101 results about "Similarity computing" patented technology

The invention belongs to the technical field of industrial data processing, and provides an intelligent material matching method and system fusing multi-modal features and fuzzy matching, and the technical scheme is that data in obtained electronic component list data is analyzed, key characters in character strings are identified, and a constructed mapping table is called to carry out mapping replacement on the key characters, so that the matching accuracy of the electronic component list data is improved. Obtaining each field parameter of the mapped electronic component; performing matching based on the constructed manufacturer alias knowledge graph and the mapped manufacturer field parameters to obtain a manufacturer matching result; screening the mapped material number parameters to obtain a material number candidate set, performing semantic similarity calculation based on the material number candidate set and a constructed special word vector model in the field of electronic components, when the similarity is greater than a set threshold, performing accurate matching, otherwise, triggering fuzzy matching, calculating a service score according to a matching result, and obtaining a service result; and performing multi-objective optimization based on a service score result to obtain an optimal matching scheme. And the matching accuracy and the purchasing decision-making efficiency are obviously improved.

A dynamic preference perception online course recommendation method based on a Hawkes process and a TimeXer mechanism comprises the steps that user data and course data are acquired, and the data comprise user attributes, historical interaction sequences and course attributes; generating a context semantic embedding vector by adopting a pre-trained BERT language model; projecting an embedded vector to a unit hyperspherical space by using a spherical Gaussian model, and measuring similarity by using a spherical distance so as to relieve the problems of data sparsity and cold start; a user dynamic preference model integrating a Hawkes process and a TimeXer attention mechanism is constructed, the Hawkes process calculates event occurrence intensity to capture self-excitation characteristics, the TimeXer attention mechanism is adjusted through the intensity information, and weighted user representation reflecting preference dynamic changes is generated; and according to the similarity between the weighted user representation and the projected course embedding vector, calculating a recommendation score, and generating a personalized online course recommendation result. According to the method, the accuracy of online course recommendation is improved by accurately capturing the dynamic preference of the user and effectively processing the sparse cold start problem.

The invention discloses an intelligent operation and maintenance alarm causal relationship analysis method based on BERT. According to the method, the semantic similarity of core switch link interruption and access layer equipment offline is improved by 40% through semantic modeling upgrading and joint coding of equipment types and alarm contents, and compared with an independent coding scheme, the misjudgment rate is remarkably reduced. Through organic combination of deep semantic modeling and dynamic time sequence analysis, the crossing of the intelligent operation and maintenance alarm causal relationship from rule driving to data intelligent driving is realized, and an efficient analysis tool is provided for intelligent operation and maintenance of a complex system. According to the method, accurate causal modeling of complex system alarm data is realized through semantic similarity calculation and multi-dimensional weight dynamic fusion under time sequence constraint, and the method is widely applied to large-scale distributed system fault analysis and root cause positioning scenes in industries such as power systems, communication networks, internet platforms, cloud computing infrastructures and industrial Internet of Things.

The invention relates to an electricity consumption prediction method and system based on fusion RAG and personalized graph federated learning, and belongs to the technical field of electricity consumption prediction. The method comprises the following steps of: preprocessing historical electricity consumption data of each municipal district, converting the historical electricity consumption data into a data frame, and improving data quality by removing a full null value column, eliminating abnormal data, filling missing values by adopting linear interpolation and the like; carrying out normalization processing on the data by utilizing a Z-score and Min-Max standardization method; a graph neural network is adopted as a basic model structure, an adjacent matrix is generated in combination with geographic position information, and the space incidence relation between communities is captured; an RAG knowledge base is introduced, special scene structured external knowledge is integrated, an embedded vector is generated through Sension-BERT to achieve historical scene retrieval, a retrieval result and model prediction are fused in combination with a gating mechanism, under a federated learning framework, a server initializes a plurality of personalized models, and a user carries out training locally to upload updated model parameters. And the server calculates an aggregation weight between the users through the functional feature similarity.

The invention discloses a data mining system and method for mail data, relates to the technical field of data retrieval processing, solves the technical problem of inaccurate retrieval matching caused by single semantic analysis retrieval, and improves data relevance by unifying multi-source data formats and combining an enterprise address book to supplement metadata. Structured data standardization and unstructured text vectorization are carried out, a computable semantic space is constructed, keywords are screened through a matching proportion, a word frequency weight is calculated in combination with a BM25 algorithm, the keyword matching accuracy is improved, a semantic vector is generated by utilizing a pre-training language model, and semantic correlation is calculated through cosine similarity. Keyword matching scores and semantic similarity scores are dynamically fused, accuracy and comprehensiveness are balanced, accuracy and recall rate are calculated in real time, retrieval deviation types are automatically recognized, keyword weights and similarity threshold values are adjusted in a targeted mode or synonym lists are expanded, and self-adaptive optimization of the system is achieved.

The invention provides an OPC UA information model automatic construction method based on a knowledge graph and a large language model. The method comprises the steps of obtaining an unstructured industrial text, performing semantic analysis on the unstructured industrial text, and extracting a main body entity, an object entity, a method entity and attribute information thereof according to a predefined hierarchical tuple structure; calling an industrial field knowledge graph in real time to perform semantic enhancement on an extraction result; performing similarity calculation and evaluation on an enhanced result, and performing information fusion and duplicate removal based on a similarity evaluation result to obtain an entity-attribute set; generating a tree-shaped hierarchical structure framework by using a large language model, and calling a knowledge graph to perform structural integrity verification; semantic association between the entity-attribute set and the tree structure framework is analyzed, attributes and methods are mapped to corresponding structure nodes, and a mapping reasoning process is output in a natural language form; converting the mapped model structure into a format of XML (Extensive Makeup Language) grammar, and executing automatic compliance check and repair; by designing a progressive task chain of information extraction, semantic fusion, model construction, interpretable mapping and standardized output, a set of complete understanding-enhancement-construction-verification automation system is constructed.

The invention discloses a semantic prompt automatic labeling method in a building construction scene, and relates to the technical field of intelligent construction, and the method comprises the steps: collecting a construction plan, on-site surveying and mapping data and real-time environment information, dividing a construction site into construction operation units, and extracting task features to generate a task priority coefficient Kt and a scene state vector Ei; calculating a semantic prompt priority index SPI based on Kt and Ei, sorting prompt words, and mapping the prompt words into feature vectors through BERT; extracting image global features by using a visual model, performing weighted fusion on the image global features and semantic prompt features, and outputting a detection region set; through comparative analysis of a semantic consistency coefficient Ssem and a threshold value Qsc, optimal prompt phrases are optimized and combined to generate a multi-granularity labeling structure; and calculating a semantic anomaly coefficient SAI by adopting cross-modal cosine similarity, comparing the semantic anomaly coefficient SAI with a threshold value Qsai to judge whether the annotation is abnormal or not, and realizing anomaly correction, history comparison and responsibility judgment by recording and tracking the whole process of the annotation link.

The invention provides an intelligent conference multi-modal interaction optimization method and system based on a large model, and the method comprises the steps: obtaining conference multi-modal data, and constructing a locked image frame set based on an image content snapshot locking mechanism; inputting each piece of modal data of the multi-modal data stream into a pre-trained large model to generate embedded vectors corresponding to the modal data, performing anchor point extraction by using similarity calculation between each pair of embedded vectors, and screening through the large model to obtain a semantic anchor point set; when the conference is carried out, each new speech is converted into an embedded vector, and then the similarity between the embedded vector and each semantic anchor point is calculated to obtain an anchor point reference frequency set; and according to the anchor point reference frequency set and the anchor point state set, generating a real-time interaction suggestion, and based on the interaction suggestion, feeding back an updated anchor point state and an updated anchor point priority, and updating the interaction suggestion. According to the invention, automatic identification, structured representation and process state perception of the conference content are realized.

The invention discloses a federated semi-supervised medical image segmentation method and system based on time sequence fluctuation aggregation and cross-client pseudo label mining, and the method comprises the steps: initializing global segmentation model parameters, and distributing the global segmentation model parameters to a plurality of federated clients; after each client receives the global segmentation model parameters, model updating is carried out based on the local annotated data and the unannotated data, and performance indexes are calculated on the verification set; the server collects the performance indexes uploaded by the clients and executes time fluctuation weight updating; each client generates a cross-client pseudo tag through feature clustering and semantic similarity calculation; constructing a neighborhood structure entropy loss function; and the server performs weighted aggregation on the model parameters uploaded by the clients according to the dynamically distributed aggregation weights to obtain updated global segmentation model parameters, distributes the updated global segmentation model parameters to the clients for iterative training until the global segmentation model converges, and performs image segmentation by using the global segmentation model.

The invention discloses a medical image treatment method and system for multi-source heterogeneous data, and relates to the field of medical image treatment, and the method comprises the steps: firstly, respectively extracting an image embedding vector and a text embedding vector from original medical image data and a description text thereof through a deep learning model; then, the vectors of the two different modes are fused, and a unified semantic embedding vector is formed; based on the unified vector, through semantic similarity calculation with a standard term library, candidate mapping can be automatically generated, and dependence on rigid artificial rules is eliminated. More importantly, a closed-loop mechanism of manual auditing-feedback learning is introduced in the scheme, high-confidence mapping is adopted automatically, low-confidence mapping is audited by experts, and an auditing result is absorbed into a mapping knowledge base, so that the system has continuous learning and self-evolution capabilities, and a semantic gap of cross-mechanism data can be eliminated more intelligently and more accurately.

The application discloses a business code processing method and device, a storage medium and an electronic equipment. It relates to the field of financial technology, and the method comprises the following steps: in the case that a business code repository is detected to have a business code modification event, a target code repository is matched according to a code file name corresponding to the business code modification event; in the case that a code class identical to the code file name is not matched, an attribute tree of a first code class and attribute trees of a plurality of second code classes are respectively constructed; similarity between the first code class and each second code class is calculated according to the attribute tree of the first code class and the attribute trees of the plurality of second code classes, and a target code class is determined from the plurality of second code classes according to the similarity. Through the application, the problem that the processing accuracy of business code is low when the code repository is adaptively modified in the related art is solved.

The application relates to an industrial process state monitoring method based on robust space-time joint projection, which comprises the following steps: data preprocessing and standardization; data space manifold structure and time dynamic information capturing: calculating the similarity degree between samples according to neighborhood overlapping order similarity, obtaining a data similarity matrix, and thus obtaining the overall space manifold information of process data; subsequently, calculating a data time derivative matrix, extracting slow change characteristics in the data, and obtaining key dynamic information; feature extraction and monitoring model building based on robust space-time joint projection: comprehensively integrating space-time information, performing dimension reduction projection on the data, obtaining key low-dimensional characteristics for retaining the representation of system operation states, constructing monitoring statistics and monitoring thresholds based on the key low-dimensional characteristics, and building an offline process monitoring model; real-time online monitoring of the state of an industrial system.

The application discloses a model updating method and device, equipment and a computer storage medium. The method comprises the following steps: acquiring edge parameters sent by n first edge servers, and a round in which the n edge servers respectively send the edge parameters; calculating a first similarity between the first edge parameters and second edge parameters; calculating a standard deviation of the first similarity; in the case that the round is greater than 1 and not greater than a target value, calculating a first difference value between the standard deviation and a standard deviation of a previous round; in the case that the first difference value is within a first preset range, updating parameters of a target model based on the edge parameters sent by the n first edge servers to obtain an updated target model. In this way, only the edge parameters that pass the test are used for updating the global model, thereby improving the accuracy of the result of the federated learning.

Techniques are disclosed that relate to skip lists. A computer system maintains a skip list having towers of varying depths and entries storing pointers to other towers. A first tower includes an entry at a particular depth storing a pointer to access an entry of a second tower. The pointer includes first similarity information indicating an amount of similarity between a key of the first tower and a key of the second tower. The computer system performs a traversal of the skip list for a search key. The computer system generates second similarity information indicating an amount of similarity between the first tower's key and the search key. Based on a comparison involving the first and second similarity information and without accessing the second tower to obtain information about its key, the computer system determines whether to traverse to the second tower using the pointer or descend the first tower.

This invention relates to the field of artificial intelligence technology and discloses an information push method. The method includes: classifying all viewed content by all users to obtain multiple content categories; calculating a first probability that a target user will view each of the content to be recommended based on the target user's preference for each of the content categories and the similarity between the content to be recommended and the target user's viewed content; calculating a second probability that each content to be recommended will be recommended to the target user based on the comment information of the user category to which the target user belongs; obtaining a popularity rating for each content to be recommended based on the first and second probabilities; and recommending the content to the target user based on the popularity rating. Through this method, this invention improves the accuracy of content recommendation.

The application provides a manufacturing cloud service recommendation generation method, comprising: obtaining manufacturing service descriptive information and label information of a cloud platform; using an LDA topic model to vectorize and model the manufacturing service descriptive information, obtaining p topic information of the manufacturing service, and obtaining p*q-dimensional service topic word vectors through Word2vec mapping expansion; inputting all the word vectors into CNN to learn the deep features of the manufacturing service; further calculating the manufacturing service similarity matrix through the deep feature similarity; using an improved K-Means clustering to cluster the manufacturing service label information to obtain the manufacturing service category, further obtaining a user category matrix model and a service category matrix model, and obtaining a coefficient to construct a user preference matrix; and combining the manufacturing service similarity matrix and the user preference matrix to obtain a manufacturing service recommendation list. The application solves the problems of incomplete manufacturing service information feature extraction and sparse user service matrix in the prior art.

The present application relates to a power consumption prediction method and system based on fusion RAG and personalized graph federated learning, belonging to the technical field of power prediction. The method comprises the following steps: preprocessing the historical power consumption data of each municipal district, converting it into a data frame, and improving the data quality by removing all null columns, removing abnormal data, and using linear interpolation to fill in missing values; using Z-score and Min-Max standardization method for data normalization; using graph neural network as the basic model structure, combining with the geographical position information to generate the adjacency matrix, and capturing the spatial correlation between communities; introducing RAG knowledge base, integrating structured external knowledge in special scenarios, generating embedding vectors through Sentence-BERT to realize historical scene retrieval, combining with the gating mechanism to fuse the retrieval results and model prediction, under the federated learning framework, the server initializes multiple personalized models, the user trains and uploads the updated model parameters locally, and the server calculates the aggregation weight between users through the functional feature similarity.

The application discloses a content-tracing-based enterprise culture hot word analysis method, and relates to the technical field of enterprise culture analysis.The method comprises the following steps: collecting culture-related original data from three dimensions, and constructing an enterprise-specific culture term library through preprocessing, density peak clustering, de-redundancy and matching with top-level culture elements; classifying enterprise internal texts to be analyzed according to departments and text types, automatically matching terms of corresponding categories in the enterprise-specific culture term library, and performing directional word segmentation based on preset rules; calculating the scene coverage of terms according to department scenarios and text type scenarios, calculating the culture correlation degree by using Jaccard similarity, and screening core hot words by combining the Apriori algorithm; locking target hot word origin texts based on the core hot words, counting the track data of the origin text transmission channels, identifying the transmission nodes and constructing the transmission track; and the method improves the accuracy of hot word analysis and provides support for targeted promotion of enterprise culture.

The invention relates to the technical field of knowledge management and data processing, and discloses an RAG document knowledge standardization method, system and device and a storage medium, and the method comprises the steps: carrying out the multi-modal content analysis of an RAG document, splitting the document content into text knowledge atoms and image knowledge atoms, and storing the text knowledge atoms and the image knowledge atoms; semantic association between the text knowledge atoms and the image knowledge atoms is established; converting the text knowledge atoms, the image knowledge atoms and the semantic association relationship between the text knowledge atoms and the image knowledge atoms into structured knowledge data in a preset format; performing multi-level similarity calculation on the structured knowledge data from the RAG documents to identify knowledge conflicts; based on a preset resolution strategy, performing intelligent resolution on the identified knowledge conflicts, and generating standardized knowledge data; according to the method, standardization, traceability and efficient utilization of knowledge are realized, and a solid knowledge base is provided for intelligent operation and maintenance of enterprises and application based on a large model.

The invention mainly provides a question and answer model verification method and system, electronic equipment and a storage medium. The method comprises the following steps: automatically generating a dynamic enhanced knowledge base Q + from an original knowledge base Q through semantic extension; respectively inputting the dynamic enhanced knowledge base Q + into the target model and at least one external reference model to obtain an answer set A and at least one reference answer set A +; performing mixed similarity calculation on the answer set A and a reference answer set A + to obtain a matching degree; and calculating the comprehensive similarity according to a preset rule, and comparing the comprehensive similarity with a preset dynamic threshold value to generate a corresponding verification report. According to the technical scheme, a diversified question method is automatically generated, the coverage range and generalization ability of a verification sample are improved, the output of an external high-precision model is combined to serve as a dynamic reference, dependence on a single model is reduced, meanwhile, semantic similarity calculation and logic consistency verification of domain knowledge enhancement are fused, and the verification precision of a professional scene is improved.

When searching based on the similarity of multiple indicators, it is possible to check the similarity of each indicator. [Solution] The drawing search system includes a search unit that searches for similar drawing information, which is existing drawing information similar to drawing information, from among multiple existing drawing information registered in an existing drawing database based on a similarity calculated from a detailed similarity, which is the similarity between drawing information and similar drawing information for each of multiple indicators, and a similarity display unit that displays the detailed similarity used by the search unit to search for similar drawing information together with the similar drawing information.

The invention relates to the technical field of power load prediction, and discloses a federated power load prediction method based on dynamic clustering and layered personalized aggregation, comprising the following steps: constructing a transverse federated learning framework; each client carries out local load data independent training and uploads updated model parameters to the server; the server calculates similarity by using cosine similarity so as to dynamically adjust the structure of the clusters, and deletes inactive clusters by judging whether the clusters are active or not according to training rounds and setting an active period so as to adjust the number of the clusters; the server executes hierarchical federal aggregation on each cluster model based on the current clustering result; and the server respectively issues the aggregated cluster model to each client of the corresponding cluster for next training, thereby realizing continuous dynamic optimization. The method has obvious advantages in key indexes such as prediction precision and convergence rate, and the load prediction performance under the federated framework is improved.

The invention discloses an employment service management method and system based on an AI large model, and relates to the technical field of human resource management, and the method comprises the steps: integrating the semantic embedding of a semantic enhancement substring in a clustering result through weighted average pooling, generating a comprehensive semantic embedding vector of the semantic enhancement substring, and carrying out the semantic embedding of the semantic enhancement substring; calculating semantic similarity of the semantic embedding vector by using cosine similarity, and generating a recessive skill vector; calculating intensity similarity and semantic similarity on the basis of the normalized recessive skill vector, calculating comprehensive matching degree on the basis of the intensity similarity and the semantic similarity, converting the comprehensive matching degree into a cost value by using linear transformation, and solving and generating a pairing set by using a Hungary algorithm. According to the method, key semantic information in job seeker resumes and post description is enhanced by combining a semantic embedding technology and a substring clustering algorithm, and deep semantic understanding of a traditional text matching method is improved by combining semantic feature extraction with weighted average pooling.

This invention discloses a method and system for multi-source office information fusion processing based on big data, belonging to the field of office data processing technology. The method includes acquiring standardized multi-source office information that has been cleaned, standardized, and de-identified; performing feature extraction and quantitative weighting, initial cluster center optimization, hybrid distance calculation, and cluster iteration on the standardized multi-source office information to generate office information clustering results; combining the clustering results, performing multi-dimensional similarity calculation, temporal feature weighting, and two-layer association fusion to obtain office information association fusion results; classifying and integrating the association fusion results to generate and output fused office data. The system includes data preprocessing, big data fusion processing, and output display modules, enabling full-process fusion processing of office information, effectively breaking down information silos, simplifying office operations, and improving office efficiency.

The invention provides an index similarity screening method and device based on a hierarchical index system, and relates to the technical field of data index management.The method comprises the steps that an external data source is collected to a data warehouse ODS layer and then processed to form a DWD layer width table, and the data blood relationship is recorded; a three-layer index system comprising an atomic index, a derived index and a composite index is constructed, the atomic index is a non-divisible value of a business activity, the derived index is generated based on an atomic index association time period and a modifier, and the composite index is generated by calculating the derived index through an operation rule; constructing a graph structure which takes the ODS layer table, the DWD layer table, the atomic index, the derived index and the composite index as nodes and takes the data flow direction relation as an edge; and calculating the similarity of the indexes on the basis of node types in the graph structure, and calculating the similarity of data sources in combination with the similarity of edge adjacent nodes so as to screen the high-similarity indexes.

The invention provides an intelligent component recommendation method and device and electronic equipment, and the method comprises the steps: carrying out the original data collection of different types of components in advance, carrying out the extraction and analysis of performance parameters in the original data, and determining a key performance parameter list; determining index evaluation standards of the key performance parameters in advance and classifying the evaluation standards; performing similarity calculation on a first text corresponding to the component in the original data and a second text corresponding to the required component, and determining the semantic similarity between the component and the required component; determining a performance recommendation index of the key performance parameters in any classification according to the evaluation standard of the classification; and respectively determining weight coefficients of the semantic similarity and the performance recommendation index, and determining a fusion recommendation index of the component according to the weight coefficients and the recommendation index. The features of the text information and the key performance parameters are fused, so that quick and accurate component recommendation is realized.

The invention relates to the technical field of generative models, and relates to a multi-agent confrontation cooperation-based photographable script generation method and system, and the method comprises the steps: obtaining text data inputted by a user, and carrying out the feature extraction of the text data through a trained feature extraction model, so as to obtain at least one keyword; inputting all keywords into the trained large language model to obtain script text data; performing word segmentation processing on the script text data to obtain a plurality of lexical elements; calculating the relevancy between the lexical elements and the sensitive word knowledge graph according to the semantic similarity between the lexical elements and each node in the sensitive word knowledge graph; traversing all lexical elements in the script text data to judge whether danger tags are added to the lexical elements or not; and correcting the text data, performing iteration, and outputting the current script text data when a preset termination is met. According to the method, the script generation quality is guaranteed, and meanwhile, the safety and compliance of the content are effectively improved.

The application discloses an employment service management method and system based on an AI large model, relates to the technical field of human resource management, and comprises the following steps: using weighted average pooling to integrate semantic embedding of a semantic enhanced substring in a clustering result, generating a comprehensive semantic embedding vector of the semantic enhanced substring, calculating semantic similarity of the semantic embedding vector by using cosine similarity, and generating an implicit skill vector; based on the normalized implicit skill vector, intensity similarity and semantic similarity are calculated, based on the intensity similarity and the semantic similarity, comprehensive matching degree is calculated, the comprehensive matching degree is converted into a cost value by using linear transformation, and a pairing set is generated by using the Hungarian algorithm for solution. The application enhances key semantic information in a job seeker's resume and a post description by combining semantic embedding technology and substring clustering algorithm, and improves deep semantic understanding of a traditional text matching method by combining semantic feature extraction and weighted average pooling.

The application provides an employment post recommendation method and system based on big data analysis, a device and a storage medium. Firstly, talent demand data, basic characteristics of job seekers and trajectory data of job seekers in the talent market in a target administrative region are obtained. Then, multi-dimensional features of talent demand are extracted to construct a demand feature set, and combined with the basic characteristics and the demand feature set, industry inclination data of the job seekers is obtained by using a support vector machine model. Then, the inclination data is adapted and screened based on the trajectory data to obtain the target industry corresponding to the job seekers. The K-nearest neighbor algorithm is used to calculate the similarity between the job seeker characteristics and the post characteristics corresponding to the target industry, and all target posts with a similarity value greater than a preset threshold are sorted to generate a post recommendation list for the job seekers. The method can effectively solve the problem of insufficient post matching accuracy in the regional talent market, and further realize more accurate and adaptive regional employment recommendation.

The invention discloses a multi-dimensional similar enterprise recommendation method and system. The method comprises the following steps of S1, multi-dimensional information collection and field judgment, S2, dynamic word segmentation processing of a subdivision technical field, S3, word vector generation of the subdivision technical field, S4, multi-dimensional word vector fusion, and S5, scene similarity calculation and recommendation. According to the method provided by the invention, multiple official platforms are accessed to obtain multi-dimensional data information, so that each enterprise obtains comprehensive and accurate capability representation; through combination of a technical lexicon in the subdivision technical field and a keyword extraction strategy, the word segmentation and feature extraction precision is improved; compared with the prior art, the field consistency factor is introduced to ensure comparison only in the same field, and the scene filtering factor is introduced to meet specific risks or potential preferences, so that different requirements of investment institutions and banks can be flexibly adapted, and the purpose of high scene adaptability is achieved.