761 results about "Multi feature fusion" patented technology

The invention discloses a point cloud differential defect detection system and detection method based on multi-feature fusion, which are mainly used for high-precision detection of surface defects of a side wallboard of a railway passenger car, and the method comprises the following steps: obtaining datum point cloud data through a designed standard side wallboard model of the railway passenger car, and collecting real-time point cloud data through a laser scanner; preprocessing the obtained point cloud data; calculating the difference between the real-time point cloud and the reference point cloud by adopting a difference method for fusing geometric features such as Euclidean distance, curvature and normal vector, namely calculating the Euclidean distance difference, curvature difference and normal vector difference between the reference point cloud and the real-time point cloud; and a comprehensive abnormal score is generated by further combining a dynamic weight fusion mechanism, and different region characteristics (such as a plane, a curved surface and an edge region) are adapted through dynamic threshold adjustment. According to the method, the defect region is segmented through the clustering algorithm, and the defect area and depth are quantified. The method has the advantages of high detection precision, strong adaptability and good real-time performance, and is suitable for complex surface defect detection in industrial production.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention discloses a millimeter wave radar human body tumble detection method based on multi-feature fusion, and the method comprises the steps: obtaining an original point cloud frame from each radar, and carrying out the timestamp calibration and space coordinate system conversion; performing noise filtering, ground segmentation and human body point cloud extraction on each frame of point cloud; dividing the preprocessed point cloud into a static cluster and a dynamic cluster; filtering false dynamic clusters; extracting a residual dynamic cluster set, and identifying and tracking the human body dynamic clusters in continuous frames by adopting a tracking algorithm; extracting a corrected time sequence feature from the tracked human body cluster; inputting the time sequence characteristics into a pre-trained deep time sequence network, learning a falling time sequence dependency relationship, and outputting an abnormal index; and performing multi-source fusion with the abnormal index to obtain a comprehensive index to judge whether to trigger an alarm. The method can adapt to a complex home environment, improves the detection accuracy and real-time performance, and reduces the false alarm and missing alarm.

The invention provides a multi-feature fusion-based cover film defect intelligent detection method and system, and the method comprises the steps: firstly obtaining a plurality of groups of image units of a to-be-detected cover film under different shooting parameters to form an image data set, carrying out the feature screening of the image data set, and obtaining a candidate feature set of a potential defect region; the method comprises the following steps: selecting a candidate feature set comprising regional gray features and morphological structure features, then performing association mapping on the candidate feature set, establishing an association relationship between the features to obtain an association feature spectrum, then calling a pre-constructed defect identification model to analyze the association feature spectrum, and generating an identification result of a defect prediction category identifier and a regional range parameter; and finally, generating a detection report containing defect position coordinates based on an identification result, and sending the detection report to a detection management system. Therefore, the accuracy and efficiency of cover film defect detection are improved.

The invention relates to the technical field of road surface detection, in particular to a road surface skid resistance detection system based on multi-feature fusion, which comprises the following steps: applying broadband sweep frequency excitation by using a frequency modulation vibration sensor, matching with the inherent frequency of road surface texture to generate local resonance, and collecting the temperature, humidity and rainfall of a road surface in real time; performing fast Fourier transform on the collected vibration signals to obtain a resonance response spectrum, extracting key parameters through Gaussian fitting, and fusing frequency domain, material and environment data to form a comprehensive feature set; the frequency domain features are converted into three-dimensional energy distribution of pavement microtextures, the actual contact area ratio is calculated according to the three-dimensional energy distribution, an environment temperature and humidity compensation factor is introduced, and a dynamic friction attenuation coefficient is calculated; and comparing the calculated dynamic friction attenuation coefficient with a third-level safety threshold, and outputting a corresponding anti-skid performance level. Multi-source data fusion enables a detection result to be more fit with an actual driving scene, and misjudgment caused by single data is avoided.

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention discloses a black pig image segmentation method based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a black pig image, and carrying out the contrast enhancement processing; inputting the enhanced image into a residual convolutional network to generate a depth feature map, extracting a local shape feature map through curvature threshold segmentation and a double fitting strategy, and fusing the two feature maps to generate a black pig feature map; establishing a spatial position prior probability graph based on the black pig sample library, calculating regional correlation and performing adaptive weighting to obtain a fusion feature graph; boundary segmentation and iterative optimization are carried out on the fused feature map based on the dynamic behavior pattern map and the attitude constraint rule, and an initial segmentation map is generated; and adopting a group behavior model as an optimization criterion, correcting the boundary of the initial segmentation image, and outputting a final segmentation result. According to the method, the segmented enhancement function based on the double-peak characteristic and the local texture feature self-adaptive adjustment strategy are constructed, so that differential enhancement of image preprocessing is realized.

The invention discloses a power battery health scoring method and system based on multi-feature fusion. The method comprises the following steps: preprocessing and classifying obtained power battery operation data; constructing a consistency evaluation index system; determining a consistency evaluation index weight based on a combination weighting method of the game theory; calculating a total evaluation score of the power battery by combining each consistency evaluation index and the weight and the threshold value thereof; obtaining each data set and carrying out feature extraction, screening and construction to obtain an optimal feature set of each power battery; the optimal feature set is divided into a training set, a verification set and an evaluation set to be used for training a BP neural network model and a LightGRM model respectively, then evaluation weights of the two models are calculated, and finally a battery health evaluation score is calculated. According to the hybrid modeling method based on the lightweight space-time diagram convolutional network, the precision and robustness of battery health state estimation are remarkably improved by fusing physical mechanism constraints and data driving advantages.

The invention relates to the technical field of emotion recognition, in particular to an electroencephalogram emotion recognition method based on an attention mechanism and multi-feature fusion, which comprises the following steps: acquiring electroencephalogram signal data, preprocessing the electroencephalogram signal data, and obtaining an electroencephalogram emotion recognition result through a constructed TSS-ENet network model; and carrying out time domain, frequency domain and space domain feature extraction and fusion on the preprocessed electroencephalogram signal data, and finally obtaining an emotion classification result of the electroencephalogram signal data. According to the method, features are directly extracted from electroencephalogram signal data in an end-to-end manner, and electroencephalogram signal emotion classification is adaptively performed in combination with the TSS-ENet network model, so that the accuracy of emotion recognition is effectively improved.

The invention provides a drug target affinity prediction algorithm based on protein three-mode and multi-feature fusion, and the algorithm makes full use of sequence information and two-dimensional and three-dimensional structural features of drugs and proteins, and realizes deep integration of data. The technical innovation point lies in that a multi-modal data fusion mechanism and an advanced graph neural network technology are introduced, and the sensitivity and recognition capability of the model to key biomarkers are enhanced through a dynamic attention mechanism. According to the technical advantage, the method has higher accuracy and stability when processing complex biological data, and compared with the prior art, the generalization ability and the interpretability are remarkably improved. In the aspect of technical maturity, the algorithm has passed multiple rounds of verification and shows good prediction performance and adaptability. The method is simple in operation and wide in market application prospect, can assist in new drug discovery and accelerate the drug design process, can also promote the development of the precision medical field, and particularly has important value in targeted therapy and disease prevention strategy formulation.

The invention relates to the technical field of computer image processing, and particularly discloses a double-branch defogging method based on a Laplacian pyramid, and the method comprises the following steps: S1, building a training data set A containing an original clear image and a corresponding foggy image; s2, performing cutting and data enhancement processing on the input image; s3, a defogging network LPSDNet based on the Laplacian pyramid is constructed, the defogging network LPSDNet comprises a multi-feature fusion focusing branch and a Laplacian pyramid supplementary branch, and after feature extraction of the two branches is completed, features learned by the two branches are fused through a learnable fusion module; s4, training the defogging network LPSDNet by adopting the training data set A until a preset loss function is converged; obtaining a trained defogging network LPSDNet in combination with an objective function; and S5, inputting an image to be defogged into the trained defogging network LPSDNet for testing and verification to obtain a defogging result. The global structure information of the image can be fully captured, and the distortion phenomenon is prevented.

The invention provides a privacy protection federated distillation and backdoor defense method for large model fine tuning, and belongs to the technical field of artificial intelligence security and federated learning, and the method comprises the steps: 1, carrying out the distillation and core representation extraction of a data set based on contribution degree weighted federated pre-training and local neural feature function matching; step 2, self-adaptive noise back door defense processing based on multi-feature fusion; according to the method, a dataset distillation mechanism based on neural feature function matching and a self-adaptive noise defense strategy are adopted, so that effective balance of the large model among data simplification, privacy protection and backdoor defense robustness is realized; the method is of great significance in improving the safety and reliability of an artificial intelligence system in a distributed environment.

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 invention provides a brain aneurysm intelligent detection and positioning method and system based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: receiving and preprocessing a head angiography image; utilizing a multi-scale feature extraction network to obtain feature maps and fusing the feature maps; constructing anatomical candidate regions by using density clustering; extracting a tumor contour through a graph cut energy function; constructing a vascular network topological graph based on probability feature mapping, and calculating a position feature descriptor; and finally carrying out classification discrimination and marking and displaying a detection result. According to the method, various characteristics are fused, the accuracy and sensitivity of cerebral aneurysm detection are improved, and the misdiagnosis rate is reduced.

The invention relates to the technical field of gas extraction drilling machine vibration signal processing, in particular to a gas extraction drilling machine vibration signal self-adaptive noise reduction method based on multi-scale feature fusion. The method comprises the steps of collecting a vibration signal, converting the vibration signal into a two-dimensional waveform image, recognizing a drilling working condition area, constructing a multi-scale objective function to optimize VMD parameters, decomposing the signal and screening a dominant IMF component for reconstruction. Through multi-objective optimization and multi-feature fusion strategies of energy distribution and transient impact characteristics, the problems that in a traditional method, the noise reduction effect is poor, and the coal rock character recognition precision is insufficient are solved, the waveform similarity, the power spectrum density coincidence degree and the correlation coefficient are remarkably improved, and reliable guarantee is provided for coal mine safety production.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention provides a lithium battery residual life prediction method based on a multi-feature fusion large model, and relates to the field of lithium battery health management and life prediction, and the method comprises the steps: carrying out the segmentation processing of original data through a sliding window technology, constructing a key health index soft measurement module through a KAN, and carrying out the prediction of the residual life of a lithium battery; converting the original data into key indexes representing the health state of the battery; the method comprises the following steps: splicing data and key indexes of a lithium battery to form fusion features, inputting the fusion features into a large language model LLM to construct a fusion feature prediction module, obtaining future fusion features through pre-training word embedding, a multi-head attention mechanism and natural language prefix prompt, inputting the future fused features into a sparse KAN, and obtaining a fusion feature prediction model; and constructing a residual life prediction model. And a regression relation with the residual life of the battery is established, real-time prediction of the residual life of the lithium battery is realized, and the method is suitable for state monitoring and maintenance decision of the lithium battery in scenes of electric vehicles, energy storage systems and the like.

The invention provides an L1-L4 lumbar vertebra segment-oriented multi-feature fusion diagnosis system and method, and the system comprises an image preprocessing module which is used for receiving a lumbar vertebra CT image sequence of a patient; a centrum anatomy partition module; the multi-dimensional image feature extraction module is used for extracting four types of quantitative features from each sub-region; the clinical multi-modal data coding module is used for independently acquiring and processing three types of clinical data: a multi-modal graph attention fusion network; and the segment-level diagnosis output module outputs diagnosis results of three levels. Through a parallel processing architecture and an optimized feature extraction algorithm, the whole diagnosis process only needs 45 seconds from data input to report generation, time is saved compared with manual film reading, and the consistency of diagnosis results is remarkably improved.

The invention relates to the technical field of image target tracking, and discloses a video image target tracking method and processing device based on multi-feature fusion, and the method comprises the steps: obtaining an initial position parameter and a current frame detection parameter of a target; calculating according to the initial position parameter to obtain a pixel-level motion vector field; performing prediction according to the pixel-level motion vector field to obtain a prediction position parameter of the target; comparing the predicted position parameter with the current frame detection parameter, and when the difference between the predicted position parameter and the current frame detection parameter is smaller than a preset threshold value, judging that the target is not shielded; and when the difference between the predicted position parameter and the current frame detection parameter is greater than a preset threshold value, tracking error accumulation may be caused by too early view angle switching in the prior art, and in a resource-limited scene, the calculation overhead of a deep learning model is generally large, resulting in poor tracking accuracy and real-time performance.

The invention discloses an ice condition prediction method based on multi-feature fusion and physical constraint, and belongs to the technical field of hydrological forecasting. Firstly, various types of data of a target area are collected and preprocessed to serve as a data set, features of the various types of data are extracted, feature fusion is conducted on obtained image features, time sequence features and environment features through a multi-head attention mechanism, and a fusion feature vector is generated. Secondly, adopting a physical information neural network model, taking the fusion feature vector as input, taking ice thickness and ice stress as output layers, carrying out constraint by using a composite loss function, carrying out model optimization by using a verification set, carrying out processing through a full connection layer in the network, and carrying out end-to-end training and regularization of a prediction model; and finally, evaluating the final prediction model obtained by training through the test set, and verifying the effectiveness and generalization ability of the final prediction model. The method combines multi-source and multi-mode observation data, can effectively capture complex relations between ice surface changes and various factors, can improve prediction precision, stability and reliability, and can improve model training efficiency and generalization ability.

The invention provides a multi-modal information fused steel pipe inner surface defect area segmentation method and system, and the method comprises the steps: obtaining a steel pipe inner surface defect RGB image and a depth map at the same time, and carrying out the preprocessing and marking; constructing a support data set and a query data set; the support and query image feature extractor is used for respectively extracting support image multi-scale aggregation features FS, support image multi-mode semantic features IS, query image multi-scale aggregation features FQ and query image multi-mode semantic features IQ by sharing the multi-mode feature extraction backbone network; the FS, the IS, the FQ and the IQ are input into a multi-feature fusion device, a graph semantic guide module is supported to generate class guide features FA by using the FS and the MS, a similar prior feature generation module is supported to generate similar prior features FM by using the IS, the IQ and the MS, the FA, the FM and the FQ are spliced on a channel dimension, and a fusion feature graph is output and decoded by a multi-mode decoder to obtain defect area segmentation output. The method can be used for segmenting the defect area on the inner surface of the steel pipe.

The invention discloses an antibacterial peptide recognition method and system based on a sequence-structure dual-channel neural network, and the method comprises the steps: splicing amino acid features and amino acid-level manual features extracted by ProtT5 to obtain peptide embedding, and transmitting the peptide embedding to a sequence channel composed of a plurality of Transform blocks to extract the sequence features of the peptide; predicting a three-dimensional structure of the peptide by using ESM-Fold to construct an adjacency graph, taking amino acid features obtained by ESM-2 as node features of the adjacency graph, and performing layer-by-layer extraction and enhancement by fusing structural channels of multi-head graph attention, a residual network, layer normalization and a feedforward neural network; and carrying out maximum pooling and splicing on the sequence features and the structural features, and then, carrying out antibacterial peptide prediction. According to the method, a multi-feature fusion strategy is adopted, meanwhile, the three-dimensional structure information of the antibacterial peptide is introduced, and the sequence and the structural features are fused through a two-channel architecture, so that the recognition accuracy of the antibacterial peptide is effectively improved.

The invention relates to the technical field of engineering process control, and discloses a ball mill granularity soft measurement method based on a time sequence large model. Constructing a multi-feature fusion module for extracting multi-scale features based on Convld K3, Convld K5 and Convld K1, and constructing a soft measurement model in combination with multi-head attention and a large language module with a fixed weight; the soft measurement model generates a first feature and a query matrix, generates a key matrix and a value matrix according to a fixed weight of the large language module, generates a second feature based on the query matrix, the key matrix and the value matrix, and fuses the second feature with the first feature to obtain a fused feature; and then a granularity prediction result corresponding to the field data is obtained through a large language module, so that the problems that an existing soft measurement model is too high in dependence on large-scale sample data, insufficient in modeling capability for complex nonlinear process parameters and low in prediction precision are solved.

The invention discloses a digital-analog linkage life prediction method based on nonlinear Wi engine process degradation modeling. The digital-analog linkage life prediction method comprises the following steps: selecting features with high association degree with a health state based on a Pearson's correlation coefficient; carrying out normalization and de-noising on the selected features; constructing a comprehensive health index based on multi-feature fusion; carrying out degradation modeling and life prediction on the evolution process of the constructed comprehensive health indexes by adopting a non-linear Wi-ener process; estimating values of degradation model parameters based on maximum likelihood estimation and a Bayesian parameter updating method; and optimizing model parameters, updating a weight coefficient of multi-feature fusion and a failure threshold value of degradation modeling, and obtaining a more accurate life prediction result. The invention provides a digital-analog linkage life prediction method based on nonlinear Wi-er process degradation modeling in order to solve the problems that an existing RUL prediction method based on statistical modeling does not consider the nonlinear degradation condition and feature extraction and degradation modeling links cannot form interactive linkage, so that residual life prediction is not accurate, and provides a digital-analog linkage life prediction method based on nonlinear Wi-er process degradation modeling. And high-precision life prediction and uncertainty quantification of a prediction result are realized.

The invention discloses a short-time traffic flow prediction method and system based on a multi-feature map, and relates to the technical field of traffic flow prediction, and the method comprises the steps: respectively constructing an occupancy rate feature map, an average speed feature map and a traffic flow feature map through three kinds of data, including traffic flow, occupancy rate and average speed data, collected by a road sensor at the same time; node feature information of three feature maps is aggregated by using a GCN, supervised learning training of occupancy features and average speed features is realized by using an improved Transform encoder, and feature interaction fusion of obtained feature vectors of occupancy and average speed is realized in a multi-feature fusion traffic flow prediction model. The occupancy and average speed features are utilized to assist a multi-feature fusion traffic flow prediction model to better capture traffic flow features, and more accurate traffic flow prediction is provided.

The invention relates to the technical field of cow breeding early warning, and discloses a cow breeding early warning system and method based on multi-feature fusion analysis, and the system comprises a data collection module, a feature fusion analysis module, an abnormity early warning module, a decision and suggestion module, and a breeding data display module. The feature fusion analysis module is used for analyzing the health condition, the production performance and the environmental comfort of the dairy cow based on the physiological data, the feeding data and the colony house environment data of the dairy cow; and the decision suggestion module is used for making a dairy cow breeding optimization suggestion according to the feature fusion analysis result, and the dairy cow breeding optimization suggestion comprises optimization of feed distribution and dynamic division of activity areas. The system effectively solves the problems of health management lagging, resource waste, difficulty in recognition of environmental hidden dangers, unreasonable region division and the like in traditional cow breeding, remarkably improves the intelligent level of pasture management and breeding efficiency, and has a wide application prospect.

The invention belongs to but is not limited to the technical field of state monitoring, and particularly relates to a multi-feature fusion power field effect transistor online state monitoring and evaluation method and system, which are used for understanding the aging mechanism of a device by selecting a proper aging signal and calculating the features of the aging signal. Then, effective feature information strongly correlated with the life sequence is screened out by using a mutual information correlation coefficient algorithm; next, dimension reduction fusion is performed on the screened features by using a KPCA polynomial kernel function algorithm, so that a health index is constructed, and the degradation state of the device is reflected; and monitoring and evaluating the health state of the device by using CNN, XGBoost and RF classifiers according to the constructed health indexes so as to determine the health state of the device. In addition, the RF classification model with the best classification effect is deployed on the DSP development board, the performance of the prediction model is further optimized, and the prediction time of the model is shortened. According to the method, the health state of the power device is described by integrating the multi-feature information.

The invention discloses a TCN network and multi-feature fusion-based CMC residual life prediction method, belongs to the technical field of material nondestructive testing and life prediction, and constructs a TCN network-based CMC residual life prediction model in combination with acoustic emission signal clustering analysis and fatigue hysteresis behavior features. By introducing features such as a clustering analysis result and a hysteresis loop area, the model can more comprehensively capture multi-scale features of CMC fatigue damage, and the capability of distinguishing different damage modes is enhanced. According to the method, contributions of different damage modes to the residual life can be quantified more accurately, the model can automatically learn the influence weight of each damage mode, so that the prediction precision of the CMC residual life is remarkably improved, in addition, the universality and reliability of the model are further improved due to the advantages of the TCN network in processing long sequence data, and the prediction precision of the CMC residual life is improved. And effective prediction of the residual life of the CMC is realized.

The invention discloses a PET waste plastic intelligent sorting method and system based on multi-feature fusion, and the method comprises the following steps: collecting an RGB-HSI original data set of PET waste plastic, carrying out the dimension reduction processing of the HSI data set based on a genetic algorithm to generate MSI data, and forming a feature fusion data set with RGB data; constructing a feature fusion network based on a YOLACT model, and training by adopting a feature fusion data set; the feature fusion network comprises an RGB branch, an MSI branch and a fusion prediction module, RGB is used for extracting features of RGB data, the MSI branch is used for extracting features of MSI data, and the fusion prediction module is used for combining output of the RGB branch and output of the MSI branch to make comprehensive prediction; and installing the trained feature fusion network to an industrial personal computer to realize intelligent sorting of the PET waste plastics. Compared with a traditional recognition algorithm, the method can improve the recognition precision.