1444 results about "Feature screening" patented technology

The invention relates to the technical field of flood early warning, and discloses an urban flood disaster early warning method and system based on artificial intelligence, and the method comprises the steps: collecting five types of information, i.e., meteorological perception, hydrological monitoring, geographic space, urban operation and social perception in real time, and obtaining multi-source data with precise space-time coordinates; through preprocessing, gridding space-time alignment and key feature screening, rainfall accumulation and confluence evolution related features are extracted; constructing a physically constrained space-time fusion deep learning model, and outputting a future ponding depth prediction result in combination with a multi-head attention mechanism; environmental changes such as urban terrains and drainage facilities are adapted through incremental updating and transfer learning; and fusing the ponding depth, the influence range and the regional vulnerability characteristics to generate multi-level early warning, and synchronously outputting a spatial distribution map, a time evolution trend and affected object evaluation information. According to the invention, urban flood control and disaster reduction decision making and public accurate risk avoiding can be effectively supported.

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

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 smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention belongs to the technical field of landslide susceptibility analysis, and relates to a landslide susceptibility ensemble learning evaluation method considering spatial heterogeneity partitioning and factor feature screening, which comprises the following steps: generating a landslide sample based on historical landslide catalog data, and selecting a non-landslide sample through environmental factor frequency ratio analysis; the method comprises the following steps of: extracting static and dynamic environment factor data sets, realizing factor space interpretation force transformation by utilizing a t-SNE-ISO clustering algorithm and a feature screening strategy, eliminating high-correlation factors through a Pearson correlation coefficient method, quantifying interpretation force of each factor on landslide space differentiation by combining a geographic detector, screening optimal feature combinations under global and partition frameworks respectively, and performing landslide space differentiation on the landslide space. According to the method, a Stacking integrated learning framework is combined with CNN, DNN, MLP-based learners and LR element learners, a landslide susceptibility probability prediction model is formed, the generalization ability and prediction accuracy of the model are improved, and the method is especially suitable for landslide high-incidence areas with severe topographic relief and complex geological conditions.

The invention belongs to the technical field of high polymer material design and intelligent manufacturing, and discloses a machine learning driven epoxy resin based composite material thermal-mechanical property aided design method, which comprises the following steps: S1, data acquisition and feature construction; s2, performing feature screening; s3, constructing and training an interpretable prediction model; s4, carrying out reverse design and optimization; and S5, performing closed-loop verification and updating. According to the method, the quantitative relation of structure-process-performance is constructed through an interpretable machine learning model, and the contribution mechanism of each factor is revealed by means of SHAP analysis. And finally, reversely designing an optimal epoxy resin monomer structure and a matched curing process according to the performance target. The limitation of a traditional trial and error method is broken through, collaborative optimization of the material structure and the forming process can be achieved, and the development efficiency of the epoxy resin-based carbon fiber composite material is remarkably improved.

The invention relates to an AI Agent-driven credit risk early warning strategy automatic evaluation method and device, control equipment and a computer readable storage medium, and the method comprises the steps: obtaining structured financial data, unstructured text data and dynamic time sequence data of an enterprise through multi-source data, carrying out the standardization processing, and outputting a standardized risk data set; based on the standardized risk data set, static financial features, dynamic derivative features and associated network features are extracted, and a high-discrimination feature set is generated through feature screening and dimensionality reduction optimization; a multi-model integration strategy is adopted, the high-discrimination feature set is input into a neural network, a Stacking integration model and a graph neural network, and enterprise risk scores and grades are output; and automatically matching an early warning rule based on the enterprise risk score and the grade, and carrying out real-time monitoring and early warning on enterprise credit according to the early warning rule.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The invention discloses a fault diagnosis method and system based on a multi-source data association rule and a graph neural network. The method comprises the steps of extracting high-frequency operation data and low-frequency time sequence state data based on historical data, and establishing an equipment operation feature set; an Apriori algorithm is utilized to screen correlation characteristics to calculate a correlation relation, and a fault symptom set is constructed; and taking the association relationship of the features as an adjacent matrix embedded graph neural network, and training the constructed fuzzy graph neural network based on historical data to obtain a fault diagnosis model. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a feature screening module, a feature association relationship analysis module, a fault diagnosis model training module, a fault diagnosis module and a database storage module, and can perform multi-source data fusion analysis and training and updating of a fault diagnosis model. According to the method, the fault diagnosis model is constructed by combining multi-source data fusion, feature extraction, association relationship mining and the fuzzy graph neural network, so that more accurate and efficient fault diagnosis is realized.

The invention discloses a multi-mode equipment maintenance and inspection knowledge intelligent recommendation system combined with digital twinning, and belongs to the technical field of equipment maintenance and inspection. The method is used for solving the technical problem that in an existing scheme, recommendation strategy staticizing and edge cloud collaborative global optimization are difficult to consider at the same time. According to the method, multi-modal data synchronous acquisition driven by digital twinning is carried out, a long-short-term memory network model of an attention mechanism is fused, a loss function is weighted through an attenuation rate deviation, strong correlation feature screening and dynamic weighting based on mutual information entropy are carried out, and a time-varying feature matrix is utilized to capture an evolution rule of an equipment state along with time; a knowledge graph with physical entity association precision and causal reasoning ability is constructed, and a three-layer architecture including full-link interpretability of data, features, entities, causals and decisions, edge-end high-frequency response-cloud global optimization-federated learning parameter synchronization is realized. The contradiction between high-frequency data real-time processing requirements and global knowledge graph dependence in industrial equipment maintenance can be solved.

The invention discloses an industrial bolt intelligent identification and accurate grabbing control method and system based on depth vision, and the method comprises the steps: obtaining multispectral image data, carrying out the fusion detection of the nut edge, thread texture and other features of a bolt through an enhanced YOLOv8 network, screening an effective bolt, and eliminating the interference of a gasket, chippings and the like; extracting feature points to solve a 6DoF attitude, fusing vibration data of the mechanical arm to realize motion compensation, and outputting a three-dimensional coordinate and an attitude angle under a coordinate system of the mechanical arm; the three-dimensional coordinates are converted into motion coordinates, and the grabbing position is corrected in combination with the normal vector of the assembly surface; planning a path and generating grabbing strategies such as clamping jaw opening amplitude and clamping force matched with the bolt specification; the clamping force is dynamically adjusted based on feedback of the six-dimensional force sensor, and accurate grabbing is achieved. The system comprises a visual perception subsystem, an intelligent decision-making subsystem and a precise execution subsystem. The bolt recognition precision and grabbing stability in the complex environment are improved, and the assembly requirements of bolts of multiple specifications can be met.

The invention discloses a ginseng seed lossless classification method and a ginseng seed lossless classification system, relates to the technical field of computer image detection, and solves the problem that in the prior art, a ginseng seed classification method based on image and spectral characteristics of ginseng seeds is lacked. Respectively collecting image data of the ginseng seeds and hyperspectral data of the ginseng seeds; respectively preprocessing the image data of the ginseng seeds and the hyperspectral data of the ginseng seeds; respectively carrying out feature screening on the preprocessed image data of the ginseng seeds and the preprocessed hyperspectral data of the ginseng seeds; fusing the image data of the ginseng seeds after feature screening and the hyperspectral data of the ginseng seeds; after the RBMO algorithm is improved, the RBMO algorithm is combined with the RF model to construct an ORBMO-RF model; and respectively inputting the fused image data of the ginseng seeds and the fused hyperspectral data of the ginseng seeds into an ORBMO-RF model for processing, thereby completing classification of the ginseng seeds.

The invention provides an enterprise production safety monitoring and checking system based on multi-source data fusion, and the system is characterized in that the system comprises a data collection module which collects production environment data, processes the data, and generates a time-space alignment data set; the feature screening module is used for screening features highly related to security from the space-time alignment data set to generate a feature matrix; the model construction module is used for constructing a risk assessment model, calculating a dynamic risk value according to the multi-dimensional features, and performing risk grading on the dynamic risk value; the response control module is used for starting a corresponding response strategy according to the risk classification and outputting an early warning instruction set and an equipment control signal; the monitoring calculation module is used for monitoring control, comparing risk value changes before and after treatment and calculating response efficiency; and the adjusting and optimizing module is used for carrying out dynamic adjustment according to the response efficiency and optimizing the response strategy. And the limitation of traditional single-dimensional monitoring is broken through, accurate correlation analysis of equipment, environment and personnel risks is realized, and the composite hidden danger recognition capability is remarkably improved.

The invention belongs to the technical field of flight simulator maintenance, particularly relates to a flight simulator predictive maintenance method based on machine learning, and solves the problems that existing maintenance depends on regular inspection and passive maintenance, fault early warning lags behind, and the false and missing report rate is high. The method comprises the following steps: acquiring historical operation data, sensor time sequence data, fault records and environmental parameters of a flight simulator, and carrying out cleaning, labeling and feature fusion preprocessing on the historical operation data, the sensor time sequence data, the fault records and the environmental parameters; constructing a composite health feature set containing statistical features, dynamic health state values and aerial material reliability parameters; a mixed prediction model (random forest feature screening + LSTM time sequence prediction + adaptive correction reliability evaluation) is adopted to train a model, prediction result fusion analysis and multistage decision rule post-processing are combined, and a maintenance work order and a spare part demand plan are output. According to the method, the accuracy and timeliness of fault prediction are improved, the maintenance conversion from passive response to active pre-judgment is realized, and the maintenance cost and the non-planned shutdown risk are greatly reduced.

The invention discloses a drainage pipeline defect detection system and method based on multi-scale feature fusion and occlusion perception, and the system comprises a data set construction module which is used for constructing a drainage pipeline data set covering multi-defect, multi-scale and multi-occlusion scenes; the model training module is based on establishment of a defect collaborative detection model, and a backbone network of the model training module adopts a feature pyramid sharing convolution module to reinforce the multi-scale detail extraction capability; the neck network introduces an advanced screening path aggregation network and a selective feature fusion module to realize dynamic feature screening and fusion; the detection head is integrated with an MCFEM module, and shielding perception and scale adaptability are enhanced. According to the method, the problems of high omission ratio and poor robustness caused by large defect scale change, serious shielding and complex background in drainage pipeline detection are effectively solved, the small target defect identification precision and the shielding scene detection accuracy are remarkably improved, and the method is suitable for high-precision and light-weight detection of multi-scale defects in a complex shielding environment.

The invention relates to an infectious bacterium detection method and system based on double effects of a reflection spectrum and autofluorescence, and belongs to the field of biomedical detection. The method comprises the following steps: acquiring hyperspectral images of different samples under different light sources; performing data preprocessing on the collected sample hyperspectral image, realizing pixel-level alignment of the dual-effect image based on feature matching and image registration, and constructing a data set; the method comprises the following steps: constructing a wound infection bacterium detection model based on double effects of a reflection spectrum and autofluorescence, and comprising a double-branch feature extraction module which comprises a reflection branch and a fluorescence branch which are respectively used for extracting reflection hyperspectral image features and fluorescence hyperspectral image features; the redundant feature screening module screens the features extracted by the reflection branch and the fluorescence branch through a channel attention mechanism; and the feature fusion module adopts a cross-branch cross attention mechanism to fuse the features of the double branches. According to the method, learning is carried out from two physicochemical characteristics, and the effectiveness and adaptability of the technology are improved.

The invention relates to the technical field of data processing, in particular to a business process dynamic optimization decision-making system based on AI industrial big data processing, and the system comprises the steps: obtaining stress-strain transmission data of a petrochemical pipeline network in different operation states and corresponding pipe network operation parameters, and constructing a historical decision-making case library; adaptively determining a core transfer feature set from stress-strain transfer data, and iteratively optimizing a feature screening weight through historical data; inputting the transfer feature set and the pipe network operation parameters into a dynamic correlation modeling engine, generating a self-updating transfer-operation correlation model, and correcting correlation coefficients according to real-time data deviation; and based on the transfer-operation association model and the historical decision case library, executing a dynamic adjustment decision of the pipe network operation parameters. Stress-strain and operation parameters are collected through distributed sensing, space-time deviation is eliminated, a space-time association data set is formed, screening weight is optimized through historical feature importance iteration, and a key transmission path is automatically identified.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to the technical field of multi-sensor identification, in particular to a coal gangue detection and classification method and system based on multi-sensor fusion discrimination, and the method comprises the following steps: obtaining visible light and infrared images, calculating brightness and intensity judgment feature conditions, executing edge detection to extract gray segments, and fusing textures and a thermal field to generate a vector set. And performing clustering analysis to finish classification judgment, and outputting a coal gangue detection classification result. According to the method, a precise trigger mechanism is established through brightness and thermal radiation double-feature screening, boundary recognition sensitivity is enhanced through gray abrupt change point division, salient region extraction capacity is enhanced through weighted fusion of texture energy and gray gradient, and a cross-modal consistency feature group is constructed through combination of two-dimensional vector construction and similarity screening. The recognition expression integrity is improved, static threshold classification is replaced by vector distribution clustering, accurate mapping and classification decision making of material attributes in a complex scene are achieved, and the stability and the recognition rate of a coal gangue detection result are guaranteed.

The invention provides a mining area surrounding soil heavy metal spatial distribution inversion method based on hyperspectral data, and the method comprises the steps: processing multi-source monitoring data of mining area surrounding soil, and obtaining a consistent reflectivity data set; performing soil spectrum purification based on the consistent reflectivity data set to obtain a pure soil signal; spectrum key features are screened out from the pure soil signals; obtaining a modeling data set in combination with the spectrum key features and the heavy metal concentration labels so as to construct a multi-task inversion model, and outputting each metal prediction interval and an over-standard risk probability graph; and outputting a multi-layer package based on the model, performing global and local interpretation and mechanism verification, and generating an interpretation report and traceable evidence. According to the method, full-link unification and purification can be achieved, domain deviation and mixed pollution are remarkably reduced, the accuracy and interpretability of feature screening can be considered, and the model generalization ability, prediction accuracy and space credibility are improved.

The invention discloses a data center refrigeration energy consumption short-term prediction method based on feature screening-fusion and deep learning. The method comprises the following steps: 1) acquiring historical refrigeration energy consumption of a data center and historical refrigeration energy consumption influence factor data; calculating the correlation between each refrigeration energy consumption influence factor data and the refrigeration energy consumption, extracting the refrigeration energy consumption influence factor data with the correlation greater than a preset threshold as refrigeration energy consumption characteristic data, and constructing an input characteristic matrix X; (2) a refrigeration energy consumption short-term prediction model based on SEN-MH-BiLSTM is constructed; 3) training a refrigeration energy consumption short-term prediction model based on SEN-MH-BiLSTM by using the sample data set to obtain a refrigeration energy consumption short-term prediction optimal model; and 4) predicting the refrigeration energy consumption of the data center at the future t time by using the refrigeration energy consumption short-term prediction optimal model. According to the method, the dependency relationship between the refrigeration energy consumption of the data center and the characteristic sequence can be better mined, and the prediction precision of the refrigeration energy consumption of the data center is effectively improved.

The invention relates to a protective clothing loss prediction method based on big data analysis, and the method comprises the steps: collecting multi-dimensional data, such as temperature and humidity, pollutants, wearing duration, motion amplitude and cleaning, through a high-precision sensor, and outputting a high-consistency loss feature set in combination with normalization, denoising and feature screening algorithms; multi-stage causal chain dynamic construction and node adaptive capacity expansion are realized through working condition clustering and causal inference, a deep neural network model is accessed, and the deep neural network model is used to accurately predict the loss of the protective clothing under complex and abnormal working conditions. According to the scheme, the real-time performance, the accuracy and the environmental adaptability of loss prediction of the protective clothing are remarkably improved, and a scientific basis is provided for management optimization and risk prevention and control.

The invention relates to the technical field of spacecraft orbit control and space safety, and discloses a space debris orbit prediction and avoidance method, which comprises the following steps that: a ground computing center screens candidate targets and generates enhanced data packets for uploading; the spaceborne computer combines real-time navigation data to carry out geometric feature screening, and an instant high-risk target list is generated; controlling the star sensor to execute optical observation, and resolving an optical measurement relative orbit state by utilizing displacement superposition and Kalman filtering; selecting a high confidence coefficient or a conservative probability threshold according to the optical measurement state acquisition condition, and generating an avoidance instruction when the collision probability exceeds the limit; and the propulsion system responds to the instruction to execute avoidance, verifies the effect by confirming the semi-major axis variation and executes track recovery by selecting an aircraft. According to the method, the ground-air collaborative screening and shift superposition enhancement technology is adopted, the problems that the low-precision ephemeris false alarm rate is high and the detection capability of a satellite-borne sensor on a dark and weak target is insufficient are solved, and the accuracy of evasion decision making and the execution reliability are improved.

The invention relates to the technical field of semiconductor manufacturing, in particular to a wafer pre-alignment device and method, and the device comprises a mounting platform, an XY motion platform, a rotating platform, a suction cup, and an edge detection assembly. The method comprises the steps of device correction, coarse scanning and circle center positioning, fine scanning and notch accurate positioning and alignment execution. Through software and hardware cooperation, online self-correction is used for eliminating system errors, high-order fitting and feature screening algorithms are used for restraining random errors and model errors, the repeated positioning precision, notch orientation precision and equipment consistency of the wafer pre-alignment device are improved, and the high-precision requirement for a wafer transmission system under the advanced manufacturing process can be met.

The invention relates to the field of nondestructive testing and automatic sorting of agricultural products, and discloses an X-ray-based walnut internal defect feature optimization detection method and device, and the method comprises the steps: collecting an X-ray image of a moving walnut, extracting a region of interest (ROI) through preprocessing, extracting and fusing spatial domain and frequency domain texture features to form a multi-dimensional feature set, and obtaining a feature set; an optimal feature combination is screened out through dimensionality reduction; and finally, the types of the internal defects of the walnuts are identified by a pre-trained classifier, and a result is output. The device comprises a feeding mechanism, a conveying mechanism, an X-ray detection mechanism, a sorting execution mechanism and a processing and control system electrically connected with all the mechanisms. The problems of single identification category and insufficient model stability and precision are solved, and high-throughput and automatic lossless sorting of walnuts is realized.

The invention relates to a few-sample image classification method based on hyperbolic space image-text local feature alignment, and belongs to the technical field of image recognition and artificial intelligence, and the method comprises the steps: generating word-level attribute description for a support set image through employing a multi-mode large language model; encoding the image and the text by adopting a vision-language model; constructing a hyperbolic local feature alignment module in a hyperbolic space, screening most relevant image local features for text local features by calculating hyperbolic cosine similarity, and fusing by using hyperbolic weighted average; designing a hyperbolic cross attention module, and aggregating key information from the multi-modal local features of the support set to construct a category prototype by taking query image aggregation features as guidance; and finally performing classification based on the hyperbolic geodesic distance. According to the method, the hierarchical modeling capability of the hyperbolic space and the semantic priori knowledge of the large language model are fully utilized, fine-grained multi-modal feature alignment is realized, and the small sample image classification performance is improved.

The invention relates to the technical field of fault diagnosis, in particular to a multi-modal bearing fault diagnosis method based on cross-domain transfer learning, which comprises the following steps: constructing a source domain and a target domain; calculating fault frequency characteristics and revolution frequency of the bearing; calculating a first feature vector; performing correlation coefficient and strategy feature standardization pipeline operation, collaborative feature screening strategy and dimension reduction on the first feature vector, and obtaining a second feature vector by using inherent importance and arrangement importance of a random forest classifier; training a plurality of benchmark test models by using the second feature vector of the source domain; evaluating the effectiveness of the second feature vector and determining a performance baseline; and training the target domain by using the 1D-CNN network, carrying out end-to-end cross-domain migration training on the 1D-CNN network by using the comprehensive loss function of the source domain and the target domain, and outputting a predicted fault type. The problem that the accuracy of transfer learning is affected due to lack of multi-modal data screening in an existing method is solved.

The invention relates to the technical field of material nondestructive testing, in particular to a CFRP wing skin damage positioning system and method based on multi-modal signal processing and Bayesian optimization DSCN, and the method comprises the steps: carrying out permutation entropy analysis and Higuchi fractal dimension analysis on a signal data set received by a sensor; performing time-frequency feature extraction on the sensor receiving signal data set deviating from the reference; performing damage feature screening on the signal data in the effective time period; training the DSCN structure to obtain an initial CFRP wing skin damage positioning prediction model; performing hyper-parameter optimization on the initial CFRP wing skin damage positioning prediction model according to a Bayesian algorithm; and performing damage positioning prediction on a to-be-detected sample according to the final CFRP wing skin damage positioning prediction model to obtain a corresponding CFRP wing skin damage positioning result. According to the method, effective damage features can be accurately and efficiently extracted from complex signals, and the accuracy and efficiency of CFRP wing skin damage positioning are remarkably improved.

The invention discloses an abnormal data monitoring method and device based on artificial intelligence, and the method comprises the steps: 1, dividing an original data stream through a sliding window, extracting statistics, time sequence and change rate features, and dynamically screening features adaptive to data distribution based on an SHAP value; 2, constructing a double-flow model, capturing a global isolated mode by adopting an improved isolated forest in a static flow, capturing time sequence dependence on the basis of LSTM-AE in a dynamic flow, and fusing two-flow scores through performance-driven dynamic weight distribution; 3, combining a density peak value algorithm with historical density attenuation weighting, and dynamically adjusting an abnormal threshold value; 4, realizing low-delay incremental learning through a double-trigger mechanism and experience playback; 5, multi-granularity interpretation is generated, manual annotation feedback is supported, feature engineering and model training are integrated, and a'detection-interpretation-feedback-optimization 'closed loop is formed; high-adaptability anomaly monitoring is realized through dynamic feature screening, double-flow fusion detection, threshold value self-adaption and man-machine collaborative optimization.