2160 results about "Feature screening" patented technology

The invention relates to the technical field of deep learning algorithms, and provides an electromechanical system fault diagnosis system based on deep learning, and the system is characterized in that a data collection and preprocessing module collects the operation data of an electromechanical system in real time, and carries out the dynamic window length setting, cleaning, noise reduction and standardization processing on the operation data; the interpretable deep learning diagnosis module carries out feature screening and decoupling learning by means of causal gating and a double-branch network, extracts fault related features and generates a diagnosis result containing a causal path and an abnormal prompt, and the physical constraint fusion module obtains physical principle data of the electromechanical system and parameter data of the electromechanical system in normal work. Carrying out physical constraint on the interpretable deep learning diagnosis module through an electromechanical system physical principle; according to the method, causal feature screening, dynamic causal mask generation and path extraction, multi-physical field law modeling and physical constraint injection are fused, and time sequence instantaneous causal analysis is combined, so that causal dominant expression and physical logic consistency guarantee of fault diagnosis is realized.

The invention discloses an intelligent power distribution network line state real-time monitoring, analysis and evaluation system, and relates to the technical field of power system monitoring and control, the intelligent power distribution network line state real-time monitoring, analysis and evaluation system is constructed, data is collected in multiple dimensions, features are extracted through edge calculation, data is screened, a dynamic threshold model is constructed at a cloud end, and abnormity is identified. The method has the advantages that a three-dimensional digital twin network is constructed based on GIS data and an SCADA system through the power grid topology modeling module, and a unique'topology fingerprint 'containing electrical connection, physical position and logic control relation is generated for each piece of equipment; an upstream and downstream equipment list, a load influence range and a power flow transfer path are marked, and an association state evaluation module adopts a graph convolutional neural network and can accurately capture electrical coupling, physical proximity and historical fault association effects among equipment.

The invention discloses a document information input method and system based on big data processing, and belongs to the technical field of document information processing. According to the method, deep risk feature screening is performed on document data by combining big data mining and a knowledge graph, potential sensitive elements can be accurately identified, risk categories and influence ranges are analyzed based on association rules, automatic early warning and real-time risk notification are realized, a dynamic rule knowledge base is adopted, multi-dimensional compliance verification is supported, and the risk assessment efficiency is improved. Service constraint conditions can be updated in real time, the flexibility and adaptability of rules are ensured, the accuracy and timeliness of compliance check are improved, pattern matching and multi-level check are performed on compliance data objects through an intelligent check process and a recheck rule set trained by big data, the preciseness and reliability of data check are ensured, and the data check efficiency is improved. Through comprehensive quantitative calculation of the business complexity, the risk coefficient and the timeliness priority, the task scheduling weight parameter is generated, the receipt auditing path is optimized, and the auditing efficiency is improved.

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 relates to a data processing method and system for multi-source complex biological information data. According to the method, expression profile data, gene variation data and clinical survival data are collected, and unified standardization processing is carried out on the collected data. Feature alignment is performed on different source data based on sample identifiers, a joint feature expression matrix is constructed, and a context dependency relationship across data types is maintained. On the basis, multi-stage feature screening is carried out through Lasso regression and information gain evaluation in sequence, and an optimal feature subset used for modeling is obtained. And further training a risk scoring model by adopting a Cox proportional risk regression method, and calculating a risk scoring value of the sample by utilizing the constructed continuous scoring function. And finally, dividing the score value into a plurality of risk levels, and generating a survival curve of each level in combination with a Kaplan-Meier estimation method so as to verify the risk layering effect and prediction significance of the model. According to the method, the accuracy of biological information modeling can be improved, and the method has good universality and practical value.

The invention relates to the field of target detection, in particular to a UMFNet-YOLO-based joint detection algorithm under a low-light condition, which adopts a multi-scale decomposition and high-frequency-low-frequency feature collaborative enhancement strategy, separates image details from global features through high / low-pass filtering, realizes multi-resolution feature complementation in combination with dynamic weight distribution, and improves the detection accuracy. Target contour and texture information degraded in severe weather can be effectively recovered; a parallel channel compression and spatial semantic enhancement module is designed, rain and fog noise interference is suppressed through cross-dimensional adaptive feature screening, and significance expression of a key target is enhanced; an image enhancement network UMFNet is cascaded with YOLOv8, and strong correlation between feature expression and a detection task is ensured by synchronously optimizing an enhancement module and detecting network parameters through back propagation. Compared with a baseline YOLOv8 detector, the method has the advantages that the average precision (mAP50) is obviously improved, and the method is obviously superior to a traditional enhancement-detection separation scheme. The method can be applied to robust target detection scenes in complex environments such as automatic driving and security monitoring.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health, voice navigation and the like, and discloses a voice processing method, device, equipment and medium based on voiceprint feature screening. The method comprises the following steps: screening voice of a near-field main speaker based on voiceprint similarity and signal intensity, executing duration filtering and confidence verification, dynamically constructing a voiceprint feature library, generating a voiceprint mask matrix based on the voiceprint feature library, performing frequency band suppression on a to-be-processed voice signal, and outputting a purified voice signal. According to the method, the voiceprint library is dynamically constructed, the voiceprint mask matrix is generated based on the voiceprint feature library, frequency band suppression is performed on the to-be-processed voice signal, non-target voiceprints are effectively shielded, the voice signal quality is improved, and thus target voice is accurately captured in a high-noise environment.

The invention discloses a reinforcement learning intrusion detection method and system based on dynamic network feature screening, and the method comprises the steps: constructing a self-adaptive reward reinforcement learning network based on Transform framework enhancement, carrying out the network flow feature screening in combination with a crayfish optimization algorithm, and constructing a self-adaptive reward reinforcement learning network based on Transform enhancement. Through combination of a Transform mechanism and reinforcement learning, adaptive learning and decision optimization capabilities in a dynamic network environment are significantly enhanced. A crayfish optimization algorithm is combined to perform feature extraction on the high-dimensional network traffic to capture a feature set which is most valuable for intrusion detection, so that a most representative feature subset can be efficiently extracted from complex and changeable network traffic data, and the automation and precision of a feature selection process are improved; the method effectively overcomes the defects of sample imbalance, feature selection, dynamic adaptability and the like in the prior art, and provides a more intelligent and efficient intrusion detection solution.

The invention provides an equipment telemetry data fault analysis method and system based on machine learning, and relates to the technical field of fault analysis, and the method comprises the steps: carrying out the standardization processing of multi-dimensional telemetry data, extracting time sequence features and statistical features, screening a core feature set, and constructing a lightweight feature extraction rule; according to the method, feature component contribution degree scores are calculated, adaptive weight coefficients are distributed, fault feature representation is generated, and a fault diagnosis rule base is matched, so that the fault type and probability are determined, edge end data processing lightweight and timely generation of fault early warning are realized, and the accuracy and efficiency of fault diagnosis are improved.

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 side slope monitoring and early warning, in particular to a mine site side slope remote online real-time monitoring and early warning system which comprises a multi-source data monitoring unit, a data processing and analyzing unit, a side slope instability judging unit, an instability grading early warning unit and an early warning verification pushing unit. The method comprises the following steps: collecting monitoring data of a plurality of data sources to obtain time sequence monitoring data of each data source, carrying out noise reduction on the time sequence monitoring data, then decomposing the time sequence monitoring data, calculating a time-frequency feature vector through an energy density function, and optimizing the time-frequency feature vector through feature screening; calculating a slope stability evaluation index by optimizing the time-frequency feature vector, constructing an instability risk index to judge a slope instability risk level, constructing an early warning index, and combining the early warning index with the slope instability risk level to establish a graded early warning mechanism to obtain a slope instability early warning level; and verifying the slope instability early warning grade, and performing early warning according to the slope instability early warning grade passing the verification. According to the invention, the false alarm rate of slope monitoring and early warning can be reduced.

The invention relates to the technical field of equipment anomaly detection, and discloses an anomaly detection method and system for an intelligent motor, and the method comprises the steps: collecting multi-source heterogeneous data when the intelligent motor works, carrying out the feature-level fusion, and carrying out the potential local learning, and obtaining optimized data features; performing data fragment segmentation on the optimized data features to obtain segmentation features, and determining a time-frequency feature spectrum of the segmentation features under multiple resolutions; mining a feature association relationship of the multi-dimensional operation features of the intelligent motor to construct a reinforcement learning strategy, and performing target feature screening on the multi-dimensional operation features of the intelligent motor to obtain a screened feature subset; and carrying out confrontation generation processing on the screening feature subset to obtain a simulation sample, carrying out feature ensemble learning processing on the simulation sample and the screening feature subset to obtain a judgment feature, and carrying out anomaly analysis on the intelligent motor to obtain an anomaly detection report. According to the invention, the detection precision of non-obvious or weak-feature anomalies in the intelligent motor can be improved.

The invention provides a satellite navigation interference detection and suppression system and method, and belongs to the technical field of signal analysis. The sensor module is used for determining a target area where a sensor is installed according to a target detection sky, and deploying and installing the sensor in the target area to obtain original signal data of a satellite; the signal processing module is used for standardizing the original signal data to obtain standard signal data and converting the standard signal data into a first frequency spectrum; the feature recognition module is used for analyzing features of the first frequency spectrum through fast Fourier transform and screening signal points with abnormal fluctuation; the interference analysis module is used for classifying the features of all the signal points and matching corresponding interference categories to obtain all interference categories of the original signal data; and the interference suppression module is used for adjusting parameters in real time to reduce interference by using an adaptive filter according to the dynamic characteristics of the interference category. The reliability of navigation signals is improved, and the anti-interference capability is enhanced.

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 provides a self-adaptive radar target detection method, system and device and a medium, and belongs to the technical field of self-adaptive radar target detection, and the method comprises the steps: carrying out the high-resolution decomposition and Gaussian fitting of a radar echo signal, analyzing the spectral centroid and spectral width characteristics, and determining the frequency domain interval of the radar echo signal; decomposing the radar echo signal into a plurality of intrinsic mode components, and screening out the mode components in a target frequency domain interval for reconstruction; key features in the reconstructed radar echo signals are extracted from the time domain dimension and the frequency domain dimension, and features with distinguishing force on target detection are reserved through a feature screening method; the method comprises the following steps: segmenting a long-time signal in a radar echo signal into a plurality of small windows, and respectively carrying out VMD decomposition and reconstruction; and carrying out target detection on the extracted key features by using a CA-CFAR algorithm. The method improves the precision of target detection under the background of complex sea conditions and strong sea clutters, and improves the accuracy and robustness of target detection.

The invention provides a sensor anomaly detection method and device based on a long short-term memory network, electronic equipment and a medium, which are applied to the technical field of fault detection, and effectively reduce data noise and improve the input quality of a model by performing preprocessing of null value removal and feature screening on acquired sensor data. Inputting the preprocessed sensor data into an improved attention mechanism-long and short-term memory network model, capturing a long-term dependency relationship of a time sequence by utilizing a long and short-term memory network, and dynamically distributing weights and focusing on key information by virtue of an attention mechanism, so that the model prediction precision is remarkably improved; and finally, the prediction result is compared with historical data and real-time data, so that the sensor abnormality is accurately judged, the defects of a traditional method in processing complex time sequence data are effectively overcome, the accuracy and reliability of abnormality detection are remarkably improved, and meanwhile, the method has prediction capability and adaptivity and is suitable for popularization and application. And characteristic differences of different sensors can be adapted.

The invention provides a sodium ion battery health state estimation method in an energy storage scene. The method comprises the following steps: establishing a sodium ion battery complete attenuation data set under two working conditions and extracting a plurality of battery degradation characteristics; respectively measuring the correlation between the battery degradation characteristics and the SOH by using three correlation analysis methods, and carrying out preliminary screening on the battery degradation characteristics and the SOH to obtain battery degradation characteristics after preliminary screening; carrying out secondary feature screening by using an integral gradient algorithm, and carrying out interpretability analysis by using a KAN network as an agent model, so as to obtain a feature combination with relatively strong contribution to battery life prediction; and the battery degradation features in the feature combination are sent to a Temporal Residual KAN-LSTM network model for training, and the SOH of the sodium ion battery is estimated, so that high-precision SOH estimation and interpretability of the sodium ion battery are realized.

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 discloses a multivariable underground water level time sequence prediction method and device, equipment and a medium, and relates to the technical field of artificial intelligence. Performing feature variable contribution proportion calculation, feature screening and normalization processing on the processed data based on a random forest regression model by using an average impurity reduction method to obtain target data; constructing a prediction model based on a convolutional neural network and a long-short term memory network, and performing structure setting, cross validation, parameter setting and model training on the prediction model to obtain a target prediction model; the method comprises the following steps: inputting target data into a target prediction model to obtain a predicted value, generating an evaluation index based on the predicted value and an actual measured value, optimizing the target prediction model by using the evaluation index, improving the applicability, generalization ability and training speed of the model, optimizing the convergence of the model, and improving the accuracy and stability of multivariable groundwater level time sequence prediction. And the sustainable utilization efficiency of underground water resources is improved.

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 provides a power consumption behavior diagnosis system and method based on an anomaly detection model, and relates to the technical field of power system anomaly diagnosis, and the system comprises a data collection module which is used for collecting the data of the power consumption behavior of a user; the data preprocessing module is used for obtaining basic time sequence data; the feature extraction module is used for extracting multi-dimensional features; the feature selection module is used for screening the multi-dimensional features by adopting a feature screening function to obtain a screened feature set; the model processing module is used for inputting the screened feature set into a pre-trained anomaly detection model to generate a reconstructed feature sequence; the anomaly recognition module is used for calculating an anomaly score; and the diagnosis output module is used for outputting the abnormal level and generating an abnormal diagnosis report of the power consumption behavior of the user. The method can effectively solve the problems that in the prior art, the modeling capacity of complex multi-dimensional time series data is insufficient, feature extraction and selection depend on manual work, information loss is likely to be caused, and the anomaly detection precision and the calculation efficiency are difficult to balance.

The invention discloses a construction method of a dyskinesia phenotype classification model of a Parkinson's disease patient and a diagnosis system, and belongs to the field of medical auxiliary diagnosis devices. The system comprises an image acquisition module and a phenotype classification module, wherein the classification module is composed of a space-time diagram convolution feature extraction module, a hierarchical node fusion module, a functional brain network construction and feature extraction module, a local brain region feature screening module, a hierarchical brain feature pairing fusion module and an output module. According to the method, multi-modal brain image data are fused, space-time and complex relation characteristics of a brain region are deeply mined, a key lesion brain region is positioned, and dyskinesia phenotypes are accurately classified by training a neural network. The system can significantly improve the diagnosis accuracy of the Parkinson's disease dyskinesia phenotype, has good expansibility and adaptability, and provides a scientific basis for early diagnosis and personalized intervention. The invention further relates to an operation method of the system, an auxiliary diagnosis device and a computer readable storage medium.

The invention provides an unstructured data processing method and system, and relates to the technical field of natural language, and the method comprises the steps: carrying out the tensor decomposition of unstructured data, obtaining a standardized feature vector, constructing a self-organizing critical state feature learning network in an isolation calculation space, constructing a feature interaction network based on quantum entanglement perception, and carrying out the recognition of the feature interaction network. And establishing a hierarchical causal graph for feature screening, realizing semantic feature vector generation by adopting multi-scale feature fusion, and establishing an intelligent protection mechanism. The feature extraction accuracy can be effectively improved, the data processing reliability is enhanced, and the system operation efficiency is improved.

The invention relates to a dynamic scene vision SLAM method and system based on point-line complementation, and belongs to the field of simultaneous mapping and positioning of mobile robots. According to the method, target prior information monitoring is carried out on an image through a semantic module, and dynamic feature points inconsistent with camera motion are identified; visual feature extraction is carried out on the image through a tracking module, and the visual feature extraction comprises point feature extraction and line feature extraction; removing the point features preliminarily extracted by the tracking module according to the dynamic feature points identified by the semantic module, fusing the obtained static point features and line features, expanding the static point features according to the fused line feature constraints, and screening out key frames according to the point and line features; and finally, completing map construction and optimization updating through a local mapping module and a loopback detection and global BA optimization module. According to the method, mapping limitation and deviation possibly caused by single scene texture information can be overcome, and the real-time performance, the mapping precision and the robustness of mapping can be improved.

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 an aquaculture organism health state monitoring system based on multi-dimensional feature association, and belongs to the technical field of aquaculture, and the system comprises a visual feature collection layer which is composed of an image collection module, an image preprocessing module, a target analysis module and a feature extraction module; the molecular biology verification layer is composed of an immune gene monitoring module and a metabolic gene monitoring module; the data association prediction layer is composed of a standardization module, a feature screening module, a deep learning module and an evaluation output module; a self-adaptive optimization mechanism, a fault-tolerant mechanism and an environment adaptability adjustment mechanism. According to the aquaculture organism health state monitoring system based on multi-dimensional feature association, intelligent monitoring of the health state of aquaculture organisms is achieved by integrating a computer vision technology and a molecular biology detection method, and a scientific basis is provided for aquaculture production management.