1576 results about "Source image" patented technology

The invention discloses a visual detection system and method for tiny flaws of industrial products, and belongs to the technical field of product detection, multi-source image data of a target industrial product under multiple detection angles and illumination conditions are acquired, and an image information matrix is established; performing region segmentation and texture enhancement on the image, and extracting local texture direction inconsistency parameters; carrying out normalization analysis on the pixel ratio under different spectrum channels, and calculating a multispectral reflectance ratio abnormal index; constructing a deep convolution recognition model; reasoning the image by using the model, and outputting a defect judgment result and a confidence score; judging whether the area is a flaw area based on a dynamic threshold mechanism, and outputting a detection report containing flaw position information and a visual heat map; according to the method, multi-dimensional fusion identification of texture structure disturbance and spectral response abnormity is realized, the micro defect identification precision is effectively improved, and the method has high robustness, automation and engineering practicability and is suitable for high-precision quality control requirements of various industrial scenes.

The embodiment of the invention provides a swimming pool robot positioning and trajectory prediction method and system fusing sonar and vision. The method comprises the following steps: collecting terahertz sonar data for a target area; based on terahertz sonar data, constructing a motion track model of the swimming pool robot; acquiring multi-source image data by adopting a multi-spectrum and event camera collaborative acquisition strategy; a dynamic SLAM algorithm based on deep learning is adopted, dynamic objects in the swimming pool environment are recognized and processed in real time based on the multi-source image data, and the environment image data with the dynamic objects removed are adopted to obtain a swimming pool environment model; performing deep fusion on the motion track model and the swimming pool environment model through a knowledge graph network, and predicting the operation state of the swimming pool robot by adopting a quantum machine learning algorithm to obtain a panoramic model; and creating and updating a digital twinborn model corresponding to the panoramic model in real time. The real-time performance and accuracy of positioning and track prediction of the swimming pool robot are improved, and a user can conveniently maintain and manage the swimming pool robot.

The invention relates to the technical field of underwater fish school monitoring, and discloses an underwater fish school monitoring statistical system based on image fusion. According to the system, underwater video streams and sonar reflection intensity data of different spectral bands are acquired through an underwater multi-source image acquisition module, and time-space synchronous multi-modal image data streams are generated through timestamp alignment; a fish school contour reconstruction module is used for segmenting a fish school contour boundary and fusing visible light texture and sonar geometric features to generate an underwater three-dimensional fish school distribution set; the dynamic track mapping module tracks the mass center displacement, calculates the movement rate and the direction deviation angle, and correlates the water area depth to generate a dynamic track topological graph; the behavior anomaly analysis module extracts environment data based on the track mutation node, and detects aggregation density change and direction dispersion to mark an anomaly feature cluster; and the population statistics output module integrates the data, performs classified statistics on population distribution, a quantity threshold value and a migration path overlap ratio, and finally generates a fish school quantity distribution statistics thermodynamic map.

The invention relates to the technical field of multi-image fusion recognition, in particular to a data fusion method and system for CCD visual detection, and the method comprises the following steps: obtaining a horizontal pixel row calculation gradient construction trend sequence, repairing an edge fracture to generate an integrity index, extracting a gray value to detect feature mutation, and distributing fusion weights to establish a mapping relation. And executing image fusion and balancing the contrast to generate a fusion matrix result. According to the method, the fracture edge region is identified, interpolation compensation is executed, the structural similarity index of the local gray sequence in the image overlapping region and feature direction mutation detection are combined, accurate identification of the edge matching result is guided, and fusion weight factor mapping corresponding to signal-to-noise ratio distribution is introduced; according to the method, the distribution relation between the pixels in the region and the credible weight is effectively established, the edge transition among the multi-source images is more natural through Poisson constraint and contrast balance adjustment of the fusion region, and the structural fidelity and the judgment stability of the fusion image are remarkably enhanced.

The invention discloses an educational resource image intelligent recommendation and multi-scale matching system and method based on machine learning, and relates to the technical field of intelligent education, a permeability evaluation model is constructed by collecting interactive behavior data of students and educational resource images to quantify cognitive stability and knowledge mastery parameters; synchronously performing multi-scale analysis on the image to generate a visual feature, a knowledge association feature and a cognitive guide feature; and determining an optimal recommendation level based on dynamic granularity selection processing, generating an enhanced image with a permeability feedback mark, and interactively generating learning evaluation data through the enhanced image. According to the method, cognitive dynamic evaluation and multi-scale feature matching are fused, so that the technical bottleneck of a traditional recommendation system on image granularity selection and cognitive adaptation is solved, and the educational resource recommendation accuracy and learning efficiency are remarkably improved.

The invention belongs to the technical field of digital restoration of computer vision and cultural heritage, and particularly relates to a traditional picture restoration method fusing low-resolution prior and efficient visual selection, which comprises the following steps: constructing a multi-source image data set, taking images in the multi-source image data set as high-resolution images, preprocessing the high-resolution images to obtain low-resolution images, and carrying out high-resolution priori and high-efficiency visual selection on the low-resolution images. The high-resolution image and the low-resolution image are respectively masked to generate simulated damage mask images, and the simulated damage mask images comprise a regular damage mask image and an irregular damage mask image; taking the multi-source image data set and the preprocessed multi-source image data set as training data, and training a multi-source image model; the dual-stage repair network comprises a coarse repair network and a fine repair network; according to the method, the problems of structural semantic loss, high priori information dependency and insufficient global and local coordination when an existing image restoration method is used for processing a complex scene and a large-range missing region are solved.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention provides a tunnel crack identification method and system based on multi-source image processing, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining multi-source data of a horizontal rock stratum tunnel; performing spatial registration on the multi-source data to generate a multi-modal image under the same reference system; extracting multi-modal features according to the multi-modal image, and constructing a multi-source feature map under the same space grid; performing crack initial detection on the multi-source feature map in combination with multi-scale filtering and structure tensor analysis to obtain a crack candidate region mask; accurate crack identification is carried out through the crack candidate area mask, a crack identification result is obtained by combining bedding direction constraint and a microconditional random field, and the crack identification result comprises a crack segmentation map and a crack category. The method solves the problem that existing crack identification does not consider the bedding characteristics of the horizontal rock stratum tunnel.

The invention relates to the technical field of fire-fighting early warning, and discloses a fire-fighting early warning system based on image data relevance. The system comprises a multi-source image acquisition module used for acquiring fire-fighting scene multi-source heterogeneous image data; the correlation feature analysis module is used for performing cross-data-source correlation analysis on the data to generate feature vectors; the spatio-temporal dynamic modeling module is used for constructing a multi-dimensional feature fusion space to generate an associated spatio-temporal feature tensor; the resource optimization scheduling library is used for constructing a double-layer collaborative library; and the intelligent early warning control module generates a real-time fire early warning instruction and an emergency response decision through a hierarchical reinforcement learning framework. The system also has the functions of building structure deformation detection, smoke diffusion prediction and the like. Through cooperation of multiple modules, accurate fire-fighting early warning and efficient emergency response are realized, the fire prevention and control capability is improved, and life and property safety is effectively guaranteed.

The invention relates to an infrared and visible light image fusion method based on a cross-domain Transform, and belongs to the field of computer image processing. The method comprises the following steps: respectively carrying out preprocessing operation on an infrared image and a visible light image to obtain a training data set; an end-to-end image generator network is designed, an encoder module is used for extracting deep semantic features of an infrared image and a visible light image, a fusion module introduces an axial attention mechanism to enhance the global modeling capability of the features, and feature fusion is carried out in combination with information of a spatial domain and a frequency domain; the fused features are gradually recovered to an image space through a decoder module, and a fused image is generated; constructing a fusion loss function module, and guiding the network to focus a significant feature difference between the source image and the fusion image based on a comparative learning idea; and finally, inputting the infrared and visible light image Y channel into the network model, generating a fusion image, completing a training process, and realizing unified optimization of fusion performance and visual quality.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention discloses an intelligent detection method for fault detection of an outdoor power line, and the method comprises the following steps: 1, carrying out the collection and preprocessing of multi-modal data, and carrying out the collection and preprocessing of the multi-modal data through an unmanned plane cluster, a distributed optical fiber sensor, a laser radar and meteorological monitoring equipment; visible light image data, infrared image data, laser point cloud data, vibration waveforms, temperature distribution and environmental parameters of the power line are synchronously obtained, and multi-source image data are processed, namely the visible light image data, the infrared image data and the laser point cloud data are processed; and 2, intelligent fault diagnosis: inputting the data acquired in the step 1 into a multi-task neural network model, and outputting a fault positioning and type identification result. According to the novel detection method based on multi-modal data fusion, an intelligent algorithm and closed-loop optimization, the fault identification precision, the dynamic decision-making capability and the comprehensive protection efficiency are improved, and the intelligent operation and maintenance requirements of a modern power grid are met.

The invention discloses an intelligent safety monitoring method and system for a glass curtain wall, electronic equipment and a storage medium, and relates to the technical field of curtain wall detection. The method comprises the following steps: acquiring multi-source image data of an external facade of the glass curtain wall, wherein the multi-source image data comprises a front view image, an infrared thermal imaging image and an ultraviolet spectrum image of the glass curtain wall, which are acquired at different time periods and different angles; performing registration fusion and identification analysis on the multi-source image data to obtain crack characteristics, deformation characteristics and temperature distribution characteristics of the surface of the glass curtain wall; establishing an abnormal characteristic spectrum of the glass curtain wall based on the crack characteristics, the deformation characteristics and the temperature distribution characteristics; performing spatio-temporal evolution analysis on the abnormal characteristic spectrum to determine a multi-level risk area of the glass curtain wall; and generating a monitoring report of the glass curtain wall according to the multi-level risk area. By implementing the technical scheme provided by the invention, comprehensive monitoring of the glass curtain wall can be realized, so that the monitoring accuracy of the glass curtain wall is improved.

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

The invention belongs to the technical field of super high-rise building three-dimensional reconstruction, and particularly relates to a multi-source data fusion super high-rise building group live-action three-dimensional model construction method. According to the method, an initial three-dimensional model is generated through a series of processing such as aerial triangulation encryption and triangulation network construction based on multi-source image data, in the process of recognizing a fuzzy region and performing data supplementary collection, regions with texture loss and structure distortion in the initial three-dimensional model can be positioned, supplementary collection requirements are determined according to characteristics of different regions and a preset threshold value, and the recognition accuracy of the initial three-dimensional model is improved. The method comprises the following steps of: performing oblique photography on an unmanned aerial vehicle to acquire data in a supplementary manner, fusing the data with original data, extracting a building structure contour, matching high-resolution texture data, performing texture binding and processing and the like to form a building monomer model, and performing spatial position and texture fusion on the building monomer model and a process three-dimensional model to generate a regional three-dimensional live-action model. And finally, splicing and fusing the three-dimensional live-action models of all the areas to form a complete super high-rise building group live-action three-dimensional model.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention relates to the technical field of machine vision structure health monitoring, and discloses a steel structure weld fatigue cracking real-time monitoring system based on machine vision. The system comprises a space-time registration and fusion module, a multi-scale feature analysis module, a health monitoring module, a crack deduction calculation module and a regulation and control strategy generation module. Performing space-time registration and pixel-level fusion through the visual data of the plurality of image sensors to generate a synchronous multi-source image stream; a multi-level welding seam characteristic spectrum is constructed through multi-scale characteristic analysis, and a welding seam structure knowledge base is dynamically updated; the knowledge base and the real-time characteristic spectrum are used for monitoring the welding seam health state, and abnormity is recognized; deducing a crack initiation position and an evolution path in combination with historical damage data; and real-time load information is fused to pre-estimate the remaining service life, and a structural integrity regulation and control strategy is generated online. According to the invention, high-precision fusion of the multi-source visual data and active prediction of the crack trend are realized, and the monitoring accuracy and the early warning capability are improved.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention discloses a feature fusion-based loaded coal and rock mass instability failure early warning method and system, and the method comprises the following steps: obtaining the information of an internal strain field and an external strain field through a coal and rock loading deformation failure test, and constructing a whole space-time sample data set; pre-training an auto-encoder for realizing feature extraction and constructing a multi-modal image; integrating depth features extracted from different source images by adopting a fusion strategy based on a self-attention mechanism so as to realize feature fusion; during training, inputting a multi-modal image in the data set into the neural network to obtain a strain field spatio-temporal evolution model; after the test is completed, the corresponding stage of the strain field measured by the sensor and the prediction result of the model are compared and verified, the relevant parameters of the model are adjusted according to the accuracy, and the fitting and generalization ability of the model is enhanced.

Disclosed in the present invention is a soft tissue photoacoustic / ultrasonic multi-modal image fusion method based on deep learning. The method comprises the following steps: an ultrasonic-photoacoustic imaging device collecting a human body soft tissue photoacoustic image and ultrasonic image, and performing size normalization processing; inputting the images into a spatial conversion module to convert same into a YCbCr space; inputting the images into a pre-convolutional module to change the number of data channels; inputting the images into a multi-scale feature extraction module to extract salient features of the source images; inputting the features into a filter prediction module to obtain a multi-scale filter; and inputting the filter into a filtering fusion and adaptive enhancement module and combining same with the input source images, so as to obtain a final fusion result. Compared with several traditional fusion methods and deep-learning-based fusion methods, the method provided in the present invention has a greater fusion effect, and more importantly, has an excellent real-time performance. Moreover, multiple modes of photoacoustic / ultrasonic fusion extension experiments are performed on a photoacoustic / ultrasonic multi-modal imaging system, verifying the effectiveness of the method in the present invention.

The invention discloses an image editing method and system based on diffusion model inversion and attention optimization, and relates to the technical field of image editing, and the method comprises the steps: mapping a source image to a potential space through a pre-trained automatic encoder, and obtaining an initial noise feature; an EF noise space inversion algorithm is adopted to process the initial noise features, a high-variance noise graph is generated, and an intermediate result under each time step is obtained through a decoder; constructing an improved U-Net network based on edit-friendly feature reweighting and an edit-friendly attention mechanism, fusing the target prompt information into a denoising process of the improved U-Net network through a cross attention mechanism, and performing feature optimization based on the improved U-Net network; and reconstructing the potential features through a U-Net decoder, and generating an edited image conforming to the target prompt information. On the low-cost premise that complex model fine tuning and retraining are not needed, the method is superior in text-guided controllable editing tasks, and the image generation quality is improved.

The invention provides an image processing method and a related device. The embodiment of the invention can be applied to various scenes such as artificial intelligence. The method comprises the following steps: acquiring a source image and a query image; performing feature extraction on the source image and the query image, and performing feature fusion on the obtained source object texture feature information and physical attribute feature information to obtain texture fusion feature information; performing regional semantic recognition and identity feature recognition on the source image through a regional response network to obtain a regional semantic information set and identity feature information; taking the identity feature information and the regional semantic information set as denoising conditions, and performing denoising processing on the texture fusion feature information through a denoising network to obtain target feature information; and decoding the target feature information to obtain a target image. According to the method provided by the embodiment of the invention, the quality and authenticity of the generated image can be improved, the key attributes and features of the source image can be better maintained, and the generation of a more vivid and natural target image is facilitated.

The invention discloses an industrial robot image processing method based on image fusion, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting an original image in real time through deploying an image collection device integrating visible light, polarization and multispectral imaging, and extracting suspended matter density, water body light transmittance and illumination intensity change information; generating a first image set; suppressing suspension interference through image filtering and enhancement processing to obtain a first corrected image; extracting an aquatic product individual region, performing multi-source image fusion, identifying color deviation, texture interruption and reflection feature anomaly regions, and constructing a lesion candidate set; gray scale reconstruction, edge gradient and brightness normalization correction of a multispectral channel are executed based on illumination and reflection changes, and a high-quality fusion image is generated; and calculating a health anomaly probability coefficient of the target individual by using the depth recognition model, comparing the health anomaly probability coefficient with a threshold value, and recording a recognition result and collecting information if the threshold value is exceeded. The method can significantly improve the accuracy of aquatic individual lesion recognition.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

Systems and techniques are provided for processing image data. For example, a process can include processing a source image to generate a first features for the source image and a target image to generate a second features for the target image. The process can include generating a first cluster map for the source image based on prototypes and the first features for the source image, and generating a second cluster map for the target image based on the prototypes and the second features for the target image. The process can include determining a propagated cluster map for the source image based on the first cluster map and a correspondence between regions of the source image and regions of the target image. The process can include determining a loss based on a comparison of the propagated cluster map for the source image and the second cluster map for the target image.

The invention discloses an interactive three-dimensional Gaussian editing method and system based on three-dimensional geometry consistent attention priori. The method comprises the following steps: firstly, generating a multi-view source image through a three-dimensional Gaussian splashing technology, performing preliminary editing by using a diffusion model, and interactively selecting a key view; then, a CLIP model is adopted to screen reference views with consistent semantics, and three-dimensional geometric consistent attention priori is constructed; and finally, dynamically fusing the attention of the three-dimensional priori and the two-dimensional diffusion model through an adaptive cross-dimensional attention fusion network to realize a consistent editing result of multiple views. According to the method, user interactive key view selection, reference view semantic screening and attention fusion based on 3D constraint are introduced, so that the problems of multi-view inconsistency and local detail loss in the existing three-dimensional editing process are effectively solved, and the geometric consistency and detail reduction capability of the editing effect are remarkably improved.

The invention belongs to the technical field of image information processing, and discloses a scene guidance prompt representation enhanced infrared and visible light image fusion method, which is divided into two stages: a first stage, constructing a scene prompt generation network, and learning global visual semantic information covering a source image through a semantic segmentation task; in order to further enhance the prompt representation capability, a visual perception context prompt module is designed, interaction is performed by using a correlation matrix between modal specific features and text features, and the text features are refined in a dynamic weighting mode, so that scene prompt representation with richer semantics is obtained. In the second stage, a cross-modal alignment fusion network guided by prompt is provided, and infrared and visible light features are mapped to a unified shared embedding space by utilizing learned scene prompt. In the process, a pixel-text similarity matrix is established through a prompt driving feature alignment module, and accurate alignment of cross-modal features is realized, so that a fusion result of semantic consistency and detail fidelity is obtained.

The invention discloses a motor coil visual detection system and method based on multi-modal data fusion, and relates to the technical field of image recognition. The system solves the problems of single detection dimension, spatial structure information loss and the like in the existing motor coil visual detection, and comprises an image acquisition module, a data processing module, a defect identification module and a result output module. The image acquisition module comprises an RGB camera, a depth camera and a thermal imaging camera, and is used for respectively acquiring an appearance image, three-dimensional structure information and a temperature distribution diagram of the motor coil; the data processing module is used for performing registration, denoising and feature extraction on the multi-source image, and constructing uniform feature representation through a multi-modal fusion algorithm; the defect identification module analyzes the fusion features based on a deep learning model to realize appearance defect identification, three-dimensional deformation detection and thermal anomaly positioning of the coil; and the result output module classifies and evaluates the identification result and uploads the identification result to an upper system. The method is suitable for quality control and intelligent judgment of motor coil assembly.

The invention relates to the technical field of hydraulic engineering structure safety monitoring, in particular to an intelligent early warning system and method for hydraulic engineering dam body cracks. According to the system, a sensor network is deployed, multi-source image data of a dam body is collected, structural perception attention variation denoising is adopted to fuse gray scale, infrared and thermal imaging channel characteristics, noise is inhibited, and crack details are reserved; modeling a spatio-temporal evolution sequence in combination with a time sequence convolutional network, constructing a dynamic semantic structure map, mapping spatial positions, morphological characteristics and semantic levels of crack nodes, generating a neural network through map enhancement to split structure codes and semantic codes, and fusing a variation generation module to predict a future crack evolution state; a risk scoring function is constructed based on SHAP interpretability analysis, pop-up window early warning is triggered in combination with a dynamic threshold value, scheme generation is automatically responded, and expert collaborative diagnosis is carried out, so that a closed-loop early warning decision-making system is formed. According to the invention, the real-time performance of crack monitoring and the early warning reliability are obviously improved.

The invention discloses a robot autonomous disassembly method and system based on multi-source visual perception, and relates to the technical field of biological pharmacy, and the method comprises the following steps: obtaining initial image data of a target bagged product, including a color image collected by an RGB camera and a depth image collected by a depth camera, and respectively marking collection timestamps; synchronous alignment of image data is carried out based on timestamps, and spatial position information of different source images at the same moment is matched by constructing a unified time coordinate system. Through integration of technologies such as multi-source visual image synchronization, three-dimensional modeling and track control, residue detection and the like, high-precision autonomous disassembly of the biopharmaceutical bagged product by the robot is realized, the problem of misoperation caused by image time sequence asynchronization is solved, the system stability and the cleanliness control capability are improved, and good application value is achieved.