95 results about "Skeleton graph" patented technology

The invention discloses a geological map-oriented breakpoint repairing and closed curve reconstruction method and system and a medium. The method comprises the following steps of: preprocessing an original geological map to obtain a grayscale image; carrying out binarization and morphological closed operation processing to obtain a grayscale image of the edge of the smooth contour curve; performing pixel skeletonization to obtain a pixel-level skeleton diagram; performing secondary edge extraction, connected domain marking and endpoint statistics on the pixel-level skeleton diagram, and screening out a non-closed curve and an endpoint set; carrying out nearest neighbor retrieval by adopting KD-Tree to obtain a candidate pairing set of each end point; performing priority pairing in combination with the spatial distance and the direction smoothness; and generating a transition line segment according to a pairing result to supplement the fracture part of the non-closed curve. According to the method, non-closed breakpoints generated by scanning or drawing errors in the geological map can be automatically restored, and a closed curve with topological integrity and a smooth boundary is generated.

The invention discloses an internet consumption analysis method based on data analysis, and relates to the technical field of data analysis, and the method comprises the steps: a plurality of participation platforms generate local cause-effect sub-graphs according to user behavior data, convert the local cause-effect sub-graphs into Hash codes, upload the Hash codes to a central server, and aggregate the Hash codes to generate a global cause-effect skeleton graph; injecting a predefined sequential logic rule based on the global causal skeleton graph, dynamically adjusting a causal edge weight according to a user real-time behavior event, and incrementally updating nodes and edges in the dynamic knowledge graph; pre-training the policy network parameters by using each participation platform, and uploading the pre-trained policy network parameters to a central server for aggregation; and issuing the aggregated policy network parameters to each participation platform, executing an interpretable policy according to the user real-time behavior event, and returning user feedback behavior data to each participation platform. According to the method, collaborative optimization of privacy security, real-time response and causal interpretability is realized through federal causal discovery and a dynamic knowledge graph evolution architecture.

The invention discloses a crack width measurement method and system based on dynamic topological structure analysis. A system hardware basis comprises an image acquisition module, an image processing unit, a data storage module and a result display module. According to the method, the working process of an image processing unit is divided into two stages according to a time sequence: the first stage is used for realizing data processing and crack segmentation, and the second stage is used for completing dynamic topology analysis and width measurement of cracks. In the second stage, the steps of form closed operation, skeleton extraction and enhancement, end point bridging, topological statistics and segment width measurement are executed in sequence, and finally a visual result and quantized data are output. The method comprises the following steps: processing an initial skeleton image by adopting an independently researched and developed topology enhancement algorithm; the method is especially good at processing complex net-shaped cracks, and the defect that measurement is inaccurate at crossed and bent cracks in a traditional method is effectively overcome through a dynamic topology analysis method.

A method, apparatus, non-transitory computer readable medium, and system for image generation includes obtaining an input image depicting an entity and a skeleton map depicting a pose of the entity and performing a cross-attention mechanism between image features of the input image and entity features representing the pose to obtain modified image features. An output image is generated based on the modified image features that depicts the entity with the pose.

The invention relates to a multi-modal gait recognition method based on space-time semantic modeling and cross-modal cooperation, and the method comprises the steps: inputting a skeleton energy diagram, a contour energy diagram, a contour diagram and a skeleton diagram into four feature extraction branches with the same structure, and obtaining four modal features and two stage features of each modal; inputting the two stage features of each group of cross-modal combination into a double-stage feature interaction enhancement module to obtain a cross-modal fusion feature; all the modal features and the cross-modal fusion features are mapped and pooled and then input into a classification head, and a multi-modal gait recognition result is obtained. According to the method, key features under different view angles and walking conditions are effectively focused, the integrity of local channel information is reserved, modeling is performed on spatio-temporal information through multiple branches, high-level semantic features are extracted, spatio-temporal changes of gaits under different view angles and walking conditions are accurately captured, features of different modal data are fused, and the gait time-space information is obtained. And the gait recognition performance under multi-view and different walking conditions is improved.

The invention provides an ancient character defect font reconstruction method and device based on a graph structure and deep learning, and relates to the technical field of image processing, and the method comprises the steps: carrying out the graph structure representation extraction of a defective ancient character image, and obtaining a target contour graph and a target skeleton graph corresponding to the defective ancient character image; according to the target contour map and the target skeleton map, determining a defect type corresponding to the defective ancient character image, determining a font reconstruction strategy based on the defect type, and determining a map neural network corresponding to the defective ancient character image based on the font reconstruction strategy; the graph neural network comprises a contour graph reconstruction network, a skeleton graph reconstruction network or a cross-modal reconstruction network; and inputting the defective ancient character image into a pre-trained graph neural network based on a font reconstruction strategy to obtain a reconstruction result. According to the method, high-quality ancient character defect font reconstruction is realized by using the graph neural network.

The application provides a tab folding detection method and device, electronic equipment and storage medium. The method comprises: performing skeleton extraction processing on a cross-sectional image of a multi-layer tab to obtain a skeleton image of the multi-layer tab; performing merging processing on a broken connected domain in the skeleton image to obtain a merged connected domain, the broken connected domain being a connected domain that is broken in the same tab cross section; according to the merged connected domain and a non-broken connected domain, counting a target number of the multi-layer tab; and according to the target number and a preset number, detecting whether a tab in the multi-layer tab is in a folded state. The broken connected domain is merged to obtain the merged connected domain, and according to the merged connected domain and the non-broken connected domain, the target number of the multi-layer tab is more accurate, thereby making the tab folding detection more accurate.

The application provides a human motion sequence prediction method and system based on skeleton-enhanced Transformer, comprising the following steps: S1: sampling human motion to obtain original motion sequence data; S2: generating virtual joints based on real joints, adding the virtual joints to the sequence, constructing enhanced motion sequence data, and generating an enhanced skeleton graph; S3: inputting the enhanced skeleton graph into a graph convolution network, establishing the skeletal correlation between virtual joints and real joints, and optimizing feature extraction through a spatiotemporal attention mechanism; S4: decomposing the input motion sequence data and dividing it into a trend part and a residual part, wherein the trend part is used to capture the trend information of the overall motion, and the residual part is used to retain local details; S5: combining the extracted features with the trend part and the residual part to generate future motion sequence data to obtain a prediction result; the skeleton features are enhanced by simulating joint combinations and introducing virtual joints.

This application discloses a method, system, and device for detecting malicious code variants using control flow graph semantic matching, belonging to the field of computer network security technology. It includes: disassembling the target executable file, constructing a control flow graph, and extracting basic block semantic features to generate semantic vectors; obtaining semantic equivalence classes based on semantic vector clustering, identifying main anchor points, and constructing a main anchor point skeleton graph; performing semantic compression and noise processing with the anchor point skeleton as constraints, and obtaining a semantic core graph through topological verification; hierarchically normalizing the semantic core graph and the main anchor point skeleton graph to generate a composite hash signature; and matching the composite hash signature with a malicious code family signature library to complete variant determination. The above scheme, through the main anchor point constraint compression and normalization process, resists structural obfuscation disturbances, improves the consistency of homologous variant identification, and has advantages such as strong robustness, high efficiency, and good interpretability. It is suitable for detecting malicious code variants using control flow graph semantic matching in complex obfuscated environments.

The invention discloses a construction settlement dynamic monitoring method based on data analysis, and relates to the technical field of construction engineering monitoring and data-driven modeling, and the method comprises the steps: combining a causal diagram modeling method with a multi-source data analysis mechanism, achieving the structural modeling and dynamic correlation recognition of multi-level observation variables in a construction settlement region, and achieving the dynamic monitoring of the construction settlement. According to the method, a causal structure is updated in real time under multi-source heterogeneous data flow through a fast conditional independence test algorithm in combination with a modular skeleton diagram updating mechanism, logic consistency of a monitoring model is kept, and a multi-objective function optimization algorithm is combined with DAG constraint and a direction confidence coefficient matrix, so that a multi-source heterogeneous data flow is optimized. According to the method, automatic judgment and global optimal causal direction reasoning of conflict causal relationships are achieved, a priori knowledge attenuation function is combined with a data-driven confidence fusion model, time sequence dynamic evaluation of causal edge reliability is achieved, and self-evolution of a knowledge system is achieved.

The invention relates to the technical field of automatic fruit picking, and particularly discloses a skeleton-guided fine-tuning-based longan picking point identification system and method, and the method comprises the steps: introducing a region attention mechanism into a C3K2 module of YOLOv11 to form a C3K2AAttn feature extraction module, inputting a feature map into a YOLOv11 network, carrying out the deep feature extraction through the C3K2AAttn feature extraction module, and carrying out the recognition of a longan picking point. Performing multi-direction region division on the feature map, calculating the attention weight of each region, integrating multi-direction features through a weight fusion layer, outputting an enhanced feature map, and on this basis, taking a branch binary skeleton map as geometric prior information, splicing high-level semantic features of the backbone network and the binary skeleton map in a channel dimension, and obtaining a multi-directional feature map; after region attention enhancement feature expression, a 3 * 3 convolutional layer is input to generate a biased field, and the biased field is utilized to dynamically calibrate an initial prediction picking point, so that a prediction point returns to a real branch communication region, and the accuracy of automatic longan picking is improved.

The application discloses a photovoltaic power station monitoring method and system based on three-dimensional image modeling and virtual simulation fusion, relates to the technical field of image modeling, and comprises the following steps: generating a reflection response graph according to an original inspection graph of a photovoltaic power station, sequentially performing adaptive threshold segmentation and morphological processing on the reflection response graph to obtain a candidate reflection graph of a screen; performing skeleton extraction on the candidate reflection graph of the screen to obtain a candidate highlight skeleton graph, screening out attached edge pixel points according to a skeleton pixel point set in the candidate highlight skeleton graph, connecting the attached edge pixel points to obtain an edge traction sliding graph; generating a basic outer adjacent band according to a three-dimensional model of a station of the photovoltaic power station, performing region merging on the basic outer adjacent band to obtain a grounding conductor layout band, and performing projection on the grounding conductor layout band to obtain a virtual screen corridor projection graph; and the application improves the reliability of a photovoltaic power station monitoring result.

The invention discloses a real ink mark driven facsimile authentic method based on submerged space diffusion, which comprises the following steps: firstly, collecting a target facsimile image and a large number of real ink mark sample images, carrying out preprocessing operation on the images, and constructing a standardized training set and a test set; secondly, extracting a contour diagram and a skeleton diagram from the target copy image, and mixing the contour diagram and the skeleton diagram in a linear mode to generate a continuous structural feature map; constructing an ink mark encoder, encoding the authentic mark sample, and extracting the pen and ink characteristics of the authentic ink mark sample; and finally, constructing a reinking network based on a latent space diffusion model, and finally converting the optimized data representation into a pixel image through a decoder to obtain a calligraphy image with natural ink marks and real strokes. The problems that in the prior art, copy images are inflexible in ink color and lack of natural ink color change are solved, meanwhile, stability of a font skeleton and refinement of the stroke of the to-be-restored area are guaranteed, and authentic transformation work is achieved.

The application discloses a kind of fruit tree image acquisition method and system based on two-dimensional skeleton and viewpoint planning, it is related to wisdom agricultural equipment technical field, method includes: obtaining crown image obtains branch mask and depth information;To mask thinning pruning obtains initial skeleton, identifies feature point and calculates growth direction after artifact removal;Based on feature point and direction vector, topological reconstruction is carried out to broken skeleton, and complete two-dimensional skeleton graph is obtained;Based on two-dimensional skeleton graph, viewpoint planning is carried out in reference depth plane, generates viewpoint sequence and is mapped into three-dimensional pose, realizes autonomous image acquisition.The application solves the problem of information loss caused by occlusion and three-dimensional reconstruction calculation lag, and realizes real-time, safe and high-resolution image acquisition.

The invention provides a multi-person whole body grid three-dimensional reconstruction method and system based on hierarchical query and graph convolution refinement. The method comprises the following steps: performing parallel prediction on parameters of each part of a human body from coarse to fine by using a hierarchical query decoder; aggregating all the preliminary attitude parameters into a human body global skeleton graph structure, and carrying out whole body attitude collaborative optimization through an attitude graph refinement device based on a graph convolution pyramid to obtain an optimized whole body fusion parameter set; and inputting the optimized whole body fusion parameter set, the shape features and the facial expression parameters into an SMPL-X human body model to carry out three-dimensional human body grid reconstruction. According to the invention, the natural dependency relationship of each part of the human body is explicitly captured from coarse to fine; the problem of space decoupling of body parts is solved while high efficiency is kept, and the structural consistency of attitude estimation is remarkably improved; and through explicit modeling of a dependency relationship between joints and fusion of multi-scale features, distortion of a local attitude is effectively corrected, and the method has excellent robustness and accuracy.

The application discloses a method, device and medium for establishing a Parkinson's disease gait impairment evaluation model. The method for establishing the model comprises the following steps: pre-processing a gait video, extracting a skeleton graph sequence and a multi-cycle gait energy graph; using the skeleton graph sequence and the multi-cycle gait energy graph, combining a gait impairment score label obtained by a doctor according to gait video diagnosis, training a neural network to obtain a Parkinson's disease gait impairment evaluation model, wherein the gait impairment score label is used to divide the gait impairment degree. The application uses the contour and joint extracted from the gait video to generate two different morphological data streams of the gait energy cycle graph and the skeleton graph, and combines neural network training to generate a corresponding neural network model. The neural network model can analyze the gait video of a Parkinson's disease patient to be evaluated to obtain a gait score. Based on the gait score, the gait impairment degree of the Parkinson's disease patient can be evaluated conveniently and quickly, and the accuracy is high.

The application discloses a multi-robot cooperative exploration method based on a real-time environment skeleton graph and related equipment, and the method comprises the following steps: each robot acquires sensing data of itself, constructs a grid map according to the sensing data, performs binary processing on the grid map to obtain a binary map, a server constructs a global binary environment map based on the binary maps uploaded by all the robots, extracts an environment skeleton graph based on the global binary environment map, performs structural optimization on the environment skeleton graph to obtain an optimized environment skeleton graph, and sends the optimized environment skeleton graph to each robot; each robot performs state detection based on the optimized environment skeleton graph, and if the robot falls into a skeleton dead end or is stuck, the robot starts an escape strategy; each robot performs path planning and outputs an optimal exploration target and a path; and each robot executes a next exploration action according to the optimal exploration target and the path until the exploration task is completed.

The application belongs to the technical field of behavior recognition, and particularly relates to a human behavior recognition method and system based on dynamic space-time modeling and semantic quantification, which comprises the following steps: acquiring human skeleton sequence data and converting the data into space-time feature representation; constructing a Transformer network based on dynamic space-time modeling and semantic quantification, which comprises a preprocessing layer, a progressive feature extraction layer and a classifier; the preprocessing layer generates initial space-time features; in the progressive feature extraction layer, the joint importance weight is adaptively assigned by a global dynamic joint weighting module in the shallow stage, and the global dynamic joint weighting and a semantic quantification module are simultaneously used in the deep stage, the continuous features are discretized into semantic indexes by using a learnable motion primitive codebook, a semantic graph is constructed based on hard assignment and is fused with a physical skeleton graph, and the differentiable feature reconstruction is performed based on soft assignment; finally, the semantic enhanced features are input into the classifier to complete behavior recognition. The application improves the precision, interpretability and generalization ability of behavior recognition.

The embodiment of the invention provides a deposition configuration depicting method and device, a storage medium, a processor and a program product, and belongs to the technical field of oil exploration and development. The method comprises the following steps: acquiring a sedimentary configuration mode in a target work area obtained based on logging data, lithology data and a seismic data volume; based on the lithology data and the logging data, determining low-frequency, intermediate-frequency and high-frequency single-frequency bodies of each sedimentary layer in the sedimentary configuration mode from the seismic data body, and obtaining an RGB frequency division attribute fusion image of the target work area; extracting a configuration boundary skeleton graph from the RGB frequency division attribute fusion graph by adopting a morphological method; and depicting boundary features of each of the plurality of configuration units in the target work area and contact features of different configuration units in the plurality of configuration units based on the extracted configuration boundary skeleton so as to complete deposition configuration depiction in the target work area. According to the method, the limitation of low-resolution seismic data can be overcome, and the description of the configuration unit in the exploration target work area, especially the description of the river channel configuration unit, is completed.

The application relates to a rule violation video detection method and system based on skeleton behavior recognition, which comprises a video acquisition module, a key frame extraction module, an image data set establishment module and a real-time video behavior detection module.The posture estimation algorithm is used in the video key frame extraction module to estimate the joint position, the rule violation behavior detection module adopts a multi-filter dynamic graph convolutional neural network model, the real-time video behavior detection module adopts a multi-filter dynamic graph convolutional neural network, and the connection relationship between different behavior nodes is adapted through a dynamic skeleton graph.The Inception structure and the dynamic skeleton graph method based on the graph convolutional neural network can realize real-time and accurate detection and identification of rule violation sensitive action behaviors in internet live broadcast, thereby providing an important guarantee for standardizing the anchor live broadcast behavior and providing a civilized internet live broadcast environment.

The invention belongs to the field of human behavior recognition and computer vision, and provides a human behavior recognition method and device based on an improved space-time diagram convolutional neural network. The method relates to a graph neural network modeling technology and a spatial-temporal feature extraction technology. The method comprises the following steps: firstly, acquiring an original human body behavior video through a video acquisition system, and converting the original human body behavior video into a fixed-length image frame sequence; extracting human skeleton key point coordinates and confidence based on a YOLOv8-pose algorithm, and constructing a time sequence skeleton graph structure; screening skeleton targets with relatively high confidence coefficients to serve as skeleton feature data; the method comprises the following steps of: constructing a skeleton graph structure and a joint feature matrix on a space structure, automatically dividing the graph structure by utilizing a Laplacian feature mapping method, and extracting local and global features; in a time domain, a multi-scale cavity convolution method is adopted to extract human body action change trends with different time lengths; and finally, space and time dimension information is fused, and recognition and classification of behavior categories are completed. The human body behavior recognition method based on the improved space-time diagram convolutional neural network has the advantages of being clear in structure, easy to deploy, high in parameter automatic optimization capacity, suitable for various complex human body action scenes and the like, and meets the dual requirements for recognition precision and real-time performance in practical application.

The invention discloses an English letter recognition method and device based on machine vision and a medium, and relates to the technical field of image recognition, and the method comprises the steps: collecting an original image, carrying out the graying and denoising processing, obtaining a denoised gray-scale image, carrying out the correction and binarization processing of the denoised gray-scale image, and delimiting a connected domain, and obtaining a binary letter image; iteratively trimming the binary alphabet graph, setting nodes and edges, summarizing the nodes and the edges into a skeleton graph structure, calculating the path length of the edges and the curvature and the splitting degree of the nodes, and labeling the path length and the curvature and the splitting degree on the skeleton graph structure to obtain a skeleton graph structure with topological filtering labeling; constructing input of a graph neural network model according to a set topological complex of the skeleton graph structure with the topological filtering label, and completing training of the graph neural network model; and inputting the node feature matrix into the graph neural network model to obtain a letter recognition result. According to the invention, through completing the training of the graph neural network model, the recognition accuracy of complex, scribbling or deformed letters is improved.

The application discloses a skeleton quality driven tubular structure segmentation closed loop optimization method and system, which is applied to the technical field of biomedical image processing and computer aided diagnosis, and the method comprises the following steps: using a trained segmentation model to infer medical volume data to obtain a segmentation mask; skeletonizing the segmentation mask to extract a skeleton graph; performing topological defect detection on the skeleton graph to obtain a structured defect report; reversely mapping three-dimensional coordinates in the report back to a voxel space, taking each defect point as a center, a preset radius as a range, and generating a defect density weight graph according to a severity score; and using the weight graph as a spatial weighting parameter of a loss function to optimize the segmentation model; and the application reversely maps the topological defects detected by skeletonization into a weight graph and integrates the weight graph into a loss function for closed loop iteration, realizes directional repair on high-occurrence areas such as fractures and false branches, and improves the conduction efficiency of segmentation improvement to skeleton quality.

The invention relates to a method for lightweighting and / or design of an additively manufactured article. The invention also relates to a computer program product suitable for carrying out the method of the invention and to an additively manufactured article obtainable by all methods according to the invention following the lead of the independent claim. The method comprises the step(s) of filling and / or building with quasicrystalline structures each of one or more constituent article parts. The additively manufactured article obtainable by carrying out the method comprises quasicrystalline structures and / or quasiperiodic minimal surface fillers and / or quasiperiodic minimal surface design structures and / or non-periodic minimal surface design structures and / or non-periodic minimal surface fillers. The invention also relates to the use of skeleton graphs for pre-processing in an additive manufacturing process.

The invention discloses a path cost calculation method and device based on discrete graph key point recognition and a medium, and relates to the technical field of path planning, and the method comprises the steps: obtaining a working environment image corresponding to an intelligent agent working environment, and a current node and a target node corresponding to an intelligent agent; dividing the nodes into key nodes and non-key nodes based on the communication relationship, and drawing a skeleton graph based on the key nodes and the non-key nodes; calculating the distance between any two key nodes in the skeleton diagram based on a multi-source Dijkstra algorithm, and creating a distance table based on the distance; and judging whether the current node is a key node or not and whether the target node is a key node or not, and determining the optimal path cost corresponding to the intelligent agent based on the judgment result and the distance table. The method and the device are used for solving the problems of low efficiency and poor accuracy when path cost calculation is carried out by directly utilizing a discrete graph structure in the prior art, and the efficiency and the accuracy of path cost calculation are improved.

The application discloses a hierarchical three-dimensional modeling method and system taking a scene skeleton as an anchor point, and belongs to the technical field of computer vision and three-dimensional reconstruction. k The local subgraph is constructed and the local community is divided based on the abutment screening structure; the island-level graph is constructed based on the local community, the skeleton subgraph is constructed through the maximum weight edge disjoint spanning tree merging and the selective verification of the double-view disambiguator; the non-skeleton image is attached to the skeleton node in the corresponding community as a leaf node; and the scene skeleton is reconstructed based on the skeleton subgraph, and the complete reconstruction is completed by gradually absorbing the leaf node. Through the hierarchical graph construction and the selective verification mechanism, the application solves the problem that the robustness and the efficiency are difficult to be considered in the large-scale scene reconstruction, can effectively inhibit the visual ambiguity propagation while significantly reducing the calculation overhead, and realizes the high-integrity three-dimensional modeling.

The invention relates to the technical field of computers, and provides a point cloud-based planar graph generation method, device and equipment and a storage medium, and the method comprises the steps: obtaining to-be-detected point cloud three-dimensional data, and carrying out the two-dimensional projection of the point cloud three-dimensional data, and obtaining point cloud raster data; performing skeleton refinement on the point cloud raster data to obtain a skeleton diagram; processing the skeleton diagram to obtain gradient change characteristics, and detecting to obtain a wall line candidate line segment set based on the gradient change characteristics; obtaining a point chain topological relation graph based on the skeleton graph, and determining a connection relation of each wall line candidate line segment in the wall line candidate line segment set based on a preset wall line connection strategy by taking the point chain topological relation graph as a constraint model; generating final wall line data based on the connection relation of the candidate wall line segments; the wall line data is used for generating a plane graph. According to the method, the plane graph with high precision and good connection quality can be automatically generated.

The invention discloses an image compression method oriented to key point detection optimization, and aims to solve the problem that downstream task performance cannot be ensured at a low code rate in traditional compression. The method comprises the following steps: generating a track main code stream and a skeleton diagram from an original image frame through attitude token extraction and track parameterization; applying a reversible skeleton to render the track main code stream and the skeleton graph so as to synthesize a basic reconstruction image and a skeleton rendering state; calculating a residual image between the original image frame and the basic reconstruction image; coding the residual image based on task hard constraints derived from a skeleton rendering state and a key point similarity threshold, and generating an auxiliary residual code stream and a coding sequence strategy; and outputting the track main code stream and the auxiliary residual code stream. According to the method, the motion structure and the detail residual error are separated, and the task hard constraint is introduced to carry out code rate allocation, so that the performance of the key point detection task can reach the preset threshold value under the condition of low bit rate.

The invention relates to the technical field of artificial intelligence, in particular to a curve structure segmentation method and system based on edge-skeleton double constraint, and the method comprises the steps: obtaining a curve-shaped target image, carrying out the preprocessing of the curve-shaped target image, and obtaining an edge image, a skeleton image and a text prompt; inputting the edge graph, the skeleton graph and the text prompt into an edge-skeleton double-constraint generator for training to obtain a trained edge-skeleton double-constraint generator; generating a composite image-mask pair by using the trained edge-skeleton double-constraint generator, and fusing the composite image-mask pair with a real data set to obtain a training data set; training the curve-shaped structure segmentation model by using the training data set to obtain a trained curve-shaped structure segmentation model; inputting a to-be-segmented curve-shaped target image into the trained curve-shaped structure segmentation model to obtain a segmentation result; the segmentation performance of the curved structure can be improved.

The application is suitable for the technical field of data security and federated learning, and provides a data synchronization and verification method, system, device and program product. The method comprises: obtaining and mask processing health index features to obtain a masked federated feature set; based on the masked federated feature set and the last round of updated graph, obtaining the current layered cross-domain causal skeleton graph; under the dual constraints of privacy loss and federated consistency, iteratively updating the causal edge weight in the graph; based on the updated weight, calculating the privacy security attribution score of each feature and identifying the causal path; based on the attribution score, calculating the federated incremental trusted verification threshold, and combining the threshold to calculate the privacy entropy lock strength of the causal path; based on the weight, attribution score, trusted threshold and entropy lock strength, calculating the synchronization factor; if the synchronization factor meets the condition, the substandard feature is modified, and the causal skeleton graph is incrementally updated to form a closed loop. The application improves the overall security and stability of the system by constructing a deep endogenous coupling closed loop process.