Electrical operation full-life-cycle intelligent safety management and control platform

Abstract

The invention relates to the technical field of electrical operation safety management, in particular to an electrical operation full-life-cycle intelligent safety management and control platform which comprises a data acquisition module, a data processing and feature extraction module, a hidden fault mode recognition module, a hidden fault prediction module, an early warning module and a data storage module. The data acquisition module acquires equipment multi-mode operation state data in real time through a distributed sensor network, the data processing and feature extraction module preprocesses the data and extracts potential features of hidden faults, and the hidden fault mode identification module distinguishes a normal operation mode from a hidden fault mode. The hidden fault prediction module predicts the future operation state and fault risk of equipment, the early warning module generates risk prompts and maintenance suggestions through real-time monitoring and a multi-stage early warning mechanism, and the data storage module realizes classified storage of data; according to the invention, comprehensive monitoring, accurate prediction and intelligent early warning of the operation state of the equipment are realized, and the operation safety of the electrical equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical operation safety management, and in particular to an intelligent safety management and control platform for the entire life cycle of electrical operations. Background Art

[0002] With the rapid development of modern industry, electrical equipment is increasingly used in various operations, and its safety and reliability are crucial to the entire production process. However, the operating environment of electrical equipment is complex and changeable. Equipment aging, operating parameter fluctuations and potential hidden failures may cause a series of problems, such as reduced production efficiency, equipment damage and even major safety accidents.

[0003] Existing technologies have conducted relevant research in the operation monitoring and hidden fault management of electrical equipment. For example, the operation data of the equipment is collected by sensors, and the equipment is analyzed in combination with simple feature extraction and status monitoring methods. However, these technologies have many limitations. First, the collection and fusion of multimodal data often lacks real-time and comprehensiveness, and it is difficult to reflect the overall picture of equipment operation. Second, the recognition accuracy of hidden fault modes is low, and traditional methods are difficult to accurately distinguish between normal operation modes and hidden fault modes. Finally, existing fault predictions are mostly based on static analysis, which cannot capture the dynamic change characteristics of multimodal time series data, resulting in unreliable prediction results. Summary of the invention

[0004] Based on the above objectives, the present invention provides an intelligent safety management and control platform for the entire life cycle of electrical operations.

[0005] An intelligent safety management and control platform for the entire life cycle of electrical operations, comprising a data acquisition module, a data processing and feature extraction module, a hidden fault pattern recognition module, a hidden fault prediction module, an early warning module and a data storage module, wherein;

[0006] The data acquisition module collects multi-modal operating status data of electrical equipment in real time through a distributed sensor network, wherein the multi-modal operating status data includes equipment operating parameters, dynamic performance parameters and transient abnormal signals;

[0007] The equipment operating parameters include voltage, current and power factor;

[0008] The dynamic performance parameters include temperature rise, harmonic components and operating frequency;

[0009] The transient abnormal signals include current spikes and overload vibrations;

[0010] The multimodal operating status data is formed into multimodal time series data by timestamp marking;

[0011] The data processing and feature extraction module is used to preprocess the multimodal time series data, the preprocessing includes data cleaning and denoising, and uses a convolutional autoencoder to perform feature learning on the preprocessed multimodal time series data to extract potential operating features of hidden faults;

[0012] The latent fault mode recognition module constructs a latent fault mode recognition model based on the extracted potential operation characteristics, so as to recognize the normal operation mode and the latent fault mode;

[0013] The hidden fault prediction module is used to establish a hidden fault prediction model based on the hidden fault pattern recognition to predict the future equipment operation status;

[0014] The early warning module establishes a real-time monitoring and early warning mechanism according to the prediction results of the hidden fault prediction model;

[0015] The data storage module is used to store multi-modal time series data, potential operation characteristics, fault mode recognition results and prediction results.

[0016] Optionally, the data acquisition module includes:

[0017] Equipment operation parameter acquisition unit: collects equipment operation parameters through embedded electrical sensors, including voltage sensors, current sensors and power factor sensors;

[0018] Dynamic performance parameter acquisition unit: collects dynamic performance parameters through environmental sensors, including temperature rise sensors, harmonic analyzers and operating frequency detectors

[0019] Transient abnormal signal acquisition unit: collects transient abnormal signals through high-frequency vibration and impact sensors. Transient abnormal signals include transient current spike signals and overload vibration signals.

[0020] Data synchronization unit: The data synchronization unit arranges the collected multi-modal operating status data in chronological order to form multi-modal time series data.

[0021] Optionally, the data processing and feature extraction module includes:

[0022] Data preprocessing: preprocess the collected multimodal time series data, including data cleaning and denoising;

[0023] Feature learning: Use convolutional autoencoders to learn features from preprocessed multimodal time series data;

[0024] Feature output: After feature learning of multimodal time series data through convolutional autoencoder, the potential operation characteristics of hidden faults are extracted, including equipment operation characteristics, dynamic performance characteristics and transient signal characteristics.

[0025] Optionally, the hidden fault mode identification module includes:

[0026] Data input: Obtain potential operation characteristics from the data processing and feature extraction module, including equipment operation characteristics, dynamic performance characteristics, and transient signal characteristics;

[0027] Cluster analysis: K-Means clustering algorithm is used to classify the patterns of potential operation characteristics;

[0028] Clustering result optimization: Dynamically adjust the parameters of the K-Means clustering algorithm to improve the accuracy of pattern recognition;

[0029] Pattern labeling: Label each sample according to the clustering results to generate pattern classification data, which includes a normal operation pattern feature vector set and a hidden fault pattern feature vector set.

[0030] Optionally, the hidden fault prediction module includes:

[0031] Input data: receiving the pattern classification data and historical multimodal time series data output by the hidden fault pattern recognition module;

[0032] Model structure construction: Use long short-term memory network to establish a hidden fault prediction model. The model structure includes input layer, hidden layer and output layer;

[0033] Model training: Divide the input data into training set and validation set for model training and performance evaluation;

[0034] Model output: Model output includes future time series trends of multimodal features and predicted classification results of equipment operating status (normal operation mode or hidden failure mode).

[0035] Optionally, the hidden fault prediction module further includes:

[0036] Hidden fault state prediction: Use the hidden fault prediction model to infer the input multimodal time series data and predict the feature change trend of future time steps;

[0037] State transition analysis: Use the hidden Markov model to calculate the state transition probability of the predicted feature change trend;

[0038] Fault risk assessment: Calculate the risk probability of future hidden faults based on the predicted state transition probability and feature change trend;

[0039] Prediction result output: Output the hidden fault prediction results, including the characteristic change trend in the future time window, the time point and occurrence probability of the hidden fault state transfer, and the fault risk level (low, medium, high).

[0040] Optionally, the early warning module includes:

[0041] Data reception: receiving multi-modal time series data collected in real time and receiving the prediction results output by the hidden fault prediction module;

[0042] Real-time status comparison: compare real-time multimodal time series data with prediction results to determine the deviation of the operating status;

[0043] Anomaly detection: Anomaly detection using dynamic performance parameters and transient abnormal signals;

[0044] Monitoring result output: Real-time output of monitoring results of the current status of the device.

[0045] Optionally, the early warning module further includes:

[0046] Warning level classification: define multi-level warning rules based on the probability of hidden faults;

[0047] Warning signal generation: combining multimodal time series data from real-time monitoring with forecast results to generate warning signals;

[0048] Response suggestion generation: Dynamically generate equipment maintenance and operation suggestions based on warning levels and real-time multimodal time series data.

[0049] Optionally, the data storage module includes:

[0050] Data classification and storage: Classify storage content by data type;

[0051] Storage structure optimization: For multi-modal time series data, a hierarchical storage method is used to group data according to data type and time sequence. For fault mode recognition results and prediction results, an efficient indexing mechanism is used to facilitate fast query and update;

[0052] Data backup and security management: Back up stored data regularly and set up encryption and access control mechanisms to ensure data security and integrity.

[0053] Beneficial effects of the present invention:

[0054] The present invention realizes the real-time collection of equipment operating parameters, dynamic performance parameters and transient abnormal signals through a distributed sensor network. The collected data is marked with timestamps to form multimodal time series data, and combined with dynamic sampling frequency adjustment technology, the integrity and real-time performance of the data are ensured. In the data processing and feature extraction module, a convolutional autoencoder is used to perform deep feature learning on the pre-processed multimodal time series data, and to extract the potential operating characteristics of hidden faults, providing a high-quality data foundation for subsequent pattern recognition and fault prediction, and effectively solving the problem of scattered and inaccurate data in traditional electrical operations.

[0055] In the present invention, the hidden fault pattern recognition module classifies the potential operating characteristics through a clustering algorithm, accurately identifies the normal operating mode and the hidden fault mode, and dynamically optimizes the boundary of pattern recognition, solving the problem of inaccurate recognition of hidden fault patterns by traditional methods. At the same time, the hidden fault prediction module is based on a long short-term memory network and combines the hidden Markov model to analyze the state transition probability of the fault mode, realizing an accurate prediction of the future equipment operation state, providing a time window and risk probability for the occurrence of hidden faults, significantly improving the recognition efficiency and prediction accuracy of hidden faults, and providing a strong guarantee for the safety of equipment operation.

[0056] In the present invention, the early warning module dynamically generates multi-level early warning signals by real-time monitoring of the deviation between the equipment operating status and the prediction results, and outputs targeted maintenance suggestions in combination with the risk level, which significantly improves the timeliness of risk response and the effectiveness of decision-making. At the same time, the data storage module ensures the efficiency and reliability of data management by classifying and storing multimodal time series data, pattern recognition results and prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A schematic diagram of the control platform process of an embodiment of the present invention;

[0059] Figure 2 Schematic diagram of the hidden fault prediction module flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0061] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0062] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0063] like Figure 1-Figure 2 As shown, an intelligent safety management and control platform for the entire life cycle of electrical operations includes a data acquisition module, a data processing and feature extraction module, a hidden fault pattern recognition module, a hidden fault prediction module, an early warning module and a data storage module, wherein;

[0064] The data acquisition module collects multi-modal operating status data of electrical equipment in real time through a distributed sensor network. The multi-modal operating status data includes equipment operating parameters, dynamic performance parameters and transient abnormal signals, among which;

[0065] Equipment operating parameters include voltage, current and power factor;

[0066] Dynamic performance parameters include temperature rise, harmonic content and operating frequency;

[0067] Transient abnormal signals include current spikes and overload vibrations;

[0068] Multimodal operating status data is marked with timestamps to form multimodal time series data;

[0069] The data processing and feature extraction module is used to preprocess the multimodal time series data, including data cleaning and denoising. The convolutional autoencoder is used to perform feature learning on the preprocessed multimodal time series data to extract the potential operating characteristics of hidden faults.

[0070] The latent fault mode recognition module builds a latent fault mode recognition model based on the extracted potential operation characteristics, which is used to identify the normal operation mode and the latent fault mode;

[0071] The hidden fault prediction module is used to establish a hidden fault prediction model based on the hidden fault pattern recognition to predict the future equipment operation status;

[0072] The early warning module establishes a real-time monitoring and early warning mechanism based on the prediction results of the hidden fault prediction model;

[0073] The data storage module is used to store multimodal time series data, potential operating characteristics, fault mode recognition results and prediction results.

[0074] The data acquisition module includes:

[0075] Equipment operation parameter acquisition unit: collects equipment operation parameters through embedded electrical sensors, including voltage sensors, current sensors and power factor sensors, among which;

[0076] The voltage sensor is used to monitor the real-time voltage value of the equipment;

[0077] The current sensor is used to obtain the real-time current signal of the equipment;

[0078] The power factor sensor is used to calculate the power factor change value of the equipment;

[0079] Dynamic performance parameter acquisition unit: collects dynamic performance parameters through environmental sensors, which include temperature rise sensors, harmonic analyzers and operating frequency detectors, among which;

[0080] The temperature rise sensor is used to detect the temperature rise of the equipment and the surrounding environment;

[0081] Harmonic analyzer is used to obtain harmonic components;

[0082] The operating frequency detector is used to monitor the operating frequency fluctuation of the equipment;

[0083] Transient abnormal signal acquisition unit: collects transient abnormal signals through high-frequency vibration and impact sensors. Transient abnormal signals include transient current spike signals and overload vibration signals.

[0084] Data synchronization unit: The data synchronization unit arranges the collected multi-modal operation status data in chronological order to form multi-modal time series data;

[0085] The data acquisition module can comprehensively and accurately obtain the operating status data of electrical equipment through real-time acquisition of distributed sensor networks and timestamp synchronization of multimodal data, providing high-quality data support for subsequent data processing and analysis.

[0086] The data processing and feature extraction modules include:

[0087] Data preprocessing: Preprocess the collected multimodal time series data, including data cleaning and denoising, specifically including:

[0088] (1) Data cleaning: Detect and remove outliers from multimodal time series data. The removal formula is:

[0089]

[0090] Among them, X i is the sample value, μ is the mean of the modal data, σ is the standard deviation, and k is the tolerance coefficient;

[0091] Use interpolation to fill missing values ​​and ensure data integrity and consistency;

[0092] (2) De-noising: For dynamic performance parameters and transient abnormal signals, wavelet transform method is used for denoising. The denoising formula is:

[0093]

[0094] Among them, X raw is the original signal, ψ i is the decomposed wavelet basis function, n is the number of decomposition layers;

[0095] After removing noise, the data are reorganized into high-quality multimodal time series data;

[0096] Feature learning: Use convolutional autoencoders to learn features of preprocessed multimodal time series data, including:

[0097] The structure of the convolutional autoencoder includes input layer, convolution layer, pooling layer, encoding layer, decoding layer and reconstruction layer. The model expression is:

[0098] h = f(W*X+b);

[0099] Where W is the convolution kernel weight, X is the input time series data, b is the bias, f(·) is the activation function, and h is the extracted feature;

[0100] The pooling layer uses the maximum pooling (MaxPooling) operation, expressed as:

[0101] hpool =max(X local );

[0102] Among them, X local is the local receptive field data window;

[0103] Feature optimization: Perform principal component analysis on the features extracted by the convolutional autoencoder to remove redundant information. The optimized features are used as the input of the hidden fault pattern recognition module.

[0104] Feature output: After feature learning of multimodal time series data through convolutional autoencoders, potential operating features of hidden faults are extracted, including equipment operating characteristics, dynamic performance characteristics, and transient signal characteristics;

[0105] The data processing and feature extraction module extracts the key operating characteristics of hidden faults through data cleaning and denoising technology, combined with deep feature learning of convolutional autoencoders and feature optimization of principal component analysis, providing reliable feature input for the hidden fault pattern recognition module and significantly improving data quality and fault identification accuracy.

[0106] The hidden fault mode recognition module includes:

[0107] Data input: Obtain potential operation characteristics from the data processing and feature extraction module, including equipment operation characteristics, dynamic performance characteristics, and transient signal characteristics;

[0108] Cluster analysis: K-Means clustering algorithm is used to classify the potential operation characteristics into patterns, including:

[0109] The input feature vector is divided into two categories, representing the normal operation mode and the hidden failure mode;

[0110] The objective function of the K-Means algorithm is:

[0111]

[0112] Among them, J is the clustering objective function, k is the number of clusters, and F j is the jth feature vector sample, C i is the i-th cluster, μ i is the i-th cluster center;

[0113] Clustering result optimization: Dynamically adjust the parameters of the K-Means clustering algorithm to improve the accuracy of pattern recognition, including:

[0114] Dynamic cluster center update: recalculate the cluster center based on the latest feature data input in real time, expressed as:

[0115]

[0116] Among them, |C i | is the number of samples in the i-th cluster;

[0117] Dynamic adjustment of thresholds: Dynamically adjust clustering thresholds based on historical data and real-time feature data to ensure clear boundaries of hidden faults;

[0118] Mode labeling: label each sample with its own mode according to the clustering results to generate mode classification data, which includes a normal operation mode feature vector set and a hidden fault mode feature vector set;

[0119] The latent fault pattern recognition module accurately distinguishes normal operation mode from latent fault mode through the K-Means clustering algorithm and dynamic optimization strategy based on potential operation characteristics, and marks key pattern features, providing high-quality pattern classification results and decision support for the latent fault prediction module.

[0120] The hidden fault prediction module includes:

[0121] Input data: receiving the pattern classification data and historical multimodal time series data output by the hidden fault pattern recognition module. The historical multimodal time series data includes historical equipment operating parameters (such as voltage, current, power factor), historical dynamic performance parameters (such as temperature rise, harmonic components, operating frequency) and historical transient abnormal signals (such as current spikes and overload vibration);

[0122] Model structure construction: Use the long short-term memory network to establish a hidden fault prediction model. The model structure includes input layer, hidden layer, and output layer. The mathematical expression of the long short-term memory network model includes:

[0123] The forget gate update is expressed as:

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

[0125] The input gate update is expressed as:

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

[0127]

[0128] The cell state update is expressed as:

[0129]

[0130] The output gate update is expressed as:

[0131] o t =σ(W o ·[h t-1 ,x t ]+b o );

[0132] h t =o t *tanh(C t );

[0133] Among them, x t is the input of the current time step, h t is the output of the current time step, h t-1 is the hidden state of the previous time step, C t is the current unit state, W f , W i , W o , W c is the weight matrix, b f , b i , b o , b c is the bias term, σ is the activation function;

[0134] Model training: The input data is divided into a training set and a validation set for model training and performance evaluation. The loss function uses the mean square error, which is expressed as:

[0135]

[0136] Among them, y i is the true value, is the predicted value, N is the total number of samples;

[0137] Model output: Model output includes future time series trends of multimodal features and predicted classification results of equipment operating status (normal operation mode or hidden failure mode).

[0138] The hidden fault prediction module also includes:

[0139] Hidden fault state prediction: The hidden fault prediction model is used to infer the input multimodal time series data and predict the feature change trend of future time steps, which is expressed as:

[0140]

[0141] in, is the predicted feature vector for the next time step, h tis the current hidden state, X t is the feature vector at time step t;

[0142] Iterative prediction of the feature sequence of the next T time steps is expressed as:

[0143]

[0144] Where T is the number of time steps for prediction;

[0145] State transition analysis: The hidden Markov model is used to calculate the state transition probability of the predicted feature change trend, which is expressed as:

[0146] (1) Define the state set of hidden faults, expressed as:

[0147] S={s normal ,s fault};

[0148] Among them, s normal In normal operation, s fault It is a hidden fault state;

[0149] (2) Calculate the state transition probability, expressed as:

[0150] P(s t+k |s t+k-1 )=a ij ;

[0151] Among them, a ij Indicates that from state s i Transfer to state s j The probability of

[0152] Fault risk assessment: Based on the predicted state transition probability and characteristic change trend, the risk probability of future hidden faults is calculated, expressed as:

[0153]

[0154] Among them, P fault is the cumulative probability of hidden faults occurring in the future time window;

[0155] Prediction result output: Output the hidden fault prediction results, including the characteristic change trend in the future time window, the time point and occurrence probability of the hidden fault state transfer, and the fault risk level (low, medium, high).

[0156] The early warning module includes:

[0157] Data reception: Receives multi-modal time series data collected in real time and receives the prediction results output by the hidden fault prediction module. The prediction results include:

[0158] Feature change trends in future time windows;

[0159] Predicted operating state (normal operating mode or hidden failure mode);

[0160] The time point and probability of hidden fault state transition;

[0161] Real-time status comparison: Compare real-time multimodal time series data with prediction results to determine the deviation of the operating status, including:

[0162] The difference between real-time data and forecast data is calculated using the following formula:

[0163]

[0164] Where D is the average deviation between real-time data and predicted data, is the real-time characteristic value, is the corresponding predicted value, n is the feature dimension;

[0165] When D exceeds the preset threshold, the status abnormality mark is triggered;

[0166] Anomaly detection: Anomaly detection using dynamic performance parameters and transient abnormal signals, including:

[0167] (1) Perform sliding window analysis on real-time changes in temperature rise, harmonic components, and operating frequency to detect abnormal trends;

[0168] (2) Monitor whether current spikes and overload vibrations exceed the set safety threshold;

[0169] Monitoring result output: Real-time output of monitoring results of the current status of the device, including:

[0170] Current operating status (normal or abnormal);

[0171] Deviation between real-time data and forecast data;

[0172] Detected abnormal signal information;

[0173] The early warning module detects abnormal conditions in equipment operation in a timely manner through real-time monitoring and dynamic comparison of hidden fault prediction results, providing basic support for the activation of subsequent early warning mechanisms.

[0174] The early warning module also includes:

[0175] Warning level classification: Based on the probability of hidden faults, multi-level warning rules are defined, including:

[0176] Low risk warning: When the failure risk probability P fault ∈[0.1,0.3], display low risk prompt;

[0177] Medium risk warning: When the failure risk probability P fault ∈(0.3,0.6], triggering a medium-level alarm;

[0178] High risk warning: When the failure risk probability P fault >0.6, triggering a high-risk alert and recommending emergency treatment;

[0179] Warning signal generation: Combine multimodal time series data from real-time monitoring with forecast results to generate warning signals, including:

[0180] Indicate risk level through signal strength (e.g. color or sound prompt);

[0181] The signal formula is expressed as:

[0182] S=α·P fault +β·A;

[0183] Among them, S is the warning signal strength, P fault is the fault risk probability, A is the real-time abnormal state mark (1 indicates abnormality, 0 indicates normality), α and β are weight coefficients;

[0184] Response suggestion generation: Dynamically generate equipment maintenance and operation suggestions based on warning levels and real-time multimodal time series data, including:

[0185] For low-risk warnings, routine inspections are recommended;

[0186] For medium risk warnings, it is recommended to focus on and conduct preventive maintenance on related equipment;

[0187] For high-risk warnings, it is recommended to shut down immediately or take emergency measures.

[0188] The data storage module includes:

[0189] Data classification and storage: Classify the storage content by data type, including:

[0190] Multimodal time series data: stores equipment operating parameters, dynamic performance parameters and transient abnormal signals collected in real time from the data acquisition module and marked with timestamps;

[0191] Potential operation characteristics: store key operation characteristics extracted by the data processing and feature extraction module to reflect the potential characteristics of hidden faults;

[0192] Fault mode recognition results: store the mode classification results generated by the hidden fault mode recognition module, including the classification and labeling of normal operation mode and hidden fault mode;

[0193] Prediction results: store the future operation status prediction information output by the hidden fault prediction module, including the time window of fault occurrence, risk probability and status classification;

[0194] Storage structure optimization: For multi-modal time series data, a hierarchical storage method is used to group data according to data type and time sequence. For fault mode recognition results and prediction results, an efficient indexing mechanism is used to facilitate fast query and update;

[0195] Data backup and security management: Back up stored data regularly and set up encryption and access control mechanisms to ensure data security and integrity.

[0196] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0197] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent safety management and control platform for the entire life cycle of electrical operations, characterized in that: It includes a data acquisition module, a data processing and feature extraction module, a hidden fault pattern recognition module, a hidden fault prediction module, an early warning module and a data storage module, wherein; The data acquisition module collects multi-modal operating status data of electrical equipment in real time through a distributed sensor network, wherein the multi-modal operating status data includes equipment operating parameters, dynamic performance parameters and transient abnormal signals; The equipment operating parameters include voltage, current and power factor; The dynamic performance parameters include temperature rise, harmonic components and operating frequency; The transient abnormal signals include current spikes and overload vibrations; The multimodal operating status data is formed into multimodal time series data by timestamp marking; The data processing and feature extraction module is used to preprocess the multimodal time series data, the preprocessing includes data cleaning and denoising, and uses a convolutional autoencoder to perform feature learning on the preprocessed multimodal time series data to extract potential operating features of hidden faults; The latent fault mode recognition module constructs a latent fault mode recognition model based on the extracted potential operation characteristics, so as to recognize the normal operation mode and the latent fault mode; The hidden fault prediction module is used to establish a hidden fault prediction model based on the hidden fault pattern recognition to predict the future equipment operation status; The early warning module establishes a real-time monitoring and early warning mechanism according to the prediction results of the hidden fault prediction model; The data storage module is used to store multi-modal time series data, potential operation characteristics, fault mode recognition results and prediction results.

2. According to claim 1, an intelligent safety management and control platform for the entire life cycle of electrical operations is characterized in that: The data acquisition module comprises: Equipment operation parameter acquisition unit: collects equipment operation parameters through embedded electrical sensors, including voltage sensors, current sensors and power factor sensors; Dynamic performance parameter acquisition unit: dynamic performance parameters are collected through environmental sensors, which include temperature rise sensors, harmonic analyzers and operating frequency detectors; Transient abnormal signal acquisition unit: collects transient abnormal signals through high-frequency vibration and impact sensors. Transient abnormal signals include transient current spike signals and overload vibration signals. Data synchronization unit: The data synchronization unit arranges the collected multi-modal operating status data in chronological order to form multi-modal time series data.

3. According to claim 2, an intelligent safety management and control platform for the entire life cycle of electrical operations is characterized in that: The data processing and feature extraction module includes: Data preprocessing: preprocess the collected multimodal time series data, including data cleaning and denoising; Feature learning: Use convolutional autoencoders to learn features from preprocessed multimodal time series data; Feature output: After feature learning of multimodal time series data through convolutional autoencoder, the potential operation characteristics of hidden faults are extracted, including equipment operation characteristics, dynamic performance characteristics and transient signal characteristics.

4. According to claim 3, the intelligent safety management and control platform for the entire life cycle of electrical operations is characterized in that: The hidden fault mode identification module comprises: Data input: Obtain potential operation characteristics from the data processing and feature extraction module, including equipment operation characteristics, dynamic performance characteristics, and transient signal characteristics; Cluster analysis: K-Means clustering algorithm is used to classify the patterns of potential operating characteristics; Clustering result optimization: Dynamically adjust the parameters of the K-Means clustering algorithm to improve the accuracy of pattern recognition; Pattern labeling: Label each sample according to the clustering results to generate pattern classification data, which includes a normal operation pattern feature vector set and a hidden fault pattern feature vector set.

5. The intelligent safety management and control platform for the entire life cycle of electrical operations according to claim 4 is characterized in that: The hidden fault prediction module comprises: Input data: receiving the pattern classification data and historical multimodal time series data output by the hidden fault pattern recognition module; Model structure construction: Use long short-term memory network to establish a hidden fault prediction model. The model structure includes input layer, hidden layer and output layer; Model training: Divide the input data into training set and validation set for model training and performance evaluation; Model output: Model output includes future time series trends of multimodal features and predicted classification results of equipment operating status.

6. The intelligent safety management and control platform for the entire life cycle of electrical operations according to claim 5, characterized in that: The hidden fault prediction module also includes: Hidden fault state prediction: Use the hidden fault prediction model to infer the input multimodal time series data and predict the feature change trend of future time steps; State transition analysis: Use the hidden Markov model to calculate the state transition probability of the predicted feature change trend; Fault risk assessment: Calculate the risk probability of future hidden faults based on the predicted state transition probability and feature change trend; Prediction result output: Output the hidden fault prediction results, including the characteristic change trend in the future time window, the time point and occurrence probability of the hidden fault state transfer, and the fault risk level.

7. The intelligent safety management and control platform for the entire life cycle of electrical operations according to claim 6 is characterized in that: The early warning module comprises: Data reception: receiving multi-modal time series data collected in real time and receiving the prediction results output by the hidden fault prediction module; Real-time status comparison: compare real-time multimodal time series data with prediction results to determine the deviation of the operating status; Anomaly detection: Anomaly detection using dynamic performance parameters and transient abnormal signals; Monitoring result output: Real-time output of monitoring results of the current status of the device.

8. The intelligent safety management and control platform for the entire life cycle of electrical operations according to claim 7 is characterized in that: The early warning module also includes: Warning level classification: define multi-level warning rules based on the probability of hidden faults; Warning signal generation: combining multimodal time series data from real-time monitoring with forecast results to generate warning signals; Response suggestion generation: Dynamically generate equipment maintenance and operation suggestions based on warning levels and real-time multimodal time series data.

9. The intelligent safety management and control platform for the entire life cycle of electrical operations according to claim 8, characterized in that: The data storage module comprises: Data classification and storage: Classify storage content by data type; Storage structure optimization: For multimodal time series data, a hierarchical storage method is used to group data according to data type and time sequence, and an index mechanism is used for fault mode recognition results and prediction results; Data backup and security management: Back up stored data regularly and set up encryption and access control mechanisms.

Images