472 results about "Relational graph" patented technology

The invention discloses an intelligent target allocation method and system based on deep reinforcement learning, particularly relates to the field of multi-agent systems, and is used for solving the real-time decision problem under multi-dimensional constraints in a dynamic confrontation simulation scene. Time, space and resource constraints are decomposed into independent models, and the decision complexity of a multi-agent system in a dynamic confrontation scene is reduced; by means of adaptive Monte Carlo sampling, prediction perception path planning and dynamic threshold value resource allocation, diversified high-quality candidate schemes are quickly generated; when the environment changes, the multi-layer influence propagation network and the spatial topological relation graph are used for positioning an affected agent, and scheme fragments are generated through local adjustment, so that global re-calculation is avoided, the overhead is reduced, and real-time response is ensured; meanwhile, an efficient and reliable optimal solution is screened out through resource delay coupling and path efficiency comprehensive evaluation in combination with multi-objective optimization and task consistency verification.

The invention belongs to the field of natural language processing, and provides a task complexity driven graph semantic multi-agent collaborative decision-making method and system.The task complexity driven graph semantic multi-agent collaborative decision-making method comprises the steps that a task text is obtained and subjected to semantic coding to obtain a task semantic vector, evaluation is conducted based on the task semantic vector to obtain a complexity vector, and a task complexity score of the complexity vector is calculated; the task semantic vector and the complexity vector are fused to obtain a task representation vector, an agent capability relation graph is constructed, the participation probability of each agent node is obtained according to the task representation vector and the agent capability relation graph, and a dynamic agent combination scheme is formed; and performing task decomposition according to the agent combination scheme, constructing a sub-task dependency graph, scheduling the execution sequence of the sub-tasks through topological sorting, realizing cooperative execution of the agents, and generating a task result. According to the method, precise matching and efficient cooperation of the agent combination are realized, and the capability of processing complex tasks and the resource utilization efficiency of the multi-agent system are remarkably improved.

The invention provides an industrial chain atlas modeling method and system based on deep learning, and relates to the technical field of industrial chain analysis and modeling, and the method comprises the steps: collecting multi-source heterogeneous data in an industrial chain, including demand information, resource states, process features and operation and maintenance data, and preprocessing the multi-source heterogeneous data to form a standardized data set; and performing semantic vectorization processing on the text type data in the standardized data set, and performing node embedding on the multi-relation graph to generate entity low-dimensional representation fusing text and structural features. According to the method, by integrating multi-source heterogeneous data, fusing text and structural features, constructing an industrial chain knowledge graph and performing dynamic optimization, accurate adjustment of production line configuration and task allocation strategies is realized, meanwhile, the industrial trend is effectively predicted, risk paths are analyzed and resource bottlenecks are identified in combination with a risk propagation model, a scientific basis is provided for industrial chain decision-making, and the risk propagation model has a wide application prospect. And the operation efficiency and stability of an industrial chain are improved.

The invention discloses a medical history recognition method and system based on a large model and an expert strategy, and the method comprises the steps: collecting data in a medical network, and carrying out the cleaning and standardization processing of the data; extracting medical entities and relationships thereof from the text, and optimizing the rule set; the extracted entities and relations are fused into a knowledge graph, a multi-relation graph is constructed, and attributes are defined; storing the knowledge graph in a graph database, and receiving new data through a real-time data stream processing system; basic information and symptom description of a patient are collected, and key information is extracted by using a large language model; an inquiry path is dynamically adjusted, and specific symptoms are clearly and fuzzily answered or deeply discussed by questioning; and generating a structured electronic medical record by using the large language model. Through an intelligent pre-inquiry process, the system can collect detailed medical history information and generate a standardized electronic medical record before a patient officially sees a doctor, so that the information verification and supplement time when a doctor receives the doctor is greatly shortened.

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

The invention relates to the technical field of point cloud data modeling, and discloses a point cloud data processing and modeling method, system and device for power distribution equipment and a storage medium, and the method comprises the steps: collecting the point cloud data of the power distribution equipment, and constructing a topological relation graph according to the topological relation of the power distribution equipment; semantic segmentation is performed on the point cloud data based on the topological relation graph, a component association model is constructed, and missing data detection is performed in combination with the topological relation graph; and generating a high-precision three-dimensional model based on the component correlation model. According to the method, semantic segmentation is guided through the topological relation graph, the accuracy of point cloud data classification is improved, and the problem of misclassification in a traditional method is avoided. Meanwhile, a part correlation model is generated based on the segmentation result, the geometric and functional connection relation between the devices is comprehensively described, and deep fusion of the device connection information and the three-dimensional geometric model is achieved.

The invention provides a multi-robot collaborative task dynamic scheduling method and related equipment, which are applied to the technical field of robots, and the method comprises the following steps: collecting task scheduling scene information; the task scheduling scene information is used for constructing a multi-level graph structure, multi-dimensional input information is obtained based on the multi-level graph structure, the multi-level graph structure comprises a relation graph layer of multiple dimensions, and the relation graph layer comprises relation information between objects of the dimensions corresponding to the relation graph layer; determining a task allocation strategy based on the multi-dimensional input information; and based on the task allocation strategy, sending a task execution instruction to each target robot, and controlling the target robot to execute a task corresponding to the task execution instruction. Complicated relations among the robot, the environment and the tasks are expressed by using the multi-level graph structure, rich and complete relation information is provided for generating the task allocation strategy, the accuracy of the generated task allocation strategy is improved, and conflicts generated during task allocation are avoided.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention relates to the technical field of database management, in particular to a two-way linkage database table and supervision submission form field synchronous construction method which is applied to a supervision data submission scene. According to the scheme, the method comprises the steps that physical structure metadata of a database table and definition metadata of a supervision submission form are obtained, a semantic vector set is formed by combining metadata semantic analysis and semantic coding, and a bidirectional mapping relation graph is constructed through relation weaving; constructing a linkage propagation path according to a field change event, realizing adaptive bidirectional structure adjustment, generating a field synchronous construction scheme, and driving bidirectional mapping relation graph evolution optimization through feedback learning; according to the method, the bidirectional mapping relation graph and self-adaptive bidirectional structure adjustment are creatively combined, and efficient and self-adaptive bidirectional field synchronous construction is realized.

The invention discloses an agent inference system based on multiple maps, and relates to the technical field of artificial intelligence, and the system comprises the steps: based on industry report, academic literature and business manual multi-source data, extracting entity-relationship-attribute, and constructing a knowledge map; dynamically capturing cooperation, report and task allocation relationships among entities, and constructing a production relationship graph; constructing a decision graph based on a field expert heuristic rule; based on the thinking engineering theory, human thinking modes and emotional states are analyzed, and a thinking map is constructed; integrating a knowledge graph, a production relation graph, a decision graph and a thinking graph, performing entity and mode alignment, mapping multi-source nodes and edges into the same vector space, introducing conflict resolution, and constructing a unified multi-mode heterogeneous knowledge graph; and guiding a large language model to generate a reasoning direction through path cue word injection, querying a constraint reasoning boundary, outputting an optimal reasoning result, and realizing agent reasoning of multiple maps. The method has the beneficial effect that the reasoning accuracy is improved.

An approach for determining how well a graph neural network (“GNN”) learns a relation in a multi-relational graph (“MRG”) and generating a generalized relation to substitute in the MRG to more effectively train the GNN to learn the relation. Layers of interest in a GNN may be identified along with which parameters to monitor based on the architecture of the GNN and the model type of the MRG. A MRG may be input into the GNN to generate embeddings for the entities and relations of the MRG. A comprehension score can be generated for the relations and the relations can be ranked. A more generalized relation can be identified for relations ranked below a threshold and can be substituted into the multi-relation graph in place of the lower ranked more specific relations. The updated MRG can be input into the graph neural network to further train the GNN.

The invention is applicable to the field of artificial intelligence, and particularly relates to a drug response prediction method capable of explaining a multi-relational graph neural network, which comprises the following steps of: constructing a directed heterogeneous graph containing cell lines, drug nodes, sensitivity, resistance, drug similarity and cell similarity relation types; an R-GCN model is adopted to process the directed heterogeneous graph, node representation is updated through node feature aggregation, positive and negative samples are generated for training, and a link relation between a potential cell line and a drug is predicted; the method comprises the following steps: generating a calculation graph by combining an original graph and a prediction result, extracting a to-be-explained edge neighborhood graph, obtaining an explaining sub-graph by maximizing mutual information and optimizing a structure score by utilizing an optimizable mask and an edge type weighting mechanism, and defining an explaining process from two aspects of mutual information and structure score. According to the method, link prediction is carried out by using the R-GCN model integrated with the attention mechanism, the interpretation subgraph is obtained by using the mask and the adaptive weighting mechanism, and the biological mechanism of the prediction result is clearly explained from multiple aspects.

The invention discloses a method for realizing element positioning in an RPA system, which comprises the following steps of: 1, obtaining an original request input by a user and a current webpage of the user, and obtaining a view relation graph by utilizing the webpage; 2, recognizing similar elements according to the view relation graph; splitting the original request into a condition judgment instruction or a loop execution instruction; step 3, acquiring element coordinates for the loop execution instruction based on the view relation graph; step 4, disassembling the condition judgment instruction into an element query; then recalling visible elements to be recognized in the similar elements; and step 5, for the visible elements to be identified, acquiring element coordinates by using the view relation graph or the multi-modal large model. According to the invention, element positioning can be rapidly realized in the RPA working process, and the misjudgment condition is reduced at the same time.

The invention discloses a multi-priori knowledge fused cerebral apoplexy rehabilitation map convolutional network evaluation method, and belongs to the technical field of cerebral apoplexy rehabilitation evaluation. Firstly, a high-precision prior information matrix is automatically generated through a collaborative and causal relationship automatic reasoning method based on Riemannian manifold geometry and transfer entropy, the problem that in the prior art, engineering depends on artificial features is effectively solved, and interpretable physical prior guidance is provided for a model. Then, through a multi-relation graph construction and attention weighting multi-modal adaptive fusion method, spatio-temporal features and priori knowledge are adaptively fused, an optimized graph structure is constructed, and the representation ability and interpretability of the model to complex joint interaction are improved. And finally, through a space-time diagram convolutional network guided by prior information and a comparative learning collaborative optimization method, the generalization ability and evaluation precision of the model in a small sample scene are remarkably improved through data enhancement and loss function optimization.

The invention relates to the technical field of report generation, in particular to an enterprise business report generation method and system based on a large model. The method comprises the following steps: acquiring component operation state data and production plan data of industrial equipment; performing master-slave relation division on internal components of the equipment according to the component operation state data, designating a driving device as a master component, designating a transmission component and an execution component as slave components, and constructing a component transfer chain relation graph; monitoring operation state parameters of the main component in the component transmission chain relation graph; extracting the master component of which the fluctuation of the operation state parameter exceeds a preset threshold value, and measuring the response parameter change condition of the connected slave component; and generating abnormal transmission confirmation data according to the response parameter change condition. According to the invention, based on abnormal transmission characteristics between components, early fault identification and propagation path tracking are realized, and a problem source is found before fault expansion.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map completion method based on semantic-structure multi-level fusion. According to the technical scheme, for a training set positive example triple, a structured rationality score and a semantic correlation score are fused to screen high-quality training samples; in order to complement a query search relation path, screening a guiding path through a uniqueness index; integrating the query text, the entity description, the neighbor facts and the guide path to construct an enhanced input prompt; dynamic structure embedding is generated by using a relational graph convolutional network, the dynamic structure is mapped by an adapter module and then injected into a large language model, and completion prediction is completed by combining a fusion input fine tuning model. According to the method, through multi-level fusion of semantics and structural information, intelligent sample screening, prompt enhancement construction and structural information dynamic injection are achieved, the accuracy, reasoning ability and training efficiency of a large language model in a knowledge graph completion task are effectively improved, and the method is especially excellent in performance in complex relation reasoning and inductive completion scenes.

The invention discloses a multi-unmanned aerial vehicle air combat advantage competition strategy generation method based on a graph attention network. The method comprises the following steps: a, representing air combat observation data as a topological graph; b, according to the topological graph, adopting a graph attention neural network as a strategy model, and combining priori knowledge to obtain an abstract relation graph representing an air combat relation; and c, carrying out maneuvering decision making according to the abstract relation graph. According to the invention, a graph attention network and a method for establishing an abstract relation graph are adopted to learn a strategy which is more beneficial to cooperation; and the air combat relationship and communication among the unmanned aerial vehicles are enhanced, so that the finally output actions achieve effective cooperation.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention relates to the technical field of data intelligent traceability, and discloses a whole-process business data intelligent traceability method and system. The method comprises the following steps: collecting first data of a preset business link and creating a semantic tag to form a semantic tag data set; the semantic tag data set is stored in a Merkle tree structure, and a service block chain is constructed; extracting second data of each business link from the business block chain, and generating a fusion data set based on the second data; and establishing a relation graph according to the fused data set, after receiving a traceability query request, starting to search a traceability path from a starting service point in the relation graph, and outputting a traceability result, thereby ensuring the reliability of the traceability result, and solving the problems of serious data island, missing association relationship and low traceability precision of the traditional traceability technology.

The invention discloses a financial knowledge graph construction method and system based on artificial intelligence, and relates to the field of artificial intelligence data processing. The method comprises the following steps: performing multi-dimensional semantic analysis on a heterogeneous financial data source, and extracting a structured semantic fragment; constructing a financial entity perception unit, identifying a multi-granularity entity and generating a unique code; generating a preliminary relation graph based on the event cascade relation and the attachment structure, and injecting a semantic translation label; normalizing the atlas relationship through semantic separation and a label reconstruction mechanism to form a financial relationship network with consistent semantics; executing evolution increment iteration in combination with the newly added corpus, and dynamically updating nodes and edge sets; and performing semantic consistency and structural integrity evaluation on an iteration result, and outputting a stable financial knowledge graph structural body. By introducing a multi-factor semantic analysis model, a causal relationship modeling mechanism and a graph evolution iteration strategy, systematic improvement of the financial knowledge graph in the aspects of structural expression precision, semantic reasoning ability and dynamic adaptability is achieved.

A document-level intelligent manufacturing process flow relation extraction method comprises the following steps: S1, acquiring a process document, and labeling process entities in the process document and a relation between the process entities; s2, performing deep coding on the process document by using a pre-training language model, extracting core semantic information of the process document, and generating process entity representation with consistent semantics based on an entity aggregation strategy of context sensing; s3, nodes and edges of a heterogeneous graph are constructed according to process entity representation, and a relation graph convolutional network R-GCN and a hierarchical attention mechanism are introduced to fully capture the relation between process entities; according to the method, the modeling capability of technological process sequence constraint and the fusion effect of document semantics and graph structure features are respectively enhanced by a sequential construction strategy and a process perception double-graph distillation mechanism oriented to a manufacturing process, the integrity and the utilization rate of information transmission are improved, the performance loss caused by error layer-by-layer propagation is relieved, and the method is suitable for popularization and application. Accurate and efficient extraction of the entity relationship in the process document is realized.

The invention relates to a data recommendation method which is suitable for a video recommendation scene. The method comprises the following steps: acquiring an article knowledge graph, an interaction graph between an object and an article, and modal content of the article; fusing the article knowledge graph and the interaction graph to obtain a collaborative knowledge graph, determining an article relation graph based on modal content of the article, and constructing an object co-occurrence graph based on the interaction graph; heterogeneous graph learning is carried out based on the interaction graph and the collaborative knowledge graph, and object features and article features are obtained; performing isomorphic composition learning based on the object co-occurrence graph and the object features to obtain a target object representation, and performing isomorphic composition learning based on the object relation graph and the object features to obtain a target object representation; and according to the target object representation of the target object and the target object representation of each object, performing object recommendation on the target object. By adopting the method, the recommendation performance of the strict cold start article can be improved on the premise of keeping the recommendation performance of the hot start article.

The invention provides an aspect-level emotion triple extraction method and system based on bidirectional generation, and the method comprises the steps: outputting an adjusted sentence through a term output template; utilizing a term output template to obtain a hidden state feature output by a semantic encoder; inputting the adjusted sentences into a syntactic editor to obtain hidden state features of the relational graph attention network; processing the hidden state features output by the semantic encoder and the hidden state features of the relational graph attention network by adopting a dynamic gate mechanism to obtain semantic syntactic information fusion hidden state features; respectively obtaining the probability of the aspect mark feature vector and the probability of the opinion mark feature vector based on the aspect mark feature vector and the opinion mark feature vector; and obtaining prediction distribution of the candidate indexes through decoding by a prediction decoder, and obtaining a prediction result. According to the method, mutual information clues between word pairs can be fully considered, a complex structure can be processed, and more accurate aspect-level emotion triple extraction is realized.

The invention discloses a scientific and technological achievement analysis and prediction method and system based on big data, and relates to the technical field of machine learning and big data analysis, and the method comprises the steps: collecting and preprocessing multi-source scientific and technological achievement semantic data, and constructing a scientific and technological concept relation graph; the method comprises the following steps: performing training by taking a time sequence diagram convolutional network as a basic framework and taking a scientific and technological concept relation graph as a training sample, constructing a dynamic knowledge flow semantic model, performing evolution feature extraction on the scientific and technological concept relation graph by utilizing the dynamic knowledge flow semantic model, and outputting a knowledge flow feature vector; and inputting the causal enhanced space-time diagram into a space-time diagram neural network, aggregating semantic association and causal relationships among the scientific and technological achievements in a space dimension, capturing a dynamic change mode of scientific and technological achievement characteristics in a time dimension, and outputting a scientific and technological concept time sequence predicted value sequence. According to the method, the causal enhancement space-time diagram is constructed, so that trend deduction and causal traceability analysis are carried out for the time dimension, and the accuracy of scientific and technological achievement development trend prediction is improved.

The invention discloses a house quality problem detection method and system based on graph theory correlation analysis, and the method and system have the advantages that a correlation graph model is established, a correlation graph between factors is provided by integrating construction process, material and maintenance data and dynamically recognizing high-risk nodes, the recognition of potential risks is improved, and the detection efficiency is improved. The node weight and the edge association strength can be adjusted in real time, and the quality can be effectively supervised; a multi-stage evolutionary game model is constructed, analysis is carried out in the early stage, the middle stage and the later stage of construction in combination with an association graph model, and a more accurate risk assessment model is formed; a sub-shortest path algorithm is introduced, a quality problem extension path is accurately predicted, a potential problem source can be quickly positioned, and the early warning efficiency is improved; historical data and game results are utilized to form a quality risk assessment model, high-risk nodes are deeply analyzed, potential quality problem extension trends are identified, prevention measures are taken in advance, later rework cost is reduced, and delivery safety and reliability are improved.

The invention discloses a player character and BOSS battle real-time analysis method, system and device and a medium, and the method specifically comprises the steps: taking verification battle data as input, recognizing battlefield dynamic obstacles through an improved YOLOv5 model, and generating environment perception data through a three-dimensional space interpolation algorithm; constructing a dynamic attribute relation graph according to the environmental perception data and preset role state data, modeling a time-varying restriction relation between role skills by adopting a bidirectional graph attention network, and updating an edge weight matrix through a time sliding window mechanism; and inputting the dynamic attribute relation graph and player historical operation data into a reinforcement learning decision model together to generate a battle strategy tree, and dynamically adjusting the search depth of the battle strategy tree through a real-time calculated battle complexity index to generate an optimal battle strategy. The analysis efficiency and accuracy of player character battle BOSS are improved, players can be helped to quickly understand battle dynamics, and battle strategies and operation skills are improved.

The invention discloses a graph structure-based domain penetration attack path generation method, which belongs to the technical field of network security, and comprises the following steps of: constructing a multi-level relation graph comprising a host node, a service node and a user node through automatic detection by taking any host in a domain as a starting point; assigning a weight attribute to the atlas edge based on a vulnerability library and an attack pattern library; an improved heuristic graph search algorithm is adopted, all feasible attack paths and threat scores thereof are generated by integrating the path length, attack difficulty and permission improvement effect, and the problem that the threat scores of all the feasible attack paths are influenced in various scenes and dynamic change domain environments is solved. The technical problems of realizing comprehensive automatic penetration testing, accurately identifying potential attack paths and establishing a systematic threat assessment mechanism are solved, the automation, intelligence and high-efficiency level of domain penetration testing is remarkably improved, and the method has good adaptability, expansibility and practical value and is suitable for popularization and application. And attack path discovery and risk early warning work in a dynamic network environment with high security requirements can be effectively supported.

The invention relates to the technical field of natural language processing, in particular to a multi-modal data-oriented graph neural network classification method, which comprises the following steps of: forming a cross-modal relation graph by multi-modal data by utilizing an attribute relation and a semantic relation of the multi-modal data, and jointly learning multi-modal node representation through a multi-layer graph neural network to obtain a multi-modal relation graph; and a model is trained by utilizing multi-modal node classification and node pair classification loss. According to the method, the incidence relation between the samples is effectively utilized through the graph neural network, the problem that modal information in the data samples is lost is relieved, and compared with an existing multi-modal data classification model, the classification performance is remarkably improved.

The present application can be used in the technical field of artificial intelligence. Provided are a relationship-graph-based decision processing method and a device. The method comprises: determining initial decision results respectively corresponding to multiple decision objects; determining association information between the multiple decision objects, the association information comprising the similarity between any two decision objects in terms of a decision factor; constructing a first relationship graph on the basis of the association information between the multiple decision objects, each node in the first relationship graph corresponding to one decision object, and when there is a first connection edge between the two decision objects, the edge weight of the first connection edge being positively correlated with the association information; and by means of the first relationship graph, updating the initial decision results respectively corresponding to the multiple decision objects, so as to obtain a target decision result of each decision object. In the present application, on the basis of the initial decision results, the association information between the decision objects can be fully utilized, and a dynamic damping coefficient can be constructed for decision processing, so that the decision accuracy can be effectively improved.

The invention relates to the field of integrated circuit design, and discloses an SAR ADC full-automatic size optimization design method based on formula constraint, which is used for solving the problems that the traditional manual design is low in efficiency, depends on experience and is difficult to optimize high-dimensional parameters. The method comprises the following steps: 1) establishing an analytic mapping relation between system-level indexes and sub-circuit specifications, and decomposing performance indexes to each sub-circuit module through a mathematical equation; 2) constructing a directed acyclic dependency graph, representing design variables by nodes, and representing constraint relationships by edges; 3) carrying out topological sorting on the dependency graph, and generating an execution sequence containing simulation verification, knowledge calculation and size optimization; and 4) adopting a double-layer optimization mechanism: verifying the performance of the sub-circuit and the system through circuit simulation, and performing iterative optimization until a device size scheme meeting all indexes is output when the performance does not reach the standard. According to the method, the full-automatic optimization design of the SAR ADC from the system index to the transistor size is realized.