193 results about "Connectivity" patented technology

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention relates to a task and communication combined path planning method for a multi-base unmanned system. The method comprises the following steps: mapping task distribution into a target node layer, and constructing a communication link into a transmission link layer to form a task and communication double graph; establishing a dynamic coupling edge between the graphs to represent the influence of a task execution behavior on the communication quality; an interactive influence propagation model is introduced in the time dimension, and the feedback effect of communication link fluctuation on the task progress is simulated; providing time-space dynamic cost evaluation input for path planning; defining a task feasible domain and a communication strong connectivity domain in a time-space domain based on the double-layer graph output by the technology; constructing a joint feasibility domain (JFE), only allowing path planning to be expanded in the domain, and processing a splitting problem of communication repair after the path planning; performing multi-target compression on the path, and balancing task path time consumption, node connection stability and path redundancy consumption indexes; a graph homotopy simplification mechanism is introduced, and redundant edge paths in the JFE are compressed.

The invention discloses an injection-production well connectivity modeling and yield prediction method and device, and relates to the technical field of oil reservoir engineering. Comprising the following steps: on the basis of preprocessed time sequence injection-production data, constructing a spatial topological graph structure according to screening conditions; constructing a yield prediction model based on the LSTM-GAT network, taking the spatial topological graph structure as the input of a graph attention mechanism, extracting the time dependence characteristics of the gas injection well and the production well by using the long short-term memory network, and extracting the spatial dependence relationship of the gas injection well and the production well by using the graph attention mechanism so as to output the predicted yield of the production well at the future moment; constructing a fitness function, and introducing a whale optimization algorithm to perform global optimization on the injection-production parameters to obtain an optimal injection-production parameter combination; and outputting the optimal injection-production parameter combination. The problems that when an existing method is used for determining the inter-well communication relation, a large amount of geological data and experiment cost are needed, the analysis process is complex, consumed time is long, and accurate optimization of injection-production parameters is difficult to achieve are solved.

The invention provides a geological reservoir fracture segmentation method fusing a 3DU-Net graph attention mechanism and graph theory connectivity detection and a system thereof, and aims at solving the technical problems of multi-modal data registration, 3D U-Net graph attention feature extraction, graph theory connectivity detection and the like. The problem of insufficient geological reservoir fracture segmentation precision caused by data heterogeneity and connectivity abnormity is effectively solved, high-precision segmentation of a complex geological reservoir fracture network is realized through accurate alignment of multi-modal data and a graph attention feature extraction and graph theory connectivity detection technology based on 3D U-Net, and the method has the advantages of high precision and high efficiency. The problems of local misjudgment and abnormal connectivity caused by the fact that a traditional method only depends on amplitude or texture features are effectively solved, and therefore segmentation precision and robustness are remarkably improved.

The invention provides a land planning method based on data analysis, and the method comprises the steps: obtaining an initial distance matrix between land parcels in a farmland land parcel set around a town, calculating the direct connectivity between the land parcels and other land parcels based on a distance threshold value, and constructing an initial farmland ecological network topological structure diagram; the overall network connectivity, the node centrality and the average path length of the initial farmland ecological network topological structure diagram are calculated through a graph theory algorithm, so that the structural integrity of the ecological network is evaluated, and an ecological network structural evaluation index initial value is obtained; according to the adjusted distance matrix between the land parcels, recalculating the connectivity between the land parcels, reconstructing a farmland ecological network topology structure, and calculating various indexes of network structure integrity to assess an ecological network structure; and combining the plot connectivity and the land utilization efficiency to evaluate the feasibility and cost effectiveness of plough layer stripping and reutilization, and determining an optimal adjustment scheme.

The invention discloses a splicing curved surface reinforcement method fusing a graph theory and UV parameterization. The splicing curved surface reinforcement method comprises the steps of creating a component BIM three-dimensional model, inputting reinforcement parameters, searching an arrangement path and generating reinforcing steel bars. In order to solve the technical problem of splicing curved surface reinforcement in the BIM three-dimensional design process of complex structure engineering, a graph theory is introduced to construct an edge and surface topological connected graph model, so that the problem of steel bar path planning in disordered splicing surfaces is effectively solved, and the construction efficiency is improved. The rapid routing of the steel bar arrangement path in the curved surface topology network and the rapid positioning of the steel bar arrangement path in a plurality of sampling points on the topology edge are realized. Meanwhile, by combining a UV bidirectional parameterization mapping mechanism, the problem of self-adaptive arrangement of the reinforcing steel bars in the three-dimensional curved surface is solved, so that efficient solving and accurate control of the reinforcing steel bars of the complex spliced curved surface are achieved, a traditional reinforcing steel bar mode dominated by manual experience is broken through, the automation degree and engineering reliability of the reinforcing steel bar design of the complex spliced curved surface are remarkably improved, and the construction cost is reduced. Good application prospects are realized.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to predicting an optimized result for a graph neural network (GNN). A system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components comprise: a fusion component that that models non-linear modality correlations within and across entities through Hirschfeld-Gebelein-Re'nyi maximal correlation (MaxCorr) embeddings that generates a multi-graph that preserves identities of modalities and entities; and a multi-graph neural network (MGNN) component for task-informed reasoning in multi-graphs, that learns parameters defining entity-modality graph connectivity and message passing in an end-to-end fashion.

The invention discloses an unmanned aerial vehicle automatic homeward voyage method based on a global topological map and a graph search algorithm, which comprises a global passable area topological connected graph module, a homeward voyage path search module, a homeward voyage path optimization module and a homeward voyage path error processing module. According to the method, global path searching is carried out in combination with a Dijkstra algorithm, path smoothness, flight time and obstacle avoidance are optimized by using a B spline curve, the precision and speed of path planning are effectively improved, and meanwhile, the system can cope with changes in the environment by updating a topological connected graph and optimizing the path in real time, so that the path planning efficiency is improved. And secondly, the path safety and the flight stability are both considered, and an obstacle distance penalty term is introduced, so that the flight path can avoid the obstacle, and the collision risk is reduced.

The invention discloses a three-dimensional fracture network seepage and solute transport numerical simulation method based on topological pruning, which comprises the following steps of: analyzing a fracture network topological structure from global and local scales by applying a graph theory, establishing a quantitative relation between fracture connectivity and an underground flow field, and accurately describing an influence mechanism of topological characteristics on a seepage field; on the basis of network topology analysis, the key fracture sub-network is extracted through a pruning method, the key fracture sub-network is used for replacing a complete network for high-precision numerical simulation, and the calculation efficiency is remarkably improved while the calculation precision is guaranteed. The invention provides an efficient and reliable solution for the problems of seepage and solute transport in a hard rock fractured medium.

Some aspects of the disclosure provide a method of mesh decoding processing. The method includes receiving a bitstream including coded information of a polygon mesh, the polygon mesh including vertices that are connected into polygons, the coded information indicates connectivity information of the vertices. The method also includes determining a first connectivity of a first polygon-fan with a first vertex being a pivot vertex of the first polygon-fan according to the coded information; detecting that a second vertex in the first polygon-fan is a a visited vertex that has existing neighborhood information; checking whether the existing neighborhood information of the second vertex and new neighboring information of the second vertex in the first polygon-fan satisfy a condition; and updating the existing neighborhood information of the second vertex based on the new neighboring information when the condition is satisfied.

The invention relates to an APT attack detection and machine learning technology, in particular to an APT attack detection method fusing comparative learning and cross-domain recommendation. A traceability graph is constructed based on a data set, and a bipartite graph is constructed according to the traceability graph; generating an r-ego network for all nodes in the bipartite graph; generating positive and negative sample pre-training graph encoders in the source domain, and transferring the pre-trained graph encoders to the target domain; generating initialization embedding in a target domain by using a pre-trained graph encoder; the matrix decomposition model is finely adjusted by using the initialized embedding, and the final embedding of each node in the target domain is obtained by using the finely-adjusted matrix decomposition model; and predicting whether the two nodes interact based on the final embedding. According to the method, the high-order connectivity is formed by using the side information of the system entities to predict the possibility of interaction between the entities. And meanwhile, a cross-domain recommendation method is used, so that the problem of data sparsity of APT attacks is relieved while the recommendation performance on a target domain is improved.

The invention provides a reef limestone crack connectivity prediction method and device in combination with a graph neural network, and the method comprises the following steps: S1, taking the intersection points of reef limestone crack line segments as graph nodes, and recording the three-dimensional space coordinates of each node; taking actual crack line segments between adjacent nodes as edges of the graph, and endowing each edge with an edge feature vector; the edge feature vector at least comprises a crack trend azimuth angle, a crack section length, a crack section curvature and a minimum included angle formed by the crack section and all adjacent crack sections; s2, constructing an adjacent matrix and a node feature matrix, and constructing structured graph data for describing the geometrical morphology and topological connection of the crack network in combination with the edge feature vectors; and S3, inputting the structured graph data into the graph neural network to obtain a connected probability graph. A reef limestone fracture network is converted into a graph structure and a graph neural network, and collaborative breakthrough of precision and efficiency of seepage capacity evaluation and decision reliability is achieved.

The invention discloses an oil reservoir water content prediction method based on a graph neural network and Transform, and belongs to the technical field of oil and gas field development, and the method comprises the following steps: S1, converting an oil reservoir injection-production model at each time point into a graph structure, and constructing a connection network from a water injection well to a production well; and S2, constructing a GNT proxy model in combination with the graph neural network and Transform, training the GNT proxy model, and predicting the water content of each production well. According to the constructed GNT proxy model, an oil reservoir injection-production structure is converted into graph data through a GCN module, the information transmission strength between nodes is dynamically adjusted through an edge feature enhanced message transmission mechanism, and inter-well communication features are accurately captured; and the multi-head self-attention mechanism of the Transform encoder module is combined to process the time sequence dependence, so that the long-distance time dynamic relationship is effectively modeled, and an efficient and reliable agent model is provided for the dynamic analysis of the oil reservoir.

The invention relates to an agent scheduling method and device of a charging system, a storage medium and a program product. The scheduling method comprises the following steps: a charging system network topology construction step: constructing a network topology; a centralized task allocation step: in response to the plurality of charging requests, modeling a task allocation problem into a cooperative game optimization model for the heterogeneous multi-agent system; a distributed path planning step: taking the distributed charging request as a target, and establishing a competition-cooperation game model based on a local communication fully connected graph constructed by utilizing dynamic topology; and a planning strategy design step: based on the game theory, solving the game models in the centralized task allocation step and the distributed path planning step by utilizing model predictive control until a Nash equilibrium solution sequence which enables a target function in a corresponding model predictive control optimization model to be minimum is converged, and outputting a planning control sequence in the prediction view as a control vector.

The invention provides a node clustering method based on communication network connectivity analysis, and belongs to the technical field of network analysis. The method comprises the following steps: constructing a graph model according to input nodes and a connection relationship thereof, wherein the construction of a graph supports a single network or multi-network relationship; traversing each node in the graph by using a depth-first search algorithm, recording an access path, and classifying the nodes with a communication relationship into the same communication component; and outputting a set of all connected components. According to the method, when the graph is constructed, topology analysis of various network conditions in the undirected graph is supported by dynamically judging the nodes and the neighbor relation of the nodes. The method has the advantages of low calculation complexity and high expansibility, is suitable for the fields of network communication, equipment connectivity check, complex command and control system information transmission modeling and the like, and provides an efficient and accurate solution for connectivity analysis.

Systems and methods for ray intersection against primitives are described. Primitive data is encoded efficiently into arrays of fixed-size data blocks using a data format which can be directly consumed for ray traversal. Vertex data in a block is pre-quantized and stored using a fixed-bit quantization grid. Mesh connectivity is encoded using a triangle strips based on control values representing triangle interconnectivity, and a compressed index buffer storing indices for vertices in each strip. Primitives can alternatively be quantized to generate primitive packets, that are stored compactly in, with, or near a leaf node of an acceleration structure. Low-precision intersection testers test a ray simultaneously against primitives to find candidate triangles that require full-precision intersection. Primitives that generate an inconclusive result during low-precision testing are retested using full-precision testers to definitively determine ray-triangle hits or misses.

The invention discloses a topological communication coverage path planning method and system of an unmanned ship cluster for wide-area target search and detection, and belongs to the technical field of multi-agent collaborative path planning. According to the method, turning frequency optimization is converted into a minimum rectangular coverage problem, an integer programming model is established, a layered relaxation integer programming acceleration strategy based on maximum extension candidates is proposed, and the bottleneck of model expansion and calculation efficiency caused by candidate solution space explosion in a large-scale scene is broken through; then, constructing a rectangular topological connected graph, designing an improved spectral clustering algorithm fusing load balancing constraint and subgraph connectivity guarantee, realizing efficient and balanced distribution of regions in a complex environment, and ensuring internal connectivity of subregions; and finally, providing a dynamic pruning optimization method for the minimum corner spanning tree based on strong and weak connection characteristics, iteratively evaluating the corner cost and removing redundant edges, and finally generating a coverage path spanning tree with the minimum corner cost, thereby providing an accurate and efficient full-coverage operation path for scenes such as regional exploration and environment monitoring.

The invention discloses a visual process automatic layout method based on dependency graph optimization, and relates to the technical field of visual layout optimization, and the method comprises the following steps: S1, carrying out the global scanning of an overall dependency graph, employing a self-adaptive structure entropy algorithm, extracting the connectivity and path span information of each node, and constructing a structure bit order vector of the connection strength between the nodes; according to the method, low coupling dependence is identified through structure bit sequence vectors and semantic confidence scores, spatial distribution of weak connection subgraphs is optimized by combining a field model and repulsive force, path smoothness and layout stability are ensured through dynamic path adjustment and local simulation optimization, and visual hierarchy definition is improved through hierarchy consistency verification and global correction. According to the scheme, the readability and interactivity of the flow chart and the stability and adaptability in dynamic updating and industrial application are remarkably enhanced.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and graph neural network combined earth surface deformation space-time prediction method, which is suitable for space-time prediction of earth surface deformation. Based on DS-InSAR, obtaining radar sight line-to-time sequence earth surface deformation of the high-coherence measuring points; calculating the geographic distance between the measuring points and the mutual information between the deformation sequences corresponding to the points; the method comprises the following steps: judging connectivity between high-coherence measuring points according to a geographic distance between the measuring points and mutual information to generate an adjacent matrix, and organizing an original InSAR deformation monitoring result into graph structure data; and inputting the graph structure data into the LSTM-GCN model to predict and obtain the surface deformation of all the high-coherence measuring points in the whole area. The method is high in prediction precision and wide in application range, and can be effectively applied to the fields of space-time prediction of deformation of earth surfaces and buildings (structures) caused by mine closure, underground resource development, natural disasters and the like.

A method and system provide the ability to optimize a structural engineering design. A dataset is synthesized by acquiring a structural skeleton design of an entire building. The skeleton defines locations and connectivities of bars that represent columns or beams. The skeleton design is represented as a structural graph with each bar represented as a graph node and edges connecting graph nodes. Structural simulation results are computed for the synthetic dataset based on the structural graph, various loads, and a structural analysis. A simulation model and a size optimization model are trained based on the structural simulation results with the size optimization model determining cross-section sizes for the bars to satisfy a building mass objective, building constraints, and output from the simulation model. The structural engineering design is output from the size optimization model.

The invention relates to a distribution network topology panoramic perception method and system based on an AI technology, and a medium. The method comprises the following steps: collecting and fusing a line public information model CIM model file and equipment ledger information in a distribution network homologous system; carrying out structure analysis and field correction on the collected data; a main feeder path is extracted, main line segments are divided, and key control switch positions and segment boundary points are identified; branch level-to-level management and modeling are supported; the graph model can be folded or unfolded for display; closed-loop splicing and overall connectivity analysis of multiple lines are realized; panoramic perception of the distribution network is realized; performing problem identification and deep analysis on the non-standardized structure of the current graph model; according to the method, automatic identification and optimization of the topological structure of the power distribution network are realized, the accuracy, the generation efficiency and the service adaptation capability of the graph model are remarkably improved, and unified support is provided for digital operation and maintenance and intelligent management of the power distribution network.

The invention discloses a global optimization defect skeleton extraction system and method based on an energy function, and the system comprises a refining module, a node and branch extraction module, an energy calculation module, a graph construction and maximum spanning tree pruning module, and a skeleton reconstruction module. A morphological refinement algorithm is applied to the binary region to generate a preliminary skeleton, and the node and branch extraction module is used for calculating 8-neighborhood connectivity of each pixel on the skeleton; the energy calculation module calculates normalized features of each branch; the graph construction and maximum spanning tree pruning module obtains a global optimal skeleton structure based on the normalized features; and the skeleton reconstruction module generates a final pruning skeleton according to edges reserved in the global optimal skeleton structure, so that the trunk trend of the defect area can be reserved, pseudo branches can be effectively removed, and the accuracy and stability of skeleton extraction can be improved.

The invention particularly relates to a CAD part feature recognition method based on a graph neural network, and the method comprises the steps: extracting geometric entity information and topological relation information based on C # and NX Open API, and generating standardized AAGJSON format data; converting the geometric data into a graph structure which comprises a topological adjacent matrix and a multi-dimensional node feature vector comprising geometric, topological and shape features; a graph neural network model comprising a graph convolution layer, a graph attention layer and a graph sampling aggregation layer is adopted for training, and feature categories and confidence degrees of holes, grooves, bosses and the like are output; and the prediction result is corrected based on geometric consistency, topological connectivity and context rationality constraint. The method has the characteristics of high recognition precision, strong adaptability and good robustness of complex parts, can be integrated in UG NX to realize real-time recognition and visualization, provides important technical support for design automation and manufacturing industry digitization, and solves the problems of low recognition precision, poor adaptability and insufficient topological information utilization in the existing CAD part feature recognition method.

The invention discloses an efficient routing algorithm for offshore buoy networking. The problem that existing offshore buoy networking information transmission is poor in real-time performance and stability is mainly solved. According to the scheme, the method comprises the following steps: inputting the position of an offshore buoy network, constructing a communication link connectivity model, and abstracting a network link topology by means of a graph theory representation mode; comprehensively considering network efficiencies such as time delay, packet loss rate and network load balancing, and determining link cost between two buoy nodes; aiming at the defect that the ant colony algorithm is easy to fall into local optimum, two-point improvement is carried out on the basic ant colony algorithm. The method comprises the following steps: firstly, during construction of initial pheromones, in a first iteration process of an algorithm, 20% of ants in a population are selected, a path with the minimum cost is selected based on a neighbor algorithm, and the remaining ants adopt random selection paths, so that the convergence of the algorithm is improved, and meanwhile, the diversity of the population is ensured; and a second point, aiming at the problem that a large number of ants are easy to walk to a repeated path when the ant colony is converged to the local optimum in the later stage of iteration, adding repeated check operation in an algorithm updating process. According to the specific operation, when 20% of ants select repeated paths, the ants of the repeated paths select new paths in a random mode, the path search space is expanded, and the algorithm performance is improved. According to the method, the efficient route of buoy networking communication can be dynamically planned, and the real-time performance and the stability of the network are met.

Aspects of the disclosure includes methods and apparatuses for coding connectivity information of a polygon mesh. A method for decoding the connectivity information includes selecting an active vertex from a plurality of active vertices in a plurality of vertices as a pivot vertex that is to be processed based on a priority of the active vertex. The priority of the active vertex is based on one of (i) a number of processed faces to which the active vertex is incident, (ii) a sum of an angle of each processed incident corner of the active vertex, (iii) a number of unprocessed faces to which the active vertex is incident, and (iv) a number of unprocessed face fans to which the active vertex is incident. The connectivity information of the polygon mesh is reconstructed using the selected active vertex as the pivot vertex.

The invention provides a three-dimensional steel structure full-coverage path planning inspection method based on topology reconstruction, and relates to the technical field of industrial robots, and the method comprises the steps: obtaining a model file of a to-be-inspected three-dimensional steel structure; constructing an original vertex set and an original edge set according to the model file; performing adaptive clustering on each original vertex in the original vertex set through a density-based spatial clustering algorithm to obtain a cluster; taking the geometric center of the original vertex in each cluster as a topological node, and establishing a mapping relation between the original vertex and the topological node; carrying out topology reconstruction on the original edge set, and constructing an undirected weighted graph; stitching non-connected sub-graphs in the undirected weighted graph through a K-dimensional tree and a minimum spanning tree algorithm to obtain a fully connected graph; generating a full-coverage continuous path sequence through a Chinese postman algorithm; the full-coverage continuous path sequence is converted into a three-dimensional space coordinate sequence, and the three-dimensional steel structure to be inspected is inspected according to the three-dimensional space coordinate sequence.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The invention discloses an FMCW radar moving target detection method based on graph connectivity density, which systematically fuses a graph signal processing framework and an FMCW radar system for the first time on the basis of the prior art, and introduces graph features into moving target feature extraction through graph feature extraction of radar echo signals, so that the moving target detection accuracy is improved. A spectrum distribution diagram of a radar echo signal is converted into a connected graph, features of the connected graph are extracted for detection, moving target detection is realized, and actually measured data verify that the method improves the low signal-to-noise ratio target detection rate. According to the method, traditional signal processing and graph theory analysis are combined, and the detection capability of a weak target is enhanced by utilizing topological characteristics of a graph structure while the operation of a traditional signal detection algorithm (such as CFAR detection and a constant threshold value method) is kept.

The invention discloses a narrow space automatic driving path planning method based on artificial intelligence, and the method comprises the following steps: S1, collecting laser radar point cloud and image data, executing semantic segmentation and clustering, and building a topological graph; s2, mapping topological graph nodes into pulse coupling neural network neurons, and constructing a double-domain coupling mechanism comprising a structure coupling item and a state coupling item; s3, generating a path direction vector, constructing a path intention gating factor, and dynamically adjusting a coupling input value; s4, periodically updating the neuron membrane potential, and adaptively adjusting the discharge threshold; s5, recording a first discharge node number and time, and generating a path activation sequence according to a discharge sequence; s6, performing connectivity check and curvature smoothing on the path activation sequence to generate a continuous track; and S7, converting the trajectory into a control instruction, and driving the vehicle to execute path tracking. According to the invention, high-precision, high-connectivity and high-control-stability output of path planning in a narrow space is realized.

The invention belongs to the technical field of computer storage, and particularly discloses a piggyback coded single block repair cross-rack flow optimization method, which comprises the following steps that: based on each data block in a piggyback coding result, a target map is constructed, a vertex in the target map represents the data block, and a weight value of an edge is a coupling degree between two data blocks; aiming at each coupling degree, executing a graph connectivity algorithm to obtain a group of connected components corresponding to each coupling degree; according to the number of the racks, each group of connected components is adjusted, corresponding uniform groups are obtained, the number of the data block sets in the uniform groups is equal to the number of the racks, and the difference of the number of the data blocks between the different data block sets in the uniform groups is smaller than a preset threshold value; and in the plurality of uniform groups, selecting one item with the minimum cross-rack traffic overhead as a target group. Through the method and the device, the single block repair for piggyback coding is realized, the cross-rack flow is optimized, and the repair performance is improved.