7026 results about "Very high resolution" patented technology

The invention discloses a machine vision-based resistor disc defect online detection system and a grading method, relates to the technical field of industrial machine vision detection, and solves the defect problems in the aspects of multi-scale defect dynamic perception, cross-level feature interaction and process adaptive optimization in the prior art. According to the scheme, metal reflection interference is inhibited through Retinex illumination correction and a combined denoising model; adopting a deformable convolution kernel and cavity space pyramid pooling to realize gradient entropy driving dynamic sensing of the multi-scale defect; constructing a bidirectional cross-layer attention network to realize early fusion of high-resolution details and high-level semantics; modeling local-global feature physical association based on a graph attention network and a self-supervised message passing mechanism; integrating reinforcement learning and a memristor random calculation unit to form a closed-loop parameter optimization system; according to the method, the multi-scale defect detection precision, the cross-modal feature fusion efficiency and the system adaptive capacity under complex working conditions are remarkably improved.

The invention discloses a deep learning-based small target defect recognition model training method, relates to the technical field of defect recognition model training, and aims at meeting small defect detection requirements, starting with high-resolution diversified data construction and accurate labeling, highlighting weak targets through multi-scale feature fusion and spatial attention, and realizing high-resolution target defect recognition. A hard case scene is processed in cooperation with layer-by-layer screening and secondary intensified training, real-time iterative optimization is achieved through multi-model fusion and online dynamic adjustment and optimization, finally, multi-mode and time sequence dimensions are expanded to capture deeper and dynamic defect information, the missing detection and false detection rate is greatly reduced, and the detection efficiency is improved. The detection efficiency and adaptability of micron-sized defects under a complex process background are improved; furthermore, by means of multi-source data such as infrared, X-ray or 3D morphology and a time sequence modeling means, multiple dimensions are fused, and hidden or early cracks are brought into a detection and prediction range, so that a high-reliability and evolvable intelligent recognition system for the tiny target defects is constructed.

The invention relates to the technical field of anti-cracking performance testing, in particular to an ultra-high performance concrete anti-cracking performance testing system and a dynamic monitoring method thereof.The system comprises a dynamic stress field simulation feedback module used for sensing three-dimensional distribution of a micro stress field in concrete in real time by constructing a flexible loading array to obtain dynamic stress field data; the multi-scale damage evolution tracking module is used for deploying a high-resolution acoustic emission network and a distributed optical fiber sensing layer based on dynamic stress field data of the flexible loading array to form a full-scale evolution graph from microdefects to macroscopic cracks; and the intelligent healing efficiency evaluation module obtains stress redistribution data and a full-scale evolution graph, and a repairing medium containing a tracer agent is injected into a preset crack path. According to the method, the whole process of crack resistance of the material from defect initiation to repair and regeneration can be quantitatively evaluated; and finally, outputting a dynamic evolution rule of the crack resistance and enhancing potential evaluation.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The invention discloses a high-resolution radar echo extrapolation prediction method fused with satellite data, and the method specifically comprises the following steps: firstly, inputting historical radar echo sequence preprocessing at a previous T moment, including denoising, normalization processing and data set segmentation, and obtaining cleaned data; then, through a deterministic modeling method (SimVP), a fuzzy prediction sequence of a future T duration is obtained, then a variational auto-encoder (VAE) maps an original radar echo image and the fuzzy prediction sequence to a low-dimensional potential space, and two-stage diffusion modeling is carried out on the basis; in the first stage, a space-time converter (ST-Translator) is used to extract space-time evolution characteristics of radar echoes; in the second stage, satellite data at the corresponding time of the previous T moment is input, preprocessing including normalization processing, feature selection and data set segmentation is carried out, cleaned data is obtained, and the influence of the satellite data is dynamically adjusted in the diffusion process by adopting a multi-source fusion denoising network Fsrform so as to make full use of satellite information; and finally, inversely transforming output results of the two stages into a pixel space to obtain a high-resolution radar echo extrapolation prediction result of the future T duration. According to the invention, computing resource consumption can be effectively reduced, and the precision and detail fidelity of short temporary rainfall prediction are improved.

The invention relates to the field of remote sensing image processing, in particular to a high-resolution remote sensing image semantic segmentation method based on multi-scale feature fusion, which comprises the following steps: acquiring a public remote sensing image data set, preprocessing the image, and constructing a training and testing set of semantic segmentation; a CTMFNet is designed, an encoder is composed of a lightweight residual module and an MS-Transform, and local space details and global context information are extracted; rID is adopted to reduce spatial information loss, LSFE is introduced to improve spatial positioning capability, and feature calibration is carried out in space and channel dimensions through DecoderAttn to realize boundary fine segmentation; inputting the training sample into the network for training to obtain a converged optimal semantic segmentation model; and inputting the test set into the model to obtain a semantic prediction map, and outputting a fine segmentation result of the remote sensing image through multi-scale fusion and boundary restoration. According to the method, the precision and robustness of ground feature extraction are effectively improved, the calculation cost is remarkably reduced while high segmentation precision is kept, and the method has good practical value and popularization prospects.

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 present invention relates to a method and system for estimating forest carbon storage that combines artificial intelligence algorithms and multimodal remote sensing data. This approach comprehensively utilizes laser radar satellites, multi- / hyperspectral satellites, radar satellites, high-resolution optical imagery, etc. A hybrid technical system is employed for different forest coverage areas, resulting in high-precision forest carbon storage mapping with a resolution of 10 meters and area coverage. This provides technical support and assurance for assessing global forest carbon storage and supporting forestry carbon sequestration transactions.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention relates to the technical field of additive manufacturing, and particularly discloses a real-time defect detection and in-situ repair method and device in the additive manufacturing process, and the method comprises the steps that a multi-mode sensing system is integrated on additive manufacturing equipment, the multi-mode sensing system comprises a high-resolution CCD camera, a laser scanner and a thermal infrared imager, and data acquisition is synchronized; a hierarchical scanning strategy is adopted, two-dimensional surface images, three-dimensional point cloud data and thermal field distribution information are collected before and after each layer is machined, and the data synchronization precision is smaller than 0.1 ms; according to the invention, a multi-modal sensing system and an advanced algorithm are integrated, so that real-time detection and accurate repair of defects are successfully realized; a high-resolution CCD camera, a laser scanner, a thermal infrared imager and other sensors are adopted, a cross-scale RANSAC algorithm and a depth feature fusion network are combined, surface and internal defects are accurately detected, and laser remelting parameters are adjusted in real time for in-situ repair.

Systems and methods for monitoring and evaluating time-series real-time environment data to create a high-resolution, high-fidelity actual (e.g., nowcast) and predicted (e.g., forecast) representation of an environment of interest. In some aspects, the system comprises instructions to obtain a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period, identify one or more precursory signals within the set of real-time environment measurements, determine at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold, generate a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements, and display, at a user interface, the generated time-series representation of the actual environment state.

The invention provides an underground water dynamic evolution prediction method, and belongs to the technical field of underground water prediction based on deep learning. Hydrological parameters and geographic position data of monitoring points are collected, and a regional hydrogeological input tensor is constructed; then establishing an initial finite element model to carry out space extrapolation and flow field completion on underground water level distribution; based on an initial finite element model structure, designing a global optimization algorithm based on target decomposition, carrying out real-time structure optimization on the finite element model, then simulating underground water evolution of a non-monitoring area through a finite element numerical solution, and generating multi-node physical consistency hydrological time series data after resolution is improved; and inputting the generated hydrological time series data into a designed perceptual water level prediction model, and outputting underground water level prediction results at multiple moments and multiple positions in the future and corresponding underground water level thermodynamic diagrams. According to the method, more accurate, continuous and interpretable high-precision prediction of the underground water system is realized, and reliable support is provided for scientific management of underground water resources.

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 relates to the technical field of oceanographic engineering surveying and mapping, and provides an oceanographic engineering surveying and mapping deep sea terrain reconstruction method, which comprises the steps of multi-modal data synchronous acquisition, frequency domain feature cross enhancement, dynamic convolution feature fusion, inverse projection terrain reconstruction and terrain credibility verification. According to the invention, an acousto-optic multi-mode fusion technology is adopted, and the advantages of acoustic penetrability and optical high resolution are combined, so that the complex terrain detail capturing capability is improved; the dynamic convolution network adaptively adjusts convolution kernel parameters through frequency domain energy, the multi-scale feature fusion effect is enhanced, and the accuracy of terrain texture expression is improved; according to the motion distortion compensation method, carrier attitude drift is corrected in real time through inertial navigation data, and the geometric fidelity of a reconstruction model is guaranteed. The method is suitable for a deep sea complex environment, and provides high-precision terrain reconstruction method support for ocean engineering surveying and mapping and ocean resource exploration.

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 relates to the field of artificial intelligence, in particular to a medical image computer-aided analysis method based on deep learning, and aims to solve the problems that an existing medical image analysis method is low in high-resolution image processing efficiency, insufficient in tiny focus recognition precision, weak in model generalization ability and insufficient in multi-modal image fusion. According to the method, a lightweight multi-scale feature extraction network is constructed to improve the high-resolution image processing efficiency, a fine-grained lesion recognition module is introduced to improve the detection precision of a tiny lesion, and a self-adaptive regularization strategy is adopted to enhance the model generalization ability. And a multi-modal deep fusion mechanism is designed to make full use of complementary information of different modal images. According to the invention, medical image analysis which is more efficient, more accurate, higher in generalization ability and capable of effectively fusing multi-modal information can be realized, so that clinical application of deep learning in the field of medical images is promoted.

The invention discloses a medical image segmentation method based on high-resolution modal guidance and cross-modal boundary perception, and the method comprises the steps: carrying out the data preprocessing and enhancement of multi-contrast magnetic resonance imaging data, obtaining a boundary mask through a Canny operator and a Dilatation operation, constructing a multi-modal low-resolution data set, and carrying out the recognition of the multi-modal low-resolution data set; meanwhile, a high-resolution T2f modal data set is reserved, and the data set is divided into a training set, a verification set and a test set; a segmentation model is constructed, and the segmentation model comprises a high-resolution mode-guided double-encoder architecture module, a cross-level attention collaboration mechanism module, and a segmentation branch and boundary prediction branch decoder module; designing a training strategy of joint optimization of boundary contour detection and region segmentation, training the segmentation model by using a training set, and storing optimal model parameters on a verification set; and carrying out model performance verification in the test set, and segmenting a to-be-tested medical image by using the verified segmentation model.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention discloses an intelligent planning method for space monitoring of an unmanned aerial vehicle. The method comprises the steps that intelligent path allocation is realized by constructing a task demand priority matrix; the method comprises the following steps: firstly, collecting geographical, climate and environmental parameters of a monitoring area, quantifying regional complexity and color features of a monitoring target by combining high-resolution image data with a neural network model, and generating a priority matrix according to task importance, change frequency and risk level; a Dijkstra algorithm is adopted to plan an initial flight path giving consideration to priority and flight limitation, a path complexity index is calculated, and the index comprehensively considers a target priority weight, a task detouring coefficient and a path relaxation degree; and finally, dividing a monitoring area into height layers according to a path complexity index threshold value, performing height layer adjustment on the initial path, and generating a dynamic flight path containing height layer switching, thereby realizing efficient resource allocation and accurate risk prevention and control in a complex monitoring scene.

The invention relates to a high-fidelity cloud rendering cluster scheduling method and system, and the method comprises the steps: receiving a rendering task, generating a multi-level priority queue according to the task complexity, timeliness and resource demand classification, and automatically optimizing a scheduling strategy; monitoring the resource state of the heterogeneous computing node in real time; allocating tasks by using preemptive and round-robin scheduling strategies, and adjusting and coping with resource fluctuation in combination with a dynamic code rate; the tasks are decomposed by adopting a spatial blocking and time framing strategy, and an execution sequence is controlled according to a topological sorting algorithm; abnormal nodes are identified through heartbeat detection, and affected subtasks are migrated through incremental task updating. According to the method, efficient resource allocation, scheduling algorithm and idle key frame scheme can be realized, the GPU directly outputs the video stream, the rendering speed is remarkably improved, resource waste and operation cost are reduced through a dynamic resource allocation mechanism, a fault-tolerant mechanism is provided, the task is ensured to be normally completed under the condition of node fault, and the task efficiency is improved. And distributed rendering and synthesis of large-scale high-resolution images are supported.

The invention discloses a powder metallurgy gear hardness detection method and system, and belongs to the technical field of metallurgy gears, and the method comprises the steps: obtaining a to-be-detected gear three-dimensional model, and dividing a hardness evaluation region; a thermal response image is collected, a thermal response time gradient parameter TRTG is extracted, and a thermal diffusion characteristic matrix T is formed; establishing a hardness initial model H1 based on the TRTG and compactness; constructing a finite element stress field model, extracting a stress distribution modulation coefficient SDMC, and forming a stress characteristic matrix S; performing weighted correction on H1 based on T and S, and establishing a comprehensive hardness prediction model H2; outputting a hardness prediction result of the whole gear and the key area, and identifying a potential weak area; according to the method, thermal response and stress modulation parameters are fused, non-destructive and high-resolution hardness distribution prediction is achieved, and the method is suitable for quality evaluation and failure early warning of the powder metallurgy gear of a complex structure.

The invention discloses a coal mining subsidence area surface crack intelligent extraction method based on an improved Transform model, and belongs to the technical field of remote sensing image processing. Firstly, an unmanned aerial vehicle carrying a high-resolution optical camera is used for collecting images, the image overlapping rate of 70%-80% is guaranteed, and a training data set is constructed through professional labeling, cutting screening and data enhancement. The encoder of the innovative model is very distinctive, and the adaptive multi-scale patch mapping layer can dynamically adjust the patch size according to the local complexity of the image and efficiently extract features; double-attention fusion is combined with optimization position coding, and long-distance dependency capture is enhanced; and the calculation amount and the overfitting are reduced by the dynamic sparse connection full-connection layer. Residual attention enhancement pyramid pooling and a space-channel attention bottleneck mechanism are adopted, key features are highlighted, and noise is suppressed; and a breakpoint detection and connection rule determination module is utilized to realize complete restoration of the ground fracture. After the data set is used for training a model, deployment is carried out, and through preprocessing, encoding and decoding and post-processing, ground fracture information can be accurately obtained, and a data foundation is built for mining area safety management and geological disaster prevention and control.

The invention discloses a slope disease identification method and device based on an unmanned aerial vehicle and a machine vision technology, and the method comprises the steps: carrying out the multi-angle image collection of a slope region on a preset flight path through an unmanned aerial vehicle carrying a high-resolution camera; preprocessing the image, and performing feature extraction and image enhancement; recognizing and classifying disease features by combining an improved YOLOv8-seg instance segmentation algorithm, wherein slope diseases such as cracks, landslides, collapse and the like are covered; quantifying the risk level of the slope disease by using a rule model or a multi-factor analysis method based on the identification result; and carrying out dynamic change prediction on the acquired disease time sequence image by adopting a long-short term memory network (LSTM), and predicting the development trend and the potential instability time of the disease. On the basis of image data collected by the unmanned aerial vehicle, real-time monitoring and dynamic analysis are carried out on the slope diseases, potential hidden danger areas are found in time, disease features can be accurately recognized, and subjective errors possibly caused by manual judgment in a traditional method are avoided.

The invention discloses a plateau mountain road disaster identification method and system based on multi-source remote sensing image restoration and super-resolution reconstruction. The method comprises the following steps: acquiring and preprocessing a multi-source remote sensing image of a plateau mountain region, and extracting landform measurement parameters based on a digital elevation model; a super-resolution reconstruction network fusing deformable convolution and Transform is constructed, and a low-resolution image is reconstructed by using constraint training of a composite loss function containing geomorphic measurement parameters; performing feature extraction and adaptive weighted fusion on the preprocessed image and the reconstructed high-resolution image; based on the fused image, utilizing a multi-task deep learning model to identify landslide, debris flow and roadbed subsidence disasters along the highway; and carrying out morphological optimization and boundary refinement under GIS constraint on an identification result, and outputting a disaster thematic map. According to the invention, the precision and reliability of road disaster identification in a complex terrain environment are effectively improved.

The invention discloses an internal wave characteristic field information rapid prediction method based on deep learning, and belongs to the technical field of ocean internal wave detection. According to the method, rapid prediction is carried out on the basis of traditional numerical simulation data by using deep learning, so that the efficiency of inner solitary wave prediction is remarkably improved; through multi-channel coupling learning and an iterative prediction strategy, the inner wave field prediction achieves the effect of high precision and high efficiency. Meanwhile, in combination with high-resolution numerical mode data, the consistency of a model prediction result and a physical process is ensured, and rapid extension period prediction of a solitary wave characteristic field in the ocean is realized while low error and high relevancy are kept. The method is suitable for application scenes of real-time ocean internal wave monitoring and early warning and the like, and can be widely applied to the technical fields of ocean environment monitoring and early warning, intelligent ocean observation, ocean engineering safety and the like.

The invention relates to the field of image comparison, and discloses a multi-layer PCB alignment deviation detection system and method based on image comparison, and the method comprises the steps: obtaining high-resolution image data before and after lamination of a multi-layer PCB, carrying out the unified registration preprocessing of an original image through the combination of a multi-mode image fusion algorithm and a geometric distortion correction technology, and obtaining a multi-layer PCB alignment deviation detection result; constructing a standardized image registration input set; key alignment feature extraction is carried out on the image registration input set, and a multilayer structure graph model is constructed based on a graph neural network; in combination with the alignment reference map, dynamically adjusting an image comparison window and a search region by adopting a region attention mechanism and a local adaptive matching algorithm, and constructing an alignment deviation mapping map; constructing a deviation evolution model by using a time sequence behavior recognition network according to the constructed alignment deviation mapping graph and historical process deviation data; and performing comprehensive evaluation on the image registration input set based on the deviation evolution model and the generated early warning information. The method has the advantage of improving the accurate detection level.

The invention provides a wavelength division multiplexing-fused multi-core optical fiber temperature detection method and system. Wherein the broadband light source is divided into multiple wavelength channels through wavelength division multiplexing, and a wavelength-fiber core mapping relation is established, so that each fiber core of the multi-core optical fiber transmits a single-wavelength optical signal. An optical fiber is integrated to the surface of an object according to a three-dimensional path, each fiber core covers a plurality of sensing sub-areas, the wavelength drift amount caused by temperature is extracted by detecting the attenuation characteristic of an optical signal at a bending position, the wavelength drift amount is converted into a temperature value in combination with a calibration curve, and a temperature gradient model bound with the fiber core position is constructed. And further performing spatial solution by using fiber core spacing, wavelength mapping and transmission time delay parameters, establishing a corresponding relationship between the coordinates of the sensing sub-regions and the temperature, and finally reconstructing three-dimensional temperature field distribution. According to the technical scheme provided by the invention, high-resolution dynamic reconstruction and gradient analysis of a three-dimensional temperature field on a complex surface are realized through wavelength-space two-dimensional decoupling.

The invention discloses a feature integration method for interactive convolution and dynamic focusing of infrared images. The feature integration method comprises the steps that infrared image pairs with different resolutions in various real scenes are obtained through an infrared camera; performing degradation preprocessing on a part of original high-resolution images to obtain low-quality high-resolution images to form a mixed low-resolution data set, and dividing the processed data set into a training set and a test set; constructing a double-layer feature extraction module for feature modeling; training a network by using the processed training set, and optimizing a loss function; and inputting a low-resolution infrared image into the trained network, and outputting a high-resolution reconstruction result. According to the method, local and global features are fused, so that the super-resolution reconstruction quality of the infrared image in complex scenes such as low contrast and fuzzy edges is remarkably improved, and meanwhile, relatively high calculation efficiency is kept.