579 results about "Feature combination" patented technology

The invention discloses a multi-field part size and appearance defect intelligent detection system, and relates to the field of image analysis. The system comprises an image acquisition module, an image preprocessing module, a feature extraction module, a defect identification and size measurement module, a data processing and analysis module, an automatic control module and a man-machine interaction module. According to the method, CNN and LBP, Hough transform and SIFT algorithms are fused, high-level semantics and bottom-level texture / geometric features are considered, 2304-dimensional fusion feature vectors are formed through feature splicing, the feature extraction integrity of parts in multiple fields is improved, texture detail features can be accurately extracted, geometric shape features can be accurately obtained, and the method is suitable for large-scale popularization and application. Meanwhile, the adaptive feature selection mechanism dynamically optimizes the feature combination according to the detection result, the problem of calculation redundancy is reduced, and the detection precision is ensured while the detection efficiency is improved.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention discloses a method for identifying impurities and bubbles in a wine bottle based on multi-feature comprehensive judgment, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: employing a high-resolution industrial camera to be combined with an LED light source, and continuously shooting multiple frames of images of the wine bottle under the consistent shooting angle and illumination condition; carrying out the preprocessing of the collected image through employing a self-adaptive histogram equalization and Gaussian blur method; extracting the edges of impurities and bubbles in the wine bottle on the image by adopting a Canny edge detection algorithm to obtain an edge image; segmenting the edge image into a plurality of target areas by using a contour detection technology, and extracting possible bubble and impurity areas from the edge image; extracting target area features including circularity, gray average, gray uniformity, texture features and color space; and according to the extracted features, bubbles and impurities are distinguished by applying a comprehensive judgment rule. According to the method, the bubbles and the impurities are comprehensively judged by adopting multi-feature combination, so that the accuracy and the robustness of detection are greatly improved.

An intelligent regulation and control method for a DevOps platform monitoring period comprises the following steps: firstly, acquiring a multi-dimensional index data stream of a system, and forming an instant multi-dimensional vector through stream-oriented calculation, window processing, feature combination and the like so as to judge the state of the system; and quantifying event information by using an event level model to determine an emergency degree. A confidence vector is obtained through operation of a preset rule base, a corresponding scheduling strategy is triggered, and resources are allocated according to the fuzzy logic and the event emergency degree. Meanwhile, the event priority is determined in combination with expert experience and fuzzy logic, and a flexible management plan is executed and a result is evaluated accordingly. And finally, according to the rule base evaluation, dynamically adjusting the rule weight ratio, and optimizing the judgment on the multi-dimensional indexes of the system. According to the invention, dynamic regulation and control of a monitoring period, accurate evaluation of systems and events, intelligent scheduling and handling are realized, and the stability, high efficiency and self-adaptability of a DevOps platform are improved.

The invention discloses a transformer partial discharge detection method and system based on multi-modal feature combination, and relates to the technical field of transformer detection, and the key points of the technical scheme are that the method comprises the steps: collecting an electromagnetic wave signal, a pulse current signal and a sound wave signal of a target transformer; respectively extracting an electromagnetic wave time-frequency matrix, a pulse current waveform parameter and a sound wave space coordinate; performing multi-modal feature fusion on the electromagnetic wave time-frequency matrix, the pulse current waveform parameters and the sound wave space coordinates based on a dynamic weighting strategy to generate a joint feature tensor; and inputting the joint feature tensor into a pre-constructed neural network model, identifying a discharge type and optimizing a positioning coordinate. According to the method, multi-modal features are subjected to tensor fusion, cross-modal correlation learning is realized through the neural network model, information islands can be eliminated, and the misjudgment rate is effectively reduced; and electromagnetic wave-current-sound wave signal adaptive fusion is realized through a dynamic weight coefficient, and the signal-to-noise ratio is obviously improved through characteristic complementation in an oil paper insulation discharge scene.

The invention provides a multi-source spatio-temporal data fusion processing method and device. The method comprises the following steps: preprocessing multi-source spatio-temporal data; performing standardization processing on the preprocessed multi-source spatio-temporal data to obtain standardized data; performing alignment processing on the standardized data by using a space-time registration technology to obtain registration data; constructing a multi-layer network structure, and performing data arrangement on the registration data by using the multi-layer network structure to obtain structured data; and establishing a unified geographic element classification system according to the structured data. Sampling the standardized feature vectors by adopting an MCMC urban sampling algorithm to obtain feature distribution parameters; calculating a covariance matrix based on the feature distribution parameters and performing feature value decomposition; and defining geographic element categories according to the distinction degree of the feature combination. The method also comprises a corresponding device implementation. According to the method, the problems of low efficiency and poor precision of multi-source spatio-temporal data fusion in the prior art are solved, the precision of data fusion is improved, and the method has relatively high practicability and adaptability.

The invention provides an abnormal behavior recognition method and system based on multi-dimensional data analysis, and the method comprises the steps: carrying out the cleaning and standardization processing of structured data and unstructured data, obtaining a data set in a unified format, employing a natural language processing technology, processing the document text in the data set, extracting the entity, relation and behavior information, and carrying out the recognition of abnormal behaviors. Generating a structured feature vector; constructing behavior statistics, time sequence and association features by using the feature vectors and the structured data, selecting a feature combination by adopting a sampling algorithm, establishing an entity relation graph according to association data in a constructed multi-dimensional feature set, calculating association strength among entities, and optimizing the entity relation graph according to the association strength to establish a network structure model; a feature weight system is set according to the model and a multi-dimensional feature set, a multi-dimensional abnormal score is calculated by using a pruning tensor structure measurement method, an abnormal score is comprehensively calculated by using a low-rank tensor recovery technology, and an early warning list is output, so that the recognition accuracy and efficiency can be improved.

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 display panel defect detection method and system based on multi-modal data fusion, and the method comprises the steps: obtaining multi-type representation data generated in a detection process of a display panel, and carrying out the feature mapping of the multi-type representation data; respectively mapping the panel appearance characterization data, the panel signal characterization data and the panel interlayer characterization data to a preset feature space to generate a cross-type mapping feature set, and performing feature association enhancement on the cross-type mapping feature set to obtain a cross-type mapping feature set; establishing an association relationship among the appearance mapping features, the signal mapping features and the interlayer mapping features, generating an association enhanced feature set, performing defect feature reasoning on the association enhanced feature set, extracting a feature combination conforming to a defect feature mode, and generating a defect feature reasoning result; and outputting a defect detection result of the display panel. According to the invention, the accuracy and reliability of the defect detection result of the display panel can be improved.

The invention discloses a camouflage target detection method and system based on cross-scale global and local feature combination, and the method comprises the steps: extracting feature maps of M scales from an input image # imgabs0 # through an encoder, inputting a multi-branch convolution block to obtain enhanced features, inputting the enhanced features into a pre-trained decoder, and carrying out the multi-scale fusion to obtain final fusion features, a decoding result obtained through up-sampling is classified to obtain a camouflage target detection result; wherein the decoder is provided with a # imgabs1 # layer, any # imgabs2 # layer comprises # imgabs3 # decoder nodes, all the decoder nodes are subjected to up-sampling layer by layer and step by step, fusion is carried out through double convolution operation, and then the output features of the decoder nodes are obtained through multi-stage combined scanning feature fusion blocks. The invention aims to solve the detection difficulty when the camouflage target is highly similar to the background, the edge is not obvious or a large amount of shielding exists under the complex background, and overcome the problem that the camouflage target detection precision is low due to the fuzzy edge and the small target size.

The invention relates to a construction safety risk early warning method and system based on cross-modal visual language retrieval, belongs to the technical field of combined image retrieval and multi-modal retrieval, and designs a multi-granularity image-text semantic feature decoupling module, a multi-granularity feature combination module and a multi-granularity combination-target alignment module. Through a multi-granularity semantic decoupling technology, local features and global features of an image and a text are decoupled respectively, alignment between multi-modal query and a target image is considered at the same time, multi-granularity combination is carried out on the decoupling features of the image and the text in the multi-modal query, and multi-granularity combination features of the multi-modal query are obtained. According to the method, combined features are pushed to approach target image features from a multi-granularity level, cross-modal alignment of a multi-granularity detail level is realized, semantic information between multi-modal query and a target image is aligned more accurately, and overall cross-modal understanding and application effects are improved.

The invention discloses a channel siltation probability early warning method and system for water flow feature recognition, and relates to the technical field of channel maintenance, and the method comprises the steps: obtaining channel characteristic parameters, inputting the channel characteristic parameters into an interaction knowledge graph, and determining a key water flow feature index set; performing random feature combination based on the index set, screening K coordination feature groups in combination with historical operation data, training K reference deposition prediction models, and performing integration to generate a deposition probability prediction model; collecting real-time water flow data through a sensor, processing the data and inputting the data into the model to predict the channel siltation probability; if the prediction result meets the early warning condition, siltation early warning is triggered. Therefore, the technical effects of improving the early warning perspectiveness, reducing the misjudgment rate and enhancing the complex environment adaptability are achieved.

The invention provides a coral reef remote sensing image multi-modal feature generation and restoration method and system, and relates to the technical field of remote sensing image restoration, and the method comprises the steps: obtaining coral reef image data based on a multi-source sensor, and carrying out the preprocessing; performing image morphological feature and spectral feature extraction on the preprocessed coral reef image data by using a convolutional neural network, and establishing a feature database; carrying out weighted fusion on the multi-source features based on a feature combination network, generating comprehensive feature expressions, and storing the comprehensive feature expressions into a feature database; the coral reef image to be restored is matched with the feature database, and guidance parameters are generated; and inputting the guidance parameters into the generative adversarial network, and performing pixel-level reconstruction on the missing region of the coral reef image to be restored. According to the method, a complete closed-loop system is constructed, multi-source information scheduling, pixel-level guide reconstruction and semantic feedback verification are covered, image information can be supplemented, ecological information can be reasonably reconstructed, and a high-quality data basis is provided for subsequent classification, monitoring and protection work.

The invention provides an artificial intelligence advertisement pushing method based on multi-dimensional historical data analysis, and the method comprises the steps: constructing second-level, minute-level, hour-level and other behavior feature views of different time granularities for extracted original features, and forming multi-time scale feature representation; analyzing feature views under different time scales through a graph mining algorithm, mining an association rule between feature nodes, and constructing a cross-scale feature association graph; in the feature correlation map, according to the spectral clustering result of the feature nodes and the gravitation direction of the feature edges, the temporal correlation degree of different feature combinations is judged; if the time correlation degree of the feature combination exceeds an empirical threshold value, endowing the time weight of the feature combination with a high weight coefficient of power law distribution; and dynamically adjusting the network depth and the connection density of the association atlas according to the vertical dimension and the semantic hierarchy of the features, and optimizing the feature weight distribution in the atlas.

The invention relates to the technical field of image processing, and discloses a morphology-based image texture feature extraction method, which comprises the following steps: an image preprocessing step: carrying out graying processing on an original image, and carrying out smoothing processing on a grayscale image by using a Gaussian filter; a morphological operation step: selecting structural elements suitable for image texture characteristics, performing expansion and erosion operation on the smoothed image, performing opening operation on the expanded image, and performing closing operation on the eroded image; and an adaptive feature selection step: carrying out local binary pattern feature extraction on the image after morphological processing, adaptively determining a threshold according to statistical distribution of LBP features, and segmenting the image into a foreground and a background. The image texture feature extraction method based on morphology aims at solving the problems that feature extraction is inaccurate, noise interference is likely to happen, and feature combination is complex in an existing image texture feature extraction method.

The invention provides a target tracking method and system based on global and local two-way extraction and asynchronous enhancement, and the method comprises the steps: carrying out the initialization of a template image and a search image, and carrying out the sequence division through block embedding; extracting global features and local features of the template image and the search image at the same time, and performing feature fusion on the global features and the local features of the template image and the search image; the output of the global and local joint module is input into an asynchronous enhancement module for asynchronous interaction and feature enhancement; and inputting the finally enhanced output feature to a prediction head to obtain a tracking result. According to the method, global and local features are extracted at the same time through the feature combination module, and the features after interaction are enhanced by using asynchronous enhancement operation in the feature fusion stage, so that the robustness and the feature expression ability of the model are improved.

The invention provides an intelligent electric energy meter service life prediction method and device and a readable storage medium, and belongs to the technical field of device service life prediction.The method comprises the steps that multi-dimensional input is constructed by fusing key environment data and an operation time sequence, and the microcosmic, periodic and long-term characteristics of the input are extracted through multi-scale convolution support; the feature combination is dynamically optimized through an adaptive fusion module; constructing a time sequence prediction model, embedding a convolution attention mechanism in an encoder, and compressing redundant information of key features by combining sparse self-attention distillation through key features of feature combination of channel-space double attention dynamic enhancement optimization; the decoder adopts a mask probability sparse attention and iterative prediction strategy to generate an error prediction sequence based on the global features of the encoder to avoid future information interference; and calculating the residual life according to the intersection point of the error prediction sequence and the failure threshold. According to the method, the long sequence prediction precision, the key feature utilization rate and the calculation efficiency are improved, and reliable support is provided for preventive maintenance of the intelligent electric meter.

The invention discloses an intelligent monitoring method and system for preventing an old person from falling down based on multiple sensors, and relates to the technical field of falling monitoring, and the method comprises the following steps: merging behavior data, physiological data and environmental data into a data set; obtaining a feature combination from the related features based on an association rule, and quantifying the influence of the feature combination on the falling probability of the elderly; based on a machine learning algorithm, learning a relationship between the related features and whether the old person falls down, and predicting an old person falling probability corresponding to each sample of the new data set; optimizing the falling probability of the elderly based on the quantification result; if the optimized falling probability of the old people exceeds a certain threshold value, executing a corresponding early warning scheme; the system comprises a data acquisition module, a feature combination acquisition module, a probability prediction module, a probability optimization module and an early warning execution module. According to the invention, the fall probability corresponding to each sample can be obtained, the quantitative influence of the detected feature combination is superposed to the prediction result, and the accuracy of fall risk assessment is further improved.

The invention belongs to the technical field of medical clinic and health monitoring, and discloses an operation hypotension early warning system and prediction method based on multi-modal physiological parameters, and the system comprises a data acquisition module, a feature engineering module, a multi-modal integration and prediction module, a decision support module, a result display module and the like. The feature engineering module constructs a multi-dimensional feature space including key indexes, composite indexes and dynamic indexes based on various real-time physiological parameter data of a patient, the multi-modal integration and prediction module carries out feature screening, constructs optimal feature combinations corresponding to different prediction time windows, and carries out prediction on the optimal feature combinations. And designing two-stage model processing to obtain a hypotension risk prediction result of a plurality of prediction time windows. According to the method, the possibility of hypotension events in multiple time periods in the future in the operation process can be predicted, and clinical intervention suggestions are provided, so that medical personnel can intervene and prevent the hypotension events in advance, and postoperative complications related to hypotension are reduced.

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 defect detection method combined with an unsupervised image feature extraction technology, and the method comprises the steps: collecting a qualified image of a product as training data of an unsupervised convolution auto-encoder, and obtaining a feature extractor; defect images are collected and marked; inputting the defect image in the training set into YOLOv8 for training to obtain a multi-scale feature map; inputting the same defect image into a feature extractor to obtain an unsupervised feature map; fusing the unsupervised feature map with the multi-scale feature map; the fused image is subjected to feature combination through a neck network, and a detection result is output through a head network; optimizing the model by using the test set, and evaluating a detection result output by the model by using the verification set; and carrying out product defect detection by utilizing the trained model. According to the method, the global features of the images are captured from a large number of defect-free images, effective feature representation under current imaging is formed, and the problems of model over-fitting and substandard model performance caused by defect data deficiency are avoided.

The invention discloses a terrain classification method based on a random forest algorithm, a server and a storage medium, and belongs to the technical field of terrain classification, and the method comprises the steps: obtaining DEM data and remote sensing image data of a research area, and extracting terrain features, spectral features, index features and texture features; designing a feature combination scheme, and generating an optimal feature combination data set as input data of terrain classification; calculating an optimal parameter combination of a random forest algorithm, and generating an optimal random forest classifier; establishing a terrain classification system suitable for the research area, and constructing a terrain classification training sample set and a verification sample set; performing terrain classification by using a random forest classifier and the optimal feature combination data set to obtain a terrain classification result of the research area; and calculating and evaluating the precision of a terrain classification result by utilizing a verification sample set and a Kappa coefficient evaluation method. By adopting the method, the terrain classification refinement degree and the terrain classification calculation efficiency and classification efficiency can be improved.

A radio terminal (1) performs a first random access using a random access resource associated with a feature combination. If the first random access fails, the radio terminal (1) falls back to a second random access using a random access resource associated with a feature subset included in the feature combination. This helps, for example, to provide a fallback in the event of a failed random access procedure using a random access resource associated with a feature combination.

The invention discloses a security transaction system automatic test system based on result feedback. The system comprises a use case generation module, a use case execution and feedback module, a use case iterative optimization module and an abnormality diagnosis module. The case generation module reads and analyzes a drive data file input by a tester, extracts features in the drive data file, generates a plurality of test case sets covering different feature combinations, and performs priority ranking on all test cases; the use case execution and feedback module executes the test use cases according to the priority sequence and generates feedback data according to execution results; the case iterative optimization module identifies an uncovered scene based on the feedback data, generates a new test case, and performs priority ranking on the newly generated test case; the abnormality diagnosis module collects various indexes during execution of the test cases, sets baseline values of the indexes, marks abnormal cases, classifies abnormal conditions, feeds classified abnormal information back to the case iterative optimization module, and improves a generation strategy of the cases.

The invention relates to the field of digital mapping, and discloses a landslide susceptibility mapping method and device and a medium, and the method comprises the steps: obtaining multi-source remote sensing data and historical landslide data, and making a disaster-inducing factor data set and a landslide sample data set; inputting all landslide sample data sets into an AGNES clustering algorithm and a VIIQ feature optimization strategy module to obtain an optimal feature combination; dividing the optimized data features into a training set and a test set, inputting the training set and the test set into a machine learning classifier and a deep learning classifier, and performing model precision evaluation to obtain a preliminary landslide susceptibility map; inputting the landslide susceptibility map into the Blending heterogeneous integrated model to obtain a final landslide susceptibility mapping result; the integrated model is deeply explained in combination with an SHAP algorithm, and a landslide susceptibility result graph is optimized; according to the method, the landslide susceptibility can be predicted more effectively, and a scientific basis is provided for risk assessment, early warning and prevention of geological disasters.

The invention discloses a multi-channel electroencephalogram signal spatial filtering and feature enhancement method and device for intelligent glasses, and the method comprises the steps: taking a multi-channel electroencephalogram signal collected by the intelligent glasses as an original signal, generating a corresponding electrode mapping relation for analysis, determining a spatial structure and a filtering scale, and generating a filter group according to the filtering scale; performing component separation on the original signal according to the filter bank, obtaining component information and a corresponding signal source, and constructing an independent component sequence according to the signal source; spatial mapping is carried out on the independent component sequence, main components are determined, a weight matrix is constructed for spatial filtering, a spatial feature sequence is generated for spatial reconstruction, and enhanced features are generated; performing multi-scale decomposition on the filter bank and the enhanced features, acquiring multi-scale features to construct a feature combination, acquiring corresponding time-frequency features to perform spatial mapping, acquiring interference modes corresponding to the time-frequency features to perform feature enhancement, acquiring corresponding feature representations to perform spatial reconstruction, and acquiring spatial enhanced features.

The invention provides a passive perception radar target classification method and system based on electromagnetic detection feature extraction, and the method comprises the steps: carrying out the multi-dimensional signal decomposition of an original scattered signal sequence through obtaining an electromagnetic radiation signal set of a target region; obtaining a time-frequency distribution feature set and a polarization response feature set corresponding to each signal acquisition period, extracting a target electromagnetic feature combination from the time-frequency distribution feature set and the polarization response feature set according to a preset electromagnetic feature association rule, generating a weight distribution parameter, and performing weighted fusion processing on the target electromagnetic feature combination to obtain a target electromagnetic feature combination; and the fusion feature vector set is input into an adaptive classification network, hierarchical classification operation is performed on the fusion feature vector set, the category identifier of the target scatterer and the corresponding classification confidence are output, and a radar detection optimization instruction is generated based on the category identifier and the corresponding classification confidence. According to the method, high-precision distinguishing of multiple types of scatterers is achieved under the passive sensing condition, and meanwhile the environmental adaptability and the calculation efficiency of the classification process are guaranteed.

The invention discloses an unmanned aerial vehicle target detection method and device based on scale adaptive recognition and a medium, and relates to the field of machine learning, and the method comprises the steps: carrying out the data collection based on an unmanned aerial vehicle, and obtaining video data corresponding to a target scene; analyzing based on the video data to obtain image data of a continuous sequence, and labeling the image data to generate a training data set; performing data enhancement on the image data in the training data set; and training the convolutional neural network through the training data set after data enhancement, and performing target detection through the trained convolutional neural network. The image is used as input, and then the feature extraction fusion part is used for feature extraction. A multi-scale convolution strategy is adopted, convolution operations of various different sizes are specifically covered, and feature fusion is carried out on feature maps of different scales to obtain a feature combination.

The invention relates to the technical field of low-altitude aircraft chip safety monitoring, and discloses a low-altitude aircraft chip safety monitoring system which comprises a state acquisition module, a risk assessment module, an anomaly detection module, a strategy generation module, an instruction execution module, an effect verification module, a threshold setting module, a data output module, a feedback optimization module and a system regulation module. The state acquisition module analyzes the operation parameters and the environment interference to obtain a state value; the risk assessment module extracts and screens abnormal protection feature combinations; the anomaly detection module combines a sample variation matching feature with an evaluation combination; the strategy generation module maps a feature strategy based on real-time data and sets a rule; the instruction execution module captures feature data and analyzes a trend; the effect verification module adjusts rules according to errors; the threshold setting module determines a monitoring threshold interval; the data output module generates standardized data; the system realizes comprehensive monitoring of the chip state through cooperation of multiple modules, and the operation safety and stability are improved.

The invention discloses an intelligent medical big data information management system, and the system comprises a data collection layer which serves as a sensing neural network, employs an Internet of Things gateway to be compatible with a medical equipment protocol, and deploys an edge calculation node for physiological signal preprocessing; the intelligent preprocessing layer is used for cleaning and standardizing the original data transmitted by the data acquisition layer; the informatization service layer is used for realizing fine-grained authority control based on an RBAC (Role Based Access Control) model, realizing inspection process automation by integrating an LIS (Line Information System) interface through a Hyperdger Fabric block chain evidence storage key operation hash, and constructing a zero-footprint DICOM (Digital Imaging and Communications in Medicine) viewer; the intelligent analysis layer is used for carrying out data life cycle management by adopting a mixed storage architecture, carrying out feature engineering on data, carrying out label matching and classified storage by using efficient neighbor search and similarity / distance measurement, quantifying contribution of each feature or feature combination to a single prediction result through an SHAP value, generating an interpretation result, and outputting the interpretation result; and presenting an interpretation result to a clinician or related personnel through a query interface.