6 results about "Vector space model" patented technology

The invention discloses a medical knowledge graph-driven diagnosis and treatment decision support system, and relates to the technical field of medical knowledge graph data processing, and the method comprises the steps: obtaining a clinical data flow of a patient, mapping the clinical data flow into an updating instruction for a pre-constructed medical knowledge graph, and dynamically updating the attribute data of entity nodes and relation edges; generating a vectorization representation of the node by taking the target entity node as an original point according to the attribute data of the relation edge between the target entity node and the associated node; screening nodes to be compounded according to a preset rule, and calculating a composite vector between the target core node and the vectorized representation of the nodes to be compounded; and finally, generating a clinical state evaluation result according to the mathematical characteristics of the composite vector. According to the method, the complex medical relationship is quantified into the computable vector space model, dynamic, objective and quantitative evaluation and risk early warning of the clinical state of the patient are achieved, and the accuracy and foresight of diagnosis and treatment decisions are remarkably improved.

The application provides a kind of complex product design knowledge service method and system based on knowledge graph, comprising: through text tree structure and improved TFIDF algorithm combination N-gram strategy, reference document is semantically annotated;Knowledge carding template and rule extraction method are used to extract design process knowledge, generate knowledge triple and build knowledge graph in Neo4j graph database;On this basis, product design knowledge super network is built, and the semantic matching degree between tasks and knowledge is calculated by combining super edge correlation degree and information entropy modified vector space model;Finally, the query push of process knowledge is realized based on Cypher language, and the sorting recommendation of reference knowledge is completed by multidimensional comprehensive evaluation method.The application realizes the task-oriented knowledge structured expression, semantic correlation calculation and intelligent push in complex product design, and improves the enterprise knowledge utilization efficiency and design decision quality.

The application discloses an AI Agent intelligent adaptation skill calculation method. The method comprises the following steps: constructing a three-dimensional unified vector space model of AI Agent capability image, skill demand label and task constraint condition; performing hard rule preliminary screening, filtering the skills that do not meet the strong constraint condition based on resource, time delay, permission dependence and necessity label hit constraint; calculating the discrete label accurate matching score, the Embedding semantic similarity, the capability adaptation weighted score and the quality stability correction score of the skills that pass the preliminary screening, wherein the upper limit of the single capability adaptation ratio is capped at 1; performing weighted summation on the scores of the above four dimensions to obtain a comprehensive matching total score, and scheduling based on the threshold and the total score ranking; and updating the AI Agent proficiency and the historical success rate in reverse after calling is completed. The application improves the accuracy, efficiency and scene adaptability of AI Agent skill adaptation.

The application provides an automatic reading method and system for antibody identification results based on a vector space model, which comprises the following steps: constructing an antibody specificity vector model, mapping the antigen spectrum corresponding to each antibody specificity in an antibody identification pedigree table into an antibody specificity vector with a fixed dimension; mapping the reaction results of antibody identification of a serum sample to be tested into a to-be-tested vector through the antibody specificity vector model; calculating the similarity between the to-be-tested vector and the antibody specificity vector; and performing reading based on reading logic according to the similarity calculation results to obtain antibody identification results. The antibody identification results can be automatically read by calculating the vector similarity, the antibody identification can be realized quickly, accurately and standardly, and the work efficiency is improved significantly, and the manpower is liberated.

A method for automated therapy discovery includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model based on proximity to domain descriptors in the vector space model; deriving association scores and action characteristics between connected concepts, based on proximity and action descriptors in the vector space model; generating a semantic network; receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes; identifying subsets of concepts along the set of edges; generating hypotheses for directions and magnitudes of effects of subsets of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal.

This invention relates to a personal identification information classification method based on an information vector space model, belonging to the field of network information security technology. This method first extracts characteristic textual information transmitted in network traffic through network traffic analysis and transforms it into a dataset containing service, location, information, and frequency feature dimensions. Then, the dataset description is transformed into a sample space for text classification. Next, a generative model based on three-layer Bayesian methods is established in conjunction with the text classification model. Model parameters are obtained through data sample training, automatically representing services-locations and their transmitted information as vectors, and obtaining the probability distributions between services-locations, information, and types. Finally, new services are inferred by calculating the probability distributions between each service-location, information, and type. This method can more precisely describe the distribution characteristics of different information semantics transmitted in network traffic, achieving the goal of accurately classifying personal identification information.