761 results about "Graph embedding" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent anomaly recognition and intervention processing method, device, equipment and medium. The method comprises the following steps: carrying out feature fusion by using a gating fusion network and generating a preliminary abnormal score, determining a reconstruction error through an auto-encoder and triggering abnormal early warning, calculating a causal effect value of key features in combination with a causal graph model and anti-factual reasoning, and calibrating the abnormal score to generate a final abnormal score and an intervention instruction. And executing an intervention action and recording a result. According to the method, the multi-dimensional feature information and the causal reasoning mechanism are fused, the self-encoder reconstruction error is combined to carry out anomaly judgment, the intervention instruction is generated and executed, closed-loop control of anomaly detection, reasoning analysis and intervention execution is achieved, and the recognition accuracy of complex events and the system response capacity are improved.

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention relates to the technical field of information resource fusion management, and particularly discloses a fusion and management system for multi-source heterogeneous science and technology information resources. Analyzing an equipment fault chain from an unstructured text of a historical operation and maintenance log, extracting a rated parameter constraint from a structured table of an equipment manual, and collecting an operation feature vector from a real-time sensing data stream to generate a knowledge graph containing N entity relationships; based on an entity attribute constraint rule of the knowledge graph, designing a bidirectional attention mapping network to calculate semantic similarity weights of multi-source data and knowledge nodes, and generating a graph embedding vector set with weight marks through Hadamard product operation; according to the method, the embedded vector set is input into the pre-trained graph neural network model, and the root cause equipment set causing feature offset is positioned, so that efficient fault diagnosis and positioning are realized, decision support is provided for a subsequent preventive maintenance strategy, and the reliability and the operation and maintenance efficiency of the system are improved.

The invention relates to the technical field of industrial internet security and artificial intelligence crossing, in particular to an industrial production process APT attack detection method and system based on a knowledge graph, and the method comprises the steps: obtaining industrial production data, carrying out the preprocessing of the obtained industrial production data, and obtaining an APT attack detection result; the preprocessed industrial production data are used as input for dynamic construction of a knowledge graph, known attack mode reasoning is carried out based on the knowledge graph, the known attack mode reasoning comprises the steps that a known attack chain is recognized through multi-hop matching of graph embedding, a time sequence graph convolutional network and an attention mechanism are fused to detect unknown abnormal behaviors, and the known attack chain is subjected to known attack mode reasoning. Data fusion is performed based on the topological relation of the knowledge graph, an attack entry node, an associated entity and a propagation path are positioned according to a data fusion result, and real-time detection and traceability of the hidden attack chain are realized by constructing the equipment-protocol-data stream three-dimensional semantic dynamic knowledge graph and fusing a graph embedding technology and a graph convolutional network.

The invention discloses an urban road traffic entrance and exit influence evaluation method based on big data, and relates to the technical field of urban traffic management. Constructing an urban road traffic network topological graph based on the traffic feature vectors, proposing a dynamic weight graph embedding algorithm, and establishing a road network association mapping model; applying a graph neural network algorithm based on an attention mechanism to the road network association mapping model, performing road node influence factor evaluation, and quantitatively analyzing the road entrance and exit influence degree; fusing influence factor evaluation results, and constructing a multi-dimensional traffic influence evaluation model by adopting a cross-domain ensemble learning method; and according to a performance evaluation result of the multi-dimensional traffic influence evaluation model, generating urban road traffic entrance and exit optimization decision suggestions through an intelligent recommendation algorithm, and completing accurate scheduling of traffic network nodes. The intelligent recommendation algorithm is developed based on reinforcement learning, and reliable optimization suggestions are provided for traffic management decisions.

The invention relates to a knowledge graph data intelligent management method and system based on a semantic web technology, and the method comprises the steps: obtaining text data from a high-frequency data flow in real time, and generating a first semantic set through segmentation processing and semantic extraction; performing noise filtering and sorting on the multi-source data to generate a second semantic set; constructing semantic representation compatible with the knowledge graph; utilizing a graph embedding algorithm to generate graph updating data through source weight optimization; based on a historical conflict mode and a credibility weighting model, intelligent resolution of semantic conflicts is completed; and generating dynamic situation awareness data through real-time incremental loading and multi-dimensional association analysis. According to the method, through time sequence priority dynamic weighting, multi-source noise accurate filtering and cross-modal credibility evaluation, the problems of response lag, redundancy accumulation and insufficient conflict resolution during high-frequency dynamic data processing of a traditional method can be solved, and therefore real-time updating and consistency maintenance of the knowledge graph are achieved.

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 a knowledge question and answer processing method fusing a large model and a knowledge graph, and relates to the technical field of artificial intelligence and natural language processing. Aiming at the defects of a traditional retrieval enhancement generation technology in the aspects of complex semantic association, context consistency and dynamic knowledge updating, the scheme adopted by the invention comprises the following two stages: knowledge graph construction and mixed index generation: collecting and processing internal and external multi-source data of an enterprise, and performing cleaning preprocessing such as coding normalization and de-duplication to obtain a mixed index; entities and relations are extracted through a pre-training model to generate a triple, a knowledge graph is constructed and stored in Neo4j, then a mixed index is generated through text and graph embedding fusion, and two types of retrieval are supported; retrieval and answer generation: obtaining user query, preprocessing, vectorizing, obtaining a candidate list through low-level semantic retrieval and high-level reasoning retrieval, fusing multi-dimensional indexes, rearranging and screening top-M candidates through Cross-encoder, constructing a JSON evidence list, and generating traceable answers through small model draft, large model fine calibration and consistency verification.

The invention provides an intelligent risk identification and analysis method based on multi-modal heterogeneous data fusion, which relates to the technical field of risk management, and comprises the following steps of: acquiring multi-source data, constructing symbol-nerve double-space processing, generating semantic vectors, extracting features through orthogonal matrix decomposition and a bidirectional long-short-term memory network, and obtaining an intelligent risk identification and analysis result; according to the method, multi-modal information is fused by using an adaptive weight mechanism, and a risk propagation topology network is constructed by applying a graph embedding algorithm, so that accurate identification and early warning of risks are realized, and the risk prevention and control capability and prediction accuracy are effectively improved.

The invention discloses a virtual fitting personalized clothing recommendation method based on artificial intelligence, and the method comprises the following steps: S1, collecting a user image, text description and behavior data, and generating a multi-modal user feature set; s2, constructing a multi-modal heterogeneous semantic map, and carrying out structural modeling and embedded representation; s3, graph embedding and feature fusion are carried out, positive and negative sample pairs are constructed, and semantic alignment training is carried out; s4, optimizing network structure parameters and training hyper-parameters by using a raccoon optimization algorithm; s5, calculating a semantic matching score, generating recommendation candidates, inputting body type parameters, and generating a multi-angle virtual fitting image; and S6, collecting user behavior feedback, updating a map edge weight and a training sample, and executing closed-loop optimization. The method has the advantages that personalized clothing recommendation and virtual fitting image generation based on the multi-modal features and the body type parameters of the user are achieved, a recommendation-fitting-feedback closed loop is constructed, and recommendation accuracy and user experience are improved.

The invention relates to the technical field of machine learning, and discloses a decision tree data model establishment method, which comprises the following steps of: acquiring heterogeneous data sources such as a structured data table, a time sequence data stream and graph structure data through distributed nodes, sampling the time sequence data stream by using a dynamic sliding window, and vectorizing the graph structure data through a graph embedding algorithm; a multi-stage feature selection model is constructed to screen features, and a dynamic decision tree generation framework adopting an adaptive splitting criterion is established based on the features. A tree structure is adjusted by applying a multi-objective optimization algorithm, and the performance is improved by introducing an incremental pruning mechanism. And the online model updating module monitors data distribution change, reconstructs a local sub-tree in good time, and injects noise to protect data privacy in combination with a differential privacy protection mechanism. According to the method, heterogeneous data is effectively processed, the model classification precision is improved, the complexity is reduced, the generalization ability is enhanced, the model can be updated online, and the data privacy is protected. The electronic equipment calls related instructions to execute the method, and efficient data processing and analysis can be achieved.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

The invention discloses an embedded hydraulic engineering risk intelligent regulation and control system based on a knowledge graph, and belongs to the technical field of hydraulic engineering. The system comprises a multi-level knowledge graph construction module for constructing a multi-level knowledge graph and forming a dynamically updated knowledge network; the atlas embedding expression module is used for encoding entity attributes, relation characteristics and time sequence evolution characteristics thereof in real time; the risk identification and evaluation module is used for integrating atlas embedded features and expert knowledge rules and analyzing hydraulic engineering risk features and spatial propagation modes thereof so as to realize accurate identification and quantitative evaluation of risks; the reinforcement learning regulation and control decision module encodes the risk state and the regulation and control target into a state-action space based on a deep reinforcement learning framework; the dynamic feedback optimization module is used for automatically updating the entity state and the relationship strength so as to form a closed loop of continuous optimization and knowledge accumulation; and the visual interaction module is used for visually displaying the risk situation, the regulation and control effect and the propagation path of the water conservancy project.

The invention discloses a document analysis method based on an RAG model and a dynamic knowledge graph, and relates to the technical field of artificial intelligence. The method is combined with an RAG model and a dynamic knowledge graph technology, and is realized by the following steps of: performing entity relationship joint extraction on an input document, generating a structural triple, and constructing a dynamically updatable knowledge graph; based on the knowledge graph, mapping entities and relationships into low-dimensional vectors by adopting a graph embedding model, and constructing a local vector knowledge base with a topological structure; receiving user questions in real time, encoding the user questions into query vectors, executing approximate nearest neighbor search based on the vector knowledge base, and matching related map fragments; and combining the retrieved graph fragments with the large language model, and generating a structured answer through path constraint of the injection knowledge graph. The method is used for solving the problem that in the prior art, a model cannot capture document deep semantics and dynamic relations insufficiently.

The invention provides a long text intelligent review and prediction method fusing a dynamic knowledge evolution mechanism, and relates to the technical field of text review, and the method comprises the steps: carrying out the structural analysis of a long text, constructing an initial knowledge graph, and generating an evolution knowledge graph through combining a time sequence change mode of an entity relationship in a historical text; calculating semantic similarity between word vectors and graph embedding to realize information interaction; deep semantic features are extracted to calculate the mahalanobis distance between the deep semantic features and an abnormal category prototype to determine an abnormal mode; and combining historical evolution trajectory modeling time sequence characterization to predict an abnormal development trend. And the accuracy and prediction capability of long text review can be effectively improved.

The invention provides a water conservancy field retrieval enhancement generation method and device based on a knowledge graph and a medium. The method comprises the steps of extracting entity relationships in a water conservancy field document; constructing a directed unweighted graph by utilizing the entities and the relationships; summarizing description information of each entity and relation description between the entity and other entities by using a large model to generate an entity abstract, performing multi-level semantic modeling on the entity abstract, and converging semantic information of adjacent entities as graph embedding representation of the entities; associating the entity abstract and the graph embedding representation thereof with entity nodes in the directed unweighted graph to obtain an optimized graph; dividing the entities into a plurality of communities according to the modularity among the entities in the optimization graph; carrying out community summarization on each community by utilizing the large model to obtain a community abstract; when a user query request is received, carrying out knowledge graph recall by utilizing multi-graph inquiry and multi-path sorting fusion; and according to the sorting score of the retrieved atlas information, optimizing the task cue word to guide the generation model to optimize the answer.

The invention provides an intelligent electronic medical record generation method based on a portable robot, and the method comprises the steps: firstly constructing a medical knowledge graph from an ICD-11 term library, real-time data and real-time data, generating a standardized entity relationship set, and generating knowledge vectors supporting semantic reasoning through a graph embedding algorithm; then converting the doctor-patient dialogue audio into a structured text through a medical voice model, and generating semantic annotation data in combination with a knowledge vector matching atlas entity; when the matching degree exceeds a dynamic threshold value, the data are automatically converted into HL7 FHIR standard data, and after the compatibility with an existing map is automatically checked, synchronous updating of the electronic medical record is finally completed through template alignment and API adaptation. According to the method, the automatic conversion from the unstructured dialogue to the standardized electronic medical record is realized, and the quality and the utilization efficiency of the medical data are improved.

The invention discloses a circuit breaker fault positioning method based on a graph neural network, and the method comprises the following steps: S1, collecting operation data, constructing a node set and an edge set, and forming a power grid topological graph; s2, preprocessing the operation data, and embedding the operation data as node attributes into a power grid topological graph; s3, constructing a graph convolutional neural network, and generating a multi-scale graph embedded representation; s4, introducing a residual error coding module, constructing a residual error response characteristic graph, and fusing adjacent node residual error characteristics in a graph convolution mode; s5, constructing a global sensitive node sorting function, and extracting node indexes with activation response amplitudes exceeding a threshold value as a candidate fault node set; and S6, performing confidence calculation on each node, and selecting the node number with the highest confidence value as a positioning result of the circuit breaker fault. The fault circuit breaker node can be accurately positioned in the complex power grid topology, and the fault diagnosis efficiency and the positioning precision are improved.

The invention relates to the technical field of intelligent operation and maintenance, in particular to an artificial intelligence operation and maintenance decision support method and system for multi-source information fusion. The method comprises the following steps: acquiring multi-source operation and maintenance data to perform multi-dimensional feature extraction to obtain multi-dimensional operation and maintenance feature data; performing continuous spatial cross-modal embedding according to the multi-dimensional operation and maintenance feature data to obtain cross-modal embedded data; performing heterogeneous feature coupling graph generation on the cross-modal embedded data to obtain coupling graph data; performing heterogeneous space fusion coding according to the coupling graph data to obtain fusion coding data; performing expert knowledge driving graph embedding on the fusion coding data to obtain operation and maintenance fusion graph data; performing root cause positioning reasoning according to the operation and maintenance fusion graph data to obtain root cause positioning data; and performing operation and maintenance decision generation according to the root cause positioning data to obtain operation and maintenance decision data. Through multi-source information fusion and intelligent reasoning, the root cause positioning accuracy and intelligent operation and maintenance decision efficiency of the system can be effectively improved.

The invention provides a tunnel risk reasoning method fusing a knowledge graph and a large language model, which comprises the following steps of: obtaining structured monitoring data and unstructured text data, adopting methods such as field standardization for the structured monitoring data to realize a unified format, adopting methods such as sentence segmentation and word segmentation for the unstructured text data to realize the unified format, and obtaining the structured monitoring data and the unstructured text data; the method comprises the following steps of: extracting entities from data by utilizing a model, extracting a relationship between the entities based on the entities, forming basic triads, forming a sub-graph by the basic triads, integrating to form a knowledge graph, generating a natural language, extracting the sub-graph related to the natural language from the knowledge graph, and converting the sub-graph into a sub-graph in a vector form by utilizing a graph embedding algorithm. The entities and the relation paths of the entities serve as explicit reasoning clues, the natural language, the sub-maps in the vector form and the explicit reasoning clues are input into a large language model, natural language output is generated, multi-source data information is integrated, and high-precision and interpretable tunnel risk early warning is output through the large language model.

A graph anomaly detection method based on multi-view graph embedding and node prototype comparison comprises the following steps: S1, collecting and preprocessing a graph anomaly detection data set, and constructing a data set label; s2, building a graph anomaly detection model based on multi-view graph embedding and node prototype comparison; s3, performing iterative optimization on the model by using the training set; s4, repeatedly executing S2 and S3 until the total loss value tends to be stable; completing the training of a graph anomaly detection model; and S5, predicting the abnormal probability of the node on the test set, and if the probability exceeds a specified threshold, regarding the node as an abnormal node. According to the method, the multi-view adjacency matrix is constructed through the KNN algorithm, the node attribute similarity and the original adjacency relation are fused, the enhanced adjacency matrix reflecting the attribute similarity is generated, and richer input is provided for the graph neural network. According to the method, through multi-view image embedding learning and a node prototype comparison strategy, the distinction degree of normal nodes and abnormal nodes is remarkably improved, and the image anomaly detection performance is effectively improved.

The invention relates to the technical field of artificial intelligence, in particular to a sheet part performance rapid prediction method based on deep learning, which comprises the following steps: collecting multi-working condition simulation data to generate a training sample, constructing and coding a grid topological structure to extract multi-dimensional features, and inputting a perceptron to predict stress and evaluate errors after feature fusion and self-attention mechanism processing. According to the method, a structured training sample set is constructed by introducing simulation information, a geometric structure feature set is formed by combining node space coordinates, boundary constraints and a connection relation, so that mutual positions and constraint conditions among nodes are completely expressed in a graph structure, and through node-level feature extraction and feature fusion processing, a graph structure is obtained. According to the method, deep embedding of node geometric layout and boundary interrelation is realized, learnable expression of a stress evolution path in a space structure is established through local subgraph and context analysis, a multi-layer feature aggregation and attention mechanism is introduced in a node graph embedding process, and feature response expression of a key area is enhanced.

The invention discloses a legal history document classified storage method based on big data analysis, and relates to the technical field of knowledge maps, and the method comprises the steps: collecting texts, images and metadata of legal history documents, carrying out OCR recognition and multi-language alignment, detecting an image seal region, and segmenting a format to obtain a multi-modal data set; the method comprises the following steps: coding a legal provision cross-forensic revision record into a graph embedding vector, and constructing a time decay function to obtain a provision space-time trajectory vector set; according to a clause keyword and a time range input by a user, activating related fragments and calculating a trajectory vector cosine similarity to obtain a retrieval result; and fusing the revision record of the newly added literature into the article spatio-temporal trajectory vector set in real time, and triggering the dynamic extension of the knowledge graph node to obtain a classified storage database. Classification labels and knowledge graph nodes are obtained through legal logic label matching, the knowledge graph nodes are dynamically expanded, and cross-legal provision similarity accurate calculation is achieved.

The invention provides a cold rolling mill roller micro displacement real-time monitoring and dynamic compensation control early warning method, which relates to the technical field of monitoring control, and comprises the following steps: monitoring roller temperature field distribution through infrared thermal imaging, establishing a mapping relation between displacement and temperature gradient, calculating thermal deformation and compensating displacement drift; constructing a rolling mill equipment domain knowledge graph and a diagnosis rule base, and performing fault diagnosis by utilizing graph embedding learning; and generating a compensation optimization strategy and establishing fuzzy correlation mapping to calculate an optimal parameter. According to the method, accurate monitoring and compensation of the micro displacement of the roller are achieved, and the equipment fault early warning capacity and the rolling precision are improved.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention relates to a distributed source-load collaborative optimization method based on high-order topology and multi-scale attention, and the method comprises the steps: firstly providing a high-order graph construction method driven by structural interaction, and achieving the structural embedded expression of a physical interaction relation between multi-source equipment through a hyperedge-line graph mapping mechanism and functional attribute coding; secondly, a graph feature extraction method based on a multi-scale joint attention mechanism is designed, topology and state information are fused, and the inter-node adjustment collaboration recognition capability is improved; further constructing a source-load collaborative optimization scheduling model, introducing a particle swarm optimization algorithm to obtain an initial feasible strategy, and establishing a state-action mapping relation based on a deep reinforcement learning framework driven by graph embedding to realize autonomous learning and rolling optimization of a distributed control strategy; and finally, constructing an operation feedback closed loop mechanism, and introducing a graph structure migration and strategy adaptive updating method to enhance the response capability of the system to topological change and dynamic disturbance.

The invention discloses an industrial equipment interconnection and intercommunication method based on an industrial control platform, and relates to the technical field of industrial equipment interconnection and intercommunication, and the method specifically comprises the following steps: calculating a behavior entanglement degree between a resending data frame with repeated content and a resending data frame, and if the entanglement degree exceeds a preset threshold value, determining that the resending data frame is the resending data frame; if yes, extracting a sequence offset, a time delay amount and a field fluctuation amplitude, and generating an index vector used for describing the aging degree of the data identifier; the generated index vector is input into a pre-trained graph embedding algorithm model, a first parameter and a second parameter are generated, the first parameter is used for describing the consistency of the data frame and a historical evolution path, and the second parameter is used for describing the aging degree of the data frame identifier. According to the method, the problem that the reissued data of the industrial equipment cannot be accurately identified is solved, and timeliness judgment and validity identification of the state data are realized based on the causal atlas and the graph embedding model.

The invention discloses a data warehouse multi-hop relationship quick retrieval method and system based on a graph embedded index, and relates to the technical field of multi-hop relationship retrieval data processing. The quick retrieval method for the multi-hop relationship of the data warehouse based on the graph embedded index comprises the following steps of: monitoring data flow interference; monitoring index updating timeliness; monitoring the query accuracy of the multi-hop relationship; and timely monitoring of data query response. According to the method, whether to perform graph model modeling to obtain the graph model is determined by judging whether to trigger the interference suppression mechanism, then the multi-level index is constructed based on the graph model, and whether to trigger the index step length updating mechanism is determined based on the index updating timeliness judgment result; and finally, based on a multi-hop relationship result query accuracy verification result, determining whether to trigger a candidate node screening range shrinkage mechanism, thereby achieving the effect of improving the multi-hop relationship retrieval efficiency of the data warehouse, and solving the problem of low multi-hop relationship retrieval efficiency of the data warehouse caused by data flow interference in the prior art.

The invention discloses a power grid state characterization method and system based on multi-modal fusion. The method comprises the following steps: characterizing power grid topological structure data through a graph embedding algorithm to generate topological feature vectors; encoding the time sequence operation data through a long short-term memory network to generate a time sequence feature vector; extracting an equipment state feature vector through a multi-layer perceptron; projecting the three types of feature vectors to a shared semantic space, and realizing cross-modal feature alignment by using cosine similarity loss; and adopting a gating multi-mode unit GMU to carry out adaptive weighted fusion, and generating a unified power grid state representation vector. The system comprises a multi-modal data acquisition and preprocessing module, a topological structure characterization module, a time sequence dynamic characterization module, an equipment state characterization module and a cross-modal fusion and decision module. According to the method, the problems of incomplete single-mode characterization, characteristic isomerism and insufficient dynamic characteristic capture are solved, the accuracy and robustness of power grid state sensing are improved, and the method is suitable for real-time monitoring and decision support of an intelligent power grid.

The invention discloses a knowledge graph-based old building reconstruction scheme automatic recommendation method. The method comprises the following steps of S1, obtaining and preprocessing old building house data; s2, extracting a semantic entity and attribute relationship from a transformation case library, a building specification library and a construction scheme library, and constructing a knowledge graph; s3, mapping the house data to knowledge graph entity nodes, and executing graph embedding to generate building semantic representation; s4, constructing a graph neural network, calculating node semantic relevancy, and generating a building state vector; s5, inputting the building state vector into the reinforcement learning decision network, and optimizing the strategy to obtain an optimal transformation action; s6, screening reconstruction measures from the knowledge graph according to the optimal reconstruction action, and performing scoring to form candidate schemes; and S7, sorting the candidate schemes, selecting the scheme with the highest score, and recommending and updating the knowledge graph. According to the method, intelligent generation and self-optimization recommendation of the old building reconstruction scheme are realized, and the reconstruction efficiency of the scheme is remarkably improved.