Power equipment fault early warning system

Abstract

The invention relates to the field of power equipment, and discloses a power equipment fault early warning system, which comprises a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning judgment module and an information interaction module. Key operation parameters are cooperatively acquired through multiple types of sensors, time series data are uniformly calibrated by adopting a timestamp mechanism, the problems of fragmentation of operation state information of power equipment and superposition of acquisition errors are effectively solved, and then feature fusion and dimension reduction compression are performed on high-dimensional heterogeneous data by introducing a principal component analysis and auto-encoder neural network, so that the operation state information of the power equipment is acquired. According to the method, redundant information is eliminated, meanwhile, key discrimination features are reserved, the sensing dimension of the system for the equipment operation state is more comprehensive, the representation capacity is higher, the Bayesian network and the support vector machine are adopted to jointly evaluate the equipment state health level, higher state recognition accuracy is achieved in a dynamic scene, and the method is suitable for popularization and application. And the model generalization ability is enhanced through historical samples, so that the equipment state can be judged more stably.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment, and in particular to a power equipment fault early warning system. Background Art

[0002] With the continuous improvement of the intelligence and automation level of power systems, real-time monitoring of the operating status and fault warning capabilities of power equipment, as the core foundation for stable power system operation, are becoming key factors in ensuring power supply security and improving operation and maintenance efficiency. Currently, traditional equipment detection methods rely mostly on periodic manual inspections and scheduled maintenance, which cannot meet the demand for "all-weather, all-scenario, and all-factor" intelligent monitoring of power equipment. They suffer from multiple issues such as delayed response, inaccurate warnings, and incomplete diagnosis.

[0003] On the one hand, the operation of power equipment will be affected by many complex factors, such as load fluctuations, environmental changes, aging and wear, structural looseness, etc., which will cause the equipment status to exhibit nonlinear, multi-variable coupling and time-varying instability characteristics. A single type of sensor or a single dimension of data cannot fully reflect the operating status of the equipment, and it is urgently needed to be supported by the fusion perception technology of multi-source heterogeneous data.

[0004] On the other hand, existing early warning methods mostly adopt simple judgment strategies based on thresholds or rule bases, which make it difficult to make forward-looking predictions on potential failure trends of equipment. They lack the ability to intelligently identify and deduce trends in the evolution of equipment status, resulting in serious "lag or hypersensitivity" problems in system early warnings, which restricts the initiative and accuracy of power grid operation and maintenance.

[0005] At the same time, some current systems also have significant deficiencies in the transmission of early warning information and interactive response. They lack real-time push capabilities and closed-loop feedback mechanisms for operation and maintenance personnel, resulting in difficulty in timely implementation of early warning information, difficulty in forming system optimization feedback for response measures, and low efficiency in operation and maintenance scheduling. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a power equipment fault warning system, which solves the "all-weather, all-scenario, and all-factor" intelligent monitoring needs of power equipment, and solves the problems of delayed response, inaccurate warning, and incomplete diagnosis.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a power equipment fault early warning system, including a data acquisition module, a data fusion module, a status assessment module, a trend prediction module, an early warning determination module, and an information interaction module;

[0008] Data acquisition module:

[0009] Based on the need to monitor various operating conditions of power equipment, a multi-type sensor acquisition method is adopted to collect key operating parameters such as current, voltage, temperature, humidity, partial discharge, and vibration. The data is time-corrected through a timestamp synchronization mechanism and encoded in a unified format and transmitted to the subsequent processing module to generate the original operating data set;

[0010] Data fusion module:

[0011] Based on the original running data set, principal component analysis and autoencoder neural network algorithms are used to extract features and reduce dimensionality of multi-source data to construct a unified high-dimensional feature vector. At the same time, data cleaning and normalization processing mechanisms are introduced to improve data quality and model compatibility, generating a fused feature data vector.

[0012] Condition Assessment Module:

[0013] Based on the fused feature data vector, the Bayesian network modeling method and the support vector machine (SVM) classification algorithm are used to evaluate the current health status of power equipment. The evaluation results are divided into multiple health levels. At the same time, a discriminant model is constructed based on historical fault samples to improve recognition accuracy and generate equipment health level information.

[0014] Trend forecast module:

[0015] Based on equipment health level information and combined with equipment operation time series, a long short-term memory network (LSTM) prediction algorithm is used to build a dynamic change model to predict the health change trend and potential failure probability of the equipment within a certain time window in the future. Confidence interval analysis results are also output to generate failure trend prediction data.

[0016] Early warning judgment module:

[0017] Based on fault trend prediction data, an adaptive threshold determination method and a multi-level warning strategy algorithm are used to determine the level of abnormal behavior that may occur in the future. The warning threshold is automatically set based on the importance of the equipment and environmental factors, and a clear warning level and corresponding response suggestions are output to generate warning levels and disposal suggestions.

[0018] Information interaction module:

[0019] Based on the warning level and disposal suggestions, through the visual interface management mechanism, the warning information is promptly conveyed to the operation and maintenance personnel by means of Web platform push, SMS alarm, and mobile APP notification. The system supports the warning information traceability query and feedback mechanism, enhances the system closed-loop, and generates warning information interaction records.

[0020] Preferably, the data acquisition module includes an electrical parameter acquisition submodule, an environmental quantity acquisition submodule, a structural response acquisition submodule, a time synchronization submodule, and a data encapsulation submodule;

[0021] Electrical parameter acquisition submodule: Based on the voltage, current, and frequency electrical parameters of the equipment operation, it uses signal conditioning and sampling timing algorithms to collect electrical data in real time and generate electrical parameter data sets;

[0022] Environmental quantity acquisition submodule: collects environmental data and performs synchronous monitoring of external environmental variables such as temperature, humidity, and wind speed. It uses a sensor fusion algorithm to synchronously collect environmental data and electrical parameter data, and completes numerical merging to generate an environmental data set.

[0023] Structural response acquisition submodule: uses vibration sensors to obtain the mechanical response data of the equipment through time domain analysis algorithms and generates a structural response data set;

[0024] Time synchronization submodule: Through the NTP protocol and GPS clock synchronization algorithm, it ensures the accurate synchronization of the timestamps of each sensor data and generates a time synchronization data set;

[0025] Data encapsulation submodule: encapsulates the collected data types into a unified encoding format for easy transmission and generates original data packets.

[0026] Preferably, the data fusion module includes a feature extraction submodule, a data cleaning submodule, a dimension compression submodule, a normalization processing submodule, and a feature construction submodule;

[0027] Feature extraction submodule: extracts key features of current waveform and spectrum characteristics through wavelet transform signal processing algorithm to generate electrical characteristic data;

[0028] Data cleaning submodule: uses anomaly detection algorithms to remove noise and outliers from collected data, ensuring data quality and generating a cleaned data set;

[0029] Dimensionality compression submodule: Use principal component analysis (PCA) to reduce the dimensionality of data, reduce redundant information, and generate a reduced-dimensionality feature vector;

[0030] Normalization processing submodule: uses a standardization algorithm to normalize the data to eliminate dimensional differences and generate normalized feature data;

[0031] Feature construction submodule: Through the feature fusion algorithm, the electrical features, environmental features and structural features are integrated into a high-dimensional feature vector to generate a fused feature data vector.

[0032] Preferably, the state assessment module includes a health modeling submodule, a grade classification submodule, a historical sample comparison submodule, a model training submodule, and an error checking submodule;

[0033] Health modeling submodule: Based on the Bayesian network model, it uses conditional probability to model the health status of the equipment and generate a health assessment model;

[0034] Classification submodule: Based on the evaluation results output by the health model, the support vector machine (SVM) is used to classify the device status into healthy, minor fault, and major fault levels, and generate health level information;

[0035] Historical sample comparison submodule: This module obtains historical fault data by calling the historical fault sample library, and uses the KNN (K-nearest neighbor) algorithm to compare the evaluation results, improve the evaluation accuracy, and generate fault discrimination information.

[0036] Model training submodule: Use historical data to train the health assessment model, optimize model parameters, and generate an optimized assessment model;

[0037] Error check submodule: Use cross-validation method to test the accuracy and robustness of the model, ensure the effectiveness of the model, and generate check error information.

[0038] Preferably, the trend prediction module includes a sequence modeling submodule, a trend deduction submodule, a probability output submodule, an interval analysis submodule, and a time window setting submodule;

[0039] Sequence modeling submodule: Models the device's time series data based on the long short-term memory (LSTM) network, captures the temporal dependencies in the data, and generates a time series model.

[0040] Trend deduction submodule: Uses the LSTM model to predict the trend of device health status changes in the future period and generates device health change trends;

[0041] Probability output submodule: Outputs the failure probability of the equipment in the future, generates failure probability data based on the state curve output by the trend model and combined with historical failure data for prediction;

[0042] Interval analysis submodule: Through confidence interval analysis, the error of the prediction results is estimated, and a credible interval is provided to generate confidence interval analysis results

[0043] Time window setting submodule: Based on the equipment's operating cycle and fault occurrence patterns, set the predicted time window length and generate a predicted time window.

[0044] Preferably, the early warning determination module includes a threshold calculation submodule, an early warning classification submodule, a risk association submodule, a response suggestion submodule, and a dynamic adjustment submodule;

[0045] Threshold calculation submodule: Through the adaptive threshold algorithm, the health threshold and warning threshold are calculated according to the historical status of the equipment and the current prediction results, and the threshold data is generated.

[0046] Early warning classification submodule: Based on the equipment status and threshold data, a multi-level early warning strategy is used to divide the early warning levels, output different levels of early warning levels, and generate early warning level information;

[0047] Risk association submodule: Combines equipment importance and environmental factors, uses a weighted risk assessment model to perform risk association analysis, and generates risk assessment information;

[0048] Response suggestion submodule: automatically generates emergency response suggestions and response suggestion data based on warning levels and risk assessments;

[0049] Dynamic adjustment submodule: Automatically adjusts warning strategies and thresholds based on real-time data and environmental changes, improves system adaptability, and generates adjusted warning strategies.

[0050] Preferably, the information interaction module includes a multi-terminal push submodule, an interface display submodule, a record storage submodule, a feedback transmission submodule, and a permission distribution submodule;

[0051] Multi-terminal push submodule: pushes warning information to operation and maintenance personnel through Web platform push, SMS alarm, and mobile APP notification, and generates push notification data;

[0052] Interface display submodule: displays warning information through a visual interface to help operation and maintenance personnel intuitively understand the equipment status and generate a warning information display interface;

[0053] Record storage submodule: stores warning information and feedback records for later review and analysis, and generates historical record data;

[0054] Feedback submodule: Receives processing feedback information from operation and maintenance personnel, feeds it back to the system to update the device status and generate feedback data;

[0055] Permission distribution submodule: distributes different levels of warning information according to the permissions of different operation and maintenance personnel, and generates permission management data.

[0056] The present invention provides a power equipment fault early warning system. It has the following beneficial effects:

[0057] 1. The present invention uses multiple types of sensors to collaboratively collect key operating parameters, and adopts a timestamp mechanism to uniformly calibrate time series data, effectively solving the problems of fragmentation of power equipment operating status information and superposition of collection errors, ensuring the continuity and reliability of source data, and then introducing principal component analysis and autoencoder neural network to perform feature fusion and dimensionality reduction compression on high-dimensional heterogeneous data, while eliminating redundant information while retaining key discriminant features, making the system's perception dimension of the equipment operating status more comprehensive and its representation capability stronger. By using Bayesian networks and support vector machines to jointly evaluate the health level of equipment status, it has higher status recognition accuracy in dynamic scenarios, and enhances the model generalization ability through historical samples, achieving a more stable judgment of the equipment status.

[0058] 2. The present invention constructs an equipment operation trend prediction model by combining a long-short-term memory network, continuously characterizes the health level evolution process, generates future trend prediction curves and confidence interval segments, and improves the ability to proactively control potential faults. At the same time, it adopts adaptive thresholds and multi-level hierarchical mechanisms, combines equipment criticality with environmental variables to set early warning conditions, avoids misjudgments and missed judgments, and achieves a dual improvement in alarm sensitivity and accuracy.

[0059] 3. The present invention pushes early warning information to the operation and maintenance terminal through multi-channel means, and establishes an information tracing and interactive recording system to ensure the closed-loop implementation of disposal suggestions and enhance the response and coordination efficiency of the operation and maintenance scheduling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a framework diagram of the fault warning system of the present invention;

[0061] Figure 2 This is a system diagram of the data acquisition module of the present invention;

[0062] Figure 3 This is a system diagram of the data fusion module of the present invention;

[0063] Figure 4 This is a system diagram of the status assessment module of the present invention;

[0064] Figure 5 This is a system diagram of the trend prediction module of the present invention;

[0065] Figure 6 This is a system diagram of the early warning determination module of the present invention;

[0066] Figure 7 This is a system diagram of the information interaction module of the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Example:

[0069] Please see the attached Figure 1 -Attached Figure 7 , an embodiment of the present invention provides a power equipment fault early warning system, including a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning determination module, and an information interaction module;

[0070] Data acquisition module:

[0071] Based on the need to monitor various operating conditions of power equipment, a multi-type sensor acquisition method is adopted to collect key operating parameters such as current, voltage, temperature, humidity, partial discharge, and vibration. The data is time-corrected through a timestamp synchronization mechanism and encoded in a unified format and transmitted to the subsequent processing module to generate the original operating data set;

[0072] Data fusion module:

[0073] Based on the original running data set, principal component analysis and autoencoder neural network algorithms are used to extract features and reduce dimensionality of multi-source data to construct a unified high-dimensional feature vector. At the same time, data cleaning and normalization processing mechanisms are introduced to improve data quality and model compatibility, generating a fused feature data vector.

[0074] Condition Assessment Module:

[0075] Based on the fused feature data vector, the Bayesian network modeling method and the support vector machine (SVM) classification algorithm are used to evaluate the current health status of power equipment. The evaluation results are divided into multiple health levels. At the same time, a discriminant model is constructed based on historical fault samples to improve recognition accuracy and generate equipment health level information.

[0076] Trend forecast module:

[0077] Based on equipment health level information and combined with equipment operation time series, a long short-term memory network (LSTM) prediction algorithm is used to build a dynamic change model to predict the health change trend and potential failure probability of the equipment within a certain time window in the future. Confidence interval analysis results are also output to generate failure trend prediction data.

[0078] Early warning judgment module:

[0079] Based on fault trend prediction data, an adaptive threshold determination method and a multi-level warning strategy algorithm are used to determine the level of abnormal behavior that may occur in the future. The warning threshold is automatically set based on the importance of the equipment and environmental factors, and a clear warning level and corresponding response suggestions are output to generate warning levels and disposal suggestions.

[0080] Information interaction module:

[0081] Based on the warning level and disposal suggestions, through the visual interface management mechanism, the warning information is promptly conveyed to the operation and maintenance personnel by means of Web platform push, SMS alarm, and mobile APP notification. The system supports the warning information traceability query and feedback mechanism, enhances the system closed-loop, and generates warning information interaction records.

[0082] Among them, the data acquisition module includes an electrical parameter acquisition submodule, an environmental quantity acquisition submodule, a structural response acquisition submodule, a time synchronization submodule, and a data encapsulation submodule;

[0083] Electrical parameter acquisition submodule: Based on the voltage, current, and frequency electrical parameters of the equipment operation, it uses signal conditioning and sampling timing algorithms to collect electrical data in real time and generate electrical parameter data sets;

[0084] Environmental quantity acquisition submodule: collects environmental data and performs synchronous monitoring of external environmental variables such as temperature, humidity, and wind speed. It uses a sensor fusion algorithm to synchronously collect environmental data and electrical parameter data, and completes numerical merging to generate an environmental data set.

[0085] Structural response acquisition submodule: uses vibration sensors to obtain the mechanical response data of the equipment through time domain analysis algorithms and generates a structural response data set;

[0086] Time synchronization submodule: Through the NTP protocol and GPS clock synchronization algorithm, it ensures the accurate synchronization of the timestamps of each sensor data and generates a time synchronization data set;

[0087] Data encapsulation submodule: encapsulates the collected data types into a unified encoding format for easy transmission and generates original data packets.

[0088] Among them, the data fusion module includes feature extraction submodule, data cleaning submodule, dimension compression submodule, normalization processing submodule, and feature construction submodule;

[0089] Feature extraction submodule: extracts key features of current waveform and spectrum characteristics through wavelet transform signal processing algorithm to generate electrical characteristic data;

[0090] Data cleaning submodule: uses anomaly detection algorithms to remove noise and outliers from collected data, ensuring data quality and generating a cleaned data set;

[0091] Dimensionality compression submodule: Use principal component analysis (PCA) to reduce the dimensionality of data, reduce redundant information, and generate a reduced-dimensionality feature vector;

[0092] Normalization processing submodule: uses a standardization algorithm to normalize the data to eliminate dimensional differences and generate normalized feature data;

[0093] Feature construction submodule: Through the feature fusion algorithm, the electrical features, environmental features and structural features are integrated into a high-dimensional feature vector to generate a fused feature data vector.

[0094] Among them, the status assessment module includes health modeling submodule, grade classification submodule, historical sample comparison submodule, model training submodule, and error correction submodule;

[0095] Health modeling submodule: Based on the Bayesian network model, it uses conditional probability to model the health status of the equipment and generate a health assessment model;

[0096] Classification submodule: Based on the evaluation results output by the health model, the support vector machine (SVM) is used to classify the device status into healthy, minor fault, and major fault levels, and generate health level information;

[0097] Historical sample comparison submodule: This module obtains historical fault data by calling the historical fault sample library, and uses the KNN (K-nearest neighbor) algorithm to compare the evaluation results, improve the evaluation accuracy, and generate fault discrimination information.

[0098] Model training submodule: Use historical data to train the health assessment model, optimize model parameters, and generate an optimized assessment model;

[0099] Error check submodule: Use cross-validation method to test the accuracy and robustness of the model, ensure the effectiveness of the model, and generate check error information.

[0100] Among them, the trend prediction module includes sequence modeling submodule, trend deduction submodule, probability output submodule, interval analysis submodule, and time window setting submodule;

[0101] Sequence modeling submodule: Models the device's time series data based on the long short-term memory (LSTM) network, captures the temporal dependencies in the data, and generates a time series model.

[0102] Trend deduction submodule: Uses the LSTM model to predict the trend of device health status changes in the future period and generates device health change trends;

[0103] Probability output submodule: Outputs the failure probability of the equipment in the future, generates failure probability data based on the state curve output by the trend model and combined with historical failure data for prediction;

[0104] Interval analysis submodule: Through confidence interval analysis, the error of the prediction results is estimated, and a credible interval is provided to generate confidence interval analysis results

[0105] Time window setting submodule: Based on the equipment's operating cycle and fault occurrence patterns, set the predicted time window length and generate a predicted time window.

[0106] Among them, the early warning judgment module includes a threshold calculation submodule, an early warning classification submodule, a risk association submodule, a response suggestion submodule, and a dynamic adjustment submodule;

[0107] Threshold calculation submodule: Through the adaptive threshold algorithm, the health threshold and warning threshold are calculated according to the historical status of the equipment and the current prediction results, and the threshold data is generated.

[0108] Early warning classification submodule: Based on the equipment status and threshold data, a multi-level early warning strategy is used to divide the early warning levels, output different levels of early warning levels, and generate early warning level information;

[0109] Risk association submodule: Combines equipment importance and environmental factors, uses a weighted risk assessment model to perform risk association analysis, and generates risk assessment information;

[0110] Response suggestion submodule: automatically generates emergency response suggestions and response suggestion data based on warning levels and risk assessments;

[0111] Dynamic adjustment submodule: Automatically adjusts warning strategies and thresholds based on real-time data and environmental changes, improves system adaptability, and generates adjusted warning strategies.

[0112] Among them, the information interaction module includes a multi-terminal push submodule, an interface display submodule, a record storage submodule, a feedback return submodule, and a permission distribution submodule;

[0113] Multi-terminal push submodule: pushes warning information to operation and maintenance personnel through Web platform push, SMS alarm, and mobile APP notification, and generates push notification data;

[0114] Interface display submodule: displays warning information through a visual interface to help operation and maintenance personnel intuitively understand the equipment status and generate a warning information display interface;

[0115] Record storage submodule: stores warning information and feedback records for later review and analysis, and generates historical record data;

[0116] Feedback submodule: Receives processing feedback information from operation and maintenance personnel, feeds it back to the system to update the device status and generate feedback data;

[0117] Permission distribution submodule: distributes different levels of warning information according to the permissions of different operation and maintenance personnel, and generates permission management data.

[0118] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A power equipment fault early warning system, characterized in that: It includes data acquisition module, data fusion module, status assessment module, trend prediction module, early warning judgment module and information interaction module; Data acquisition module: Based on the need to monitor various operating conditions of power equipment, a multi-type sensor acquisition method is adopted to collect key operating parameters such as current, voltage, temperature, humidity, partial discharge, and vibration. The data is time-corrected through a timestamp synchronization mechanism and encoded in a unified format and transmitted to the subsequent processing module to generate the original operating data set; Data fusion module: Based on the original running data set, principal component analysis and autoencoder neural network algorithms are used to extract features and reduce dimensionality of multi-source data to construct a unified high-dimensional feature vector. At the same time, data cleaning and normalization processing mechanisms are introduced to improve data quality and model compatibility, generating a fused feature data vector. Condition Assessment Module: Based on the fused feature data vector, the Bayesian network modeling method and the support vector machine (SVM) classification algorithm are used to evaluate the current health status of power equipment. The evaluation results are divided into multiple health levels. At the same time, a discriminant model is constructed based on historical fault samples to improve recognition accuracy and generate equipment health level information. Trend forecast module: Based on equipment health level information and combined with equipment operation time series, a long short-term memory network (LSTM) prediction algorithm is used to build a dynamic change model to predict the health change trend and potential failure probability of the equipment within a certain time window in the future. Confidence interval analysis results are also output to generate failure trend prediction data. Early warning judgment module: Based on fault trend prediction data, an adaptive threshold determination method and a multi-level warning strategy algorithm are used to determine the level of abnormal behavior that may occur in the future. The warning threshold is automatically set based on the importance of the equipment and environmental factors, and a clear warning level and corresponding response suggestions are output to generate warning levels and disposal suggestions. Information interaction module: Based on the warning level and disposal suggestions, through the visual interface management mechanism, the warning information is promptly conveyed to the operation and maintenance personnel by means of Web platform push, SMS alarm, and mobile APP notification. The system supports the warning information traceability query and feedback mechanism, enhances the system closed-loop, and generates warning information interaction records.

2. The power equipment fault early warning system according to claim 1, characterized in that: The data acquisition module includes an electrical parameter acquisition submodule, an environmental quantity acquisition submodule, a structural response acquisition submodule, a time synchronization submodule, and a data encapsulation submodule; Electrical parameter acquisition submodule: Based on the voltage, current, and frequency electrical parameters of the equipment operation, it uses signal conditioning and sampling timing algorithms to collect electrical data in real time and generate electrical parameter data sets; Environmental quantity acquisition submodule: collects environmental data and performs synchronous monitoring of external environmental variables such as temperature, humidity, and wind speed. It uses a sensor fusion algorithm to synchronously collect environmental data and electrical parameter data, and completes numerical merging to generate an environmental data set. Structural response acquisition submodule: uses vibration sensors to obtain the mechanical response data of the equipment through time domain analysis algorithms and generates a structural response data set; Time synchronization submodule: Through the NTP protocol and GPS clock synchronization algorithm, it ensures the accurate synchronization of the timestamps of each sensor data and generates a time synchronization data set; Data encapsulation submodule: encapsulates the collected data types into a unified encoding format for easy transmission and generates original data packets.

3. The power equipment failure early warning system according to claim 1, characterized in that: The data fusion module includes feature extraction submodule, data cleaning submodule, dimension compression submodule, normalization processing submodule, and feature construction submodule; Feature extraction submodule: extracts key features of current waveform and spectrum characteristics through wavelet transform signal processing algorithm to generate electrical characteristic data; Data cleaning submodule: uses anomaly detection algorithms to remove noise and outliers from collected data, ensuring data quality and generating a cleaned data set; Dimensionality compression submodule: Use principal component analysis (PCA) to reduce the dimensionality of data, reduce redundant information, and generate a reduced-dimensionality feature vector; Normalization processing submodule: uses a standardization algorithm to normalize the data to eliminate dimensional differences and generate normalized feature data; Feature construction submodule: Through the feature fusion algorithm, the electrical features, environmental features and structural features are integrated into a high-dimensional feature vector to generate a fused feature data vector.

4. The power equipment failure early warning system according to claim 1, characterized in that: The status assessment module includes a health modeling submodule, a level classification submodule, a historical sample comparison submodule, a model training submodule, and an error checking submodule; Health modeling submodule: Based on the Bayesian network model, it uses conditional probability to model the health status of the equipment and generate a health assessment model; Classification submodule: Based on the evaluation results output by the health model, the support vector machine (SVM) is used to classify the device status into healthy, minor fault, and major fault levels, and generate health level information; Historical sample comparison submodule: This module obtains historical fault data by calling the historical fault sample library, and uses the KNN (K-nearest neighbor) algorithm to compare the evaluation results, improve the evaluation accuracy, and generate fault discrimination information. Model training submodule: Use historical data to train the health assessment model, optimize model parameters, and generate an optimized assessment model; Error check submodule: Use cross-validation method to test the accuracy and robustness of the model, ensure the effectiveness of the model, and generate check error information.

5. The power equipment failure early warning system according to claim 1, characterized in that: The trend prediction module includes a sequence modeling submodule, a trend deduction submodule, a probability output submodule, an interval analysis submodule, and a time window setting submodule; Sequence modeling submodule: Models the device's time series data based on the long short-term memory (LSTM) network, captures the temporal dependencies in the data, and generates a time series model. Trend deduction submodule: Uses the LSTM model to predict the trend of device health status changes in the future period and generates device health change trends; Probability output submodule: Outputs the failure probability of the equipment in the future, generates failure probability data based on the state curve output by the trend model and combined with historical failure data for prediction; Interval analysis submodule: Through confidence interval analysis, the error of the prediction results is estimated, and a credible interval is provided to generate confidence interval analysis results Time window setting submodule: Based on the equipment's operating cycle and fault occurrence patterns, set the predicted time window length and generate a predicted time window.

6. The power equipment failure early warning system according to claim 1, characterized in that: The early warning judgment module includes a threshold calculation submodule, an early warning classification submodule, a risk association submodule, a response suggestion submodule, and a dynamic adjustment submodule; Threshold calculation submodule: Through the adaptive threshold algorithm, the health threshold and warning threshold are calculated according to the historical status of the equipment and the current prediction results, and the threshold data is generated. Early warning classification submodule: Based on the equipment status and threshold data, a multi-level early warning strategy is used to divide the early warning levels, output different levels of early warning levels, and generate early warning level information; Risk association submodule: Combines equipment importance and environmental factors, uses a weighted risk assessment model to perform risk association analysis, and generates risk assessment information; Response suggestion submodule: automatically generates emergency response suggestions and response suggestion data based on warning levels and risk assessments; Dynamic adjustment submodule: Automatically adjusts warning strategies and thresholds based on real-time data and environmental changes, improves system adaptability, and generates adjusted warning strategies.

7. The power equipment failure early warning system according to claim 1, characterized in that: The information interaction module includes a multi-terminal push submodule, an interface display submodule, a record storage submodule, a feedback transmission submodule, and a permission distribution submodule; Multi-terminal push submodule: pushes warning information to operation and maintenance personnel through Web platform push, SMS alarm, and mobile APP notification, and generates push notification data; Interface display submodule: displays warning information through a visual interface to help operation and maintenance personnel intuitively understand the equipment status and generate a warning information display interface; Record storage submodule: stores warning information and feedback records for later review and analysis, and generates historical record data; Feedback submodule: Receives processing feedback information from operation and maintenance personnel, feeds it back to the system to update the device status and generate feedback data; Permission distribution submodule: distributes different levels of warning information according to the permissions of different operation and maintenance personnel, and generates permission management data.

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