125 results about "Graph sampling" patented technology

The invention discloses a power distribution network distributed photovoltaic bearing capacity real-time evaluation method, which comprises the steps of determining a mathematical model for power distribution network photovoltaic bearing capacity evaluation, and constructing a Markov decision model for power distribution network photovoltaic bearing capacity dynamic evaluation based on the mathematical model; a soft actor-commentator SAC algorithm is used as a basic framework, graph sampling and aggregation GraphSAGE are used for improving the SAC algorithm, a graph deep reinforcement learning algorithm is constructed, the graph deep reinforcement learning algorithm is used for solving a Markov decision model, and a trained graph strategy network is obtained; carrying out the subsequent real-time evaluation of the distributed photovoltaic bearing capacity of the power distribution network through employing the trained graph strategy network; according to the method, the limitation that precise modeling is needed in a traditional method is effectively overcome, and a more accurate real-time evaluation result is provided.

The invention provides an interpretable node classification prediction method based on adversarial causal graph learning. The method comprises the steps that a constructed prediction model comprises a redundancy filtering module and an adversarial causal graph learning module; a redundancy filtering module and an adversarial causal graph learning module realize a graph information bottleneck mechanism; the redundancy filtering module adopts a two-layer graph attention network GAT structure to carry out information aggregation, and node embedding is obtained; the confrontation causal graph learning module adopts a learnable sub-graph sampler based on an attention mechanism to generate a causal interpretation sub-graph for node embedding, performs gradient disturbance optimization on interpretation sub-graph embedding based on a PGD confrontation training strategy of a causal enhancement mechanism, generates confrontation embedding, and obtains final disturbance interpretation sub-graph embedding through multiple rounds of disturbance iteration; performing end-to-end prediction model training through multi-target loss joint optimization; and after training is completed, embedding of the nodes is input into a classifier, and a prediction result is output. According to the method, the structural transparency and interpretability of the model are remarkably improved.

The invention discloses an automatic standard term recommendation method, which comprises the following steps of: collecting industry data to be evaluated, constructing a knowledge graph, carrying out discretized semantic alignment on entity vectors and path codes, constructing a matrix-graph joint index, processing user input data by using a gated attention fusion mechanism to obtain an input intention, and recommending the input intention to the industry data to be evaluated. Candidate recommendation terms associated with input intentions are searched in the knowledge graph based on the matrix-graph joint index and a graph sampling algorithm and screened to obtain recommendation standard terms, the recommendation standard terms are clustered, the standard terms in clusters are clustered by adopting a rime algorithm, representative terms of each cluster are obtained and are incrementally updated to the matrix-graph joint index, and the candidate recommendation terms and the candidate recommendation terms are combined. And if the recommended standard terms are not searched, recommending the synonyms. According to the method, the information retrieval and screening efficiency is improved through matrix-graph joint indexing, and the knowledge graph is searched through a graph sampling algorithm, so that the recommendation result fits the intention of the user.

The invention belongs to the technical field of equipment fault diagnosis, and discloses a variable working condition bearing fault diagnosis method based on a domain adversarial hybrid graph convolutional network. According to the specific technical scheme, the method comprises the specific steps that firstly, a convolutional neural network is used for conducting feature extraction on input data, obtained features serve as nodes, secondly, an adjacent matrix is obtained through a graph generation layer, an association graph is constructed according to the input matrix, and thirdly, the association graph is obtained. Inputting the constructed graph into a multi-receptive-field mixed graph convolutional network to extract structural information, capturing local structural information and global structural information of the graph from the perspective of the graph convolutional network and graph sampling and aggregation by using the multi-receptive-field mixed graph convolutional network, and finally, extracting the structural information of the graph from the perspective of the multi-receptive-field mixed graph convolutional network. And fault classification is carried out on the obtained features through a transfer learning method, so that the fault diagnosis capability of the model on a target domain is improved, and the expression capability, the generalization performance and the robustness of the model are remarkably improved.

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The invention discloses an APT (Advanced Persistent Threat) network attack detection method based on meta-path learning and subgraph sampling, which relates to the technical field of network security, and comprises the following steps of: constructing a network entity relation graph, collecting and standardizing various log data and network topology information, and introducing an external knowledge base to excavate a potential relation; the attribute graph model is used for endowing rich attributes for the nodes and the edges; a meta-path is dynamically generated through an adaptive algorithm, the meta-path is optimized in combination with frequent sub-graph mining and historical attack data, a model in which an attention mechanism and a noise reduction auto-encoder are combined with a graph convolutional network is introduced, and node features are further extracted; defining a node influence propagation index to perform sub-graph sampling, and generating a hierarchical sub-graph; and finally, fusing sub-graph topology, nodes of meta-path learning and attribute features, and adopting multi-classifier ensemble learning to optimize the performance of the attack detection model. According to the method, the accuracy, timeliness and adaptability of APT attack detection are effectively improved, and the method is suitable for a complex dynamic network environment.

The invention discloses a spatio-temporal data driven consumption behavior prediction method and system, and relates to the technical field of text travel data intelligent analysis, and the method comprises the steps: respectively defining a user node, a business district node and an event node according to user attribute data, business district attribute data and a historical external event, performing analysis in combination with user historical consumption data and business district POI data to determine a corresponding user-business district consumption edge, a business district-event influence edge and an event-user trigger edge so as to construct a heterogeneous ternary graph; performing multi-scale Motif sub-graph sampling on the heterogeneous ternary graph to extract a multi-granularity sub-graph, and extracting multi-scale spatial features of corresponding user nodes from the multi-granularity sub-graph; and the two are organically fused into a highly expressed space-time fusion feature, and the business district consumption behaviors of the user in different business districts are predicted according to the space-time fusion feature. Therefore, the prediction precision and robustness of the commercial district consumption behavior of the user are greatly improved.

The invention relates to the technical field of government affair entrepreneurship guidance service intelligent dialogue systems, in particular to a dialogue intention recognition and correction method based on a knowledge graph, and the method comprises the steps: obtaining a voice signal and text information inputted by a user, and carrying out the cross-modal fusion processing to generate a semantic vector; carrying out evolution modeling on the dialogue state by adopting a graph neural network, and outputting a dynamic dialogue state graph containing a time sequence relationship between a user entity and a business object; taking the semantic vector and the focus information as query conditions, executing dynamic subgraph sampling from the government affair knowledge graph, and generating an intention correction strategy vector; and finally, utilizing reinforcement learning to generate personalized guide statements and outputting the guide statements to the user. According to the method, through deep coupling of semantic understanding, state tracking, knowledge reasoning and decision optimization, real-time perception and active completion of dynamic updating of hidden nodes and policies of the government affair process are achieved, and the intention recognition accuracy and systematic adaptive capacity are effectively improved.

The present invention discloses a method for automatically recommending standard terms, including: collecting industry data to be evaluated, constructing a knowledge graph, discretizing semantic alignment between entity vectors and path encodings, constructing a matrix-graph joint index, using a gated attention fusion mechanism to process user input data to obtain input intentions, searching for candidate recommended terms associated with the input intentions in the knowledge graph based on the matrix-graph joint index and a graph sampling algorithm and screening to obtain recommended standard terms, clustering the recommended standard terms, using a rime algorithm to aggregate the standard terms within the cluster, obtaining representative terms for each cluster and incrementally updating them to the matrix-graph joint index, and if no recommended standard terms are searched, recommending synonyms. This method improves the information retrieval and screening efficiency through the matrix-graph joint index, and searches the knowledge graph through the graph sampling algorithm, making the recommended results conform to the user's intentions.

The invention relates to the technical field of network security, in particular to realization of self-efficient autonomous defense in an Internet of Things network. Comprising the following steps: constructing a network attack and defense scene, and modeling observation information by using a graph data structure; modeling the action space of the attacker and the defense agent; modeling an attack and defense process in the network attack and defense scene; performing normalization processing on the feature matrix of the nodes by adopting graph normalization; dynamically aggregating the current state information of each device according to the interaction between the network device and the adjacent device by using the self-attention mechanism of the GAT; selectively reserving a node host according to the self-attention score by using a down-graph sampling pooling strategy; and training a defense agent by using a graph attention network enhanced reinforcement learning algorithm. The problems that automatic defense of an existing reinforcement learning algorithm faces performance bottleneck in a complex network scene, convergence to a high return value is difficult, and an effective defense strategy cannot be made are solved.

Embodiments of this specification provide a shard storage method and apparatus for a graph and a subgraph sampling method and apparatus for a graph. In a distributed storage process of a graph, local identifiers of a vertex and an edge are implicitly stored, and data is stored in an ordered manner, so that the local identifiers of the vertex and the edge can be implicitly calculated. A connecting edge is stored in a CSR format, to ensure that a first-order neighbor of a node is contiguously stored in a memory. In this way, there can be a higher data loading speed and lower memory occupation.

The invention discloses an anti-parasitic drug target affinity prediction method based on multi-modal feature fusion. The method comprises the following steps: 1, acquiring an anti-parasitic drug related data set; 2, constructing a multi-scale convolutional neural network, and obtaining sequence features of drugs and proteins; 3, constructing a graph convolutional neural network, and obtaining structural features of the medicine; 4, constructing a graph sampling and aggregation network, and obtaining structural features of the protein; 5, constructing a self-adaptive gating network, fusing two modal characteristics of the drug and the protein, and generating drug-protein joint expression; 6, carrying out feature alignment on the two modal features of the drug and the protein by utilizing comparative learning; and 6, updating model parameters according to an output result of the prediction layer. According to the method, multiple neural networks are adopted to learn sequence features and structural features of drugs and proteins, and information of two modal features is balanced and integrated by utilizing comparative learning, so that drug-protein interaction expression is enhanced, and the accuracy of predicting the affinity of anti-parasitic drugs and proteins is improved.

The invention discloses a model multiplexing-based image-text retrieval graph network method, which comprises the following steps of: firstly, constructing a multi-field image-text pair data set from an internet public data source according to a user demand, performing multi-stage data cleaning by utilizing a pre-training model to ensure the data quality, and secondly, multiplexing a large-scale pre-trained multi-modal model as a feature extractor to obtain a multi-domain image-text pair data set; the deep semantic representation of the image text pair is efficiently obtained, and the model training cost is remarkably reduced. Then, a heterogeneous topological structure is designed, image text nodes respectively form same-proton graphs, and cross-modal edges are dynamically generated through learnable attention weights; and finally, by utilizing the constructed image text semantic relation graph, completing context information supplementation of a retrieval target through graph sampling and aggregation, further generating retrieval features, and completing a retrieval task of multi-modal combination. According to the method, the pre-training model features are multiplexed, so that the process is more efficient, and the model convergence speed is greatly improved.

The invention discloses a complex network node influence sorting method combining graph embedding and graph sampling in the field of complex networks. The complex network node influence sorting method comprises the following steps: S1, synthesizing a training data set based on different network topology models; s2, initializing the model; s3, pre-training and updating model embedding layer parameters by utilizing sorting learning; s4, model evaluation layer parameters are updated through reinforcement learning training; s5, sampling a real network by adopting random walk; s6, performing node influence sorting on each sub-network by using the trained model; and S7, calculating the node influence sequence of the original network through multi-source ranking fusion. According to the method, the model embedding layer is pre-trained through sorting learning, so that inductive graph embedding is effectively realized; reinforcement learning is used as node disconnection value learning, so that the algorithm has the capability of searching for disconnection nodes according to state iteration; the large-scale network is sampled through random walk, and the prediction efficiency and accuracy of the large-scale network are optimized.

The invention belongs to the technical field of computers and electronics, and discloses a graph neural network hardware Trojan detection method and system fused with an attention mechanism, and the method comprises the steps: S1, mapping a netlist circuit code carrying a hardware Trojan into a directed graph; s2, performing graph embedding, graph segmentation and graph sampling on the directed graph; s3, extracting inherent characteristics of the nodes through an encoder; s4, extracting node local features through a graph neural network; s5, extracting global features of the nodes through an attention mechanism model; and S6, carrying out feature fusion on the data inherent features extracted by the auto-encoder, the local structure features extracted by the bidirectional GCN convergence model and the global structure features extracted by the self-attention mechanism, and obtaining an HT detection result through a classification model. According to the method, the Trojan horse detection accuracy and efficiency are greatly improved; compared with an existing method, the detection range or type is expanded, and the method can be applied to hardware Trojan horse detection of a large-scale integrated circuit actually.

The invention provides a workpiece surface target merging and partitioning method, system and device, and relates to the technical field of image processing, and the workpiece surface target merging and partitioning method comprises the steps: obtaining a surface image of a target region of a target maintenance workpiece; performing graph sampling and visual processing on the surface image to generate a binary image of the target area, and analyzing the binary image to obtain a plurality of connected domains of the target area; performing convex hull generation according to the connected domains to obtain a convex hull corresponding to each connected domain; generating a rotation enclosing rectangle set according to the leading edge vector of each convex hull; and through a variant ant colony optimization algorithm, carrying out global optimization on the rotating enclosing rectangle set, and generating a combined partition of the target area. According to the invention, through accurate capturing of the original image and subsequent layer-by-layer optimization, the redundancy of the laser maintenance path is reduced, and the operation cost is reduced; through construction and global optimization of a high-adaptation form carrier, precise coverage of a complex form target is realized, and the maintenance precision is 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 relates to the technical field of brain anomaly detection and artificial intelligence auxiliary diagnosis, in particular to a graph convolutional network brain disease diagnosis method based on sub-graph sampling and multi-feature fusion, and aims to improve the accuracy of brain disease diagnosis. The method comprises the following steps: obtaining resting state functional magnetic resonance imaging data, preprocessing the data, and constructing a brain function connection diagram; and the brain function connection graph represents a brain interval collaborative activation relationship in a graph structure. Subgraph sampling is carried out based on function module division and node degree sorting, and an initial subgraph set is generated; and performing optimization selection on the initial sub-graph set by utilizing reinforcement learning to obtain an optimal sub-graph, introducing a node attention mechanism into the optimal sub-graph, screening key nodes based on attention scores, and generating a discriminant sub-graph. And extracting and fusing position features, neighborhood features and structural features of the discriminant subgraphs, and performing brain disease diagnosis based on the fused features.

The invention discloses a federated learning-based functional magnetic resonance imaging data classification method. The method comprises the following steps of obtaining multi-site resting state functional magnetic resonance imaging data and performing preprocessing; dividing a brain region and extracting a time sequence; constructing a brain function connection network by adopting a Pearson correlation coefficient; a graph sampling aggregation neural network fusing a residual connection structure and a multi-head attention mechanism is trained at each site, and shallow brain region features are reserved; adopting a linear kernel maximum mean value difference loss function to align the brain region node feature distribution of each site, and minimizing the data distribution difference between the sites; and carrying out classification training by adopting cross validation, aggregating parameters of each station through federal weighting, and evaluating classification performance indexes to obtain a final classification prediction result. According to the method, the graph sampling aggregation neural network and the cross-network layer feature alignment method are combined, the heterogeneity problem of multi-site functional magnetic resonance imaging data can be solved, and therefore generalization and classification performance of a global model are improved.

The embodiment of the invention relates to a method and system for training a graph neural network, and the method comprises the steps: in an offline stage, firstly, carrying out the distributed sub-graph sampling of a knowledge graph through each computing device in a distributed system by taking a plurality of graph elements in local fragmentation data of the knowledge graph as a sampling center, and obtaining a plurality of sub-graphs; the graph elements comprise nodes and / or relation edges; then, each computing device takes each graph element as a key, takes the sub-graph corresponding to each graph element as a value, and stores the plurality of sub-graphs in a target storage area in a key-value pair form; in a training stage, firstly, training equipment takes a first batch of first graph elements as keys, and queries from the target storage area to obtain a first batch of first sub-graphs; the training device then trains a graph neural network based on the first sub-graph of the first batch.

The invention discloses an ultra-short-term photovoltaic power prediction method and system based on an image derived graph structure, and belongs to the technical field of new energy development and utilization. The method comprises the following steps: taking a foundation cloud picture as input, performing image element segmentation by using a superpixel segmentation method, and extracting block high-dimensional features by using a ResNet neural network; calculating nodes and edges of a graph structure by taking a historical data time sequence feature and an image partitioning feature as input, realizing embedded construction of the graph structure, and aggregating coupling features of data and an image by using a graph sampling aggregation network; based on the photovoltaic power value, taking the time sequence feature, the image local feature and the global coupling feature as input, and performing multi-modal feature fusion by using a multi-head attention mechanism to obtain a fusion feature vector; and a multi-step prediction result is obtained through an encoder-decoder. The photovoltaic power ultra-short-term prediction precision can be improved, support is provided for safe and stable operation of a power grid, and the method has certain engineering practical value.

The invention provides an APT attack detection method and system, a storage medium and electronic equipment, and the method comprises the steps: collecting a plurality of attack logs of known attacks, and constructing an attack subgraph according to the attack logs; performing feature enhancement on nodes in the attack sub-graph based on an attention mechanism to obtain feature vectors corresponding to the nodes, and training the initial detection model according to the feature vectors to obtain a final detection model; obtaining a to-be-detected host log, constructing a traceability graph according to the to-be-detected host log, and performing sub-graph sampling on process nodes in the traceability graph to obtain at least one target sub-graph; and performing feature enhancement on the at least one target sub-image to obtain an enhanced target feature vector, and sequentially inputting the target feature vector into the final detection model to obtain a detection result corresponding to each target sub-image. According to the invention, the accuracy and flexibility of detection can be improved, and the situation of false alarm or missing alarm is reduced.

The invention discloses a data association relationship determination method, system and device, a medium and a program product applied to an artificial intelligence technology, and the method comprises the steps: taking to-be-associated data of a data association task and all source data in associated data information as node features, and inputting the node features into an association relationship recognition model; performing graph sampling on the incidence relation identification model, aggregating feature information of each sampling node and adjacent neighbor nodes based on a target polynomial, and determining an incidence relation identification result between the sampling nodes corresponding to the current graph sampling according to the current aggregation feature; and determining target data with an association relationship in the association data information for the to-be-associated data according to each association relationship identification result. The problem that task precision and task execution efficiency cannot be considered at the same time in the prior art can be solved, and on the basis of improving the task execution efficiency determined by the data association relationship, it is ensured that a high-precision data association result is output.

The invention relates to the technical field of operation and maintenance of power grid equipment, and discloses an equipment fault text implicit relation prediction method based on a graph structure learning model, and the method is based on an obtained text data set of power grid equipment fault maintenance. The objective is to model entities and relationships in equipment fault texts into a graph structure, perform message passing and relationship inference in combination with entity vectors generated by a large model and a graph neural network, and design a graph structure learning model to predict unknown fault relationships between different equipment. Finally, unknown relationships between entities are predicted using a classifier. According to the prediction method, vector representation of entities in sentences is mined by utilizing the characteristics of a large language model, a sub-graph structure among a plurality of entities is constructed by combining a graph neural network model and utilizing a random walk sub-graph sampling mechanism considering fault influence, and finally, a message passing mechanism based on a pyramid attention mechanism is used to predict the sentences of the entities. And obtaining an implicit relation prediction result in the power grid equipment fault text.

The invention relates to a heterogeneous ontology matching method and system based on BERT and graph contrast learning, and belongs to the field of combination of semantic web and deep learning. The method comprises the following steps: extracting triad information in an ontology through a semantic feature extraction module to construct a corpus, inputting a BERT model fine tuning parameter into the corpus, and generating a semantic feature vector of an entity by using a fine-tuned BERT model; performing graph sampling through a graph comparison learning module according to the topological structure of the ontology graph to generate two views; performing training by selecting positive and negative samples and setting a loss function, and generating an entity comprehensive feature vector; and finally, calculating final similarity scores of different entity pairs by adopting a similarity calculation module. According to the method, the topological structure of the ontology graph can be effectively utilized to extract the structural features, the problem that the traditional structural features cannot fully express the complex structural relationship between entities is solved, and the matching accuracy is improved.

The invention provides an information cascading prediction method and system based on a subgraph, and belongs to the technical field of social network analysis and neural networks, and the method comprises the steps: S1, constructing a deep learning information cascading prediction model CasSubTS, enabling collected user published information to pass through an input layer, and constructing an information cascading graph G; s2, inputting the G into a sub-graph sampling layer, dividing the G into a plurality of information cascade sub-graphs according to different time steps, converting the information cascade sub-graphs into adjacency matrixes, and performing node feature aggregation on the adjacency matrixes to obtain a feature representation matrix B; s3, inputting the B into a feature learning layer to obtain a feature vector # imgabs0 # with a structural feature and a time sequence feature; s4, inputting the # imgabs1 # into a feature weighting layer, and performing weighted fusion on the # imgabs2 # by using a channel attention mechanism to obtain a weighted feature vector # imgabs3 #; and S5, inputting # imgabs4 # into a prediction layer to predict a final macroscopic cascade increment. According to the method, the information cascading in the social network is effectively predicted.

The traffic flow prediction method provided by the invention comprises the following steps: acquiring historical traffic flow information of a target area road, and preprocessing the historical traffic flow information; constructing a traffic flow prediction model based on a graph sampling aggregation network and a long short-term memory network, and inputting the preprocessed historical traffic flow information into the traffic flow prediction model for training; and obtaining traffic flow information of one hour before the current moment, preprocessing the traffic flow information and taking the traffic flow information as prediction input information, and inputting the prediction input information into the trained traffic flow prediction model to obtain traffic flow information of a future target moment. According to the method, during traffic flow prediction, the influence of the historical traffic flow in the current target road section is considered, and the influence of the adjacent road sections in the road network where the current road section is located on the current road section is also considered, so that the traffic flow at the set moment in the future can be accurately predicted.

The invention belongs to the technical field of financial industry transaction fraud detection, and relates to a residual aggregation enhanced neighborhood graph sampling credit card fraud detection method, which comprises the following steps: acquiring credit card transaction data and inputting the credit card transaction data into a credit card fraud detection model to obtain a fraud detection result; the training process of the credit card fraud detection model comprises the steps of obtaining credit card transaction data and constructing a multi-relation heterogeneous graph; inputting the multi-relation heterogeneous graph into a plurality of graph aggregation layers which are sequentially connected in series to obtain a multi-relation heterogeneous graph output by each graph aggregation layer; the node features of the multi-relation heterogeneous graph output by each layer are input into a shallow layer residual error aggregation module, and final node features are obtained; inputting the final node feature representation into a classification module to obtain a fraud detection result; updating model parameters according to a fraud detection result until a trained fraud detection model is obtained; according to the method, the node features are updated under each relation type in the graph aggregation layer, and the neighbor information of the node features in various relation types is fully utilized.

The invention provides a global optimization method and device for a refinery enterprise supply chain, and the method comprises the steps: obtaining a supplier set, a sales company set, a refinery plant set, a transportation set, a product set and a spatial position set of a refinery enterprise, and constructing a supply chain model, represented by a network graph, of the refinery enterprise; simplifying the supply chain model represented by the network graph by using a graph sampling algorithm to obtain a sampled supply chain model, constructing an effective lower bound function of a supply chain optimization problem, and solving a first optimal solution according to the effective lower bound function and the sampled supply chain model; reducing the supply chain model represented by the network graph by using a graph reduction algorithm to obtain a reduced supply chain model, constructing an effective upper bound function of a supply chain optimization problem, and solving a second optimal solution according to the effective upper bound function and the reduced supply chain model; and generating a global optimal solution of the supply chain model according to the first optimal solution and the second optimal solution.

The application relates to a kind of smart grid dynamic graph data generation methods based on deep adversarial training, comprising the following steps: obtaining smart grid time series graph data, inputting the smart grid dynamic graph data generation model based on deep adversarial training, and generating smart grid dynamic data graph;The smart grid dynamic graph data generation model based on deep adversarial training includes sampling module, generative adversarial network and reconstruction module, wherein the sampling module is used to generate the center graph using the center graph sampling method;The generative adversarial network includes generator and discriminator, the generator includes decoder and encoder, the encoder is used to encode the center graph through the time series graph self-attention network constructed based on the multi-head self-attention mechanism, and obtain the hidden variable of the center graph;The reconstruction module is used to generate the classification distribution of each time series edge according to the center graph score matrix, and then generate the smart grid dynamic data graph.Compared with the prior art, the application can efficiently and reliably generate smart grid dynamic data graph.