32 results about "Vector space model" patented technology

The invention discloses a costume design assisting method and system based on artificial intelligence, and belongs to the field of costume intelligent design, and the method comprises the steps: extracting multi-dimensional style feature information from a pre-constructed costume image data set and a fashion trend database through employing a convolutional neural network, and constructing a style vector space model; vector similarity matching is carried out, and a costume design sketch is generated by adopting a graph neural network; based on the design sketch, generating a structured process data packet for proofing; the design sketch and the structural data are input into a three-dimensional human body modeling and simulation module, and wearing effect simulation of virtual clothes is realized based on human body motion capture and physical cloth simulation technologies; and constructing a human-computer interaction interface, and quickly responding to a modification request based on the generative adversarial network model. Structured information such as the garment pattern structure, the sewing sequence, the material specification and the process route is automatically generated through the reinforcement learning algorithm, and the garment proofing efficiency and accuracy are remarkably improved.

The invention discloses a material warehousing coding and inventory screening matching method and system, and the method comprises the steps: carrying out the similarity calculation of material attributes of inventory materials, the material attributes comprise text attributes and numerical attributes, and carrying out the calculation of the similarity of each attribute for the numerical attributes, then different weights are given according to the importance of the attributes, and finally the weighted attribute similarities are summed to obtain the overall similarity of the materials; for text attributes, calculation is carried out based on the similarity of a vector space model, and target materials are screened and matched by calculating the cosine similarity between two material attribute vectors. According to the method, existing stock material matching and checking with consideration of multiple material attributes can be solved, and the utilization rate of existing stock materials is increased.

The invention relates to the technical field of text processing, and discloses an artificial intelligence text analysis and extraction and key point source positioning method, which comprises the following steps: collecting PDF document data, and carrying out manual labeling and preprocessing to obtain a training set, a verification set and a test set; initializing the deep learning model, and setting parameters of deep learning model training; training, verifying and testing the deep learning model based on the training set, the verification set and the test set to obtain a PDF document layout recognition model; identifying a to-be-identified PDF document by using the PDF document layout identification model, and converting an identification result into a text format or a chart / table format; performing depth vectorization processing on the text converted into the text format or the chart / table format; and semantic retrieval is realized based on an advanced vector space model and a similarity measurement algorithm. According to the method, the accuracy and efficiency of text analysis and extraction can be remarkably improved, and key point sources can be quickly positioned.

The invention belongs to the technical field of artificial intelligence, and relates to an LSH-based retrieval enhancement generation method and device, equipment and a storage medium thereof. Carrying out serialization processing; sampling processing is carried out through the vector space model, and high-dimensional embedded vector representation is generated; inputting the high-dimensional embedded vector representation into a vector database, and carrying out matching calculation; carrying out binary code valuation conversion on a matching calculation result by adopting an LSH algorithm to obtain binary code values corresponding to all the retrieved texts after conversion; according to the binary code value, an expected retrieved text is screened out; combining and sorting the retrieval input text and the expected retrieved text to generate a to-be-enhanced text; and inputting the text to be enhanced into the text enhancement generator to generate an enhanced retrieval feedback text. The method is applied to webpage search, question and answer systems and recommendation system scenes in the field of financial services or medical services, and a faster and more accurate retrieval feedback result is provided for retrieval personnel.

A computer-implemented method for detecting attribute value pairs from corpus data using a computer comprising a processor and a computer readable medium comprising instructions executable by the processor to at least: receive the corpus data comprising at least one pair; detect a layout template of the at least one pair; measure the merit of the layout template by determining at least one of (a) relative magnitudes of content probabilities based on a probability of the contents of an attribute cell and a probability of a corresponding value cell, (b) the validity of a name-value pair, or (c) the pointwise mutual information of a frequency matrix M corresponding to a sparse vector capturing context information of a word; and output detected attribute value pairs.

The invention discloses a question answering method, device and equipment based on operation and maintenance business questions, a medium and a product, and relates to the technical field of data processing. The method comprises the steps of obtaining an operation and maintenance service problem; the operation and maintenance service problems are subjected to collaborative analysis according to the pre-constructed knowledge base and a preset algorithm, a solution of the operation and maintenance service problems is determined, and the preset algorithm comprises a vector space model, a mixed recommendation strategy and an auxiliary decision strategy. According to the method, the pre-constructed knowledge base containing rich operation and maintenance business problem templates is pre-constructed, cooperative work is performed in combination with a vector space model, a mixed recommendation strategy and an auxiliary decision strategy, and corresponding solutions are automatically and rapidly matched according to data features, historical experience and an intelligent algorithm, so that the problem processing time is greatly shortened, and the problem processing efficiency is improved. The processing efficiency of the overall operation and maintenance service is effectively improved, and the problems that a current operation and maintenance mode lacks an effective accumulation and application mechanism for new knowledge and experience and the cross-domain problem comprehensive processing difficulty is large are solved.

Systems, devices, and methods are disclosed in relation to a system for natural language based message categorization designed to identify text from a particular topic from a potentially inexhaustible set of potential topics. In one of many possible implementations, a vector space model is first used to translate text into a vector representation. This vector is used to determine if the text can be recreated by swapping words and phrases from a training corpus of documents. This is done by determining if the vector is within the conical span of the vector representations of the text in the training corpus of documents. Span composition is evaluated by a two vector boolean comparison, enabling great computational complexity and short-circuiting enabling fast real-time topic determination.

The present invention belongs to the technical field of information retrieval, and particularly relates to a multimedia information retrieval method based on semantic similarity. During the preprocessing operation of documents, the present invention learns the method of the classical vector space model and replaces the dictionary containing keyword entries with an ontology library, uses the semantic vector containing the concepts described in the document and their features to replace the document and meaningfully extracts and absorbs the content of the document. To achieve the high efficiency of the retrieval completion, semantic classification is performed on the document to prepare for a more effective use of the abduction plan and techniques of the retrieved semantic vectors. When calculating the concept similarity and attribute similarity, the existence relationship between each concept instance and the attribute power is determined, and the influencing factors are analyzed according to the concept characteristics to complete the entire process of semantic retrieval. Finally, multimedia information retrieval is completed based on semantic similarity.

The present invention relates to the field of human-computer interaction technology, and particularly to a vehicle-mounted computer interface interaction method based on human-computer interaction. The method includes collecting information such as user voice input and touch operations, and using a vector space model for quantitative representation to evaluate the degree of association between the user and the vehicle-mounted computer functions, so as to accurately identify the user's intention; by combining the recognition result of the user's intention with the vehicle-mounted computer system status information, using a Bayesian network to analyze the interaction scenario, and dynamically adjusting the interaction response mechanism to meet the user's needs in different scenarios; by using the analytic hierarchy process to determine the weights of the vehicle-mounted computer interface function modules and optimize the interface layout to improve the convenience and efficiency of user interaction; by constructing a Markov chain model based on user feedback and adjusting the vehicle-mounted computer feedback strategy, the interaction performance and user experience of the vehicle-mounted computer system are improved. The present invention is used to solve the technical problem of poor user interaction experience in vehicle-mounted information systems.

The present disclosure is directed towards systems and methods of processing audio information for a vehicular automation system. The method comprises obtaining a context of a VAS; obtaining one or more audio data streams from one or more audio sensors; extracting an audio feature from the one or more audio data streams; processing the audio feature with a neural network and a vector space model to determine the relevance of the audio feature, wherein: the vector space model comprises a Human Audio Perception Model, HAPM; the HAPM is configured to mimic how an audio signal is perceived and processed by a human; and the method comprises using the HAPM to adjust the relevance of the feature based on the context of the VAS.

The application provides a public policy participation evaluation method and system based on an LDA and a vector space model, and the method comprises the following steps: obtaining target public policy documents and target dynamic data; adopting a TF-IDF algorithm to perform word segmentation processing on the target public policy documents and the target dynamic data respectively, so as to obtain a document feature word segmentation set and a data feature word segmentation set; extracting semantic information hidden in the target public policy documents and the target dynamic data respectively through an LDA model, so as to obtain a document-data matrix; constructing a vector space model based on the document feature word segmentation set and the data feature word segmentation set, calculating the similarity between the target public policy documents and the target dynamic data, so as to obtain a similarity matrix; and performing linear weighting processing on the document-data matrix and the similarity matrix. The application can effectively evaluate the citizen participation in public policy, and also solves the problems of data sparseness and semantic loss, and improves the accuracy and effectiveness of the evaluation.

The present invention relates to a method, device, electronic device and storage medium for classifying spam text messages. The method for classifying spam text messages includes the following steps: S1, pre-processing text messages, downloading a text message data set from a communication network as a basic data set for generating word vectors, and using a vector space model to form a word-text matrix from the word set; S2, generating text message feature vectors including a topic feature vector and a context feature vector, the topic feature vector is obtained by performing a singular value decomposition of the word-text matrix, and the context feature vector is used to predict the probability of the context based on the central word; S3, neural network fusion and training, a training task for a classifier for processing text messages, the classifier is used to distinguish whether the text message is a spam text message; S4, text message classification and discrimination. According to the spam text message classification method of the present invention, the topic feature and the context feature can make up for each other's shortcomings, realize multi-feature fusion, and thus realize accurate classification of spam text messages.

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 invention provides an article classification method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a plurality of to-be-classified field articles, determining a first vector of each field article of a first type through a network model according to the content identifier and content information of each field article, and according to the feature vector of each domain article, matching with a known domain article vector of a second type in a pre-constructed vector space model, determining a second vector of the domain article of the second type, and according to the first vector and the second vector, classifying the plurality of domain articles to be classified. According to the technical scheme of the invention, the method achieves the targeted recognition of the domain articles of the corresponding types through the model and vector matching, achieves the automatic marking, finally carries out the classification, solves the problems of low efficiency and inconsistency of manual one-by-one marking, and improves the marking expansibility and accuracy.

The present invention discloses an automatic classification method for defects in two interpreters, CPython and PyPy, comprising the following steps: 1) extracting keywords from the title of the issue report based on syntactic components; 2) supplementing tags by combining quantitative analysis and the experimental results of manual classification to make up for the deficiencies in the extraction of syntactic components and improve the retrieval efficiency of defect records; 3) recommending the issue to the corresponding category based on code and text similarity and in combination with the vector space model VSM. The difficulty of the method of the present invention lies in automatically generating defect record tags by extracting keywords from the issue report. By using hybrid technology, the present invention fills the gap in the direction of automatic classification of interpreter defects and provides certain assistance to developers and maintainers of Python interpreters, developers of Python applications, and researchers in related fields.

The invention discloses an information security interaction method and system. The method comprises the steps of obtaining an original information file based on an information access request; constructing a vector space model based on the original information file; calculating a membership function of a relationship between the security features and the security types based on the original information file and the vector space model, calculating a fuzzy variable of each security feature based on the membership function, and selecting the features with fuzzy variable values meeting requirements as fuzzy variables of security type edges; key security features are selected based on the fuzzy variables of the security type edges, a support vector machine classification method is adopted to perform classification learning on the key security features, a classification model is obtained, and the security features are identified through the classification model; and analyzing the original information file based on the security feature, encrypting the analyzed original information file and transmitting the encrypted original information file to an information request end, and receiving and decrypting the encrypted original information file by the information request end to complete information interaction. The security feature recognition rate is improved, and the false alarm rate is reduced.

The invention provides a text similarity calculation method based on phrase feature items, and aims to solve the problem that text semantic information cannot be completely reflected when keywords are adopted as feature items in a traditional vector space model (VSM) method and improve the text similarity calculation accuracy. The method comprises the following three steps: defining and constructing language slices, performing formalized representation on a text by using the language slices, and performing text similarity calculation. Specifically, the method comprises the following steps: combining two words in a text according to a grammar rule to form candidate language slices; calculating the correlation of the two words by using the point mutual information amount, and screening out language slices which accord with a threshold value as feature items; a TF-IDF method is adopted to calculate the weight of the feature item, and a vector included angle cosine method is adopted to calculate the text similarity. Experiments show that when the method is used for computer test paper judgment and scoring of test paper subjective questions, the accuracy rate can reach 85.24%, the method is remarkably superior to a traditional method adopting keywords as feature items, and an optimization scheme is provided for text similarity calculation.

The invention discloses an international commercial law retrieval system and method based on artificial intelligence, and relates to the technical field of legal retrieval. Feature words are accurately extracted from a retrieval request input by a user through a natural language processing technology; in combination with semantic understanding, performing normalization processing on synonyms and synonyms; performing similarity calculation by adopting a vector space model in combination with a cosine similarity algorithm to obtain feature word similarity of each legal text and the retrieval request in the legal knowledge base; sorting the retrieval results according to the similarity, and preferentially displaying the most relevant legal text; a collaborative filtering algorithm is combined with a content-based recommendation algorithm, historical retrieval records and current retrieval requests of a user are collected, a user interest model is constructed, and legal information which the user may be interested in is recommended. According to the method, retrieval omission caused by insufficient semantic understanding can be effectively avoided, and the accuracy of a retrieval result is greatly improved; and the most relevant legal text is preferentially displayed, so that the retrieval efficiency is 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 invention belongs to the technical field of natural language processing, and relates to a knowledge association method based on semantic similarity and co-occurrence word matching degree. According to the method, keyword extraction is carried out on the text by utilizing a jieba word segmentation library, high-frequency and high-distinction-degree feature words are highlighted in combination with a classical TF-IDF algorithm, the matching degree of semantic distribution is quantified through a vector space model by adopting a measurement standard of cosine similarity, and the co-occurrence condition of vocabularies in the text is particularly concerned; and the semantic association information is used as an important index for measuring the text association degree, so that surface semantic association information possibly omitted by single semantic analysis is made up. According to the method, the semantic similarity, the co-occurrence word matching degree and the vocabulary co-occurrence condition are comprehensively considered, so that the correlation degree between the texts can be more comprehensively and accurately measured, the information retrieval accuracy is effectively improved, meanwhile, the calculation complexity is reduced, and more accurate and comprehensive information services are provided for users.

The invention relates to the technical field of man-machine interaction, in particular to a man-machine interaction-based vehicle-machine interface interaction method, which comprises the following steps of: collecting information such as voice input and touch operation of a user, and performing quantitative expression by using a vector space model, so as to evaluate the association degree between the user and a vehicle-machine function and realize accurate identification of a user intention; according to the method, the identification result of the user intention and the state information of the in-vehicle infotainment system are combined, a Bayesian network is adopted to analyze an interaction scene, and an interaction response mechanism is dynamically adjusted to adapt to user requirements in different scenes; the weights of the function modules of the vehicle machine interface are determined by applying an analytic hierarchy process, and the interface layout is optimized, so that the convenience and efficiency of user interaction are improved; the Markov chain model is constructed according to the user feedback, the in-vehicle infotainment feedback strategy is adjusted, and the interaction performance and the user experience of the in-vehicle infotainment system are improved. The method and the device are used for solving the technical problem of poor user interaction experience in a vehicle-mounted information system.

The application discloses a kind of based on BLS's NAVTEX message semantic automatic classification method, to NAVTEX message data application data cleaning and word segmentation operation, and proposed semantic label to give message label category, specifically as the summary of the message content contained in navigation warning, defined 7 kinds of semantic labels.By the application, redundant and complex original message data can be effectively simplified and the foundation for subsequent classification tasks is laid, making the trained features more representative.The application extracts features using the vector space model on the preprocessed NAVTEX message data, which has the advantages of easy calculation and simple structure.Then, based on BLS, the semantic classification is performed, which has the advantages of short training time and good classification effect, effectively alleviating the labor intensity of the crew and improving the emergency response speed and efficiency of responding to safety information, thereby improving the safety navigation performance of the ship.

The web spider thematic search system based on the improved cloud platform provided by the present invention improves the HITS algorithm, a web analysis algorithm based on a link structure, and the web analysis algorithm based on the topic similarity calculation of a VSM vector space model, proposes an improved web spider model algorithm, proposes an overall framework model based on the Hadoop cloud platform web spider, designs and implements the storage structure of the cloud platform web spider on the file system HDFS, and implements the MapReduce algorithm for each functional module based on module division; proposes an improved task allocation algorithm, which can take into account the uniform allocation and the load of each crawling sub-node, and improves the crawling efficiency and accuracy of the cloud platform web spider system; the results show that the cloud platform web spider system based on Hadoop proposed and implemented by the present invention is feasible and effective, can greatly improve the accuracy and efficiency of thematic search, and can retrieve topic-related information comprehensively, quickly and accurately.

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.

One variation of a method 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; deriving association scores and action characteristics between connected concepts in the vector space model; and generating a semantic network. This variation of the method further includes: 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 in the semantic network; identifying concepts along the set of edges in the semantic network; generating hypotheses for directions and magnitudes of effects 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 for a user to review.

The invention provides an intelligent quality problem information matching method and system based on text understanding, and the method comprises the steps: carrying out the data preprocessing, and carrying out the data dimension reduction of the preprocessed sample data; performing feature extraction, wherein the feature extraction comprises feature extraction according to distance measurement, feature extraction based on information measurement, feature extraction based on dependency measurement and feature extraction based on consistency measurement; vectorizing the quality problem information by using a vector space model; constructing a feature extraction algorithm; calculating Chinese text similarity; constructing a similarity calculation module; and a problem prediction software model is constructed, and intelligent matching of quality problem information is carried out. Unified processing and fusion are carried out on the data through the ET L process. And aiming at the fused high-quality data, researching a multi-type text intelligent understanding technology based on machine learning and a text intelligent understanding technology based on combination of rules and statistics, and obtaining core data in quality problem information in a question and answer mode.

The invention provides a knowledge graph-based complex product design knowledge service method and system. The method comprises the steps of performing semantic annotation on a reference document through a text tree structure and an improved TFIDF algorithm in combination with an N-gram strategy; designing process knowledge is extracted by utilizing a knowledge combing template and a rule extraction method, a knowledge triple is generated, and a knowledge graph in a Neo4j graph database is constructed; on this basis, a product design knowledge super network is constructed, and the semantic matching degree between the task and the knowledge is calculated by combining the hyperedge correlation degree and the vector space model corrected by the information entropy; and finally, querying and pushing process knowledge based on a Cypher language, and finishing sorting recommendation of reference knowledge through a multi-dimensional comprehensive evaluation method. According to the method, task-oriented knowledge structured expression, semantic association degree calculation and intelligent pushing in complex product design are realized, and the enterprise knowledge utilization efficiency and the design decision quality are improved.

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.

The invention provides a power generation and utilization customer intelligent scheduling management system based on a mobile internet technology, which relates to the technical field of power system scheduling management and comprises a training management module, an operation auxiliary module and an information display module. The training management module comprises a personnel gradient portrait unit and an authentication management unit, and the personnel gradient portrait unit is used for performing capability quantification on field personnel of power generation and utilization clients through a vector space model to obtain a capability quantification result; through the personnel dynamic gradient portrait unit, the vector space model is utilized to quantify the personnel ability and accurately position the ability difference of the personnel, so that more targeted training content is provided for new personnel and on-duty personnel, the training is more systematic and standardized, the problem of insufficient training caused by frequent replacement of field personnel is effectively solved, and the training efficiency is improved. The scheduling instruction understanding and operation execution capability of employees is improved, and through linkage with the operation auxiliary module, it is ensured that only the personnel with valid certificates can receive the scheduling instruction and perform operation.

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.