663 results about "Wave transformation" patented technology

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention provides a marine ranch water quality parameter real-time correction and compensation method and system for a multi-source sensor, and relates to the technical field of multi-source sensors, and the method comprises the steps: constructing a double-layer edge computing network, and connecting a sensor through a micro-service architecture to collect water quality data. And carrying out data preprocessing in combination with wavelet transform. And establishing a sensor digital twinborn model, and calculating the real-time credibility. Establishing a multi-dimensional sensor association network, optimizing a weight coefficient by adopting federal learning, and establishing a self-evolution correction parameter matrix; and fusing the sensor data by using a multi-task deep learning model to generate an initial correction value. And calculating a theoretical reference value through a space-time sequence prediction model. A compensation coefficient is adaptively adjusted by adopting a fuzzy decision tree, hierarchical water quality parameter correction is realized, and a closed-loop self-optimization intelligent correction system is formed through verification of a digital twin model. The accuracy and reliability of marine ranch water quality monitoring data are effectively improved.

The invention provides a water level distributed monitoring method and system based on a visual sensing network, and relates to the technical field of water level monitoring, and the method comprises the steps: constructing a water level propagation prediction model through multi-modal comparative learning and a graph neural network, processing a real-time water level image through the combination of adaptive wavelet transform, decoupling representation and a dense connection network, and obtaining a real-time water level prediction model; the water level measurement value and the reliability score are obtained, anomaly analysis and emergency monitoring are carried out based on state estimation, causal discovery and hierarchical reinforcement learning, high-precision and high-reliability distributed water level monitoring can be achieved, abnormal water level changes can be effectively recognized, and timely emergency monitoring parameters are provided.

The invention provides a brushless motor attitude detection and balance control method and system based on a gyroscope, and relates to the technical field of control, and the method comprises the steps: collecting angular velocity and acceleration data through a six-axis gyroscope, carrying out the noise reduction through wavelet transform, and carrying out the attitude calculation through the combination of an extended Kalman filter and a quaternion algorithm. A rotor position signal is obtained through a magnetic encoder, nonlinear compensation is carried out, and rotating speed data are calculated. A motor state is modeled by adopting a long-short-term memory network, a double-layer adaptive fuzzy neural network controller is constructed, and attitude error compensation and rotation speed fluctuation suppression are realized. A controller model is optimized through particle swarm optimization and a genetic algorithm, a compensation current vector is corrected in real time, and the control precision and stability of the brushless motor are improved. According to the method, the operation efficiency and the dynamic response capability of the brushless motor are effectively improved.

The invention discloses a fault detection and root cause analysis system for a multi-source energy system, and relates to the technical field of energy management and fault diagnosis. The system obtains various operation data and image data of the multi-source energy system in real time; preprocessing the operation data and the image data; inputting the preprocessed data into a hybrid algorithm combining wavelet transform, CNN and LSTM, and detecting an abnormal mode and a potential fault in the multi-source energy system; the digital twinborn model is dynamically updated according to data acquired in real time; and in combination with the feature extraction capability of a digital twin model and a hybrid algorithm, positioning key causes causing the fault, simulating a system operation state, analyzing potential causes of the fault, and outputting an analysis result. According to the invention, the comprehensive analysis capability of the system on various data sources is effectively improved, and a comprehensive and accurate solution is provided for intelligent operation and maintenance of the energy system.

The invention provides an embedded device online test and remote maintenance method and system, and relates to the technical field of device maintenance, and the method comprises the steps: collecting multi-source heterogeneous data, selecting a sampling period based on Shannon entropy, employing wavelet transform and fractal theory to extract features, inputting an anomaly detection model to generate an anomaly degree vector, and carrying out the detection of the anomaly degree vector; extracting features by using a capsule neural network and performing fault type probability distribution prediction; calculating system state indexes by combining a phase-space reconstruction technology; inputting a causal reasoning network to generate a fault diagnosis report; retrieving a maintenance scheme based on the fault diagnosis report; and the maintenance effect is evaluated through the twin neural network, and the maintenance instruction is iteratively optimized according to the effect until the maintenance effect reaches the standard.

The invention provides a civil air defense engineering concealed conduit leakage positioning method based on acoustic characteristics, and belongs to the technical field of civil air defense engineering. An acoustic signal acquisition network is constructed by deploying a high-sensitivity hydrophone array, and signals are preprocessed by using a time division multiple access technology and a pulse compression technology; and carrying out time-frequency analysis by applying wavelet transform to extract acoustic features. A time reversal mirror technology is introduced to identify direct propagation and multipath reflection signals, a fluid acoustic coupling propagation equation is constructed to analyze a leakage sound source mechanism, and pipe network topological information and a sound wave speed correction function are combined to compensate a measurement error. Wherein the deep learning pipe network acoustic propagation multi-modal model is fused with a pipeline structure encoder, an acoustic feature extractor and a position prediction decoder, and high-precision leakage positioning and degree evaluation in a complex environment are realized through a three-stage pre-training strategy; the technical problem that it is difficult to accurately locate the position of a leakage point in the complex pipe network environment of civil air defense engineering is solved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention relates to the technical field of data encryption, and discloses an encrypted storage method and system for flight data and a storage medium. The method comprises the steps that an encrypted data packet is compressed through a time sequence compression algorithm, a wavelet transform compression algorithm and a DCT compression algorithm, redundancy check processing is carried out, a coded data packet is obtained, and the coded data packet comprises a data frame with a predefined format; carrying out storage space distribution on the data frames in the coded data packet according to the data hierarchy to which the data frames belong, dividing the storage space into a fixed area and a dynamic area, and establishing a storage space mapping table to obtain a stored data packet; and performing cyclic redundancy check and integrity scanning on the stored data packet, performing anomaly detection by comparing the storage mapping table, and performing data repair by encoding redundant information to obtain an encrypted and stored flight data packet. According to the invention, differentiated encrypted storage and dynamic space distribution are realized, and meanwhile, the integrity and restorability of data are ensured.

The invention discloses a cross-modal pedestrian re-identification method and system based on wavelet transform, and relates to the technical field of pedestrian re-identification, and the method comprises the steps: constructing a two-stream ResNet-50 backbone network; fusing the features of the shallow layers of the two streams of ResNet-50 backbone networks through an ICB module to obtain fused features; carrying out feature extraction on the fusion features through a WEB branch module to obtain modal sharing features of high and low frequency parts; and a final identification result is obtained through the modal sharing feature output by the dual-branch center guiding loss optimization WEB branch module and the fusion feature output by the ICB module. According to the invention, through the information compensation module, the lost valuable information in the network feature extraction process is compensated by combining the features of the shallow layer of the wavelet transform aggregation network at different stages, and the quality of the final feature vector is improved; and through a wavelet enhancement module, dual-branch center guide loss is used to guide the network to mine modal invariant information in a wavelet sub-graph, so that the model performance is improved.

The invention discloses a night semantic segmentation method and device based on wavelet transform detail enhancement and text prompt, and the method comprises the steps: obtaining a night image, carrying out the preprocessing of the night image, and carrying out the reconstruction of a wavelet image; and inputting the preprocessed night image and the image after wavelet transform reconstruction into a deep learning model for semantic segmentation to obtain a segmentation result of the night scene object. A new three-stage network structure is designed and formed, in the first stage, a three-mode feature extractor composed of an image encoder, a night semantic category encoder and a wavelet image encoder is used for extracting features, in the second stage, a double-branch cross-mode feature interaction module is designed, and the feature extraction is carried out through the image encoder. In the first stage, features of different spatial resolutions and semantic hierarchies and natural language priori of a target object are integrated, all-directional semantic information from coarse granularity to fine granularity is captured, in the third stage, a multi-scale feature segmentation decoder is introduced, details of a low-light area are enhanced, fine texture edges and target contours are captured, and the target object is obtained. Through positioning and understanding of the target area by the natural language prior enhancement model, the precision of night scene semantic segmentation can be effectively improved.

The invention relates to the technical field of deformation early warning of a coal mine TBM tunneling roadway, in particular to a simulation and monitoring method for intelligent early warning of large deformation of the coal mine TBM tunneling roadway. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, feature mining and fusion, intelligent early warning model construction and real-time monitoring and early warning, and the data acquisition and preprocessing specifically comprises the steps that multiple types of sensors are installed on a coal mine TBM tunneling roadway site to acquire multiple data, three-dimensional reconstruction is conducted on image data, and timestamps and space coordinates of the data are unified; a multi-dimensional spatio-temporal data set is formed by using wavelet transform denoising, and feature mining and fusion comprises the steps of extracting geologic structure image data features by using 3D-CNN and capturing spatio-temporal change features of other data by using LSTM. According to the method, high-quality data is provided, meanwhile, the model is more suitable for complex working conditions and more accurate, the generalization ability of the model is enhanced, real-time monitoring and early warning are timely, effective and visual, decision making is facilitated, and safety accidents and economic losses can be effectively reduced.

The invention relates to the technical field of power monitoring, in particular to a station power utilization system safety and energy efficiency monitoring system based on multi-source information fusion, which comprises a signal analysis module, an anomaly identification module, a risk assessment module and an emergency response module. According to the method, the fineness and the response speed of power station monitoring are improved by integrating wavelet transformation and information entropy analysis, the time-frequency characteristics of signals are deeply analyzed through wavelet transformation, the capturing capacity for short-term and long-term changes is improved, the integrity and the accuracy of data analysis are further ensured, and the accuracy of power station monitoring is improved. The information entropy is introduced to assess the stability and uncertainty of the power station, a scientific basis is provided for predicting and recognizing potential risks of the system, and by combining real-time anomaly recognition and dynamic risk assessment, equipment faults and system failures can be effectively predicted, and energy consumption is optimized, so that early warning and prevention and control are performed in advance, the downtime is remarkably shortened, and the system reliability is improved. The maintenance cost is reduced, and the level and effect of energy efficiency management are improved.

The invention belongs to the field of nuclear power electric rotating equipment bearings, and relates to a nuclear power electric rotating equipment bearing early fault on-line monitoring and intelligent diagnosis method comprising the following steps: collecting acoustic emission signals generated during bearing operation; performing filtering, noise reduction and gain preprocessing on the acoustic emission signal; parameters of the acoustic emission signals are extracted according to waveform features, statistical features and energy features of the acoustic emission signals, and change features, distribution features and correlation features of the parameters of the acoustic emission signals under different working conditions and fault types are analyzed; performing time-frequency analysis on the acquired acoustic emission signals by adopting wavelet transform, optimizing the scale of the wavelet transform and the waveform parameters of the base wavelet by utilizing a genetic algorithm, and extracting the time-frequency characteristics of the acoustic emission signals; building a bearing fault diagnosis model by using a convolutional neural network and a support vector machine, and training and testing the bearing fault diagnosis model; and carrying out online monitoring on the nuclear power electric rotating equipment bearing by using the trained fault diagnosis model.

The invention discloses a multispectral satellite fire prevention method and system based on data identification, a multispectral satellite optical sensor array scans a target area for 30 minutes, the acquisition precision is guaranteed through a high-precision attitude system and a formula to obtain data, and the quality is guaranteed through adaptive filtering and multi-source data correction. A dynamic threshold value and wavelet transform are used for extracting fire features to construct vectors, the vectors are input into a fusion model to discriminate a fire in real time, early warning information including positions, fire behaviors and the like is transmitted to a ground command center within 8 minutes, data are updated every 10 minutes during the fire, the fire behaviors are evaluated according to multiple indexes in combination with a GIS and a time sequence model, strategies are adjusted, and the data are stored. The system is fast in data acquisition and processing, early warning is performed within 8 minutes, the accuracy rate exceeds 92%, the fire behavior is pre-judged 15 minutes in advance, the system is stable, data is efficiently processed, and ecological and life and property safety is protected.

The invention discloses a human body posture reconstruction and tracking method based on a neural network, and relates to the technical field of posture reconstruction and tracking. Target echo data are acquired by adopting a multiple-input multiple-output (MIMO) antenna array, and a low-resolution three-dimensional radar image is constructed by adopting space-time weighting in combination with wavelet transform noise reduction and self-adaptive gain compensation; in the feature extraction stage, a Transform-based feature extraction network is introduced, short-distance joint details are processed in combination with a local convolutional neural network (CNN), the spatial correlation of a far-end joint is calculated by adopting a global self-attention mechanism, and a global feature enhancement (GFE) module performs feature completion on a low-confidence region; a hybrid memory network HMN is adopted to carry out short-time and long-time information fusion, a short-time memory unit STM is used to store current frame features, a long-time memory unit LTM is used to store historical key point data, a drift error is corrected in combination with time sequence alignment, and time sequence consistency of attitude estimation is guaranteed.

The invention relates to a multi-focus image fusion method based on multi-layer semantics and multi-scale self-attention, and the method is characterized in that the method comprises the steps: obtaining a source image, inputting the source image into an MSI-Dtrans model, carrying out the feature extraction of the source image through Haar wavelet transform, obtaining the feature information of different scales, and carrying out the fusion of the multi-focus image through the MSI-Dtrans model. Inputting the feature information of different scales into a dynamic Transform for processing, and obtaining an initial focus image; wherein the network architecture of the MSI-Dtrans model adopts a multi-stage up-down sampling structure of a U-shaped network and is obtained through training of a training set, and the training set comprises multiple image pairs generated based on blurred images of different degrees and training labels; and fusing the initial focus image with the source image to obtain a final fused image. According to the invention, image distortion is effectively avoided and a good visual effect is achieved.

The invention relates to the technical field of wireless communication, and particularly discloses a self-adaptive anti-interference communication method and system based on star flash-Bluetooth dual-mode, and the method comprises the steps: monitoring channel interference parameters in real time through a dual-mode radio frequency module on a communication terminal, including the out-of-band radiation power and the pulse interference duty ratio at the switching moment; instantaneous interference intensity features are extracted by using synchronous compression wavelet transform and sparse time-frequency analysis, periodic pulse interference features are identified by combining Hawkes process modeling, and a comprehensive anti-interference feature vector is constructed by fusion; the feature vectors are input into a pre-trained gradient boosting tree model for communication quality evaluation, an optimal communication mode is dynamically selected according to a scoring result, intelligent response to a complex interference environment is achieved, the stability and reliability of a communication system in a high-interference scene are improved, energy efficiency optimization and resource scheduling are considered, and the method is suitable for large-scale popularization and application. The method is suitable for industrial, medical, Internet of Things and other application scenes with strict requirements on communication quality.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.

The invention discloses an electric energy quality comprehensive treatment method and device based on multilevel collaborative optimization, and belongs to the technical field of electric energy quality treatment of an electric power system. According to the method, voltage and current data of a power grid are collected in real time and multi-parameter monitoring is performed; processing the data by using an adaptive wavelet transform and singular value decomposition combined algorithm, extracting disturbance characteristics and identifying the type and position of a disturbance source; a multi-objective optimization function including harmonic suppression, voltage stabilization, three-phase balance and power factor improvement is constructed, a Pareto optimal solution set is solved, and an optimal solution is selected; and according to an optimization result, generating and executing a cooperative control strategy of each type of electric energy quality treatment equipment. Cooperative treatment of various electric energy quality problems and cooperative control of various treatment devices are realized, the accuracy, self-adaptability and economical efficiency of electric energy quality treatment are improved, and the reliability and stability of an electric power system are enhanced.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention provides a cross-modal steganography method and device, equipment and a storage medium, and the method comprises the steps: inputting a carrier image and a ciphertext image corresponding to ciphertext text data into a preset encoder, enabling a learnable wavelet transform network and a reversible neural network in the encoder to carry out the forward feature extraction of the carrier image and the ciphertext image, and obtaining a feature extraction result; obtaining a target carrier feature vector corresponding to the carrier image and a target ciphertext feature vector corresponding to the ciphertext image; and generating ciphertext cross-modal steganography result data corresponding to the ciphertext text data based on the target carrier feature vector and the target ciphertext feature vector for network transmission. According to the method and the device, the ciphertext text data of the text mode can be embedded into the carrier image of the image mode, a multi-mode steganography technology is realized, the steganography concealment and robustness of the ciphertext can be effectively improved, and the ciphertext network transmission security and reliability can be further improved.

The invention relates to the technical field of image processing, in particular to a remote sensing image segmentation method based on multilayer wavelet transform and a dynamic memory network, which comprises the following steps: decomposing an image into a low-frequency sub-band and a plurality of high-frequency sub-bands through multilayer Haar wavelet transform, and performing feature extraction on each frequency band in combination with convolution operation to obtain a final feature map; constructing a dynamic memory unit network, performing dynamic feature extraction on the feature map by adopting a query-key-value structure, generating a dynamic memory enhanced feature with global context information, performing global-local feature modeling on the feature map, and fusing the dynamic memory enhanced feature and the global-local feature to obtain a fused feature map; and segmenting the fused feature map to obtain a final segmentation result. According to the method, low-frequency smooth information and high-frequency detail information of the image can be accurately extracted, efficient fusion of global and local features can be realized, and the accuracy and robustness of remote sensing image segmentation are improved.

The invention discloses a rainfall field image extraction method based on double-domain collaboration and progressive feature decoupling. The method comprises the following steps: acquiring a rain image; respectively inputting the rain image into the rain layer branch and the background branch, carrying out corresponding image block embedding operation, and generating an initial rain layer feature and an initial background feature; performing feature extraction on the initial rain layer features through a learnable wavelet transform algorithm to obtain rain layer features, and performing feature extraction on the initial background features through frequency domain separation convolution to obtain background features; performing multi-scale progressive coupling feature extraction on the rain stripe features and the background features to obtain processed rain layer features and processed background features; and performing multi-scale up-sampling on the processed rain layer features and the processed background features to reconstruct a rain layer image and a rain-free background image, and performing weighted fusion on the rain layer image and the rain-free background image through learnable residual gating to obtain a reconstructed rain image and a rainfall field image. According to the invention, accurate separation of the rainfall field can be realized.

The invention relates to the technical field of image processing, in particular to a road anti-corrosion effect detection and evaluation method based on an image processing technology, and the method comprises the steps: obtaining to-be-detected grayscale images corresponding to two different types of images on the surface of a road anti-corrosion layer, and carrying out the segmentation to obtain to-be-detected sub-images; respectively obtaining two difference images corresponding to the to-be-detected sub-image according to the row gray difference and the column gray difference of the gray difference image corresponding to the to-be-detected sub-image; obtaining the roughness degree of the to-be-detected sub-images, obtaining an optimal scale based on the roughness degree, decomposing the two to-be-detected grayscale images by using wavelet transform according to the optimal scale, and fusing according to decomposition results to obtain a fused feature image; and obtaining a defect region of the fused feature image, and obtaining a road quality detection result according to area features of the defect region. According to the invention, a more accurate road quality detection result can be obtained.

The invention discloses a high-resolution image semantic segmentation network for underwater scene design. A high-resolution image semantic segmentation network for an underwater scene is provided based on HRNetV2. The core of the method comprises: a WDCM feature extraction module for enhancing the anti-noise capability by using wavelet transform convolution, inhibiting irrelevant features by dynamic gating, and improving the performance of a complex scene in combination with an RCA module; a multi-scale slice segmentation head is adopted, global and local features are fused, and key information is dynamically selected; and 3) proposing a sliding combination loss function, and optimizing target boundary semantic information on the basis of variable weight focus (Focal) loss and dice (dice) loss. The target boundary segmentation precision is effectively improved, and the segmentation performance in a complex scene is improved.

The invention relates to the technical field of non-destructive testing, and particularly discloses an aero-engine blade non-destructive flaw detection method for a miniature unmanned aerial vehicle, which comprises the following steps: acquiring stress data of a blade in a use process in real time by using an MEMS sensor, analyzing the data, and identifying a stress concentration area; carrying out real-time monitoring on crack data of the stress concentration area, extracting crack propagation fluctuation characteristics by utilizing a wavelet transform algorithm, and calculating a crack propagation fluctuation index; fatigue strength data of the material is monitored, fatigue failure characteristics are extracted, and a fatigue failure index is generated through a support vector machine algorithm; comprehensively analyzing the crack propagation fluctuation index and the fatigue failure index, and judging whether the blade is in a healthy state or not; and finally, based on the blade health state, determining whether to carry out further detection or maintenance.

The invention discloses an intelligent primary and secondary fusion pole-mounted circuit breaker fault monitoring method, relates to the technical field of power equipment monitoring, and is used for solving the problem of insufficient real-time performance of fault monitoring under complex environment interference. According to the invention, electrical, mechanical vibration and environmental data are synchronously acquired through a multi-mode sensor array, and a timestamp synchronization mechanism is applied; performing multi-source interference classification on the original data, separating noise by using wavelet transform, and identifying interference types by clustering; kalman filtering adaptive compensation is applied based on an interference result, and environment feedback is introduced to ensure low delay; extracting multi-dimensional fault features, and forming a robust matrix through time-frequency analysis and principal component dimensionality reduction; inputting a deep learning model to carry out space-time modeling and rapid classification; a response mechanism is triggered to execute isolation or alarm, and closed-loop optimization is formed. The method effectively solves the problem of insufficient real-time performance under the interference of a complex environment, and improves the monitoring precision and the response speed.