312 results about "Spatial consistency" patented technology

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 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 three-dimensional terrain construction method based on multi-modal semantic constraints, and relates to the technical field of three-dimensional terrain modelling, and the method comprises the steps: obtaining an optical remote sensing image and a digital earth surface model, constructing unified space reference data, and carrying out the joint semantic analysis of ground features under the guidance of the multi-modal semantic constraints; generating a semantic mask set with structural consistency; further performing regional geometric reconstruction on the digital earth surface model on the basis of a mapping relation between semantic categories and earth surface physical attributes to form a three-dimensional terrain entity conforming to ground feature space expression characteristics; meanwhile, by analyzing the project cost document and extracting the project cost data with spatial directivity, under the constraint of spatial consistency and semantic consistency, the project cost information and the three-dimensional topographic entity are subjected to association mapping, and finally a three-dimensional topographic data result fusing topographic geometric information and project economic attributes is formed.

The invention discloses an artificial intelligence-based equipment security and protection monitoring method, equipment and a medium, and relates to the technical field of equipment security and protection monitoring, and the method comprises the steps: constructing a deep learning optical flow estimation model based on image frame sequences, carrying out the optical flow motion vector calculation of adjacent image frames through employing the model, and extracting the motion characteristics of smoke diffusion; textural features are extracted from the image frames, normalization and time alignment are carried out on the textural features and smoke diffusion characterization features, and a space-time joint feature vector sequence is constructed; performing smoke event classification prediction on the sequence through a time sequence classification model containing an attention mechanism to obtain an event type and confidence; and triggering a security response according to the smoke event type and the confidence coefficient. According to the invention, the dynamic characteristics of smoke diffusion are effectively extracted, and the detection precision is improved; the capturing capability of time sequence continuity and space consistency in the smoke diffusion process is enhanced through spatio-temporal joint modeling, and missing report and false report are reduced.

The invention discloses a plane 2D point cloud map updating method based on dynamic value evaluation, which belongs to the technical field of intelligent navigation, and comprises the following steps: acquiring sensor data of a current frame, and matching the sensor data with a currently maintained global map to obtain a matching result; based on the matching result, updating and evaluating the target unit in the map, and fusing an instant cleaning criterion based on space consistency and a long-term value criterion based on historical state information; according to a result of the update evaluation, determining an update action on the target unit, the update action at least comprising a deletion operation; an update action is performed to update the global map. According to the method, the accurate map updating decision is realized through intelligent fusion of instant and long-term double criteria, and the global scoring and fault-tolerant control mechanism is combined, so that the long-term consistency and high precision of the map are ensured, the operation efficiency and stability of the system are remarkably improved, and reliable and adaptive navigation support is provided for the robot in a dynamic environment.

The invention discloses a multi-modal condition-driven closed-loop sensing and generation optimization method and device and a medium, which are applied to an automatic driving long-tail scene, firstly, multi-modal conditions such as text description, a semantic map and a 3D layout are uniformly represented, a causal inference network is introduced, a causal structure between scene elements is explicitly inferred from multi-modal input, and a multi-modal condition-driven closed-loop sensing and generation optimization model is obtained. Generating structured causal embedding to realize causal perception enhancement of generation conditions; in a diffusion generation stage, a causal consistency mechanism is deeply fused into a condition control and denoising process, and a diffusion model is guided to effectively inhibit generation of unreasonable or common sense violating scenes in a generation process through an anti-fact condition constructed by a causal inference network, so that semantic reasonability, spatial consistency and dynamic credibility of long-tail data are remarkably improved. According to the method, the problem that long-tail scene data is deficient is solved, causal constraints are introduced to a generation source, and the reliability, robustness and cross-domain generalization ability of an automatic driving system in an extreme scene are remarkably improved.

The invention relates to the technical field of multi-camera image processing and computational photography, and discloses an image processing method and system based on four-camera cross-focal-length continuous zooming fusion. The image processing method comprises the steps of system initialization, image acquisition, zoom routing, automatic ROI extraction and tracking, field-of-view cutting and geometric alignment, image fusion and binocular depth recognition. Through systematized multi-camera collaborative design, an innovative mechanism is introduced in key links such as zoom routing, geometric alignment, image fusion and depth recognition, smooth zoom, space consistency, detail fidelity, power consumption optimization and high-quality 3D perception are realized, the imaging quality is improved through the effects, the application scene is expanded, and the application prospect is wide. And a comprehensive solution is provided for mobile photography, AR and intelligent visual systems.

The invention relates to a mapping system and method for flight inspection of indoor equipment, the system comprises a data acquisition end and an offline data processing end, the data acquisition end comprises an unmanned aerial vehicle and a sensing module, and the sensing module comprises a laser radar module, a binocular camera module, an inertial measurement module and an airborne recording module; the method comprises the following steps: synchronously acquiring multi-source data through layered flight; performing space-time alignment and preprocessing; self-adaptive sampling is carried out based on point cloud geometric prior guide image features, and laser-vision joint features are generated; fusing point cloud registration, joint features and inertial data, and carrying out joint estimation on adjacent frame pose increments; performing loopback detection based on the key frame to generate a closed-loop constraint; constructing and optimizing a pose map to eliminate cumulative drift; and finally fusing to generate a global point cloud map. According to the method, the space consistency and geometric accuracy of the map can be effectively improved, and a reliable data basis is provided for digital modeling and intelligent operation and maintenance of indoor equipment inspection.

The invention discloses a method for automatically correcting radial distortion of a wide-angle lens, and relates to the technical field of computer vision and image processing, and the method comprises the steps: collecting an original image, calculating the distance from a pixel to an imaging principal point, and extracting a multi-scale image and edge features; analyzing the edge direction and radial change and linear extension of the structure, extracting a linear structure candidate region, obtaining a distortion compensation parameter, generating a multi-scale candidate linear parameter set, and calculating a multi-scale linear consistency index for adaptive adjustment; extracting a point set from the candidate straight line parameter set, executing forward and reverse mapping to calculate a dual-space consistency index, and optimizing local parameters and the point set; calculating a minimum linear complexity index based on the line segment curvature, the curvature change rate and the length, and implementing local optimization; and performing weighted fusion on each index to construct a comprehensive objective function, performing step-by-step shrinkage optimization to obtain an optimal compensation parameter, performing global geometric correction on an original image, generating a distortion correction image, and realizing improvement of the structure recognition precision and the geometric correction effect.

The invention discloses a power distribution network inspection robot system and method, and relates to the technical field of intelligent inspection, and the system comprises a data collection module, a data registration module, an anomaly detection module, a space verification module, a time sequence analysis module and a comprehensive judgment module. The data acquisition module controls the robot to acquire multi-modal data and record space-time positioning information; the data registration module registers the multi-modal data to an equipment coordinate system; the anomaly detection module extracts anomaly features and calculates confidence; the space verification module carries out space consistency verification and physical logic consistency verification according to the physical rule base and evaluates the abnormal level; the time sequence analysis module evaluates a defect evolution state; and the comprehensive judgment module performs defect judgment and generates a processing strategy. According to the method, single-mode false detection and environmental interference are effectively eliminated through a dual verification mechanism, comprehensive evaluation of the current state and the development trend of the defect is realized in combination with time sequence evolution analysis, and the accuracy and the reliability of defect detection are remarkably improved.

The invention discloses a multi-view image Gaussian reconstruction method and system guided by LiDAR point cloud. The method comprises the following steps: step S1, realizing accurate registration of LiDAR point cloud and a multi-view image in the same earth-centered earth-fixed coordinate system through GNSS constrained aerial triangulation, and uniformly carrying out center-of-gravity normalization to ensure space consistency and numerical stability; s2, estimating surface normal and scale parameters based on the local geometric features of the LiDAR point cloud, constructing a Gaussian ellipsoid consistent with the local geometry, and realizing Gaussian ellipsoid initialization of geometric perception; and S3, generating a sub Gaussian ellipsoid according to the Gaussian gradient and the tangent plane direction, and improving the modeling density and geometric continuity of the detail region through local encryption and parameter inheritance. According to the method, the fusion of LiDAR point cloud high-precision geometry and multi-view image texture information is realized, the geometric precision, the structural integrity and the detail expression capability of three-dimensional reconstruction are effectively improved, and the method is particularly suitable for high-precision live-action three-dimensional modeling of unmanned aerial vehicle multi-view images.

The invention discloses a hydropower station corridor autonomous mobile robot path navigation method, system and device and a storage medium, and the method comprises the steps: collecting hydropower station multi-source environment data, constructing a laser two-dimensional map, and fusing image semantic segmentation labels to generate a two-dimensional semantic map; the current sensor data is matched with the two-dimensional semantic map, and the initial pose of the robot is determined; a navigation path is generated on the basis of the initial pose and the target point, and the pose of the robot is dynamically adjusted and updated in the navigation process; path planning control is carried out based on the updated pose, obstacles are recognized in real time, and a driving path is optimized; recording path driving navigation data, updating the two-dimensional semantic map, and returning to the starting point based on the updated map after the task is completed. According to the method, a two-dimensional semantic map with space consistency and semantic definition is constructed, key geometric structure extraction and navigation structure index generation are combined, accurate recognition and path topology understanding of passable areas in the hydropower station gallery are achieved, and the accuracy and robustness of autonomous path navigation are improved.

The invention provides a bridge health monitoring method and system based on Beidou grid codes, and the method comprises the following steps: S1, constructing a bridge spatial index model, generating a multi-level spatial index for a bridge structure according to a Beidou grid coding rule, and building a topological relation table of components and grids; s2, performing multi-modal data feature extraction, performing defect identification on bridge image data through a pre-trained convolutional neural network, and performing abnormal mode detection on sensor time sequence data through a long-short-term memory network; according to the invention, deep coupling of the spatial index and the intelligent algorithm is realized. According to the method, health state evaluation of adjacent areas is mutually referenced, spatial continuity is enhanced, and meanwhile, multi-scale feature aggregation is realized by utilizing a grid hierarchical structure, so that the model can capture relevance of local defects and overall structure response at the same time. In the model training process, space consistency regularization is introduced, it is ensured that evaluation results of adjacent grid units are kept reasonable and continuous, and the misjudgment rate is remarkably reduced.

The invention discloses a naked-eye 3D augmented reality interactive display system, and relates to the technical field of naked-eye 3D display and reality interaction. According to the invention, the multi-source depth data acquisition module acquires and fuses a user limb depth image, the parallax physical depth mapping module completes depth and parallax mapping and visual angle correction, the occlusion relation determination module divides regions to determine the occlusion type, and the adaptive mask layer generation module generates a dynamic smooth mask layer. The shielding effect fusion display module is used for overlapping and synthesizing the shielding layer and the multi-view virtual image and then displaying the overlapped and synthesized shielding layer and the multi-view virtual image; the problems that in naked eye 3D interaction, the occlusion relation between the limbs of the user and the virtual object is not accurate, and the vision is abrupt are effectively solved, the space consistency and the vision fluency of interaction are remarkably improved, and the real and natural naked eye 3D augmented reality interaction experience is achieved.

The invention discloses a depth estimation method and device for any video and a storage medium, and belongs to the field of visual depth estimation. The method comprises the following steps: carrying out annotation processing on a scene video sample to obtain a deep annotation video data set; screening based on the depth labeling video data set and the TartanAir data set to obtain a spatio-temporal joint training sample; the method comprises the following steps: performing time sequence embedding on a multi-head attention layer in an encoder of a DepthAnything model to obtain a space-time combined multi-head attention layer, and constructing an initial TC-DepthAnything model; training the initial TC-DepthAnything model by adopting a space-time joint training sample, and performing constraint by adopting an overall training loss function formed by space consistency loss and time domain regularization loss in the training process to obtain a target TC-DepthAnything model; and inputting any video into the target TC-DepthAnything model to obtain a predicted depth video. The problem of time sequence jitter of DepthAnything in video depth estimation is solved, and flicker artifacts and motion blur in a dynamic scene are inhibited. And video depth estimation with a large application range and an accurate estimation result is realized.

According to the wafer defect detection system and method based on polar coordinate transformation and the generative adversarial network, polar coordinate expansion, geometric position coding, a double-discriminator structure and double-space consistency reconstruction loss are introduced, so that the network can accurately model a circular geometric structure of a wafer while keeping pixel details; therefore, the significance of the defect in the reconstruction error is enhanced. According to the method, the wafer image is subjected to structural constraint in the Cartesian space and the polar coordinate space at the same time, the sensitivity of the model to annular defects, edge defects and radial anomalies is improved, the defects of a traditional method in the aspects of structural consistency, edge reconstruction and weak defect detectability are overcome, and higher accuracy and engineering deployability are achieved.

The invention relates to the field of line loss treatment, in particular to a power distribution network line loss intelligent diagnosis and autonomous treatment method and system. The method comprises the following steps: collecting various data and carrying out restoration and normalization processing; checking the space consistency of topology, ledgers and archives through a block chain smart contract, and calculating a load space-time matrix; constructing a line loss gene map, and identifying an abnormal line; generating and executing a treatment strategy; and updating the gene map according to the treatment effect and carrying out closed-loop iteration. The problems that in the prior art, data processing is difficult, topology and ledger are inconsistent, and line loss diagnosis and treatment are not intelligent are solved, whole-process intelligence and automation from data collection to treatment are achieved, and the efficiency and accuracy of power distribution network line loss management are effectively improved.

The invention discloses a deep learning-based satellite remote sensing collapsible loess foundation evaluation method, which comprises the following steps of: acquiring an optical remote sensing image, a radar remote sensing image and an infrared remote sensing image of a target area, and preprocessing; collapsibility feature weighted data are screened, and feature weights are distributed; feature extraction is carried out through a local space exhibition structure and a multi-layer Transform structure of the collapsibility foundation evaluation network; collapsibility area self-supervised collaborative learning is carried out, and self-supervised training is carried out based on spatial correlation, pseudo labels and spatial consistency regular terms; and carrying out risk grade division on the target area, and outputting a collapsibility risk result of each spatial position. According to the method, multi-mode remote sensing and deep learning are fused, high-precision intelligent partitioning of the collapsible loess foundation is achieved, and the method has the advantages of being self-adaptive, low in manpower and high in spatial resolution.

The invention discloses a leakage monitoring method based on an LNG fuel gas system. The leakage monitoring method comprises the steps that a self-adaptive frequency modulation continuous wave active acoustic scanning network is established; blind source separation is carried out on mixed signals in acoustic scanning network abnormal events by adopting self-adaptive kernel independent component analysis; constructing a leakage feature mapping model of the physical information neural network; leakage source accurate positioning and quantification based on acoustic tomography and Bayesian reasoning are carried out; multi-modal decision fusion is carried out based on the multi-dimensional data sources received in parallel, a false alarm suppression mechanism is set, and time continuity verification and space consistency verification are carried out; establishing a reinforcement learning model for autonomously optimizing a monitoring strategy according to environment change and system state, and realizing adaptive optimization; the strategy network after self-adaptive optimization is deployed at the cloud, actions are generated regularly according to the current state, the actions are issued to the regional gateway and the edge node for execution, and iterative updating is carried out, so that the monitoring accuracy in a complex environment is improved, and the false alarm rate is reduced.

The invention discloses a hydrological digital twinborn model construction method and system based on an artificial intelligence technology, and relates to the technical field of hydrological monitoring and intelligent early warning, and the method comprises the steps: constructing a hydrological element measuring point monitoring model fusing multi-source sensor data, and introducing a graph convolution network to capture the complex correlation between environmental factors and hydrological elements; constructing a mapping model by using an improved long-short term memory neural network, and designing a multi-target dynamic monitoring loss function to balance data precision and space consistency; a measuring point prediction value and a global simulation value are fused through domain self-adaptive adversarial training, a basic hydrological digital twinborn model is generated, and an interaction model is constructed in combination with an extreme working condition parameter migration strategy; and constructing an inference mechanism based on the hydrological disaster knowledge graph, and outputting an entity engineering disposal scheme. The problems that a traditional hydrological model is weak in generalization ability, insufficient in data fusion, poor in extreme working condition adaptability and the like are solved, and high-precision real-time sensing and intelligent decision making in the hydrological process are achieved.

The invention discloses a vehicle track missing data complementing method and system based on space-time correlation learning, and belongs to the field of intelligent traffic and track data processing. The method comprises the following steps: firstly, preprocessing an original vehicle trajectory, then screening similar vehicle trajectories based on preset space-time constraints in a space range and a time window corresponding to a missing interval, and carrying out weighted fusion on the similar trajectories according to the correlation of a trajectory change trend and a space distance to obtain similar trajectory features for completion; performing feature extraction on historical tracks before and after the missing interval of the target vehicle by using a time sequence feature learning model to obtain historical track features; according to the relative length of the missing interval, dynamic weighted fusion is carried out on the similar trajectory features and the historical trajectory features, and fusion features used for complementation are generated; and finally, outputting the complemented vehicle track through the reconstruction network. According to the method, the complementation precision of the long-time missing segment can be improved, and the time continuity and the space consistency of the trajectory are kept.

The invention relates to a picture book generation interaction system and a picture book generation interaction method, in particular to a picture book controllable generation interaction system and a picture book controllable generation interaction method based on graph splicing-semantic fusion, and solves the problems that in the prior art, a space-semantic fusion mechanism lacks, so that a generation result lacks structural consistency and is poor in interpretability; or real-time feedback is difficult due to interaction missing. According to the method, the cross-modal alignment and fusion module is used for achieving joint modeling of the graph splicing and placing features and the semantic features, the problem of uncertainty of semantic generation is solved, logic interpretability between creation behaviors and generation results is achieved, and the structural rationality and semantic consistency of generated picture books are fundamentally improved. Meanwhile, the cross-modal alignment and fusion module adopts a cross attention mechanism and a space consistency constraint, so that the generated picture book keeps the artistic naturalness and the structural logic consistency at the same time, and the interpretability and accuracy of the picture book are further improved.

The invention relates to a water level flow intelligent monitoring system and method based on space-time prediction and a quantum sliding window. The system comprises a sensor layer, a data processing layer, an intelligent algorithm layer and an application service layer. Water level and flow point cloud data are synchronously collected through a 79G millimeter wave radar array, and after preprocessing and feature extraction, a space-time joint prediction model fuses time trend, space consistency, environmental factors and historical modes when data are missing to perform intelligent complementation; then, a quantum heuristic sliding window is used for conducting self-adaptive filtering and exception processing on the data through intelligent conversion of a superposition state, a collapsing state and a tunneling state; the integrated high-precision synchronous monitoring of the water level flow is realized, the problems of discontinuous data, insufficient precision, poor adaptability and high cost in the traditional water level flow monitoring are solved, and the reliability, the self-adaptive capability and the intelligent level of hydrological monitoring are remarkably enhanced.

The invention relates to the technical field of computer vision and video processing, and provides a residual frame video processing convolution method based on dynamic gating, which comprises the following steps of: carrying out gray conversion and Gaussian filtering processing on a current frame image and a previous frame image; calculating an inter-frame difference image and carrying out binarization processing to obtain a motion area mask; performing morphological expansion operation on the mask image; extracting the contour of the motion area and calculating a minimum bounding rectangle to obtain a bounding box; carrying out merging processing on the overlapped bounding boxes; updating the synthesized background frame based on the combined bounding box, and copying the pixels of the motion area of the current frame to the corresponding position of the background frame; and outputting the optimized bounding box set and the updated synthesized background frame. According to the method, the calculation efficiency and the resource utilization rate of video processing are improved, and meanwhile, the timing sequence continuity and the space consistency of a processing result are ensured through a strategy of keeping the static region unchanged and only updating the motion region.

Systems, methods, and apparatuses disclosed herein can map content items onto a venue using rendering-based, replication-based, and region-based mappings. In rendering-based mapping, multiple renderings of the content items are captured from multiple locations. In replication-based mapping, a single rendering is captured from one location and replicated across the venue. In region-based mapping, the venue is divided into distinct three-dimensional physical surfaces, and content items is mapped onto each region, optionally using virtual representations, transformations, and virtual texture maps to ensure geometric, photometric, and spatial coherence across the surfaces.

Systems, methods, and apparatuses disclosed herein can map content items onto a venue using rendering-based, replication-based, and region-based mappings. In rendering-based mapping, multiple renderings of the content items are captured from multiple locations. In replication-based mapping, a single rendering is captured from one location and replicated across the venue. In region-based mapping, the venue is divided into distinct three-dimensional physical surfaces, and content items is mapped onto each region, optionally using virtual representations, transformations, and virtual texture maps to ensure geometric, photometric, and spatial coherence across the surfaces.

This invention relates to a multimodal visual understanding method based on consistency learning and hybrid feature extraction. It includes constructing an end-to-end fine-grained consistency learning framework, introducing a hybrid region extractor, fusing local details and global semantics to generate high-quality hybrid visual cue embeddings, combining self-reconstruction loss and latent spatial consistency loss to force the model to establish explicit alignment between the input visual cue and the output segmentation label, utilizing the geometric boundary constraints of the localization task for description generation, and simultaneously optimizing localization accuracy using the semantic depth of the description task. Furthermore, it constructs a detailed localization index expression and segmentation task to enhance the model's reasoning ability for complex long text instructions. The aim is to address the problems of feature fragmentation and insufficient accuracy in existing large models for fine-grained visual localization and description tasks. Compared with existing technologies, this invention has advantages such as high accuracy and strong generalization ability in pixel-level localization and fine-grained description.

The invention discloses a large-tonnage static load test supervision system based on process locking and data tracing, and relates to the technical field of large-tonnage static load tests.The system is characterized in that an on-site sensing unit is activated when it is detected that a borne load exceeds a preset wake-up threshold value, and spatial position information of loading equipment is acquired; the edge control unit receives the spatial position information and performs spatial consistency verification in combination with self-positioning data and digital pile position coordinates recorded at the cloud end; the cloud supervision platform executes legality auditing on the key operation parameters before the test is started, and a test authorization instruction is issued only when a preset compliance condition is met; after the space consistency verification is passed, changing the service state of the corresponding digital pile position from'non-start 'to'in-progress'; and carrying out abnormal behavior monitoring on the uploaded load-settlement data flow to generate an abnormal evidence chain. The method has the effect of guaranteeing the authenticity, uniqueness and traceability of the static load test data from the source.

The invention discloses a land type intelligent identification and updating method and system for land investigation, and relates to the technical field of land investigation and remote sensing land type identification, and the method comprises the steps: carrying out the unified coding and weight fusion of optical, radar, terrain and auxiliary features on a standardized land block image stack through a feature fusion and dimension reduction model; generating plot-level fusion feature representation; jointly inputting the land block level fusion feature representation and the pixel statistical features in the land block into a land class recognition model, outputting a land block level land class category and confidence through a double-layer fusion structure, and generating a multi-temporal land class recognition result; and calculating a change credibility score of the land parcel based on a multi-temporal land class identification result and a time sequence observation index, and determining whether the land parcel belongs to a stable state, a suspected change or a confirmed change by comparing an index trend, class stability and space consistency. According to the method, collaborative identification of macroscopic structure information and microcosmic detail information is realized, and the accuracy and the spatial integrity of regional land class classification are improved.

This invention discloses a target trajectory association method, comprising: a preset area containing multiple detection devices, each detection device using polar coordinates as a coordinate system, reporting the first position information of the target, and each detection device acting as a pole, periodically reporting the first position information of the detected target; this target trajectory association method and system achieves temporal consistency of targets by periodically aligning two detection devices in the identification group, and unifies the state variables of targets reported by non-central devices to the coordinate system of the central device, achieving spatial consistency of targets. When the targets reported by the detection devices are consistent in both time and space, the similarity between targets reported by different detection devices in the same area is calculated to obtain the correlation between targets, thereby realizing the association of target trajectories.