604 results about "Multi feature" patented technology

A defect prediction method based on a multi-feature parallel multi-stage neural network (MF-PMSNN), includes: obtaining a trajectory dataset, and preprocessing data of a defect of a workpiece in additive manufacturing (AM); building an MF-PMSNN, and evaluating an output classification result based on evaluation indicators; and performing real-time defect prediction, and deploying a trained MF-PMSNN model to a production environment. The present disclosure combines and effectively matches thermal imaging-based in-situ monitoring data and X-ray computed tomography (XCT)-based in-situ monitoring data to ensure temporal and spatial consistency between the thermal imaging-based in-situ monitoring data and the XCT-based in-situ monitoring data. In this way, a molten pool status and a pore of the workpiece can be captured more comprehensively. The MF-PMSNN is proposed to obtain a molten pool status and the porosity distribution in the data and perform defect prediction.

The invention provides a multi-feature information hidden document integrity verification and tampering positioning method, and relates to the field of network security and digital document protection. The multi-feature information hidden document integrity verification and tampering positioning method comprises the following steps: S1, document multi-dimensional feature extraction and encryption: based on an original Word document, adopting an SHA-3 algorithm to calculate a full-text hash value as a content feature, and extracting document object model features such as paragraph hierarchy, table structure and the like through a DOM tree analyzer; after the two types of features are combined into a feature matrix, a dynamic secret key is generated through Logistic chaotic mapping for XOR encryption, and an encrypted document feature matrix is generated. A dynamic key is generated by combining Logistic chaotic mapping encryption through a multi-dimensional extraction technology fusing document content hash values and structural features, so that the anti-cracking capability of a feature matrix is remarkably improved, the confidentiality of document features in open network transmission is ensured, the block information retention rate is simulated and predicted by adopting Monte Carlo, and the watermark embedding weight is dynamically allocated.

The invention discloses a small sample radar target identification method based on multi-feature fusion. According to the invention, the natural resonant frequency characteristics of radar echo signals are introduced into a small sample learning framework, and the advantages of certain stability and simple and rapid extraction under the condition of attitude and observation angle change are fully utilized; attention multi-feature weighted fusion based on energy guidance is carried out on natural resonant frequency features and time frequency features, scattering features are described from different feature mechanisms, and the completeness of the features under the small sample condition is improved; in addition, a loss function based on combination of feature similarity measurement and classification loss is also designed, so that the model can learn feature representation with higher distinction degree while the feature space discrimination is optimized, and the performance is improved in a feature fusion-classification network. According to the method, on the premise that the reasoning time and the calculation complexity are not remarkably increased, the recognition accuracy under the condition that the observation angle changes is effectively improved.

The present disclosure describes methods, systems, apparatus, and media for object identification and classification, utilizing multi-feature and multi-modal data. This includes shape, material, brand, price, odor, taste, tactility, and sound. The system integrates a server space for data processing, a querying device for iterative searches, and a data interface module for refining results. It features AI-driven image optimization, feature extraction, and pattern recognition, employing novel techniques for fusing multi-feature and multi-modal embeddings utilizing multi-head attention. Additionally, a linker module powered by two active learning with feedback loops AI models consolidates scattered data into a unified object information database. The system also employs novel AI algorithms for isolating the object of interest through a saliency map and semantic analysis, as well as for enhancing raw images with a GAN-autoencoder.

The invention relates to the technical field of intelligent wearable health management, in particular to a health management method and device based on multi-modal data, equipment and a storage medium. Comprising the steps of obtaining multi-feature-dimension original data to construct an original data set; performing data preprocessing and feature extraction on the original data set to generate a feature vector set; performing trend capture on the feature vector set according to a time sequence to obtain a time sequence feature vector of each modal; calculating a cross-modal association weight according to each modal time sequence feature vector, outputting a joint feature vector based on the cross-modal association weight, and generating a health index value of each evaluation target dimension according to the joint feature vector; and forming a state vector by the health index value and the historical behavior data of the user, calculating action probability distribution according to the state vector, and sampling and outputting a health intervention suggestion based on the probability distribution. The problem of health assessment deviation caused by insufficient multi-modal data fusion and insufficient time sequence association mining in the prior art can be solved.

The invention discloses a DeepSORT pedestrian tracking method based on multi-feature space-time cooperative interaction, and belongs to the field of computer vision and intelligent video analysis. The method comprises the following steps: acquiring and processing pedestrian data, constructing a target detection and feature extraction model, detecting a test set after training to generate a candidate box, extracting appearance features to construct a cost matrix, matching and updating a trajectory by using a Hungary algorithm, and finally outputting a visual tracking result. In the detection stage, a small target feature enhancement pyramid is designed to improve the small target detection precision, PSConv, Triplet Attention and DyHead are fused to construct a multi-dimensional feature interaction mechanism, and the scale adaptability and the anti-shielding capability are enhanced; in the tracking stage, an IAU module is embedded into an Re-ID branch of DeepSORT, feature discrimination is enhanced through space-time and channel feature dynamic modeling, and ID Switch is reduced. The method effectively improves the perception recognition capability of a multi-scale and strong-shielding target, guarantees the detection accuracy and tracking robustness in a complex environment, and has a good application deployment value.

The invention relates to the technical field of video water flow velocity detection, and discloses a river video velocity measurement method and device based on a multi-feature fusion PSA-ResNet network, and a medium. The method comprises the following steps: constructing an STIA data set; extracting low-level textural features of the space-time image by using a CLBP multi-feature extraction algorithm, performing channel-level fusion on a feature map and an original RGB image to form a six-channel CLBP-STIA feature map, and constructing a CLBP-STIA data set according to the six-channel CLBP-STIA feature map; fusing a pyramid segmentation attention module PSA in a residual block of the residual network ResNet to construct an angle classification model, and training the model by using a CLBP-STIA data set; generating enhanced feature representation of the to-be-detected space-time image by referring to the above mode, then inputting the enhanced feature representation to the trained classification model, and outputting a corresponding texture principal direction angle; and acquiring the actual length of the velocity measurement line in the video image, and calculating the actual flow velocity of the surface flow feature on the velocity measurement line in combination with the texture main direction angle. The texture main direction is accurately estimated, so that the speed measurement precision and efficiency are improved.

The invention discloses an OFDM radar vital sign signal separation method based on FGO-VMD and multi-feature clustering, and belongs to the technical field of radar communication integration. According to the method, an OFDM signal radar is used for carrying out human body detection, multi-subcarrier phase difference linear regression is used for estimating displacement, thoracic cavity micro-motion signals are extracted, an FGO-VMD joint optimization model is provided, the modal number and penalty factor parameters of a VMD decomposition algorithm are adaptively determined through minimum envelope entropy, and a multi-feature clustering and weighted reconstruction strategy is designed. And in combination with frequency band screening and correlation coefficient weighting, the separation robustness of the respiratory signal and the heartbeat signal is enhanced, and high-precision separation of the vital sign signal is realized. According to the method, non-contact vital sign detection based on OFDM radar signals is achieved, the problems that in a traditional algorithm, parameters of modal numbers and penalty factors need to be manually set, and signal amplitude attenuation and modal aliasing are caused under complex noise are solved, the vital sign monitoring precision is remarkably improved, and high-precision separation of vital sign signals is achieved.

The embodiment of the invention provides an emphysema CT image segmentation method based on multi-feature dynamic fusion and boundary perception, and is applied to the field of medical lung CT image segmentation, the method obtains an original image by obtaining an emphysema CT image and preprocessing the emphysema CT image, and the preprocessing comprises extraction and standardization of a lung parenchyma region of interest; inputting the original image into the trained emphysema CT image segmentation model for segmentation to obtain an image segmentation result; wherein the emphysema CT image segmentation model comprises an encoder and a decoder, the encoder comprises a convolutional neural network branch, a Transform branch and a dynamic fusion module, the convolutional neural network branch is parallel to the Transform branch, and the decoder comprises an up-sampling layer and a boundary sensing module. The method improves the accuracy and reliability of the image segmentation result of the emphysema CT image.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and system with dynamic risk perception and attention focusing and a vehicle, and the method comprises the steps: predicting the track of a surrounding vehicle in real time through a multi-feature Gaussian weighted particle filtering algorithm, and improving the prediction precision through combining a vehicle kinematic model and resampling optimization; constructing a comprehensive evaluation model fusing transverse and longitudinal risks, and dynamically quantifying the collision risk of the vehicle and surrounding vehicles; and inputting the risk value as a key state feature into a double-depth Q network based on attention mechanism enhancement, focusing key information through a feature attention distribution mechanism, and generating an optimal driving decision in combination with a multi-target reward function. Compared with the prior art, the method solves the problems of insufficient quantification of uncertainty factors, incomplete risk assessment and low decision-making efficiency of automatic driving in a complex dynamic environment, and significantly improves the risk perception capability and decision-making safety of the automatic driving vehicle.

The invention provides a multi-feature trajectory prediction and cross-defense early warning method for edge and coast defense control objects, relates to the technical field of trajectory prediction and intelligent control, and solves various limitation problems of poor trajectory continuity, lack of environmental constraints and the like of an existing trajectory prediction technology in edge and coast defense scenes. The method comprises the following steps: firstly, acquiring multi-channel monitoring video data, performing environment adaptation preprocessing, and detecting each target and category; based on a detection result, splicing the track fragments in the single-path monitoring range, and obtaining global track data of the target after preset constraint inspection; extracting features of the global trajectory data from a plurality of preset dimensions, and performing feature fusion to obtain high-dimensional vector representation of the target trajectory; and constructing a multi-dimensional feature constraint trajectory prediction model, respectively modeling through time dependence and space interaction, and outputting a subsequent trajectory of the target under a preset constraint. The method can effectively realize prediction and cross-defense risk identification of future trajectories of key control objects, and is applied to the field of public security.

The invention discloses a dynamic scene robust vision SLAM (Simultaneous Localization and Mapping) method based on multi-feature collaborative optimization, which comprises the following steps of: acquiring an image sequence, carrying out dynamic target detection and segmentation through an instance segmentation network, generating a segmentation mask containing a dynamic region mark, and identifying and separating a dynamic object and a static background; removing feature points corresponding to the dynamic object based on the segmentation mask to obtain static feature points; carrying out pose estimation based on the static feature points, and for the key frame, carrying out feature matching with the previous key frame by minimizing a re-projection error, and solving to obtain the camera pose of each key frame; for non-key frames, performing camera pose tracking and data association on the previous frame by adopting an optical flow algorithm, and accumulating solving results to obtain pose tracks of all the non-key frames; the key frames and the non-key frames are subjected to differential processing by fusing feature matching and an optical flow algorithm, so that the calculation efficiency is remarkably improved while the positioning precision is ensured, and the real-time performance is improved.

The invention relates to an unmanned aerial vehicle track prediction method based on multi-feature LSTM, and belongs to the technical field of data processing, and the method comprises the following steps: 1, constructing a six-degree-of-freedom global motion state matrix of a target unmanned aerial vehicle; 2, performing joint estimation to obtain a three-dimensional wind field vector acting on the target unmanned aerial vehicle, and extracting the real-time speed direction of the target unmanned aerial vehicle; 3, fusing the historical state sequence, the three-dimensional wind field vector and the speed direction unit vector, and constructing a multi-dimensional time sequence feature vector; 4, the LSTM neural network learns a nonlinear maneuvering mode of the target unmanned aerial vehicle under the influence of the wind field through a door control mechanism of a forgetting door, an input door and an output door; and step 5, outputting the position probability distribution of the target unmanned aerial vehicle at a plurality of time points in the future through the LSTM neural network. The method has the advantages that the space-time cone representing the future trajectory is formed, and trajectory prediction of the target unmanned aerial vehicle is completed.

The invention provides a ship noise multi-feature classifier data enhancement method and system based on a multi-fine-grained conditional diffusion model. And compressing a waveform to a potential space through VQ-VAE, extracting a ship type / ship name cross semantic vector by using ResNet, and optimizing clustering in combination with a loss function. And a one-dimensional U-Net conditional diffusion model is constructed, unconditional / conditional model output is dynamically weighted and fused, and the weight is adaptively adjusted according to training loss. In the generation stage, a semantic prototype is constructed by using a high-fine-granularity label, parameters are determined by using low / medium-granularity mean value sampling and Bayesian optimization, and fine-granularity controllable waveform generation is realized. After the generated data is converted into multiple features such as MFCC and Lofar, the generated data and original data are combined to train a classifier, and a virtual class strategy relieves class imbalance. Experiments show that the MSE of generated data and real data is reduced, the classification accuracy is improved, the data diversity and the model generalization ability are remarkably enhanced, and the method is suitable for scenes such as underwater target recognition.

The invention discloses a multi-feature emotional electroencephalogram recognition method and device based on density map convolution and a medium. The method comprises the following steps of collecting original electroencephalogram signals and performing frequency band filtering division; extracting the frequency characteristics of each frequency band, calculating the electrode spatial correlation related to the emotional state, and constructing an adjacent matrix; extracting multi-dimensional spatial feature combinations in different emotional states through a density map convolutional network; integrating the spatial features by adopting a dynamic weighted fusion and channel splicing technology; enhancing the key features by using a channel attention mechanism; and outputting a recognition result through feature classification. According to the method, time domain, frequency domain and space domain features are fused, emotion related space features are fused through dynamic weighting, and the weight is adaptively optimized; constructing a multi-space adjacency matrix in combination with prior space distribution, and extracting topological association by using graph convolution; introducing a channel attention mechanism to screen key features, and collaboratively optimizing data driving and priori knowledge; and through multi-space feature fusion and dynamic modeling, the recognition precision is improved.

The invention discloses a pavement crack recognition and classification method based on multi-feature scale fusion Faster-R-CNN, and particularly relates to the technical field of road detection, and the method comprises the steps: obtaining pavement image data of a research region through an automobile carrying a high-definition camera, and carrying out the preprocessing of the pavement image data to form a required image data set; a ZFNet is used as a crack feature extraction module to perform multi-feature fusion and enhance feature information, an RPN full convolutional network is combined to perform detection by optimizing an anchor box generation mode, and back propagation and stochastic gradient descent are used to optimize and extract a disease candidate region to realize disease classification and frame regression; according to the Faster-R-CNN target detection algorithm, the detection precision is improved through a Soft-NMS algorithm, the Soft-NMS algorithm is the same as an NMS algorithm in the execution process, but function operation is used for original confidence score, the objective is to reduce the confidence score, reduce the omission ratio and improve the detection precision, and construction of the Faster-R-CNN target detection algorithm is achieved.

The invention discloses a navel orange grabbing pose estimation method based on multi-feature segmentation and a visual hedgehog algorithm, and relates to the technical field of computer vision, and the navel orange grabbing pose estimation method comprises the steps: collecting a color image and a depth image of a target navel orange, carrying out the spatial registration, and generating a three-dimensional point cloud; performing sphere fitting on the independent navel orange instance point cloud, outputting sphere center coordinates and radius parameters of each navel orange, and taking the sphere center coordinates and radius parameters of each navel orange as geometric data of the navel oranges; and collision detection and visibility analysis are conducted on the grabbing pose candidate set, collision cost and visibility cost are generated, comprehensive optimization is conducted on the collision cost and the visibility cost through a multi-target optimization function, the optimal grabbing pose is generated, and the mechanical arm is driven to execute grabbing operation based on the optimal grabbing pose. According to the method, the multi-feature segmentation and the visual hedgehog algorithm are combined, so that accurate calculation of the navel orange grabbing pose is realized.

The invention provides an underwater robot self-positioning platform based on multi-feature and multi-modal information fusion, and the platform is installed on an underwater robot, and comprises a visual sensing module which is used for collecting image information of an underwater environment, and extracting visual point features and visual line features from the image information; the acoustic measurement module is used for providing three-axis speed information and low-frequency pose information of the underwater robot based on the Doppler principle; the pressure measuring module is used for measuring water depth information of the underwater robot on the basis of a fluid statics principle; and the data processing and optimizing module is used for fusing the visual point features, the visual line features, the three-axis speed information, the low-frequency pose information and the water depth information on the basis of a factor graph optimizing framework so as to realize positioning and mapping. Compared with a traditional system, the self-positioning platform provided by the invention realizes tight coupling joint optimization of point-line features and acoustics and pressure sensor information, can stably operate in extreme underwater environments with low texture, turbidity and the like, and realizes high-precision underwater synchronous positioning and map construction.

The invention discloses a financial user behavior analysis method and system based on a multi-feature large model, and belongs to the field of financial user data analysis and processing. According to the method, financial feature vectors of a user are constructed by collecting historical financial data (including transaction unit price, quantity, financial product total price, transaction rate and the like) of the user, and the cosine similarity of any two feature vectors is calculated to perform mean clustering, so that an initial financial category is obtained. Furthermore, through a multi-threshold segmentation and factor analysis method, the initial category is optimized, independent factors and information contents thereof are extracted, a separation necessity value and a separation value are calculated, and finally a plurality of user categories are divided and financial user portraits are constructed. According to the method, a multi-dimensional data source and various data modeling technologies can be effectively fused, the refined description capability of user behaviors is improved, and accurate recommendation and differentiated services in the financial field are supported. The system comprises a data acquisition module, a clustering analysis module, a clustering optimization module, a category subdivision module, a factor analysis module, a category division module and a user portrait construction module, and is suitable for financial user behavior analysis, marketing and personalized recommendation.

The invention discloses a voice emotion recognition method based on multiple scales and multiple features, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, preprocessing an audio signal and extracting a spectrogram and a Mel-frequency cepstral coefficient; then, a residual network, a bidirectional long-short-term memory network and a HuBERT pre-training model are respectively utilized to extract spectrogram high-order spatial features, time sequence context features and voice semantic embedding features; secondly, inputting the first two features into a multi-dimensional multi-scale feature extraction module to extract richer time-frequency features, performing deep fusion by using a multi-layer cross attention mechanism, and performing weighted fusion with speech semantic embedded features; and finally, all the advanced features are spliced, and a final emotion category is recognized through a full-connection classifier. According to the invention, through combination of multi-scale feature extraction and an advanced fusion mechanism, the problem of insufficient complex emotion modeling ability in the prior art is effectively overcome, and the accuracy and robustness of voice emotion recognition are significantly improved.

The invention discloses an intelligent civil engineering construction management method and system, and relates to the technical field of civil engineering construction management, and the method comprises the following steps: obtaining the vibration data of N construction devices and the mixed vibration data of M monitoring points; and judging whether the mixed vibration data is greater than a preset threshold, if so, performing multi-feature matching with a preset vibration fingerprint database, determining responsible equipment according to a matching result, and outputting contribution degrees and vibration measured values in a descending order. According to the method, environmental interference is eliminated through preprocessing, multi-dimensional decomposition of the mixed vibration data is realized through independent component analysis, and double criteria of time domain waveform difference and frequency domain energy matching degree are combined, so that the recognition accuracy of responsible equipment is remarkably improved, and the recognition efficiency is improved. A three-dimensional geologic model is further constructed through ground penetrating radar scanning and soil layer test data, spatial distribution and attenuation characteristics of different soil layers are accurately reflected, and misjudgment of responsible equipment caused by soil layer difference is avoided.

The invention relates to the technical field of water quality detection and prediction, in particular to a short-term water quality prediction method and system based on multi-feature training and meteorological correction. The method comprises the following steps: collecting water quality parameter data and meteorological prediction data; preprocessing the water quality parameter data collected in the monitoring period; inputting the collected meteorological prediction data and the preprocessed water quality parameter data into a pre-trained water quality index prediction model for prediction; adjusting a model prediction result based on meteorological prediction data and a land type around a monitoring station; and drawing and displaying a water quality change trend chart according to the adjustment values of the prediction results of different monitoring periods. According to the method, high-precision prediction of the short-term change trend of the water quality index can be realized, and meanwhile, the adaptability of the model under extreme meteorological conditions is improved.

A multi-feature three-dimensional reconstruction 3D Gaussian method based on laser vision comprises the steps that laser radar point cloud and camera images are aligned through space-time calibration, and a unified coordinate system is established; extracting geometric features by using point cloud data acquired by the Lidar point cloud, and initializing a Gaussian ellipsoid according to the Lidar point cloud; optimizing the brightness, the contrast ratio and the structural similarity of the rendered image and the real image by combining the mean absolute error L1 and the structural similarity SSIM; the curvatures of Gaussian ellipsoids of K-nearest neighbors are forced to be consistent, and long and short axes and line and surface features of the Gaussian ellipsoids are aligned to reduce geometric distortion; the distribution density of 3D Gaussian is dynamically adjusted through line / surface features and visual structure information extracted by Lidar, and balance between geometric detail enhancement and calculation efficiency is achieved. According to the method, the position, the scale and the rotation parameters of Gaussian are uniformly optimized, and the details and the calculation efficiency of the model are balanced while the consistency of the model structure is improved.

The invention relates to an industrial robot fault diagnosis method, device and equipment and a computer readable medium. The method comprises the steps that multiple pieces of operation state data of the industrial robot are acquired; preprocessing the operation state data to obtain enhanced data; performing feature extraction on the enhanced data, and constructing an operation feature vector of the industrial robot based on the extracted features; calculating the evidence support degree of each feature subset for different faults in the operation feature vector; and fusing all the evidence support degrees to obtain a fusion support degree, and determining that the industrial robot has a fault under the condition that the fusion support degree is greater than a preset alarm threshold value. According to the method, the multi-source operation state data is collected, preprocessing, feature vector construction and multi-feature subset evidence support degree calculation and fusion are performed, the multi-dimensional operation state of the robot is covered, the risk of misjudgment of a single feature is reduced, accurate fault recognition is achieved, and the technical problem that the accuracy of robot fault prediction is low is solved.

The invention discloses a weld defect detection method based on multi-feature extraction and hierarchical SVM (Support Vector Machine) fusion, which comprises the following steps of: extracting image features of a weld surface through passive vision and laser vision sensing technologies, and inputting obtained weld parameters as features of a hierarchical SVM; and converting a multi-classification problem into a plurality of dichotomy problems through the structure of the classifier, and identifying and classifying the six welding seam targets to obtain a final result. According to the method, multi-feature extraction of weld defects is introduced, geometric shapes, texture features and spectral information in weld images can be comprehensively captured through the multi-feature extraction technology, so that the defect recognition precision and reliability are remarkably improved, multi-feature extraction of passive vision and active vision is adopted, feature information of the weld defects is obtained from multiple dimensions, and the defect recognition accuracy is improved. And a multi-classification problem is converted into a plurality of dichotomy problems, so that the welding defects on the surface of the workpiece in a welding scene can be quickly classified and identified.

The invention relates to the technical field of entity relationships, and discloses a multi-feature fused entity relationship extraction optimization model construction method and system, and the method comprises the steps: obtaining an input text sequence and a relationship label set, and carrying out the coding processing, and obtaining a text embedding vector and a relationship embedding vector; calculating the similarity between the text embedding vector and the relation embedding vector, screening in combination with a similarity threshold to obtain a candidate relation subset, and fusing the relation embedding vector and the text embedding vector in the candidate relation subset to generate a relation enhancement vector; processing the relation enhancement vector through a mixed attention mechanism to obtain a semantic enhancement vector; constructing a three-dimensional marking matrix based on the semantic enhancement vector, and marking the three-dimensional marking matrix by adopting a diagonal marking strategy; and carrying out decoding processing on the three-dimensional mark matrix, and generating a structured entity relationship triple by adopting a constraint decoding strategy. The interactive fusion of the text and the relation label can be accurately processed in a unified coding space.

The invention relates to the technical field of computer vision, in particular to a multi-feature fusion visible light-infrared pedestrian re-identification method, which comprises the following steps of: respectively extracting low-layer specific features of two modes of an input image through independent visible light and infrared branches, and inputting the low-layer specific features into a shared deep network; a wavelet enhancement multi-feature generation module WMFGM and a coordinate attention fusion module CAFblock are introduced into the modal sharing network; a statistical normalized attention prototype module SNAP is introduced; the statistical normalization attention prototype module strengthens response of a target area through a statistical significance attention mechanism and performs prototype aggregation in a normalized semantic similarity space, so that local feature information with consistent semantics is extracted from visible light and infrared modalities; local feature information and global feature information are used for pedestrian distinguishing, various loss functions are used for guidance during model training, and cross-modal and intra-modal differences are effectively relieved.

The invention relates to the technical field of fault prediction, and particularly discloses a fault prediction method and system for a high-voltage switch operating mechanism. According to the method, early-stage accurate prediction of the fault of the high-voltage switch operating mechanism is realized through multi-feature extraction and fusion in combination with a lightweight Transformer model, and compared with a traditional single current signal analysis method, the method fully considers the coupling effect of electromagnetic and mechanical states, captures electromagnetic abnormality through current-electromagnetic coordination node features, and improves the accuracy of the fault diagnosis of the high-voltage switch operating mechanism. According to the method, mechanical vibration faults are recognized through vibration energy features, motion discordance is detected through electromagnetic driving force-motion acceleration cooperative features, so that possible fault modes of the mechanism are comprehensively covered, the accuracy and the real-time performance of evaluation are further improved through efficient fusion of the lightweight Transform model, the early fault missing report rate is effectively reduced, and the reliability of the mechanism is improved. And reliable guarantee is provided for safe operation of a power system.

The invention relates to the technical field of pipeline magnetic flux leakage detection, and provides a double-wheel magnetic flux leakage detection defect alignment method and device based on multi-feature fusion, and the method comprises the steps: obtaining a magnetic flux leakage detection data set, and carrying out the multi-feature vector extraction of the data set, and obtaining a multi-feature vector set; then, generating a first Gaussian pseudo-color image corresponding to the first multi-feature vector and a second Gaussian pseudo-color image corresponding to the second multi-feature vector; defect mileage positioning is carried out based on the two images, and a common defect interval is determined; in the common defect interval, a matching degree function is constructed based on the first multi-feature vector and the second multi-feature vector, and the optimal offset of the second round of magnetic flux leakage detection data relative to the first round of data is calculated; and according to the optimal offset, performing alignment fusion on the first round of magnetic flux leakage detection data and the second round of magnetic flux leakage detection data to obtain target fusion defect data. According to the embodiment, the accuracy and stability of defect alignment can be effectively improved, so that the reliability of pipeline safety management is effectively improved.