23197 results about "Image acquisition" patented technology

The invention relates to the technical field of PCB detection, and discloses a PCB defect detection method and system, and the method comprises the steps: collecting multispectral imaging data through an image collection module, and generating an original image data set; the defect analysis server receives the synchronous imaging data to construct a three-dimensional surface topology matrix; in combination with the original image data set and the real-time imaging data, performing multi-scale decomposition on the three-dimensional surface topological matrix, extracting texture features, positioning a defect region, outputting defect type space distribution features through a layered recognition model, and updating the original image data set; and dynamically calibrating the detection parameters according to the feature categories. The system comprises an image acquisition module group, a data transmission module, a three-dimensional modeling module, a defect identification module and a parameter calibration module. According to the scheme, the accuracy, comprehensiveness and efficiency of defect detection are improved, and the detection requirements of modern PCB production are met.

The invention discloses a railway track damage detection method, and belongs to the technical field of railway track detection. According to the method, an image acquisition module and an ultrasonic detection module are installed at the bottom of a track detection vehicle, the detection vehicle is controlled to run, and track top face and side face image sequences and ultrasonic reflection signals are acquired; preprocessing the image, and respectively inputting the image into a deep convolutional neural network model and a support vector machine classifier to obtain a crack identification result and a wear level; processing an ultrasonic reflection signal, and judging a layering defect; and finally fusing the data, marking a damage position and generating a structured detection report. According to the method, the problems of incomplete detection, low precision and the like in the existing railway track damage detection are solved, efficient detection of track surface cracks, side abrasion and internal layering defects is realized through collaborative acquisition of the multi-modal sensor, intelligent algorithm processing and data fusion, and the comprehensiveness and reliability of detection are improved.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

The invention relates to the technical field of medical image diagnosis, and discloses a medical image automatic diagnosis method and system based on deep learning. According to the method, multi-modal medical image data of a target object is acquired and standardized, a two-channel convolutional neural network is utilized to extract features, the features are processed through cross-modal feature fusion, adaptive attention weight distribution and other technologies, a cascaded two-way long-short-term memory network is adopted for modeling, abnormity is detected based on a probabilistic graph model, and the target object is identified. And the nidus is segmented by a multi-scale context information enhancement module, and finally a diagnosis suggestion is generated by a diagnosis inference engine driven by a knowledge graph. The system comprises a multi-modal image acquisition interface module, a distributed feature calculation cluster, a visual interaction terminal and a security audit module. According to the method, the accuracy and efficiency of medical image diagnosis can be improved, comprehensive diagnosis reference is provided for doctors, and meanwhile data safety and privacy are guaranteed.

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

The invention relates to the technical field of image recognition, in particular to an image classification system and method based on the image recognition technology, and the system comprises an image collection module which is used for obtaining original image data to be classified; the preprocessing module is used for carrying out denoising, normalization and size standardization processing on the image; the feature extraction module is used for extracting multi-level features of the image by adopting a deep convolutional neural network; the classification decision module is used for weighting fusion features based on an attention mechanism and outputting a classification result; the output module is used for displaying the classification labels and confidence scores; according to the method, the input quality is optimized by dynamically selecting a preprocessing strategy, the multi-scale representation capability is enhanced by adopting a parallel convolution path and a feature pyramid structure, the robustness of the system is improved by integrating an adversarial sample detection and defense mechanism, and the dynamic scheduling and mixing precision acceleration of computing resources are realized by introducing an edge computing optimization technology. And the operation efficiency is obviously improved on the premise of ensuring the classification precision.

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention provides an image acquisition card multi-mode identification method and system based on intelligent security and protection. The method comprises the following steps: acquiring a multi-mode original data stream according to a global clock signal of an image acquisition card; performing space-time calibration on the multi-modal original data stream to generate a synchronous multi-modal data queue; extracting a multi-modal feature tensor of the synchronous multi-modal data queue through data preprocessing; performing hierarchical attention fusion on the multi-modal feature tensor to generate a fusion feature matrix; performing channel pruning on the fusion feature matrix through a lightweight convolutional neural network, and constructing a target detection model; and determining a detection result corresponding to the multi-modal original data stream according to the target detection model. Through the synergistic effect of a global clock signal and a dynamic space-time calibration algorithm, a time synchronization and space alignment compensation mechanism is constructed in a multi-modal data stream, and the problem of multi-modal information complementary advantage attenuation caused by space-time mismatch is effectively solved.

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 discloses an aluminum alloy surface defect detection method and system using deep learning, and relates to the related field of detection performed in combination with deep learning, and the method comprises the following steps: carrying out image acquisition on an aluminum alloy finished product, constructing a surface image data set to extract multi-scale surface features, and constructing a feature enhanced image set to carry out defect detection, and a defect detection result is obtained to dynamically optimize aluminum alloy production process parameters, and a process adjustment instruction is generated and fed back to a production system. The technical problems that traditional detection mostly depends on manual visual inspection or a machine vision system based on rules, imaging distortion, small defect missing detection and process adjustment are caused are solved, and the technical effects that deep learning is conducted through the double-branch network, the defect detection efficiency and the production yield are improved, and control over the aluminum alloy manufacturing quality is met are achieved.

The invention discloses a double-flow remote sensing image change detection method fused with Mama enhancement, which belongs to the field of image processing and comprises the following steps: acquiring images to obtain a remote sensing image data set; preprocessing the obtained dual-time-phase remote sensing image, and dividing the image into a training set and a test set; designing a dual-flow change detection network model integrated with Mama enhancement; training the constructed double-flow change detection network model by adopting training set data until the whole model is converged, and storing an optimal model; and inputting test set data into the trained optimal model, and predicting a change area in the test set. According to the method, MambaBlock and a semantic segmentation aggregation module are introduced, the global feature modeling capability is enhanced under linear complexity, and the detection precision and robustness of a change region are improved by fusing feature information of different levels and multi-scale feature learning.

The invention provides a forest area monitoring method and system based on unmanned aerial vehicle routing inspection, and the method comprises the steps: firstly obtaining a historical routing inspection data set containing a geographic position identifier and a topographic feature parameter in a target forest area, and generating an initial routing inspection route indicating a flight path and an image collection node according to the historical routing inspection data set; then, an unmanned aerial vehicle carrying a multispectral sensor is called to carry out dynamic routing inspection according to an air route, a real-time monitoring image set composed of vegetation coverage images of a plurality of monitoring areas under different timestamps is obtained, and then feature extraction and anomaly detection are carried out on the real-time monitoring image set; according to the method, an image anomaly feature set containing vegetation and surface structure anomaly indexes is determined, and finally, a forest region monitoring optimization strategy is generated based on the image anomaly feature set, so that the unmanned aerial vehicle inspection frequency and image acquisition node space distribution are adjusted, and more efficient and accurate monitoring of a forest region is realized.

The invention provides a visual inspection system and an application method thereof. The system comprises an image acquisition module used for acquiring a multi-angle optical image and laser three-dimensional point cloud data; the data preprocessing module is responsible for denoising, geometric correction and multi-modal data alignment; the feature extraction module extracts texture, edge and defect features through a convolutional neural network; the defect detection module identifies cracks, scratches and foreign matters based on feature fusion; the adaptive optimization module dynamically adjusts a detection threshold value and classifier parameters; the result output module generates a detection report and marks defect positions; the feedback calibration module corrects the weight of the detection model according to an artificial rechecking result; the equipment control module triggers the sorting device to remove defective products; and the performance monitoring module counts the detection accuracy and the system response delay. According to the invention, the detection accuracy and the system stability can be improved.

The invention relates to the technical field of PCB (Printed Circuit Board) defect identification, and discloses a method and a system suitable for identifying true and false defects of a PCB, and the method comprises the following steps: firstly, obtaining to-be-detected image information of the PCB, obtaining actual gray value distribution data of each detection area in real time under a preset detection condition, obtaining a gray difference index through difference analysis, and judging whether the gray difference index is out of a threshold interval or not. If yes, surface texture data (texture definition, uniformity and direction value) and edge contour data (contour smoothness, continuity and curvature value) of the abnormal area are obtained, a texture abnormal index and a contour deformation index are obtained through analysis, and then a preliminary defect probability value, a depth defect probability value and a comprehensive defect probability value are obtained; and taking corresponding defect identification measures after processing according to the screening rule. The system comprises an image acquisition module, a difference analysis module and the like, can improve the defect identification accuracy and efficiency, and has good real-time performance and adaptability.

The invention relates to the technical field of image data measurement and analysis, in particular to a method and a system for measuring slope deformation in a dangerous mountainous area based on image data. The method comprises the following steps: acquiring high-resolution image acquisition data and GNSS auxiliary data of the slope of the hard mountain area; correcting the high-resolution image acquisition data to obtain corrected slope image acquisition data; performing local feature extraction and matching of each time phase image on the corrected slope image acquisition data to obtain slope preliminary matching point set data; and performing mismatching elimination on the slope preliminary matching point set data to obtain transformation matrix data between the slope images. According to the method, high-resolution image acquisition and GNSS data are combined, through correction, registration, three-dimensional reconstruction and optical flow analysis, slope deformation of the hard mountainous area is accurately obtained and analyzed, and efficient deformation monitoring and visualization results are achieved.

The invention relates to the technical field of civil engineering tunnel structure safety monitoring and intelligent detection, in particular to a multi-modal fusion tunnel structure apparent disease identification and risk assessment system, which comprises an image acquisition module used for acquiring continuous images of the inner wall of a tunnel lining; a laser point cloud acquisition module; a structure sensor acquisition module; a data synchronization and preprocessing module; the multi-modal feature extraction module is used for performing depth feature extraction on the image, the point cloud and the sensor data; the heterogeneous feature fusion and disease identification module is used for fusing each modal feature and outputting a disease type identification result; and the risk assessment module is used for carrying out size estimation and parameterized expression on the identified diseases. The problems that in an existing tunnel inspection technology, the detection means is single, appearance and internal information cannot be considered, and the disease size is difficult to quantify automatically are solved.

The invention discloses a rapid three-dimensional reconstruction method for an unmanned aerial vehicle mine inspection scene, and particularly relates to the technical field of image processing and three-dimensional modeling, and the method comprises four steps: environment perception collection, inclination angle adaptive path planning, image quality optimization and three-dimensional model construction. Sensing information such as illumination, wind speed and gradient through a sensor to dynamically adjust a shooting strategy, and generating a normal supplementary shooting path in a gradient sudden change area; image quality is improved through image enhancement and feature extraction, and a confidence scoring model is constructed to screen high-quality images to participate in modeling; carrying out confidence evaluation on a modeling result, triggering a supplementary shooting mechanism, and improving the precision and integrity of the model; according to the method, the feature extraction stability of the image under the condition of illumination dramatic change is improved, the image coverage integrity of a high and steep slope area is enhanced, and stable control of image acquisition and fusion in a wind disturbance environment is realized, so that the precision, continuity and robustness of three-dimensional modeling are effectively improved.

The invention provides a posture recognition algorithm for any object under a monocular camera and an application system, and the algorithm comprises the steps: S1, constructing a target three-dimensional model, carrying out the multi-view annular shooting image collection of a target, and generating a dense grid model through feature extraction, matching, posture calculation and a multi-view geometric method; s2, generating an image depth map, and predicting depth information of a target in a motion process based on a monocular image sequence; s3, extracting a target image mask, and generating a target area mask graph through an image encoder, a prompt encoder and a mask decoder; and S4, executing attitude estimation, performing attitude initialization, correction and screening by combining the three-dimensional model, the depth map and the mask map, and outputting a six-degree-of-freedom attitude result of the target. According to the method, the target is subjected to annular shooting modeling through the method based on multi-view geometry, the three-dimensional model of the target is generated, attitude estimation is achieved in combination with the image mask and the depth map, the generalization ability of an attitude estimation algorithm in an actual scene is improved, and the application range of the attitude estimation algorithm in the actual scene is widened.

The invention relates to the technical field of concrete member detection, and discloses a concrete member defect identification and labeling system, which comprises an image acquisition equipment matching module, a defect feature analysis module and a real-time labeling regulation and control module, and a defect classification priority judgment module capable of being additionally arranged. The image acquisition equipment matching module calculates and matches the optimal equipment through the adaptive characteristic value based on the image resolution, the equipment acquisition precision, the working distance and the illumination compensation parameter; the defect feature analysis module performs quantitative analysis on features such as textures, crack forms and hole distribution of zoning images by using algorithms such as multi-scale image segmentation and frequency domain transformation; the real-time labeling regulation and control module dynamically adjusts the labeling position according to the defect position offset, the size change rate and the illumination fluctuation parameters; and the defect classification priority judgment module divides defect grades according to crack width, hole density and the like. The system improves the automation level and accuracy of concrete member defect detection, and is suitable for constructional engineering member quality detection.

The invention discloses a high-temperature airflow two-dimensional temperature field and velocity field synchronous measurement system and method based on laser-induced phosphorescence, and the system comprises phosphorescence particles, a low-frequency double-pulse laser, an image collection device, a synchronous controller, and an image processing device. The image processing device analyzes the gray intensity ratio of different wave band images acquired by the two PIV cameras at the same time, and combines a temperature response function calibrated by an experiment to realize inversion of a temperature field; meanwhile, a double-frame time-resolved image acquired by any PIV camera is subjected to cross-correlation calculation, particle displacement is extracted, and then a velocity field is reconstructed. According to the invention, synchronous acquisition of temperature and speed based on the same data source is realized, and the system has the remarkable advantages of simple structure, high measurement precision and wide application environment.

The invention relates to the field of image processing, in particular to a pin shaft forging forming quality detection method and system, and the method comprises the steps: carrying out the image collection of a to-be-detected pin shaft forge piece; then determining a total energy function of the active contour model and determining a neighborhood window, obtaining a structure tensor based on a gradient magnitude in the neighborhood window, and calculating to obtain local gradient direction dispersion; then, a defect edge structure enhancement index is calculated; then calculating a self-adaptive external energy scaling adjustment factor, and fusing the self-adaptive external energy scaling adjustment factor into an energy function of the active contour model to form an improved active contour model; and finally, accurately segmenting and extracting the surface defects of the pin shaft, and carrying out quality detection by combining the extracted defect characteristics. According to the method, by constructing local gradient direction dispersion and defect edge structure enhancement, a self-adaptive external energy scaling adjustment factor is calculated, and an active contour model is improved so as to accurately detect the surface defects of the pin shaft forge piece.

The invention discloses an unmanned car washer stain panorama identification system. The system operation process specifically comprises the following steps: acquiring panorama image data of a target car; preprocessing the panoramic image data to obtain a standardized panoramic image set; performing stain area identification on the standardized panoramic image set based on a deep learning model to generate an initial stain distribution diagram; performing stain type classification on the initial stain distribution diagram according to a stain feature database to generate a stain classification result set; generating a dynamic cleaning path instruction set based on the stain classification result set and a cleaning strategy library; real-time images in the cleaning process are collected in real time, real-time stain residue analysis is conducted, and finally a cleaning effect feedback report is generated. The method has the following advantages and effects that the system of multi-dimensional stain feature recognition, classification and dynamic decision can be fused, so that the core contradiction that the cleaning strategy is not matched with the stain features in the prior art is solved.

The invention, which relates to the technical field of structure health monitoring, discloses a sensing data-based on-line intelligent detection system for the whole body and the periphery of a house, comprising: a distributed sensor module, which is composed of a fiber grating sensor array, an MEMS environment sensor and an image acquisition unit, the system is arranged in a house bearing structure, a peripheral foundation, a drainage system and a greening area. The edge computing node module is configured to preprocess sensor data, including noise filtering, temperature compensation and abnormal data marking; the dynamic topology communication network module adopts a LoRaMesh and 5G hybrid networking technology and supports node self-repairing and bandwidth self-adaptive distribution; and the cloud analysis platform module integrates BIM model reconstruction, multi-source data fusion analysis and early warning decision, and outputs structure deformation prediction, an energy consumption anomaly map and a disaster risk assessment report.

The invention discloses an automobile steering controller fault detection method and system, and relates to the technical field of automobile electronic control. The surface image acquisition module captures an image through a visual sensor, the quality is improved through the image preprocessing module, the convolutional neural network accurately recognizes appearance defects, and the three-dimensional contour detection and thermal imaging module is triggered to be linked to re-check an abnormal area; the three-dimensional contour module confirms patch offset or tombstone standing abnormity by using a laser scanning technology; the thermal imaging analysis module is matched with a machine learning technology to analyze welding spot temperature abnormity; the predictive fault diagnosis module predicts a defect risk by using deep learning and performs early warning in advance; and the collaborative decision optimization module integrates a multi-module data dynamic optimization detection strategy. Through integration of defect detection, re-checking and prediction, the quality detection precision, coverage and efficiency of the automobile steering controller are greatly improved, the high-quality standard and production stability of products are ensured, and the requirement of the automobile industry for efficient detection is met.

The invention provides a remote sensing coastline automatic extraction method and system based on residual space pyramid segmentation and two-dimensional attention, and relates to the technical field of space analysis. The method comprises the steps of high-resolution remote sensing image acquisition and preprocessing, sea-land segmentation network reasoning, probability graph thresholding and edge extraction and vectorization processing. According to the method, the segmentation precision is improved through multi-scale feature aggregation and attention enhancement, coastline vector data with geographic coordinates are generated in combination with edge detection and topological repair, and the method is suitable for spatial analysis and coastline monitoring.

The invention provides a mechanical arm autonomous grabbing method and system based on image instance segmentation, relates to the field of computer vision and mechanical arm grabbing, and solves the limitation problems of poor generalization and stability and the like in traditional mechanical arm visual grabbing. The method comprises the following steps: performing multi-angle image acquisition on a target object, segmenting and outputting object mask information, adjusting a mechanical arm to an observation position, and obtaining depth point cloud data of the target object; performing principal component analysis on the depth point cloud data, extracting a principal direction vector representing spatial distribution of a target object, and constructing a principal direction coordinate system; generating a candidate grabbing pose set based on the depth point cloud data, and screening out an optimal grabbing pose meeting a preset direction constraint; and based on the optimal grabbing pose, the mechanical arm is driven to execute the grabbing action. The method has the advantages of being small in calculation amount and insensitive to environmental changes, the mechanical arm can autonomously move to the optimal position where the target object is observed and conduct grabbing, and the grabbing accuracy is improved.

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The invention discloses an intelligent defect detection system and a self-repairing method for a flexible base material RFID tag, and belongs to the technical field of Internet of Things electronic device manufacturing. According to the system, a three-dimensional dynamic scanning system is constructed by integrating a high-resolution image acquisition module, a multispectral sensor array and a mechanical arm motion platform, and surface and internal structure characteristics of a flexible substrate are captured in real time. A defect identification algorithm based on deep learning is combined with a multi-scale convolutional neural network and a transfer learning technology, precise classification of 12 types of defects such as microcracks, conductive layer fractures and base material deformation is realized, and the detection precision reaches 99.2%. A dual-mode self-repairing mechanism is put forward, specifically, nano-silver conductive colloid is injected through a microfluid channel for the defects of the conductive layer, and 3D structure reconstruction is achieved through a controllable temperature field; for substrate damage, a photoresponse shape memory polymer patch is adopted, and molecular-level bonding repair is achieved after ultraviolet light activation. According to the scheme, the detection efficiency is improved by more than 5 times, and the radio frequency performance of the tag is recovered to 98.7% of the initial value after self-repairing.

The invention discloses an artificial intelligence machine vision image acquisition system, and the system comprises a multi-mode perception layer which integrates a self-adaptive optical module, inhibits metal reflection, captures a visible light to short wave infrared image, and captures a motion edge; the dynamic adaptive layer adopts an illumination compensation and motion compensation module to dynamically adjust camera parameters and micro displacement compensation, feeds back an illumination trend, outputs a motion vector to the cognitive layer, generates a confrontation sample through a GAN, simulates virtual defects in combination with a physical engine, and expands training data; the cognitive reasoning layer is used for deploying a dynamic routing network, distributing computing resources according to image complexity and optimizing feature extraction efficiency; reducing data deviation through anti-fact analysis, and generating a thermodynamic diagram to explain a detection basis; and the collaborative decision-making layer is used for rapidly screening samples by edge nodes, training a global model by cloud aggregated data, automatically triggering manual rechecking when the confidence coefficient of the model is insufficient, synchronously optimizing a training set and a causal reasoning module by a rechecking result, and improving the labeling efficiency through AR assistance.