1080 results about "Graph embedding" patented technology

The invention discloses a power grid dispatching decision-making method based on a graph neural network and a large model, and the method comprises the steps: collecting an original power grid data set, and generating a fusion feature tensor through adaptive distribution mapping and recursive decomposition; importing a fusion feature tensor and a power grid topology matrix, and generating a dynamic graph embedding matrix through spectral decomposition and matrix manifold analysis; performing Markov jump process analysis and optimal transmission fusion on the dynamic graph embedded matrix to generate an integrated feature matrix; a multi-target Lyapunov function is constructed on the basis of the integrated feature matrix, and a robust decision strategy is generated by solving a Hamilton-Jacobi-Bellman equation; and performing multi-dimensional performance monitoring and statistical manifold learning on the robust decision strategy to realize strategy optimization. According to the method, the uncertainty in power grid operation can be effectively processed, and the accuracy and reliability of scheduling decisions are improved.

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 discloses an e-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, and constructing an original data set; s2, performing data cleaning and characterization processing to generate a feature data set; s3, constructing a knowledge graph and dynamically updating the knowledge graph by using the graph embedding model; s4, generating potential causal paths and weights in combination with the neural relationship reasoning model and the diffusion model, and introducing a microknowledge enhancement layer to generate causal enhancement features; s5, identifying abnormal behaviors in combination with a multi-level anomaly detection mechanism; s6, designing a dynamic game optimization model, determining a collaborative optimal solution under a multi-target conflict through Nash equilibrium, and processing a dominant and subordinate relationship in combination with a Stackelberg game to generate a collaborative optimization strategy; and S7, displaying the monitoring state and the collaborative optimization strategy through a visual interface. According to the invention, big data analysis, a knowledge graph and a dynamic game technology are combined, so that e-commerce intelligent operation monitoring and collaborative decision-making are realized.

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 a dialogue recommendation method based on combination of a multi-modal semantic graph and prompt learning, which comprises the following steps of: firstly, coding entities in dialogue history based on a knowledge graph, carrying out feature coding on a product text and an image, and then, constructing a plurality of semantic graphs with different modals, including a text semantic graph, an image semantic graph and a collaborative semantic graph, on the basis, a multi-modal semantic graph is innovatively combined with prompt learning, high-dimensional semantic association is fully mined by using a large language model, multi-modal features and structured semantic relationships between products are effectively modeled, and the multi-modal semantic graph is embedded into the multi-modal semantic graph embedded # imgabs0 # with multiple modal semantic relationships fused therein, so that the multi-modal semantic graph embedded # imgabs0 # and the multi-modal semantic relationships between the products are effectively modeled, and the multi-modal semantic graph embedded # imgabs0 # and the multi-modal semantic relationships between the products are obtained. The user preference is dynamically captured in the multi-round dialogue recommendation scene to comprehensively understand the user demand and generate a more accurate recommendation result, so that the accuracy in the recommendation task is remarkably improved, meanwhile, more natural content conforming to the context can be generated in the dialogue task, and the dialogue generation performance of the dialogue recommendation system is improved.

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 traffic prediction method based on dynamic graph space-time correlation and adaptive adversarial training. The method comprises the following steps: acquiring a dynamic graph space embedding representation by learning dynamic graph data capture by utilizing an adaptive graph convolutional network; capturing the dynamic graph data according to the time attention network to obtain a dynamic graph time embedding representation; obtaining dynamic graph embedding; after dynamic graph embedding is used as initial embedding and the last preset adversarial node embedding is defined as context node embedding to be used as input of a strategy network, an adversarial reward function and a loss function are set, then the strategy network is trained, and an updated dynamic graph is output according to the trained strategy network. And performing spatial correlation capture and temporal correlation capture on the updated dynamic graph to obtain a traffic prediction result corresponding to the current network equipment. By adopting the method, data pollution and node abnormity can be effectively handled, and traffic prediction can be realized by adapting to continuously changing network conditions.

The invention relates to the technical field of digital precise drainage sale management methods, in particular to a digital precise drainage sale management method and system fused with a knowledge graph, and the method comprises the following steps: obtaining and preprocessing multi-source heterogeneous data; multi-modal data fusion and entity identification are realized based on the preprocessed data; performing relation extraction and attribute mapping according to the fused data and the identified entities; constructing dynamic time sequence diagram embedding based on the extracted relationship and the mapped attributes; performing knowledge reasoning and graph completion by utilizing dynamic time sequence graph embedding; performing real-time updating and incremental learning on the completed knowledge graph; generating a precise drainage strategy based on the updated knowledge graph; optimizing the sale-on-sale process according to the accurate drainage strategy; according to the method, effect evaluation and feedback are carried out on the optimized sale-on-sale process, and a feedback result is used for each step of the optimization method, so that dynamic time sequence diagram embedding is realized, user interest and market trend change can be captured in real time, and recommendation timeliness is improved.

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 belongs to the technical field of power distribution network fault recovery, and provides a power distribution network fault recovery method based on graph reinforcement learning and a related device, and the method comprises the steps: constructing a power distribution network fault recovery problem graph model, and carrying out the embedding of a power distribution network model graph based on a graph attention neural network; based on the power distribution network fault recovery problem graph model, an agent set of multi-agent reinforcement learning is constructed, and the power distribution network fault recovery problem is modeled as a Markov decision process; and solving the Markov decision process, carrying out multi-agent training, and generating a fault recovery strategy by the trained agents according to the current state of the power distribution network. According to the method, the technologies of graph theory, deep learning and reinforcement learning are deeply fused, and efficient and accurate power distribution network fault recovery strategy generation is realized.

The invention discloses a knowledge graph-driven sales data multi-dimensional analysis and visualization method and system. The method comprises the following steps: constructing a sales domain knowledge graph containing products, customers and transaction entities; on the basis of the knowledge graph, preprocessing the original sales data to obtain preprocessed sales data; according to a knowledge graph and the preprocessed sales data, realizing time sequence adaptive multi-view collaborative graph embedding, and obtaining graph embedding features; fusing the graph embedding feature with the traditional sales data feature to obtain a fused feature; performing multi-dimensional analysis based on the fusion features to obtain an analysis result; performing visual presentation on the analysis result to generate visual data; on the basis of visual data, an interactive exploration interface is provided, deep analysis and insight of data are achieved, by introducing the knowledge graph technology, the complex relation between entities can be effectively captured and utilized, the association rule mining effect is remarkably improved, and the association rule mining efficiency is improved by 77.8% compared with a traditional method.

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 provides a game strategy retrieval method and device based on event-driven knowledge graph embedding. The method comprises the following steps: performing structured analysis on strategy data, and updating entities and relationships; performing graph embedding calculation on entities and relationships in the knowledge graph to obtain vector representation among the entities, and constructing a searchable vector index; vectorizing a strategy query request of a user, and performing similarity retrieval on the strategy query request and the vector index to obtain a first candidate entity set similar to the query vector; performing relation reasoning by taking the first candidate entity set as a starting point in the knowledge graph to obtain a second candidate entity set in semantic association with the first candidate entity set, and combining the second candidate entity set with the first candidate entity set to form a target entity set; and performing correlation sorting on the target entity set to obtain a strategy result. According to the method, the strategy data can be automatically and structurally managed, and deep semantic retrieval is supported, so that the accuracy of game strategy content retrieval is improved, and the dynamic expansion capability is achieved.

The invention discloses an image recognition method based on a deep neural network optimization algorithm, and relates to the technical field of image recognition, and the method comprises the steps: carrying out the preprocessing of an image data set, such as adaptive normalization and Hash coding label; coding a network structure according to a graph embedding technology, and mixing and initializing a weight threshold value; algorithm optimization features are fused, particle swarm initial determination is carried out, and then channel characterization fine tuning is carried out; using the training set to improve back propagation and adaptive moment estimation algorithm training, fusing a loss function and dynamically adjusting a learning rate; calculating evaluation and optimization of multiple indexes in the verification set, such as a genetic algorithm search structure and a Bayesian modulation learning rate; and inputting a to-be-recognized image into the optimized network, and obtaining a result by using an integrated recognition method. According to the invention, by optimizing the links of image preprocessing, neural network and recognition process, the image quality and data processing efficiency are improved, the features are accurately determined, the network training is accelerated, the network is comprehensively evaluated and optimized, the reliability of the recognition result is improved, and the image recognition precision and efficiency 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 relates to the field of intelligent contract vulnerability detection, in particular to an intelligent contract vulnerability detection method based on code graph fusion analysis and a graph matching network. The method comprises the steps of constructing an intelligent contract source code data set; through static analysis, extracting three different code feature information including a control flow diagram, a data flow diagram and an abstract syntax tree from the source code of the smart contract; carrying out information integration on the three extracted code features, and constructing a code graph covering wider features; on the basis of the generated code graph, a GraphCodeBerT model is used for pre-training; vectorizing a code graph which is output by the pre-training model and contains structural and semantic features through a graph embedding network; and learning the feature representation of the contract pair by using a cross-graph attention mechanism of a graph matching network to realize specific vulnerability detection of the smart contract source code. According to the invention, potential vulnerabilities can be accurately identified.

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 discloses a big language model tool matching method and system based on a knowledge graph, belongs to the technical field of artificial intelligence, and solves the problem that an API (Application Program Interface) tool is difficult to efficiently manage and accurately select in a real-time information processing process of an existing big language model. According to the method, a multi-source data fusion technology is introduced to collect and sort information of external API tools of different data sources, and a dynamic relation weight adjustment mechanism is utilized to dynamically adjust the weight of the relation between different API nodes according to the actual use condition of the API tools and user feedback; representing the characteristics of each API node through a graph embedding technology based on an intelligent matching algorithm of deep learning, and calculating the matching degree of each candidate API and a request by using a sequence processing model; according to the method, the knowledge graph and the intelligent matching algorithm are combined, an external API tool used by a large language model can be dynamically managed and optimized, and the information processing accuracy and the system response speed are improved.

The invention relates to the technical field of big data, in particular to a real-time commodity sales prediction system and method based on edge calculation. The system comprises a data acquisition module, an edge calculation module, a commodity sales prediction module, an optimization feedback module and an intelligent scheduling module. The system collects sales, inventory, customer behaviors and environment data in real time through the Internet of Things, and utilizes a graph embedding technology to construct a dynamic relation graph and enhance features. Modeling is carried out based on a space-time diagram network algorithm, and accurate prediction of commodity sales volume is realized. Model parameters are optimized through reinforcement learning, inventory replenishment, a distribution path, a production schedule and a promotion strategy are dynamically adjusted in combination with a discrete differential evolution algorithm, and a real-time resource scheduling scheme is generated. According to the invention, the accuracy and real-time performance of commodity sales volume prediction are improved, optimal configuration of resources is realized through intelligent scheduling, and the operation efficiency and market competitiveness of enterprises are effectively improved.

The invention provides a circuit logic synthesis method for FPGA mapping based on reinforcement learning, and aims at solving the problem that in a traditional netlist optimization method, a circuit is not seen in model optimization, and the capacity is insufficient. The method comprises the following steps: firstly, constructing a logic comprehensive data set, and then preprocessing the data set to obtain statistical characteristics and a graph structure of a circuit; then, a logic comprehensive model based on reinforcement learning and aiming at FPGA mapping is constructed, pre-training is conducted through the preprocessed data set, and finally logic optimization is conducted on a circuit through the pre-trained logic comprehensive model based on reinforcement learning and aiming at FPGA mapping. According to the circuit feature extraction module, a GNN module is adopted to analyze a logic circuit and a netlist structure to obtain graph embedding, statistical features of the logic circuit and the netlist are analyzed through an FCN module, the features of the two parts are spliced to obtain an accurate enhanced representation state of the circuit, and the understanding capacity of a reinforcement learning model on different circuits is improved.

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.