18 results about "Feature term" patented technology

The invention discloses an electronic material automatic classification method and system based on multistage feature recognition, and relates to the field of material classification, and the method comprises the steps: firstly obtaining unstructured material information, and carrying out the regularization and preprocessing of the information; subsequently, through main category identification and feature lexical element marking, accurate positioning of material core attributes is realized, which exceeds shallow identification of traditional keyword matching, and the defect that complex semantics cannot be processed is effectively overcome. On the basis, structured extraction and analysis are further carried out on the marked features, and the marked features are converted into paradigm features which can be deeply understood by a machine. And finally, through code integration and verification, a standard and unique production code, such as an SPYY. XBBBVVVV structure, is automatically generated. In this way, low-efficiency and error-prone manual operation is replaced, and the fundamental problem that an existing automatic scheme is difficult to deal with description diversity is solved through the powerful analysis capacity and robustness of the manual operation.

The present application provides a method and device for constructing a production equipment operation control function model, and relates to the field of model construction technology. The method includes: decomposing from top to bottom layer by layer to obtain a tree-structured production equipment operation control function model; obtaining interface requirement information and determining the interface content setting information of the model; determining the top-level functional activity information of the model and constructing a top-level functional reference model; determining the sub-level functional activity information of the model and constructing a sub-level functional reference model; obtaining standard text data in the field of production equipment operation control and performing topic clustering; matching the feature word topic set with the sub-level functional reference model to construct an underlying reference resource pool. By performing topic clustering on standard texts in the field of production equipment operation control, the present application can quickly extract the equipment operation control business logic, functional requirements and control methods in the standard text, thereby effectively improving the efficiency and comprehensiveness of constructing the production equipment operation control function model.

This invention discloses a multidimensional dataset analysis and processing method and system that combines AI, belonging to the field of data analysis and processing technology. It includes: cleaning the multidimensional dataset, extracting dimensional features from the multidimensional dataset to obtain dimensional feature terms, which represent the number and type of dimensions corresponding to different data in the multidimensional dataset; integrating data based on the dimensional feature terms; imputing missing data in the multidimensional dataset to obtain a processed dataset. This invention constructs a dimensional link set and implements dynamic link adjustment. The system can intelligently identify key association paths, optimize the display method, and control the display complexity by setting display thresholds, ensuring that information is presented concisely and intuitively. These mechanisms work together to not only improve the automation level of data integration, storage, and display, but also significantly improve data processing efficiency and decision support capabilities, providing enterprises with a more efficient and intelligent analysis platform.

The invention belongs to the technical field of advertisement pushing, and particularly relates to an advertisement pushing method and system based on artificial intelligence, and the method comprises the steps: obtaining feature vocabularies of a target user based on a word segmentation model; establishing a long-term portrait of the target user according to the time interval difference of the feature vocabularies of the target user; obtaining the immersion degree of the target user; combining the immersion degree of the target user with the long-term portrait to obtain a short-term portrait of the target user; based on the short-term portrait of the target user, screening a to-be-recommended advertisement of the target user through an artificial intelligence technology; and sorting the to-be-recommended advertisements of the target user based on the advertisement recommendability fed back by the purchase record of each user. According to the invention, the advertisement pushing effect is improved, and the long-term total income of the three parties of the advertisement party, the user and the platform is increased.

The invention relates to the technical field of fault diagnosis, and provides a multi-information-source adaptive fault diagnosis method and system and a storage medium, and the method comprises the steps: obtaining fault seed theme information, fault text data and equipment operation data; processing based on the seed topic model and the dynamic indication variable to obtain a topic distribution vector and a feature word distribution vector; generating feature matrix information according to the theme distribution vector and the equipment operation data; inputting the feature matrix information into a dynamic fault diagnosis model, and obtaining fault classification result information and classification weight scoring information; and generating interpretable diagnosis result information. Dynamic indication variables are utilized to extract theme features, processing efficiency is improved, model adaptability is enhanced, fault diagnosis is performed by combining fault texts and equipment running states, comprehensiveness and accuracy of diagnosis are improved, conditions indicating fault themes and feature words corresponding to the fault themes in diagnosis result information can be explained, and fault diagnosis efficiency is improved. And the interpretability is enhanced.

The invention discloses an object storage method and device, and the method comprises the steps: obtaining object description information inputted by a user, and the object description information is used for describing the features of a to-be-generated object; based on the object description information, utilizing an artificial intelligence model to generate at least one object; extracting at least one feature word from the object description information by using an artificial intelligence model, wherein the feature word is used for representing content features of the object; a storage request for a target object is detected, the object name of the target object is generated based on the at least one feature word, and the target object belongs to the at least one object; and associatively storing the target object and the object name of the target object.

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 present application relates to the field of computer technology application, and specifically provides a kind of man-machine interaction method, device, electronic equipment and storage medium, comprising: based on the current received inquiry input information, obtain k associated labels from the preset feature term knowledge base;Current received inquiry input information and corresponding k associated labels are mapped using a large language model, to obtain corresponding mapping label;Mapping label is added to the current combination label set, and based on the current combination label set and the preset knowledge graph, the symptoms associated with the current combination label set are obtained as the current inquiry label;If the preset termination condition is met, the corresponding inquiry output information is output, otherwise, the next inquiry question is generated;Based on the received answer information as the current received inquiry input information, the next inquiry operation is carried out.The present application can improve the man-machine interaction precision.

The invention discloses a data asset classification and level-to-level management method and system based on artificial intelligence, and the method comprises the steps: firstly collecting enterprise comprehensive asset data and a data classification standard in a period, carrying out the filling of numerical data, carrying out the abnormal processing, carrying out the normalization, and carrying out the cleaning, error correction and filling of text data, so as to obtain preliminary comprehensive data; performing key feature analysis on each text in the preliminary comprehensive data by using an improved natural language processing technology, collecting word conversion vectors corresponding to feature words in all texts, and obtaining feature comprehensive data; performing clustering analysis on each text in the feature comprehensive data by using a K-means technology to obtain a similar hierarchical data set; performing grade matching on each group in the similar grading data set by using a grading scheme design model to obtain a data classification grade corresponding to each group; according to the data classification level corresponding to each group, protection measures in the data classification standard are used for management, and level-to-level management of enterprise data assets is achieved.

The invention, which relates to the technical field of artificial intelligence, discloses a power production scene-oriented intelligent question number rapid construction optimization system comprising a word vector management module, a question management module, a semantic extraction module, a semantic offset management module, a semantic offset management module, a feature word screening module and a feature word compensation module. By means of dynamically adjusting a semantic weight value, accurately judging semantic offset, strengthening key feature words, compensating word vector offset and the like, the accuracy of user intention tracking and the query result matching degree in multiple rounds of dialogues are remarkably improved. According to the method, query deviation caused by semantic offset or improper feature word weight distribution can be effectively avoided, and efficient and accurate data query requirements in an electric power production scene are met.

The present invention discloses an intelligent query rapid construction and optimization system for power production scenarios, which relates to the field of artificial intelligence technology. The system includes: a word vector management module, a question management module, a semantic extraction module, a semantic offset management module, a semantic offset management module, a feature word screening module, and a feature word compensation module. By dynamically adjusting semantic weight values, accurately judging semantic offsets, strengthening key feature words, and compensating for word vector offsets, the accuracy of user intent tracking and the matching degree of query results in multi-round conversations are significantly improved. This application can effectively avoid query deviations caused by semantic offsets or improper allocation of feature word weights, and meet the needs of efficient and accurate data queries in power production scenarios.

The invention relates to the field of computer technology application, and particularly provides a man-machine interaction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining k associated tags from a preset feature term knowledge base based on currently received inquiry input information; mapping the currently received inquiry input information and the corresponding k associated tags by using a large language model to obtain corresponding mapping tags; adding the mapping tag into a current combined tag set, and obtaining a symptom associated with the current combined tag set as a current inquiry tag based on the current combined tag set and a preset knowledge graph; if a preset termination condition is met, outputting corresponding inquiry output information, otherwise, generating a next inquiry question; and taking the received answer information as currently received inquiry input information, and carrying out the next inquiry operation. According to the invention, the man-machine interaction precision can be improved.

The present application discloses a keyword extraction method, device, equipment and medium, which relates to the field of data processing. The method includes obtaining a first review text from multiple review texts, where the sentiment label of the first review text is a first sentiment label; performing word segmentation on the first review text to obtain a set of feature words of the first review text; calculating an information entropy set of the first review text according to the set of feature words, where the information entropy in the information entropy set is calculated based on the feature words in the set of feature words; and determining keywords of the first review text according to the information entropy set. The present application will obtain the information entropy set of the review text and determine the keywords in the review text according to the information entropy set. Since the information entropy represents the differences between review texts, the keywords obtained through the information entropy are more explanatory for the sentiment classification result, improving the modeling effect and interpretability.

The invention provides an HSE system audit data intelligent labeling and classifying method, a medium and electronic equipment, aims to improve the efficiency and quality of HSE system data labeling especially in the petroleum and natural gas industry and improve the interpretability and controllability of a learning model, and relates to the technical field of artificial intelligence. The invention discloses an intelligent marking and classifying method for auditing data of an HSE system. The method comprises the following steps: preprocessing a text of a document; establishing a self-defined industry word bank; performing word segmentation processing on a text in the document in combination with a user-defined industry word bank to obtain a vocabulary V; the product of the TF-IDF value and the information gain value of each word w is calculated, the feature score of each word w is obtained, the vocabulary V is sorted according to the feature score of each word w, words with the feature scores larger than or equal to a set initial threshold parameter theta0 are selected as feature words, and the remaining words serve as stop words; and after the stop words are removed, recalculating the vector representation of the document and the category, and extracting the text.

The invention provides a method and a device for extracting viewpoint feature words of comment information and electronic equipment, and relates to the technical field of big data mining, the method comprises the following steps: determining the weight of each sample feature word, and generating a sample lexicon with the weight; based on the synonym lexicon of each sample feature word, updating the weight of the sample lexicon; determining a chi-square value of each word in the updated sample lexicon with the weight, and generating a viewpoint polarity dictionary; and determining the polarity of the feature word extracted from the target comment information based on the viewpoint polarity dictionary, thereby determining the viewpoint feature word of the target comment information. Through the method, the technical problems of high cost and low accuracy of existing viewpoint feature word extraction are solved, and the technical effects of improving the accuracy and reducing the extraction difficulty are achieved.

This specification discloses methods, apparatus, devices, and systems for determining a feature effective value of business data. In one implementation, a method includes: obtaining a joint data share of a first participant based on joint data that includes feature values of a plurality of objects corresponding to a plurality of feature terms, obtaining a predictive value share and a model parameter share based on the joint data and a business prediction model, determining, through secure multi-party computation, a correlation data share corresponding to the plurality of participants, and determining, through a significance test method, an effective value of a feature term of the plurality of feature terms.

The application belongs to the technical field of artificial intelligence language information processing, and particularly relates to a service specific field scarce resource language model modeling method, which comprises the following steps: step 1) obtaining a preprocessed sentence set composed of multiple segmented sentences; step 2) obtaining a sentence-topic distribution and a topic-word distribution; step 3) obtaining selected field characteristic words; step 4) obtaining translated field characteristic words; step 5) calculating and obtaining a field topic distribution by using the translated field characteristic words; step 6) calculating the correlation between the sentence-topic distribution and the field topic distribution of each segmented sentence; step 7) judging whether the correlation of each segmented sentence exceeds a preset threshold value, and selecting the sentences meeting the condition into the language model training corpus of the scarce resource specific field; step 8) obtaining the final language model training corpus; and step 9) training the scarce resource language model to obtain the scarce resource language model.

The application provides an information identification method and device, computer equipment, a storage medium and a program product, and relates to the technical fields of artificial intelligence, big data, intelligent transportation and the like. A text feature vector of a to-be-identified text is obtained. Whether the to-be-identified text is spam is determined based on the similarity between the sample feature vectors of each sample in a sample library and the text feature vector. For each spam category, a positive context feature library corresponding to the spam category is obtained based on a positive sample set of the spam category. A negative context feature library corresponding to the spam category is obtained based on a negative sample set of the spam category. Feature words of each spam category are obtained. The positive and negative context feature libraries, topic words, category keywords or entity name words of each spam category are used to obtain the sample feature vectors of each sample in the sample library, so that the text can be identified based on multi-dimensional features, and the accuracy of identification is improved.