimization in step 9) includes:

[0105] Online learning uses: incremental learning algorithm; sliding window technology;

[0106] Transfer learning includes: cross-domain knowledge transfer; pre-training model fine-tuning

[0107] Hyperparameter optimization: grid search; random search; Bayesian optimization;

[0108] Model integration includes the following modules: Bagging; Boosting; Stacking.

[0109] Furthermore, the in-depth status analysis and long-term trend prediction in step 11) include:

[0110] State analysis: health index calculation; multidimensional state space analysis; anomaly detection algorithm;

[0111] Long-term trend forecasting: time series decomposition, including trend, seasonality, and periodicity; long short-term memory network LSTM analysis; state space model analysis; deep learning model analysis;

[0112] Scenario analysis: Monte Carlo simulation; sensitivity analysis; multi-scenario planning;

[0113] Life cycle analysis: accelerated life test; Weibull analysis; Cox proportional hazard model analysis; innovative hybrid algorithm analysis.

[0114] Among them, the innovative hybrid algorithm analysis and prediction in step 11) specifically includes:

[0115] An innovative hybrid algorithm combining deep learning and statistical models is designed to predict the remaining useful life (RUL) of equipment. The algorithm effectively combines the convolutional neural network (CNN) for feature extraction and the long short-term memory (LSTM) network for time series prediction. Specifically:

[0116] 11-1) Combining feature extraction with sequence modeling: Use CNN to extract spatial features from raw sensor data; use LSTM to capture temporal dependencies and process time series data;

[0117] 11-2) Multi-scale analysis: Use multi-scale convolution kernels in CNN to capture features of different scales;

[0118] 11-3) Ensemble learning: Combine the outputs of CNN and LSTM and integrate them through a fully connected layer to optimize the overall prediction performance;

[0119] For the convolutional layer of the convolutional neural network CNN:

[0120]

[0121] In the formula, Z i,j,k represents the kth channel of the output feature map at position (i, j), X i+m,j+n,c represents the input feature map, K m,n,c,k represents the convolution kernel, b krepresents the bias, M and N represent the size of the convolution kernel; m represents the vertical index of the convolution kernel, which is used to traverse the rows of the convolution kernel; n represents the horizontal index of the convolution kernel, which is used to traverse the columns of the convolution kernel; c represents the channel index of the input feature map;

[0122] For the gating mechanism of the long short-term memory network LSTM:

[0123] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0124] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0125]

[0126] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0127] h t =o t tanh(C t )

[0128] Among them, f t For the forget gate, i t is the input gate, C t is the cell state, o t is the output gate, h t is the hidden state, x t is the input at the current time t, W f is the weight matrix of the forget gate, W i is the weight matrix of the input gate, W C is the weight matrix of the candidate values ​​of the unit state, W o is the weight matrix of the output gate, b f 、b i 、b C 、b o is the corresponding bias term, σ is the sigmoid activation function, tanh is the hyperbolic tangent activation function; h t-1 It represents the hidden state at the previous moment; C t-1 It represents the unit state at the previous moment; Indicates the unit status.

[0129] The beneficial effects of the present invention are:

[0130] 1. Traditional battery status assessment methods have the problems of low accuracy and reliability. This invention aims to significantly improve the accuracy and reliability of battery SOC and SOH assessment by introducing advanced machine learning technologies such as deep learning, transfer learning, and graph neural networks, and ensure the stable performance of the battery under various usage environments.

[0131] 2. The present invention has designed a multi-source data acquisition and fusion module that can collect and process multi-dimensional and multi-scale data of the battery, including parameters such as voltage, current, temperature, pressure, as well as historical usage data and environmental data. Through the multimodal deep learning model, the system can comprehensively monitor the operating status of the battery and provide richer and more accurate status information.

[0132] 3. In order to extend the battery life and improve system reliability, the present invention introduces a predictive maintenance decision module based on reinforcement learning. This module can automatically generate the optimal maintenance strategy based on the battery status assessment results and historical maintenance data, prevent potential failures in advance, and reduce maintenance costs and downtime.

[0133] 4. The present invention adopts transfer learning and meta-learning technology to enable the system to quickly adapt to different types of batteries and new usage environments, improving the generalization ability and adaptability of the model. In addition, through the adaptive online learning algorithm, the system can continuously update and optimize the model according to the newly collected data, maintaining the high efficiency of evaluation and prediction.

[0134] 5. In order to improve the credibility and explainability of system decisions, the present invention introduces explainable AI technology and causal reasoning models. Users can understand the basis of battery status assessment and maintenance decisions, and enhance their trust in the system.

[0135] 6. The present invention designs a data security framework based on federated learning and blockchain technology to ensure the security and privacy protection of battery data during transmission and storage, and prevent data leakage and tampering.

[0136] 7. This invention aims to improve the efficiency of complex computing tasks, reduce data transmission delays, and improve the overall performance of the system by introducing quantum machine learning and edge computing technologies. In addition, the application of automatic machine learning (AutoML) technology can continuously optimize the neural network structure and hyperparameters, further improving the performance of the model.

[0137] 8. The present invention combines attention mechanism and digital twin technology to enhance the long-term trend prediction capability of battery status. Through self-supervised learning and generative adversarial network (GAN) data enhancement technology, the system can more accurately detect abnormal battery status and promptly warn of potential problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0138] Figure 1 This is a framework diagram of the battery SOC and SOH refined comprehensive evaluation system of the present invention;

[0139] Figure 2 The figure is a flow chart of the battery SOC and SOH refined comprehensive evaluation system of the present invention. DETAILED DESCRIPTION

[0140] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not intended to limit the scope of use of the present invention.

[0141] like Figure 1 As shown, the present invention proposes a comprehensive, accurate and intelligent battery SOC and SOH evaluation system, which specifically includes the following modules:

[0142] Multi-source data acquisition module: A variety of sensors are installed in electric vehicles to collect battery voltage, current, temperature, pressure and other parameters in real time. The sensor data is transmitted to the central control unit through the vehicle network.

[0143] Data preprocessing module: The central control unit cleans, standardizes and extracts features from the collected raw data to remove noise and outliers to ensure data quality.

[0144] Multi-source data fusion module: uses data fusion algorithm to fuse processed multi-dimensional data (such as voltage, current, temperature, etc.) with historical usage data and environmental data to form a unified data set.

[0145] Deep learning SOC estimation module: Based on the LSTM model, it uses historical charge and discharge data and real-time operation data to accurately estimate the battery's SOC. The model runs in the central control unit and updates the SOC estimation results in real time.

[0146] Transfer learning SOH evaluation module: Using transfer learning technology, the SOH evaluation model trained on a large number of similar batteries is migrated to the target battery to quickly build a personalized SOH evaluation model. The model is trained on the cloud server and regularly updated to the vehicle system.

[0147] Graph Neural Network Battery Pack Status Assessment Module: The battery pack is modeled as a graph structure, and GNN is used to capture the mutual influence between batteries to achieve the status assessment of the entire battery pack. The model runs in the central control unit and updates the battery pack status assessment results in real time.

[0148] Reinforcement learning predictive maintenance decision module: The predictive maintenance decision system based on deep reinforcement learning automatically generates the optimal maintenance strategy based on the battery status assessment results and historical maintenance data. The maintenance strategy is transmitted to the maintenance personnel through the vehicle network to guide the maintenance operation.

[0149] Big data analysis platform: Based on Spark, the big data analysis platform stores and processes massive battery data and supports model training and optimization. The data is transmitted to the cloud server via wireless network for analysis.

[0150] Edge computing nodes: Introducing edge computing nodes in the central control unit of electric vehicles to achieve local preprocessing and preliminary analysis of data and reduce data transmission delays.

[0151] Explainable AI module: Uses SHAP technology to explain the battery status assessment and predictive maintenance decision-making process, provides visual explanation results, and enhances user trust.

[0152] Adaptive online learning module: Design an adaptive online learning algorithm so that the system can continuously update and optimize the model based on newly collected data to maintain high efficiency of evaluation and prediction.

[0153] Digital Twin Module: Build a digital twin model of the electric vehicle battery system, combining real-time data and AI models to achieve virtual-real analysis and prediction of battery status.

[0154] Quantum machine learning module: Utilizes the parallel computing power of quantum computers and combines quantum machine learning algorithms to build an efficient battery state prediction model and improve the efficiency of complex computing tasks.

[0155] Federated Learning Framework: Design a distributed model training framework based on federated learning, allowing different electric vehicle users to jointly train and optimize the battery status assessment model while protecting data privacy.

[0156] Self-supervised learning anomaly detection module: It uses self-supervised learning technology to utilize a large amount of unlabeled battery operation data to automatically learn the feature representation of normal battery operation, thereby more effectively detecting abnormal conditions.

[0157] Multimodal deep learning module: Integrates multimodal data such as battery electrochemical data, thermal images, acoustic signals, etc., designs a multimodal deep learning model, and comprehensively evaluates the battery status.

[0158] Adaptive sampling strategy module: Design an adaptive sampling strategy based on reinforcement learning to dynamically adjust the data collection frequency and accuracy according to the current status of the battery and historical data.

[0159] Causal reasoning fault diagnosis module: Introduces a causal reasoning model, combines domain knowledge and data-driven methods, builds a causal relationship diagram for battery failure, and achieves more accurate fault diagnosis and root cause analysis.

[0160] Meta-learning rapid adaptation module: Using meta-learning technology, the system can quickly adapt to new types of batteries or new usage environments, improving the flexibility of the model.

[0161] Attention mechanism timing prediction module: Introduces a multi-head attention mechanism into the timing prediction model to capture key time points and features in battery state changes and improve the accuracy of long-term predictions.

[0162] Knowledge graph maintenance knowledge management module: Build a knowledge graph in the field of battery maintenance, integrate expert experience, historical maintenance records, equipment manuals and other information, and provide knowledge support for predictive maintenance decisions.

[0163] Blockchain data credibility assurance module: Use blockchain technology to record key battery status data and maintenance operations to ensure the data is tamper-proof and traceable.

[0164] Generative Adversarial Network Data Augmentation Module: Generates simulated battery degradation data using GAN to enhance the diversity of the training dataset, especially for rare failure modes, and improves the generalization ability of the model.

[0165] Automatic machine learning model optimization module: Introduces AutoML technology to automatically search and optimize neural network structures and hyperparameters, and continuously improve the performance of battery status assessment models.

[0166] The pool SOC and SOH evaluation system of the present invention can achieve the following functions:

[0167] Data preprocessing: Normalize and denoise the sensor data.

[0168] Feature Engineering: Use CNN to automatically extract features without manual intervention.

[0169] Model training: Use historical data to train the CNN-LSTM hybrid model.

[0170] Prediction and Evaluation: Model performance is evaluated through cross-validation, using metrics such as mean squared error (MSE).

[0171] Deployment and monitoring: Deploy the model in a production environment, monitor the prediction results in real time, and make adjustments based on feedback.

[0172] Correspondingly, such as Figure 2 As shown, based on the above battery SOC and SOH comprehensive evaluation system, the present invention also proposes a battery predictive maintenance method, the specific steps of which include:

[0173] 1. Data acquisition and preprocessing: Collect battery operation data through the multi-source data acquisition module, and use the data preprocessing module to clean, standardize and extract features.

[0174] Data cleaning, standardization and feature extraction specifically include the following:

[0175] 1-1) Data cleaning includes: removing duplicate data; handling missing values, including interpolation, deletion or estimation; correcting erroneous data; identifying and handling outliers;

[0176] 1-2) Data standardization includes: normalization, including Min-Max scaling; standardization (Z-score standardization can be used); logarithmic transformation; one-hot encoding, including categorical variables;

[0177] 1-3) The extracted features include: time domain features, including mean, variance, and peak; frequency domain features, including FFT and power spectral density; time-frequency domain features, including wavelet transform; statistical features, including skewness and kurtosis.

[0178] 2. Data fusion and analysis: Use the multi-source data fusion module to integrate the processed data and conduct preliminary analysis on the big data analysis platform.

[0179] Data integration processing specifically includes:

[0180] 2-1) Data fusion: integration of multi-source data, including sensors, historical records, maintenance logs; time synchronization; spatial alignment;

[0181] 2-2) Data dimensionality reduction: PCA principal component analysis; t-SNE dimensionality reduction algorithm; autoencoder;

[0182] 2-3) Data enhancement: oversampling technology (such as SMOTE); data synthesis; noise injection;

[0183] 3. SOC estimation: Use the deep learning SOC estimation module to accurately estimate the battery SOC based on the LSTM model.

[0184] 4.SOH evaluation: Through transfer learning of the SOH evaluation module, a personalized SOH evaluation model can be quickly built and applied.

[0185] The estimation methods in estimation and evaluation include: regression analysis; time series forecasting (ARIMA, Prophet, etc. can be used); machine learning model analysis (random forest, gradient boosting tree, etc. can be used);

[0186] Evaluation indicators include: MSE mean square error; MAE mean absolute error; R 2 Scores; accuracy, precision, recall;

[0187] Cross-validation: K-fold cross-validation; time series cross-validation.

[0188] 5. Battery pack status assessment: Use the graph neural network battery pack status assessment module to comprehensively assess the status of the entire battery pack.

[0189] When using a graph neural network (GNN) to evaluate the battery pack status, the battery pack is modeled as a graph structure, and the graph neural network is used to evaluate the mutual influence between batteries:

[0190] Let the graph be G = (V, E), where V is the node set (batteries) and E is the edge set (connection relationship between batteries);

[0191] The node feature update rule is:

[0192]

[0193] In the formula, is the feature representation of node i in round t+1; is the feature representation of node i in round t; is the feature representation of node j in the tth round; σ is the activation function (such as ReLU); N(i) is the set of neighbor nodes of node i; W is the weight matrix; W0 represents the self-connection weight matrix, which is used to adjust the influence of the node’s own characteristics; deg(i) represents the degree of node i, that is, the number of edges connected to node i; deg(j) represents the degree of node j, that is, the number of edges connected to node j.

[0194] 6. Anomaly detection and fault diagnosis: Combine the self-supervised learning anomaly detection module and the causal reasoning fault diagnosis module to achieve early detection and accurate diagnosis of battery abnormal conditions.

[0195] 7. Predictive maintenance decision: Generate the optimal maintenance strategy based on the reinforcement learning predictive maintenance decision module.

[0196] Abnormal and fault maintenance strategies include:

[0197] Preventive maintenance: time-based regular inspections; condition-based maintenance plans;

[0198] Predictive maintenance: Failure prediction based on machine learning; Remaining useful life RUL estimation;

[0199] Fault diagnosis: root cause analysis; fault tree analysis;

[0200] Maintenance optimization: maintenance scheduling optimization; spare parts inventory management.

[0201] The present invention generates an optimal maintenance strategy based on a deep reinforcement learning method, combining the battery status assessment results with maintenance history data, specifically including:

[0202] The state transition model is:

[0203] s t+1 =f(s t ,a t );

[0204] Reward function:

[0205] r t =R(s t ,a t );

[0206] Policy optimization algorithms (such as DQN):

[0207]

[0208] Among them, s t is the current state; a t is the current action; r t is the current reward; α is the learning rate, which controls the influence of new information on the Q value update; γ is the discount factor, which is used to balance the immediate reward and long-term benefits; s′ is the new state after the action is executed; a′ represents the learning rate after the action is executed; Q(s,a) is the Q value of taking action a in state s, which represents the estimate of long-term benefits; Represents the maximum Q value of all possible actions in the new state s'.

[0209] 8. Decision explanation and knowledge support: Use the explainable AI module to explain the decision-making process, and provide knowledge support through the knowledge graph maintenance knowledge management module.

[0210] 9. Model update and optimization: Continuously update and optimize the system model through adaptive online learning module and automatic machine learning model optimization module.

[0211] Model updates and optimizations include:

[0212] Online learning uses: incremental learning algorithm; sliding window technology;

[0213] Transfer learning includes: cross-domain knowledge transfer; pre-training model fine-tuning

[0214] Hyperparameter optimization: grid search; random search; Bayesian optimization;

[0215] Model integration includes: Bagging (such as random forest); Boosting (such as XGBoost, LightGBM); Stacking.

[0216] 10. Data security and privacy protection: Adopt the federated learning framework and blockchain data credibility assurance module to ensure data security and user privacy.

[0217] 11. Advanced analysis and prediction: Combine the digital twin module, quantum machine learning module and multimodal deep learning module to conduct more in-depth status analysis and long-term trend prediction.

[0218] In-depth status analysis and long-term trend forecasts include:

[0219] State analysis: health index calculation; multidimensional state space analysis; anomaly detection algorithms (such as isolation forest, One-Class SVM);

[0220] Long-term trend forecasting: time series decomposition, including trend, seasonality, and periodicity; long short-term memory network LSTM analysis; state space model analysis; deep learning model analysis (such as Transformer);

[0221] Scenario analysis: Monte Carlo simulation; sensitivity analysis; multi-scenario planning;

[0222] Life cycle analysis: accelerated life test; Weibull analysis; Cox proportional hazard model analysis; innovative hybrid algorithm analysis.

[0223] The innovative hybrid algorithm analysis and prediction specifically includes:

[0224] An innovative hybrid algorithm combining deep learning and statistical models is designed to predict the remaining useful life (RUL) of equipment. The algorithm effectively combines the convolutional neural network (CNN) for feature extraction and the long short-term memory (LSTM) network for time series prediction. Specifically:

[0225] 11-1) Combining feature extraction with sequence modeling: Use CNN to extract spatial features from raw sensor data; use LSTM to capture temporal dependencies and process time series data;

[0226] 11-2) Multi-scale analysis: Use multi-scale convolution kernels in CNN to capture features of different scales;

[0227] 11-3) Ensemble learning: Combine the outputs of CNN and LSTM and integrate them through a fully connected layer to optimize the overall prediction performance;

[0228] For the convolutional layer of the convolutional neural network CNN:

[0229]

[0230] In the formula, Z i,j,k represents the kth channel of the output feature map at position (i, j), X i+m,j+n,c represents the input feature map, K m,n,c,k represents the convolution kernel, b kRepresents the bias, M and N represent the size of the convolution kernel; m represents the index of the convolution kernel in the vertical direction (height), which is used to traverse the rows of the convolution kernel; n represents the index of the convolution kernel in the horizontal direction (width), which is used to traverse the columns of the convolution kernel; c represents the channel index of the input feature map.

[0231] For the gating mechanism of the long short-term memory network LSTM:

[0232] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0233] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0234]

[0235] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0236] h t =o t tanh(C t )

[0237] Among them, f t For the forget gate, i t is the input gate, C t is the cell state, o t is the output gate, h t is the hidden state, x t is the input at the current time t, W f is the weight matrix of the forget gate, W i is the weight matrix of the input gate, W C is the weight matrix of the candidate values ​​of the unit state, W o is the weight matrix of the output gate, b f , b i , b C , b o is the corresponding bias term, σ is the sigmoid activation function, tanh is the hyperbolic tangent activation function; h t-1 It represents the hidden state at the previous moment; C t-1 It represents the unit state at the previous moment. Represents the cell state, which is a key component in the LSTM network and is used to memorize information for a long time.

[0238] 12. System adaptability optimization: Through the meta-learning fast adaptation module and adaptive sampling strategy module, the system's adaptability to different types of batteries and usage environments is improved.

[0239] 13. Data enhancement and model generalization: Generate adversarial network data enhancement module to improve the model's recognition ability and generalization performance of various abnormal situations.

[0240] The present invention aims to provide a refined comprehensive evaluation system of battery SOC and SOH and an intelligent predictive maintenance method, and specifically demonstrates how to apply a variety of advanced technologies in an electric vehicle battery management system, which not only improves the accuracy and reliability of battery status evaluation, but also extends the battery life through intelligent maintenance strategies, ensuring the safety and reliability of electric vehicles. The refined comprehensive evaluation system of battery SOC and SOH has the following excellent characteristics: High-precision evaluation: significantly improves the accuracy and reliability of battery status evaluation. Comprehensive status monitoring: provides multi-dimensional and multi-scale battery status monitoring. Intelligent predictive maintenance: realizes intelligent predictive maintenance decisions and extends battery life. High adaptability and generalization ability: the system can quickly adapt to different types of batteries and usage environments. Explainability and transparency: improve the explainability and credibility of system decisions. Data security and privacy protection: ensure data security and privacy protection. Efficient computing and system performance: improve the efficiency of complex computing tasks and optimize system performance. Anomaly detection and long-term prediction: enhance the accuracy of anomaly detection and long-term trend prediction capabilities.

[0241] The present invention has broad prospects and its application scope may include the following fields:

[0242] Electric vehicle (EV) field: Electric vehicles have extremely high requirements for battery performance and life. The present invention can provide high-precision battery status assessment and intelligent predictive maintenance to ensure the safety and reliability of electric vehicle batteries, extend battery life, and improve the overall performance of the vehicle.

[0243] Renewable energy storage system field: In renewable energy systems such as solar energy and wind energy, batteries are essential as energy storage devices. The present invention can comprehensively monitor and maintain energy storage batteries to ensure stable operation and efficient utilization of energy systems.

[0244] Consumer electronics: Smartphones, laptops, tablets and other consumer electronics products are highly dependent on batteries. The present invention can be applied to the battery management system of these devices to provide accurate battery status assessment and maintenance strategies to improve user experience.

[0245] Industrial energy storage system field: In the industrial field, energy storage systems are used for load regulation and emergency power supply. The present invention can improve the reliability and safety of industrial energy storage systems and reduce downtime and maintenance costs caused by battery failure.

[0246] Drone and robot fields: Drones and robots have strict requirements on battery life and reliability. The present invention can help optimize the battery management of these devices and ensure their stable operation in complex environments.

[0247] Smart grid field: In smart grids, batteries are used for energy storage and scheduling. The present invention can provide accurate battery status assessment and predictive maintenance, improving the operating efficiency and stability of smart grids.

[0248] By integrating a variety of advanced artificial intelligence and big data technologies, the present invention can significantly improve the intelligence level of the battery management system and achieve more efficient battery status monitoring and maintenance. Through accurate battery status assessment and intelligent predictive maintenance, the present invention can effectively extend the service life of the battery, reduce the replacement frequency, and reduce user costs.

[0249] The present invention can detect abnormal conditions and potential faults of the battery in a timely manner, take maintenance measures in advance, ensure the reliability and safety of the system, and prevent accidents caused by battery failures. Through intelligent predictive maintenance, the present invention can reduce unnecessary maintenance operations and downtime, reduce maintenance costs, and improve the economic benefits of the system. The application of the present invention in renewable energy storage systems can improve energy utilization efficiency, promote the development of green energy, and help achieve carbon neutrality goals. The present invention can improve the performance of electric vehicle battery management systems, enhance the market competitiveness of electric vehicles, and promote the rapid development of the electric vehicle industry. The application of the present invention in consumer electronic products can provide longer battery life and more reliable battery performance, and improve user experience and satisfaction.

[0250] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A battery SOC and SOH comprehensive evaluation system, characterized by: Includes the following modules: Multi-source data acquisition module, used to collect battery operation data using multiple sensors, including voltage, current, temperature, and pressure parameters; Data preprocessing module, used to clean, standardize and extract features from the collected raw data; Multi-source data fusion module, which uses data fusion algorithms to integrate multi-dimensional and multi-scale battery operation data, historical usage data, and environmental data; Deep learning SOC estimation module, which uses historical charge and discharge data and real-time operation data to accurately estimate the battery's SOC; The transfer learning SOH evaluation module is used to transfer the SOH evaluation model trained on a large number of similar batteries to the target battery using transfer learning technology, and quickly build a personalized SOH evaluation model; The graph neural network battery pack status assessment module is used to model the battery pack as a graph structure, use the graph neural network GNN to capture the mutual influence between batteries, and evaluate the status of the entire battery pack; Reinforcement learning predictive maintenance decision module: A predictive maintenance decision system based on deep reinforcement learning that automatically generates the optimal maintenance strategy based on battery status assessment results and historical maintenance data; Big data analysis platform for storing, processing and analyzing massive battery data; Edge computing nodes are used to introduce edge computing nodes into the battery management system for local preprocessing and preliminary analysis of data; Explainable AI module, which uses explainable AI technology to explain the battery status assessment and predictive maintenance decision-making process; Adaptive online learning module, which is used to design adaptive online learning algorithms so that the system can continuously update and optimize the model based on newly collected data; The digital twin module is used to build a digital twin model of the battery system, combining real-time data and AI models to perform virtual-real analysis and prediction of the battery status; Quantum machine learning module, which is used to utilize the parallel computing power of quantum computers and combine quantum machine learning algorithms to build an efficient battery state prediction model; Federated learning framework, which is used to design a distributed model training framework based on federated learning, allowing different battery users to jointly train and optimize the battery status assessment model while protecting data privacy; A self-supervised learning anomaly detection module is used to use self-supervised learning technology to automatically learn the characteristic representation of normal battery operation using a large amount of unlabeled battery operation data, thereby detecting abnormal conditions; Multimodal deep learning module, which is used to integrate multimodal data of batteries, design multimodal deep learning models, and comprehensively evaluate battery status; Adaptive sampling strategy module, which is used to design an adaptive sampling strategy based on reinforcement learning, and dynamically adjust the data collection frequency and accuracy according to the current state of the battery and historical data; The causal reasoning fault diagnosis module is used to introduce the causal reasoning model, combine domain knowledge and data-driven methods, build a causal relationship diagram of battery failure, and perform accurate fault diagnosis and root cause analysis; The meta-learning rapid adaptation module is used to use meta-learning technology to enable the system to quickly adapt to new types of batteries or new usage environments; The attention mechanism time series prediction module is used to introduce a multi-head attention mechanism into the time series prediction model to capture the key time points and features in the battery state change to improve the accuracy of long-term prediction; The knowledge graph maintenance knowledge management module is used to build a knowledge graph in the field of battery maintenance and integrate relevant information to provide knowledge support for predictive maintenance decisions; Blockchain data credibility assurance module, which uses blockchain technology to record key battery status data and maintenance operations to ensure data immutability and traceability; Generative Adversarial Network Data Augmentation Module, which is used to generate simulated battery degradation data using GAN to enhance the diversity of training datasets; Automatic machine learning model optimization module, used to automatically search and optimize neural network structure and hyperparameters, and continuously improve the performance of battery state assessment models.

2. The battery SOC and SOH comprehensive evaluation system according to claim 1, characterized in that: The multi-source data acquisition module includes a voltage sensor, a current sensor, a temperature sensor and a pressure sensor; The deep learning SOC estimation module estimates the SOC of the battery using a deep learning model of a long short-term memory network LSTM; The big data analysis platform adopts the Spark distributed computing framework; The multimodal deep learning module integrates multimodal data of the battery including electrochemical data, thermal images, and acoustic signals; The relevant information integrated by the knowledge graph maintenance knowledge management module includes expert experience, historical maintenance records and equipment manuals; The automatic machine learning model optimization module adopts AutoML technology.

3. A battery predictive maintenance method implemented based on the battery SOC and SOH comprehensive evaluation system according to claim 2, characterized in that: The specific steps include: 1) Data acquisition and preprocessing: Use the multi-source data acquisition module to collect battery operation data, and use the data preprocessing module to perform data cleaning, standardization and feature extraction; 2) Data fusion and analysis: Use the multi-source data fusion module to integrate the processed data and conduct preliminary analysis on the big data analysis platform; 3) SOC estimation: Using the deep learning SOC estimation module, the battery SOC is accurately estimated based on the LSTM model; 4) SOH evaluation: Through transfer learning of the SOH evaluation module, a personalized SOH evaluation model can be quickly built and applied; 5) Battery pack status assessment: Use the graph neural network battery pack status assessment module to comprehensively assess the status of the entire battery pack; 6) Anomaly detection and fault diagnosis: Combine the self-supervised learning anomaly detection module and the causal reasoning fault diagnosis module to perform early detection and accurate diagnosis of battery abnormal conditions; 7) Predictive maintenance decision-making: Generate the optimal maintenance strategy based on the reinforcement learning predictive maintenance decision-making module; 8) Decision explanation and knowledge support: Use the explainable AI module to explain the decision-making process, and provide knowledge support through the knowledge graph maintenance knowledge management module; 9) Model update and optimization: Continuously update and optimize the system model through adaptive online learning module and automatic machine learning model optimization module; 10) Data security and privacy protection: Adopting the federated learning framework and blockchain data credibility assurance module to ensure data security and user privacy; 11) Advanced analysis and prediction: Combine the digital twin module, quantum machine learning module and multimodal deep learning module to conduct more in-depth status analysis and long-term trend prediction; 12) System adaptability optimization: Improve the system's adaptability to different types of batteries and usage environments through meta-learning fast adaptation modules and adaptive sampling strategy modules; 13) Data enhancement and model generalization: Generate adversarial network data enhancement module to improve the model’s recognition ability and generalization performance of various abnormal situations.

4. The battery predictive maintenance method according to claim 3, characterized in that: Data cleaning, standardization and feature extraction in step 1) specifically include the following: 1-1) Data cleaning includes: removing duplicate data; handling missing values, including interpolation, deletion or estimation; correcting erroneous data; identifying and handling outliers; 1-2) Data standardization includes: normalization, including Min-Max scaling; standardization; logarithmic transformation; one-hot encoding, including categorical variables; 1-3) The extracted features include: time domain features, including mean, variance, and peak; frequency domain features, including FFT and power spectral density; time-frequency domain features, including wavelet transform; statistical features, including skewness and kurtosis.

5. The battery predictive maintenance method according to claim 3, characterized in that: The data integration process in step 2) specifically includes: 2-1) Data fusion: integration of multi-source data, including sensors, historical records, maintenance logs; time synchronization; spatial alignment; 2-2) Data dimensionality reduction: PCA principal component analysis; t-SNE dimensionality reduction algorithm; autoencoder; 2-3) Data enhancement: oversampling technology; data synthesis; noise injection.

6. The battery predictive maintenance method according to claim 3, characterized in that: The estimation methods in the estimation and evaluation performed in step 3) and step 4) specifically include: regression analysis; time series forecasting; machine learning model analysis; Evaluation indicators include: MSE mean square error; MAE mean absolute error; R 2 Scores; accuracy, precision, recall; Cross validation: K-fold cross validation; time series cross validation; Step 5) When using graph neural network to evaluate the battery pack status, the battery pack is modeled as a graph structure, and the graph neural network is used to evaluate the mutual influence between batteries: Suppose the graph is G = (V, E), where V is a set of nodes representing batteries; E is a set of edges representing the connection relationship between batteries; The node feature update rule is: In the formula, is the feature representation of node i in round t+1; is the feature representation of node i in round t; is the feature representation of node j in the tth round; σ is the activation function; N(i) is the set of neighbor nodes of node i; W is the weight matrix; W0 represents the self-connection weight matrix, which is used to adjust the influence of the node’s own characteristics; deg(i) represents the degree of node i, that is, the number of edges connected to node i; deg(j) represents the degree of node j, that is, the number of edges connected to node j.

7. The battery predictive maintenance method according to claim 3, characterized in that: The abnormality and fault maintenance strategies in step 6) and step 7) include: Preventive maintenance: time-based regular inspections; condition-based maintenance plans; Predictive maintenance: Failure prediction based on machine learning; Remaining useful life RUL estimation; Fault diagnosis: root cause analysis; fault tree analysis; Maintenance optimization: maintenance scheduling optimization; spare parts inventory management.

8. The battery predictive maintenance method according to claim 7, characterized in that: Step 7) Generate the optimal maintenance strategy based on the deep reinforcement learning method, combining the battery status assessment results and maintenance history data, specifically including: The state transition model is: s t+1 =f(s t ,a t ); Reward function: r t =R(s t ,a t ); Strategy optimization algorithm: Among them, s t is the current state; a t is the current action; r t is the current reward; α is the learning rate, which controls the influence of new information on the Q value update; γ is the discount factor, which is used to balance the immediate reward and long-term benefits; s′ is the new state after the action is executed; a′ represents the learning rate after the action is executed; Q(s,a) is the Q value of taking action a in state s, which represents the estimate of long-term benefits; Represents the maximum Q value of all possible actions in the new state s'.

9. The battery predictive maintenance method according to claim 3, characterized in that: The model updating and optimization in step 9) includes: Online learning uses: incremental learning algorithm; sliding window technology; Transfer learning includes: cross-domain knowledge transfer; pre-training model fine-tuning Hyperparameter optimization: grid search; random search; Bayesian optimization; Model integration includes the following modules: Bagging; Boosting; Stacking.

10. The battery predictive maintenance method according to claim 3, characterized in that: In-depth status analysis and long-term trend forecasting in step 11) include: State analysis: health index calculation; multidimensional state space analysis; anomaly detection algorithm; Long-term trend forecasting: time series decomposition, including trend, seasonality, and periodicity; long short-term memory network LSTM analysis; state space model analysis; deep learning model analysis; Scenario analysis: Monte Carlo simulation; sensitivity analysis; multi-scenario planning; Life cycle analysis: accelerated life test; Weibull analysis; Cox proportional hazard model analysis; innovative hybrid algorithm analysis; Among them, the innovative hybrid algorithm analysis and prediction specifically includes: An innovative hybrid algorithm combining deep learning and statistical models is designed to predict the remaining useful life (RUL) of equipment. The algorithm effectively combines the convolutional neural network (CNN) for feature extraction and the long short-term memory (LSTM) network for time series prediction. Specifically: 11-1) Combining feature extraction with sequence modeling: Use CNN to extract spatial features from raw sensor data; use LSTM to capture temporal dependencies and process time series data; 11-2) Multi-scale analysis: Use multi-scale convolution kernels in CNN to capture features of different scales; 11-3) Ensemble learning: Combine the outputs of CNN and LSTM and integrate them through a fully connected layer to optimize the overall prediction performance; For the convolutional layer of the convolutional neural network CNN: In the formula, Z i,j,k represents the kth channel of the output feature map at position (i, j), X i+m,j+n,c represents the input feature map, K m,n,c,k represents the convolution kernel, b k represents the bias, M and N represent the size of the convolution kernel; m represents the vertical index of the convolution kernel, which is used to traverse the rows of the convolution kernel; n represents the horizontal index of the convolution kernel, which is used to traverse the columns of the convolution kernel; c represents the channel index of the input feature map; Among them, for the gating mechanism of the long short-term memory network LSTM: f t =σ(W f ·[h t-1 ,x t ]+b f ) i t =σ(W i ·[h t-1 ,x t ]+b i ) the t =σ(W o ·[h t-1 ,x t ]+b o ) h t =o t ·tanh(C t ) In the formula, f t For the forget gate, i t is the input gate, C t is the cell state, o t is the output gate, h t is the hidden state, x t is the input at the current time t, W f is the weight matrix of the forget gate, W i is the weight matrix of the input gate, W C is the weight matrix of the candidate values ​​of the unit state, W o is the weight matrix of the output gate, b f 、b i 、b C 、b o is the corresponding bias term, tanh is the hyperbolic tangent activation function; h t-1 It represents the hidden state at the previous moment; C t-1 It represents the unit state at the previous moment; Indicates the unit status.

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