601 results about "Spatial consistency" patented technology

The invention discloses a time sequence marine ecological environment early warning monitoring data abnormal point detection method, and relates to the technical field of marine ecological environment analysis, and the method comprises the steps: collecting a plurality of types of marine monitoring indexes, constructing a multi-source and multi-dimensional time sequence data set, and carrying out the preprocessing and unification of the data into a standard input format. And based on the causal relationship and co-evolution characteristics between the factors, constructing a graph structure model, and depicting a coupling propagation path between the factors. Generating a local time sequence model by adopting a sliding window, constructing an anti-fact path, comparing the propagation difference of an original path in a map structure, and identifying a key disturbance moment; and in combination with historical event similarity and space consistency information, the early warning credibility of abnormal points is evaluated, and efficient and interpretable graded early warning response is realized. According to the method, accurate identification and credible early warning of abnormal points are realized, causal modeling, anti-factual reasoning and hierarchical response capabilities are realized, and the intelligence and interpretability of marine ecological monitoring are improved.

A defect prediction method based on a multi-feature parallel multi-stage neural network (MF-PMSNN), includes: obtaining a trajectory dataset, and preprocessing data of a defect of a workpiece in additive manufacturing (AM); building an MF-PMSNN, and evaluating an output classification result based on evaluation indicators; and performing real-time defect prediction, and deploying a trained MF-PMSNN model to a production environment. The present disclosure combines and effectively matches thermal imaging-based in-situ monitoring data and X-ray computed tomography (XCT)-based in-situ monitoring data to ensure temporal and spatial consistency between the thermal imaging-based in-situ monitoring data and the XCT-based in-situ monitoring data. In this way, a molten pool status and a pore of the workpiece can be captured more comprehensively. The MF-PMSNN is proposed to obtain a molten pool status and the porosity distribution in the data and perform defect prediction.

The invention discloses a three-dimensional scene reconstruction method and device based on structure perception. The method comprises the following steps: constructing a sparse point cloud generation model according to a diffusion model based on a Unet architecture; inputting the information into a sparse point cloud generation model, and generating camera internal and external parameters and sparse point cloud data of the RGB image frame under the corresponding view angle; constructing a rough three-dimensional structure grid according to the sparse point cloud data; carrying out random sampling on the rough three-dimensional structure grid, and carrying out Gaussian field initialization on the Gaussian primitives according to a random sampling result to obtain an initialized Gaussian field; the loss between the current image frame and the RGB color image is calculated, parameters of Gaussian primitives of all the RGB image frames in the information are iteratively optimized in sequence according to the loss, all the optimized Gaussian primitives are combined, and a reconstructed three-dimensional scene is obtained. According to the invention, the spatial consistency and accuracy of reconstruction results are improved.

The invention relates to the technical field of image processing, in particular to a remote sensing monitoring method and system for cultivated land protection, and the method comprises the following steps: obtaining a remote sensing image sequence of a cultivated land region at a differential time node, extracting pixel gray values of the same land parcel, arranging the pixel gray values according to time, calculating the ratio of a difference value between adjacent frames to an intra-frame gray average value, and obtaining a remote sensing image sequence; and marking positions continuously exceeding the fluctuation reference ratio, and generating a pixel region set. According to the method, a sampling frequency threshold optimization strategy is adopted, abnormal areas are screened in combination with albedo change rate, short-time fluctuation interference is reduced, time sequence consistency is enhanced, a solar incident angle and a terrain slope are introduced to calculate projection influence, a slope surface shielding area is eliminated, and space grid marking and boundary integrity judgment are adopted; the method improves the continuity of cultivated land distribution information, combines time sequence analysis, sampling optimization, physical characteristic compensation and spatial consistency correction, enhances the fine recognition of the dynamic change of cultivated land, and improves the monitoring precision and data reliability.

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The invention discloses a remote sensing image multi-class ground feature recognition method based on self-supervised learning. The method comprises the following steps: S1, generating standardized remote sensing image data; s2, performing feature pre-training on the standardized remote sensing image data based on self-supervised learning, and outputting depth feature representation; s3, inputting the depth feature representation into a gradient enhancement decision tree classification module, and outputting a preliminary classification result; s4, updating a gradient enhancement decision tree classification module; s5, converting the depth feature representation into low-dimensional feature representation through a feature distillation module, and outputting an optimization classification result; and S6, implementing space consistency optimization, classification boundary refinement and category redistribution on the optimized classification result, and generating a final remote sensing image multi-category ground feature recognition result. According to the method, the classification precision and the category distinguishing capability of complex ground feature categories are remarkably improved.

The invention discloses a cross-modal remote sensing target detection method and system based on spatial consistency constraint and deep feature alignment, belongs to the field of computer vision and remote sensing science and technology and the technical field of machine learning and deep learning, and solves the problem that a conventional cross-modal method does not fully consider feature hierarchy difference. According to the invention, an improved teacher-student network model is constructed and comprises a student branch network, a teacher branch network and an optimization module for performing pseudo-label optimization, non-monitoring learning and supervised learning on the student branch network and the teacher branch network; training the improved teacher-student network model by adopting the target domain data set and the source domain data set to obtain a trained improved teacher-student network model; and carrying out cross-modal remote sensing target detection on a to-be-detected target domain image by adopting the trained improved teacher-student network model. The method is used for cross-modal remote sensing target detection.

The invention provides a liver focus three-dimensional modeling method, and belongs to the technical field of image processing based on computer vision. Firstly, a multi-view spatial registration method based on optical flow optimization is designed, pixel-level displacement information of different view images is estimated by calculating an optical flow field, accurate image alignment is achieved, and spatial consistency of three-dimensional reconstruction is improved. And secondly, a three-dimensional reconstruction strategy based on two-dimensional focus segmentation is proposed, the two-dimensional focus segmentation is completed by adopting a lightweight U-Net variant, and a segmentation result is mapped to a three-dimensional space through a voxel probability projection method, so that 3D focus reconstruction is realized, and the calculation cost is reduced. And finally, extracting high-frequency features of the three-dimensional model by adopting a local edge enhancement method based on a Laplacian operator, and strengthening a focus boundary and a key anatomical structure through interpolation optimization, so that the three-dimensional model is more accurate and clearer. Compared with a traditional method, the method has the advantages that the mode of purely depending on image superposition is avoided, and the accuracy of three-dimensional modeling is improved.

A regional extreme rainfall forecasting and early warning method based on a conditional generative adversarial network (cGAN) comprises the following steps: preprocessing multi-source meteorological data, including standardization and space consistency alignment, so as to ensure the availability of the data; and through a coding module of the cGAN, extracting and fusing interannual daily average features and horizontal features, constructing a multi-scale feature pyramid, and enhancing the characterization capability of a rainfall spatial mode. Forecasting data are generated through a cGAN generator and a condition discriminator, rainfall collaborative factors are introduced into the discriminator to serve as physical constraint conditions, and network parameters are optimized through an adversarial loss function and physical consistency loss. The forecasting data is post-processed through a graph neural network (GNN), and spatial dependency modeling and optimization correction are carried out by taking a meteorological station observation value as a true value, so that the forecasting precision and reliability of an extreme rainfall event are remarkably improved.

The invention relates to an underwater topographic measurement method based on laser radar and visual fusion, which comprises the following steps: synchronously acquiring underwater point cloud, images and physical environment sensing data, and endowing a unified space-time label to realize the space-time consistency of multi-source data; according to the method, the data quality of different modes is improved by means of preprocessing, denoising, scale normalization, feature enhancement and the like, an environment interference weight matrix is constructed in combination with an environment sensing model, feature extraction and matching algorithm parameters are adaptively adjusted according to different environment states, and multi-scale feature description, spatial consistency and physical constraint criteria are established, so that the multi-modal data quality is improved. According to the scheme, high-reliability feature matching between the point cloud and the image is achieved, finally, through environment-driven iterative optimization and fusion, the robustness and precision of space registration are improved, high-precision multi-modal data automatic registration can be stably achieved in the underwater dynamic environment, adaptability is high, and environment perception and space measurement quality is effectively improved.

The invention discloses a magnetic field measurement method and system based on a multi-sensor fusion technology, and relates to the technical field of sensor fusion and magnetic field measurement, and the method comprises the steps: deploying a multi-sensor data array, carrying out the adaptive initialization of a bistable SR parameter range, defining an SR system differential equation, and carrying out the iterative optimization through employing an MPA population. Carrying out Hilbert transform edge detection on enhanced signal component data, calculating an array inclination angle, carrying out abbe error and bidirectional projection error compensation, and carrying out metasurface grid coordinate quantization mapping; the collected and cross-scale magnetic field data set is preprocessed and packaged into data cells, quality evaluation and weight distribution are carried out on the data cells, and extended Kalman filtering data fusion is carried out; by introducing a bistable stochastic resonance system and an MPA population optimization algorithm, a weak magnetic field signal is obviously enhanced, and by calculating an array inclination angle and compensating an Abbe error and a bidirectional projection error, the space consistency of a measurement result is improved.

The invention discloses an image feature matching optimization method based on intra-class space consistency in the technical field of computer vision and image processing. The method comprises the following steps: feature point extraction and preliminary matching: extracting feature points from a query image and a reference image and performing preliminary matching; initialization and transformation model estimation: initializing a matching point set and a residual error, and calculating an initial transformation model; error calculation and matching point set updating: calculating the error of the matching point pair, and updating the matching point set by adopting a dynamic screening method; performing residual optimization: judging whether the optimal condition is reached or not based on the residual, and deciding whether to continue iteration or not; performing intra-class space consistency clustering and isolated cluster elimination: performing clustering analysis after the optimal residual error is obtained, and eliminating isolated clusters based on an intra-class space consistency separation ion structure; and outputting a result: outputting a matching point set after the isolated clusters are removed. The method solves the problem that a traditional feature matching method is difficult to completely remove mismatching in a complex scene and is sensitive to noise.

The invention provides a parallel attention convergence high-precision map construction method for assisting visually impaired people. The method comprises the following steps: firstly, collecting multi-view and multi-scale data of a traffic environment through multi-camera or multi-sensor equipment; a multi-scale feature fusion network EDS is utilized to integrate data features of different view angles, and semantic understanding of a complex scene is enhanced; then, accurate spatial position attention features are generated through a parallel attention aggregation module PAAM, and the feature aggregation effect and semantic segmentation performance are improved in combination with multi-scale context information; then optimizing multi-scale features by using a multi-resolution feature aggregation module MRFA to ensure the spatial consistency and real-time performance of the high-precision semantic map; and finally, applying the generated semantic map to a real-time navigation system for visually impaired people. According to the method, the semantic understanding and real-time map construction capabilities in a complex traffic environment are remarkably improved, and safe and accurate navigation assistance is provided for travel of visually impaired people.

The invention relates to the field of brain tumor analysis, and discloses a brain tumor image analysis system based on artificial intelligence, comprising: a spatial alignment unit for acquiring original image data of the brain of a subject; performing spatial alignment on the original image data according to a cross-modal registration algorithm to obtain standardized image data; the feature extraction unit is used for performing tumor region initial segmentation on the standardized image data according to a three-dimensional convolutional neural network so as to obtain a coarse segmentation probability graph; and extracting three-dimensional geometric feature parameters of the tumor candidate region according to the coarse segmentation probability graph. According to the method, the original image data is spatially aligned through the cross-modal registration algorithm, and the spatial consistency between different image sources is ensured, so that the image data under different modals can be accurately compared and analyzed, and an accurate spatial reference is provided for subsequent tumor region identification and processing.

The invention relates to a remote sensing water depth inversion system and method based on image space constraint and residual compensation, and belongs to the technical field of earth remote sensing detection and space remote sensing. Comprising the following steps: preprocessing satellite data, including active satellite data preprocessing and passive satellite data preprocessing; a BP neural network water depth inversion model is constructed, the preprocessed satellite data is used for training, and a preliminary water depth estimation value is output; establishing a residual compensation model and introducing spatial consistency constraint, and in the residual compensation process, introducing a spatial smooth penalty term by calculating the difference of water depth values of adjacent pixels to suppress local mutation; and carrying out optimization training on the model by using a joint loss function which is formed by weighted combination of mean square error and space consistency constraint loss, and outputting a final water depth inversion result. According to the method, an image space constraint loss term and a space smoothing penalty term in residual compensation are introduced, so that the consistency and the smoothness of an inversion result in space are ensured.

The invention relates to the technical field of image analysis, in particular to an automatic marking robot calibration method based on visual guidance, which comprises the following steps of: establishing an image set comprising different resolution levels based on operation site image data acquired by a marking robot, and performing corner detection and straight line segment detection on each level image in parallel. According to the method, through a parallel detection mechanism of a multi-resolution hierarchical image set, angular point and straight line segment features under different scales are synchronously extracted, and a multi-scale feature point set with high robustness is constructed in combination with cross-hierarchical coordinate stability measurement and response intensity quantification. And performing dynamic screening and grouping association on the feature points based on a preset geometric constraint condition, eliminating noise interference and false detection features, and generating a candidate calibration structure set with spatial consistency. Weighted contribution value fitting is adopted, cross-scale stability and detection confidence of feature points are integrated, and geometric accuracy and anti-interference capability of calibration reference point coordinates are improved.

The invention discloses a semi-supervised seismic wave impedance inversion method and system based on Swin Transform iterative gradient correction, and belongs to the technical field of earthquakes in geophysical exploration. According to the method, the impedance gradient is iteratively corrected in the model space through the deep neural network, and complex nonlinear prior information is captured; and each anisotropic structure direction regularization item is established, and constraint is carried out along the direction of the stratum structure, so that the space consistency of the prediction result is ensured. Meanwhile, the USTNet network designed by the invention combines the advantages of the local feature capture capability of the convolutional neural network and the global modeling capability of Transform, and reduces the non-uniqueness of solutions. The network is used for inverting synthetic data and actual data, and the effectiveness of the method is verified.

The invention provides a semantic aerial view visual relocation method and device in a non-exposed scene, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a multi-view image sequence under a non-exposed scene (such as a tunnel, an underground pipe gallery or an underground parking lot); semantic recognition is carried out based on a pre-trained semantic target detection model, and spatial consistency semantic features are extracted through a semantic-geometric dual-channel fusion mechanism combining a semantic mask and geometric constraints; the method comprises the following steps of: realizing three-dimensional reconstruction by using a voxel micro-renderable modeling method (VGGT), and generating a dense three-dimensional semantic point cloud fusing semantics and a geometric structure; two-dimensional semantics are mapped to a three-dimensional space through a projection and back projection relation, and point cloud semantics are endowed; main structure planes such as the ground, the left wall surface and the right wall surface are extracted, and a two-dimensional semantic aerial view with semantic annotation is generated; and pose estimation is carried out based on a reciprocal matching strategy guided by a semantic mask, so that visual repositioning with high precision, high robustness and semantic interpretability is realized. The method breaks through the problems of low precision, sparse features and poor semantic consistency of traditional visual repositioning in a non-exposed environment, and can be widely applied to the fields of intelligent transportation, underground inspection and unmanned system positioning.

The invention relates to a road disease high-precision positioning method, system and device based on multi-scale dynamic weighted curvature correction and a medium, and the method comprises the steps: constructing a road center line geometric object, obtaining a geographic coordinate, and constructing a multi-scale curvature field characterization parameter according to a spatial mapping relation between the geometric object and the geographic coordinate; establishing a correction model, calculating and generating an initial correction factor, and performing optimization to form a dynamic correction factor; the optimal projection position of the vehicle is determined according to the geographic coordinates and the road center line geometric object, the cumulative distance of the projection position is corrected based on the dynamic correction factor, and road pile number information is generated in combination with pile number reference parameters and constraints; the relative orientation is determined by comparing the distance between the vehicle position and the road end point or verifying the consistency of the driving track vector and the vector space in the tangential direction of the road, and the positioning result is output in combination with the road pile number information. According to the invention, a full-link precision guarantee system from data analysis to result output is formed, and a reliable positioning reference is provided for road maintenance.

The invention discloses an efficient image super-resolution reconstruction method and system based on degradation region guidance, and the method comprises the following steps: S1, carrying out the region-level degradation type recognition and severity quantification of an input low-resolution image, and generating a global degradation distribution map with spatial consistency; s2, according to the global degradation distribution map and in combination with semantic-texture collaborative features, repairing a region which is judged to be seriously degraded by adopting a high-capacity branch, and repairing a region which is judged to be slightly degraded by adopting a light-weight branch; s3, fusing the output of the high-capacity branch, the output of the lightweight branch and the global detail enhanced image to generate a final high-resolution image; wherein the global detail enhanced image is obtained by enhancing the semantic-texture collaborative features.

The invention discloses a change detection method based on semantic prior and fusion space positioning, and relates to the technical field of image processing, and the method comprises the steps: firstly, carrying out the pre-training of a BPNet network on a supervised remote sensing extraction task, and fully utilizing the prior knowledge in the remote sensing extraction task; and then fine tuning is performed on the pre-trained BPNet network, a twinning network structure sharing weight is adopted to expand an encoder and decoder structure, a space consistency attention module is introduced, and feature information extraction and fusion are performed on the dual-tense image by combining a self-attention mechanism and a large convolution kernel decomposition technology so as to obtain a dual-tense image. Therefore, remote sensing image target change information is obtained. Therefore, by adopting the change detection method based on semantic prior and fusion space positioning, through the space consistency attention module, the depth encoder can be helped to expand an effective receptive field, the modeling capability of a long-distance dependency relationship is enhanced, and meanwhile, double-tense change detail information can be effectively captured, and a more accurate feature map can be obtained.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses a geographic information surveying and calibration method and system based on data analysis. The method comprises the steps that S1, a geographic information data set is collected through multiple data collection devices; s2, preprocessing the geographic information data set; s3, generating a spatial topology matrix; s4, calculating the topological entropy of the geographic information data set by using the spatial topological matrix; s5, analyzing spatial consistency among the plurality of data sources according to a topological entropy calculation result; s6, constructing an error distribution model for the region with the significant topological entropy difference; s7, adjusting calibration parameters of the geographic information data according to the error distribution model; and S8, performing consistency optimization on the calibrated geographic information data. According to the method, the calibration efficiency is remarkably improved, and the problem of unstable calibration caused by insufficient data real-time performance in a dynamic scene is effectively solved.

A reference point is arranged on the ground to calibrate a ground world coordinate system, a robot base coordinate system is established based on robot base features, and a rigid transformation matrix between the two is calculated to achieve spatial alignment. A multi-sensor network is constructed, a visual sensor and a laser radar are associated to a double-reference coordinate system, and data are synchronously collected and unified to the same coordinate system. And according to the spatial consistency error of the data, dynamically evaluating the credibility weight of the sensor, constructing a joint error equation, and based on least square method optimization, weighting fusion data and outputting calibration parameters. According to the invention, the problems of dependence on single sensor precision, lack of a system-level calibration scheme and complex calibration process are solved, and the calibration precision and efficiency of the robot system are improved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention discloses a 2.5 D promptable medical image segmentation method and device based on SAM, and relates to the technical field of computer vision and medical image processing, and the method comprises the steps: carrying out the preprocessing of to-be-processed three-dimensional medical image data, and obtaining a plurality of 2.5 D data blocks; each 2.5 D data block serves as input, a trained SAM model is used for outputting a corresponding segmentation result, and a residual learning mechanism and a cross-slice attention mechanism are introduced into the SAM model. According to the method, the 2.5 D segmentation thought is introduced into the SAM, and by combining a residual learning mechanism and a cross-slice attention mechanism, the SAM model can better maintain the spatial consistency between the slices and fully capture the relevance between the slices, so that the expression ability of image features is improved, the expression of the model on a complex structure image is enhanced, and the image quality is improved. Therefore, the accuracy of the medical image segmentation result can be improved.

The invention discloses a BIM (Building Information Modeling)-based building component attribute matching method and a BIM-based building component attribute matching system, contour line segments of a target and a reference component are segmented, curvature change values are recorded, and the local feature expression capability is enhanced. Then, dynamic adjustment parameters are introduced to optimize eigenvector weighting processing, so that the matching process adapts to different shape complexity, and mismatching is avoided; and then, identifying a local similar region by comparing the corrected matching parameter set, and generating a registration mapping relation, thereby improving the accuracy of space consistency judgment. A matching priority matrix is constructed based on the mapping relation and the shape distribution difference, high-matching-degree candidate components are quickly screened, and the matching efficiency is improved. Finally, a feedback adjustment mechanism is adopted in the attribute verification stage, it is ensured that the matching result meets the preset precision requirement, and self-adaptive optimization is achieved. The method effectively solves the problems of low precision and poor stability in non-standard geometric component matching in the prior art, and has important practical value.

The invention discloses a power transmission tower deformation monitoring method and device and a computer program product, and the method comprises the steps: obtaining a multi-angle collection image of a random speckle pattern on the surface of a power transmission tower; constructing an image sequence based on the multi-angle acquired images, reconstructing a three-dimensional displacement field on the surface of the power transmission tower by using a stereoscopic vision reconstruction algorithm, and exporting a strain tensor field; performing time sequence processing on the strain tensor field under a plurality of time nodes through a strain evolution analysis model of a continuous time sequence, and extracting a strain change trend of each preset structure part; carrying out space consistency mapping and weighted interpolation processing according to the strain change trends of the plurality of preset structure parts, and calculating to obtain the overall structural dependent variable of the power transmission tower; and S5, in combination with historical strain data and the current overall structural dependent variable, identifying an abnormal deformation area of the power transmission tower by comparing the spatio-temporal evolution mode of the strain tensor field. The method has the effect of improving the accuracy of deformation monitoring of the power transmission tower.

The invention discloses an intelligent building fault prediction method based on an adaptive algorithm, and the method comprises the steps: obtaining operation state data collected by a plurality of sensor nodes, carrying out the normalization preprocessing, inputting an improved adaptive prediction model, and obtaining the implicit state representation; an initial fuzzy rule set is generated through fuzzy rule induction, a real-time evolution strategy is introduced, fuzzy rules and neural connection weights are dynamically adjusted according to error feedback, and a self-adaptive prediction model updated through evolution is formed; and then performing multi-step prediction on the key operation parameter time sequence by using the updated model, comparing a prediction result with a dynamic fault threshold value to generate a candidate fault indication, and determining the position and category of a fault to be pre-warned in the building system through time sequence stability verification and space consistency analysis. The method can significantly improve the accuracy and real-time performance of intelligent building fault prediction, and has a good application prospect.