2245 results about "Spatial relationship" patented technology

The invention discloses a PCBA circuit board welding spot detection method based on multi-modal data fusion, and relates to the technical field of electronic manufacturing quality detection.The PCBA circuit board welding spot detection method comprises the steps that a distributed sensing network is constructed, multi-modal data are collected, welding spot information is obtained in an omnibearing mode, and time-space alignment of the multi-modal data is carried out; performing feature extraction on the multi-modal data, dynamically weighting each modal feature through an attention mechanism, and highlighting key defect characterization; a welding spot spatial topological graph is constructed by using a graph neural network, and a spatial relationship between welding spots is modeled. By integrating optical, X-Ray, thermal, mechanics, electricity and other multi-dimensional data, the information limitation of single-mode detection is broken through, the complementation of different mode data is utilized, the attention mechanism is combined to dynamically weight each mode feature, the complex defect is accurately identified, the graph neural network is utilized to model the welding spot space topological relation, the associated defect is further captured, and the defect detection accuracy is improved. And the defect classification accuracy is improved.

The invention discloses a territorial space layout adjustment decision method based on planning conflict identification, and belongs to the technical field of territorial space planning and management. Comprising the following steps: constructing a space-time conflict feature vector library containing multi-dimensional attributes; generating a semantic knowledge graph containing a causal relationship, a sequential relationship, a spatial relationship and an affiliation relationship; accurate prediction of influence intensity and duration of the conflict state along with time lapse is realized; trend analysis and risk level evaluation of a cross-scale conflict propagation path are realized; automatically identifying dominant conflicts in the current layout and potential conflicts based on propagation prediction, and intelligently classifying composite conflicts by using fuzzy clustering; a staged differentiation conflict intervention strategy recommendation scheme is automatically generated; solving a territorial space layout adjustment scheme; judging whether the layout adjustment scheme meets a preset conflict relieving standard or not;

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

The invention provides an adaptive multi-modal feature fusion well logging interpretation method based on multi-task joint learning, and the model comprehensively employs a graph convolution network, a multi-head attention mechanism, a gating mechanism and an adaptive loss weight adjustment algorithm, and effectively integrates multi-well group well logging data and geological structure information. And the complex spatial relationship and geological similarity between the wells are fully captured. The model can process multi-source data in a unified manner, extracts high-level feature representation containing spatio-temporal correlation, avoids repeated feature learning, reduces calculation complexity, and ensures the consistency and complementarity of prediction task features at the same time. The self-adaptive loss weight adjustment mechanism dynamically optimizes weight distribution according to the loss change of each task, realizes balance optimization among different tasks, and comprehensively improves the prediction precision of each task. The physical constraint regularization module ensures that the prediction result not only has statistical accuracy, but also conforms to the actual geological physical law, so that the reasonability and reliability of the prediction result are improved.

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention discloses an ophthalmologic operation real-time navigation system and method based on dynamic visual field tracking, and relates to the technical field of medical systems. Comprising the following steps: matching operation data closest to a current operation from a database, and generating standard operation navigation of the current operation; the real-time image of the operation process is compared with the image of the standard operation navigation in real time, the step of the current operation is determined, and meanwhile subsequent image navigation of the operation of the patient is displayed; generating a spatial mapping matrix between an eyeball movement track and a target region by combining target region boundary data of preoperative OCT image segmentation and an intra-operative navigation coordinate system; the virtual navigation path of the surgical instrument is dynamically corrected according to the real-time eyeball movement track, so that the surgical instrument is aligned with the target area, the spatial relation between the instrument and the target area can be visually mastered through the overlapping display of the three-dimensional virtual path and the real-time image, unconscious displacement or physiological tremor of a patient in an operation is actively adapted, and the operation accuracy is improved. And the continuous accuracy of the navigation path is ensured.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention provides a building equipment operation data intelligent analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: extracting historical positioning data in a space calibration interval, and calculating a position offset vector set between a predicted trajectory point and an actual trajectory point according to the trajectory point on a predicted motion trajectory; taking the weighted sum of squares of minimizing the position offset vector as an optimization target to obtain a three-dimensional space transformation parameter optimization model; according to the optimization model, a quasi-Newton optimization algorithm is adopted to solve the optimization model, and a final calibration parameter set is generated; and based on the final calibration parameter set, correcting the predicted motion trajectory, performing spatial relationship analysis on the corrected trajectory and a spatial risk field, calculating a collision risk probability and an accident severity level, generating a comprehensive risk score, and triggering a response action according to a preset threshold value. The construction safety level and the management efficiency are improved.

The invention relates to the technical field of anomaly detection, in particular to an intelligent gas hidden danger detection system which comprises a leakage monitoring module, a time difference extraction module, a direction positioning module, a color temperature analysis module and a risk aggregation module. According to the method, the time and space relation of gas concentration response nodes is extracted, a leakage diffusion path is constructed, and a dominant propagation direction is identified, so that a leakage trend region has a clear positioning basis, regional brightness and hue changes in an image frame are combined, the time sequence stability proportion is counted, and a cross-modal trend verification channel is established; the defect of a single sensing dimension is effectively made up, in the image and trend direction matching process, abnormal area numbers are recognized through stable proportion difference value changes, intersection confirmation is carried out on trend areas, the accuracy and spatial directivity of hidden danger marking are remarkably improved, the risk recognition chain is more complete through cooperative calculation of multi-source data, and the accuracy of hidden danger marking is improved. And the method has higher linkage response capability and dynamic judgment depth.

The invention relates to a gated multi-graph convolution perception modeling method for traffic flow prediction. The method integrates multi-graph structure construction, gating graph convolution and time feature extraction, and aims to solve the problems of strong time fluctuation and heterogeneous spatial relationship in traffic data. The method comprises the following steps of: firstly, respectively constructing a geographic map and a semantic map according to the maximum mutual information measurement between the spatial distribution information of a sensor and historical traffic data; and then, designing a dual-adaptive gating graph convolution module, and dynamically adjusting an information propagation path of a multi-graph structure by introducing an attention mechanism and a gating factor, thereby improving the modeling performance of the model on spatial isomerism dependence. On the time dimension, a time sequence interactive sensing module is constructed in combination with multi-scale causal convolution and an attention mechanism, time dependence characteristics of a short period and a long period are captured, and fusion and expression of time characteristics are completed. According to the method, the modeling precision and stability of the traffic prediction model in a complex traffic scene can be effectively enhanced, and the method has relatively high practical application value.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention belongs to the technical field of knowledge graph construction, and discloses an urban rainstorm flood disaster chain affair graph construction method and system, and the method comprises the steps: firstly obtaining a research document, a first historical event and a secondary event, constructing a disaster chain directed network graph, and constructing an ontology model according to the disaster chain directed network graph; acquiring a second historical event and a secondary event, preprocessing, and inputting into the ontology model to obtain an event corpus; extracting event trigger words from a corpus to build a database by using a multi-modal dynamic fusion model MDF-Net; performing event extraction in a corpus according to the trigger word to obtain an event type and an argument; extracting a time sequence, a spatial relationship and a semantic relationship of a trigger word by using an improved ELCTRA-BiLSTM model; and finally, constructing a disaster chain affair map by taking the event type, the argument, the time sequence and the spatial relationship as nodes and taking the semantic relationship as an edge. According to the method, multi-source data and complex disaster information can be effectively integrated, and the integrity and accuracy of the urban rainstorm flood disaster chain affair map are improved.

The invention discloses an edge protection early warning method and device based on LSTM and a medium, and relates to the technical field of edge protection. The method comprises the following steps: acquiring a video stream of a target person through a camera array, and acquiring three-dimensional point cloud data of the target person through a laser radar; performing background modeling on the video stream, and fusing the three-dimensional point cloud data to generate a motion track with three-dimensional coordinates; receiving a motion track, and predicting a motion path and a motion speed in a preset time period through a stacked bidirectional LSTM network; and an audible and visual alarm, an intelligent terminal pushing device and a mechanical braking device are integrated, and graded alarm is triggered according to the predicted path, the movement speed and the spatial relationship with a preset dangerous area. According to the method, the motion path and the motion speed in the preset time period are predicted through the stacked bidirectional LSTM network, and comprehensive perception, intelligent prediction and graded alarm are performed on target personnel on a construction site.

A unified platform for multi-modal data compression and decompression that enables efficient processing of correlated data streams while preserving relationships between different modalities. The platform employs a virtual management layer to analyze and route input streams, implementing correlation analysis to identify temporal and spatial relationships between streams. Multiple compression methods, including neural network-based approaches, are utilized to compress data sets while maintaining cross-modal dependencies. A neural upsampling system leverages learned correlations between streams to enhance reconstruction quality. The platform includes a synchronization manager that maintains temporal alignment and relationship preservation throughout processing. By integrating correlation-aware compression with neural upsampling techniques, the platform provides comprehensive multi-modal compression capabilities while preserving critical relationships between different data types. The system is particularly suited for applications involving synchronized audio-visual data, sensor streams, and other multi-modal content.

The invention relates to the technical field of pneumoconiosis image detection, and discloses a pneumoconiosis-oriented chest X-ray image processing method based on an AI small shadow detection rate. According to the method, a basic mask is generated through an initial segmentation network, and then an accurate lung contour is obtained through correction of a hierarchical optimization unit. The multi-scale feature extraction module analyzes gray features in the contour and constructs an initial small shadow probability distribution map. A morphological structure analyzer identifies discrete shadow regions and marks suspicious shadow clusters, and a dynamic confidence feedback mechanism evaluates its credibility to generate an optimized feature map. A shadow cluster topological incidence matrix is established by the spatial relation modeling network and matched with a pneumoconiosis pathological feature library to screen a target shadow area. The three-dimensional reconstruction engine generates a small shadow volume density thermodynamic diagram, the hierarchical fusion module integrates the thermodynamic diagram and original image space coordinates, an enhanced distribution map is output, and finally structured diagnosis report data is generated.

The invention relates to the technical field of navigation planning, and discloses an unmanned aerial vehicle automatic inspection nest path planning method and system, and the method comprises the steps: carrying out the multi-source data fusion of a target inspection region, and obtaining space measurement environment data; analyzing an airspace limiting condition in the spatial measurement environment data, and determining a navigation path measurement constraint condition in combination with an inspection task demand of the target inspection area; performing gridding measurement on the target inspection area to identify candidate waypoints; according to the spatial position information, analyzing a spatial relationship of the candidate waypoints to construct an initial navigation network; performing multi-constraint evaluation on the initial navigation network to obtain constraint weights corresponding to the candidate waypoints, and integrating the candidate waypoints with the constraint weights higher than a preset weight threshold into a global optimal waypoint set; carrying out passable connection on adjacent waypoints in the global optimal waypoint set to obtain a navigation path; according to the invention, the efficiency of automatic inspection nest path planning of the unmanned aerial vehicle can be improved.

The described aspects and implementations enable online lane estimation and tracking in autonomous vehicle and driver assistance applications. In one implementation, disclosed is a method and a system to perform the method that includes receiving perception data indicative of driving lanes and reference objects in a driving environment of a vehicle, identifying target destinations corresponding to the driving lanes, determining candidate driving paths connecting a starting location with one of the target destinations, selecting a target driving path from the candidate driving paths based on geometric properties of each candidate driving path and a spatial relationship of a respective candidate driving path to the reference objects, and causing a motion of the vehicle to be modified based on the target driving path.

The invention relates to the technical field of surface defect recognition, and discloses an intelligent recognition method and system for surface defects of an autoclaved aerated concrete member. The method comprises the following steps: carrying out omnibearing shooting and spatial transformation matrix mapping on the surface of the autoclaved aerated concrete member to obtain a preprocessed image; performing texture feature extraction on the preprocessed image to obtain texture feature data; performing spatial feature analysis on the preprocessed image and the texture feature data to obtain spatial relation feature data; performing feature importance weighting and bidirectional cross fusion processing on the texture feature data and the spatial relation feature data to obtain comprehensive defect feature representation; and performing surface defect analysis on the autoclaved aerated concrete member based on the comprehensive defect feature representation, and outputting a surface quality comprehensive score and grade division result. According to the method, the problems of dead angles and image deformation existing in traditional single-view-angle detection are solved, and the completeness and accuracy of defect detection are ensured.

The invention relates to the technical field of building risk monitoring, in particular to an urban building safety evaluation method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a synthetic aperture radar image, an optical remote sensing image, a hyperspectral image, topographic data and building contour data of a to-be-evaluated region; based on the optical remote sensing image and the building contour data, identifying spatial form change information of each target building, and generating change parameters used for describing structure adjustment features; generating an interferogram based on the synthetic aperture radar image, extracting a corresponding deformation monitoring point, and generating a settlement parameter reflecting the displacement change of the building; based on the hyperspectral image and the topographic data, identifying spatial relationship information of the target building and the surrounding environment risk body, and generating external influence parameters; and based on the change parameter, the settlement parameter, the external influence parameter and the preset evaluation model, generating a safety evaluation result of each target building, thereby improving the efficiency of urban building safety evaluation.

The invention belongs to the technical field of three-dimensional scene modeling, and discloses a dual-branch diffusion three-dimensional scene generation method based on a multi-modal semantic graph, which comprises the following steps of: firstly, receiving multi-modal data such as sketches, texts, automatic completion instructions and scene general knowledge, extracting features and fusing the features into a unified multi-modal semantic graph; utilizing a graph neural network and an attention mechanism to enhance semantic graph features, and optimizing physical constraints through a physical engine; complementing the missing visual modality and graph structure relationship; performing quality scoring on the scene based on semantics, spatial relationships and physical constraints; and finally, respectively generating a spatial layout and a geometric shape through a double-branch diffusion model, and ensuring the coordination of the layout and the shape. The method has the advantages of multi-modal information fusion, physical rationality guarantee, high structure complementation capability, high-quality score optimization and efficient generation process, and is suitable for three-dimensional scene modeling requirements in the fields of virtual reality, augmented reality, robots and the like.

The invention discloses a video monitoring method and system, electronic equipment and a storage medium, and relates to the field of video monitoring. According to the method, a three-dimensional digital twinborn scene is constructed according to the space coordinates of each camera in a target area, and a node relation matrix is constructed according to the space relation between the cameras; according to the three-dimensional digital twinborn scene and the node relation matrix, a dynamic vision field splicing algorithm is adopted, the real-time position and the action path of the target object are determined, and the people flow density of the target area is displayed; determining a behavior mode of the target object through skeleton key point detection and interaction behavior analysis, and predicting an action track of the target object according to the behavior mode; and evaluating an initial risk level of the action track, adjusting the initial risk level in combination with the people flow density to obtain a final risk level, and generating a disposal suggestion according to the final risk level. By implementing the technical scheme provided by the invention, comprehensive and accurate video monitoring and risk management of the target area are realized.

The invention discloses a network security malicious traffic tracing method based on a generative adversarial network, which comprises the following steps: collecting network traffic data, selecting a target traffic sample, extracting a protocol behavior, a communication structure and a time evolution characteristic of the target traffic sample, and carrying out semantic coding; mapping the multiple types of features to a unified embedding space through a multi-view fusion mode; constructing a manifold set based on known malicious samples, and generating an intermediate state sample sequence; constructing an attack context and path disturbance information in combination with the target sample, and generating a guide vector; inputting the guide vector and the random variable into a generator, and outputting a potential malicious sample representation; identifying the authenticity of the sample and evaluating the path similarity by using a discrimination network; and finally, constructing an attack path graph according to the similarity and the spatial relationship, and calculating a shortest path sequence to a known malicious sample to realize traceability identification of potential malicious behaviors. According to the invention, tracking analysis of malicious traffic sources is realized by using the generative adversarial network.

The invention discloses an urban rail transit road network short-time OD passenger flow prediction method based on a space-time multi-graph neural network. The method comprises the steps of symbol definition and passenger flow data construction; a multi-relational graph structure: constructing four spatial relationships based on potential inter-station association of stations; spatial feature extraction: inputting the constructed passenger flow data, performing graph convolution operation on the constructed passenger flow data and different constructed networks, capturing global spatial features by using a Transform module, and finally performing fusion output to obtain a hidden value sequence with the spatial features and outputting the hidden value sequence; time feature extraction: using BiLSTM to respectively extract time long-distance dependence on the starting place and the destination of the OD passenger flow, fusing results, and inputting the fused results into GRU to further capture short time sequence dependence; and finally, inputting into a multi-head time convolution module, and extracting time dependence information in parallel through a plurality of convolution kernels by utilizing TCN to obtain a prediction result. The method has higher accuracy and adaptability.

The invention belongs to the technical field of artificial intelligence image generation, and particularly relates to an image generation method and system based on style feature injection, and the method comprises the steps: carrying out the multi-dimensional feature analysis of a reference image specified by a user side through a pre-trained multi-level style extraction model, extracting a style feature vector, and carrying out the feature extraction of the style feature vector; the style feature vector comprises a color feature vector, a texture feature vector, a composition feature vector and an illumination feature vector; obtaining a text description input by a user side, and outputting triple information including a scene entity, an entity attribute and a spatial relationship; performing weighted fusion on the triple information and the style feature vector to obtain a style enhanced semantic embedding vector; and inputting the style-enhanced semantic embedding vector into a generative adversarial network for splicing, and outputting an image. The method has the effect of remarkably improving the similarity between the generated image and the reference style.

An artificial intelligence system based on spatial-temporal information pairs is provided by the present disclosure. By integrally deploying paired vision, auditory and olfactory acquisition devices, a device that can collect the data within a 720-degree area is constructed, and the multi-dimensional continuous spatial-temporal information pairs such as positions, morphologies, motion states, sounds and odors from the ambient environment of the acquisition device or a same spatial object in the environment are recorded in real time. These information pairs can not only contain spatial relationships and a clock attribute, but also contain rich label attributes, for example, identifiers of acquisition devices, and a name, a category and a behavior pattern of the spatial object.

The invention relates to the technical field of defect detection, in particular to a heat-conducting adhesive tape defect detection method and system based on machine vision, and the method comprises the following steps: obtaining a surface image of a heat-conducting adhesive tape, extracting a texture trend and marking an aggregation block, adjusting the trend and offset of a connecting line segment, and analyzing the angle change of a sideband node. And recognizing a skeleton trend reverse section and closing a boundary, and extracting a continuous direction changing region to obtain a defect structure image annotation layer. According to the method, the turning position in the sideband region is extracted by constructing the direction aggregation graph of the texture aggregation block, enhancing the direction identification of the direction of the regional texture, combining the grouping organization and coordinate offset processing of the line segment path, enhancing the continuous association of the image structure, and by means of the repeated distribution characteristic of the angle mutation node; and through a skeleton trend reverse paragraph and boundary closing mode, an identification range of a structure disturbance block is expanded, and coherent tracking and multi-level labeling of a structure abnormal region are completed by combining a spatial relationship between a contour trend difference and a turning point.

The invention belongs to the technical field of bridge damage identification, and relates to a bridge structure intelligent damage identification method based on a physical information neural network, which comprises the following steps: 1, acquiring spatial relation data of a bridge structure, 2, acquiring real-time data, 3, preprocessing the data after the data is acquired, 4, performing hyper-parameter adjustment, 5, constructing a model, 6, carrying out model training and evaluation; 7, carrying out early warning and repairing scheme matching; the health state of the bridge can be monitored in real time, data support can be provided for future maintenance and repair, the safety of the bridge is improved, and the service life of the bridge is prolonged; therefore, a novel efficient tool is provided for bridge health monitoring and damage assessment through a unique data and physical driving method, an efficient and intelligent bridge monitoring and assessment system is constructed, and the safety and maintenance efficiency of the bridge are improved.

The invention relates to the technical field of cross-view image positioning, in particular to a cross-view image geographic positioning method and system for an unmanned aerial vehicle and a satellite image, and the method comprises the steps: employing a Dinov2 large model after Conv-LoRA fine adjustment as a feature encoder, and extracting an unmanned aerial vehicle image feature vector and a satellite image feature vector; designing a spatial relation perception feature aggregator MSRA based on a Mama module for aggregating image features, and embedding spatial configuration features into a global descriptor; an InfoNCE loss function is adopted to train the model; and after feature vectors of a to-be-queried image and each image in the reference image library are extracted by using the trained model, a similarity score is calculated by using cosine similarity, and the unmanned aerial vehicle-satellite image pair with a high matching degree is selected to realize geographic positioning of the to-be-queried image. According to the method, Conv-LoRA is adopted to carry out fine tuning on a Dinov2 large model, so that the quantity of parameters needing to be trained is greatly reduced; a spatial configuration relationship between features is fully mined through a spatial relationship perception feature aggregator based on a Mama module.