1692 results about "Matching methods" patented technology

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a dynamic question and answer matching method and system based on context awareness. The method comprises the following steps: acquiring history of multiple rounds of dialogues and user background information, and generating a semantic understanding model of a current dialogue; based on the semantic understanding model and the current user input, matching the most relevant answer from the question and answer library, and dynamically adjusting the matching strategy; acquiring real-time feedback data of the user, updating a personalized recommendation algorithm based on the interest change of the user, and generating a personalized answer recommendation list; dynamically updating the content of the question and answer library according to the personalized recommendation list and the user behavior data; and inputting the user feedback and the updated question and answer library data into a personalized recommendation system to generate final personalized answer recommendation. Through context sensing and real-time dynamic adjustment, the matching precision of the question answering system is remarkably improved, the interaction experience of the user is improved, and the adaptability and flexibility of the system are enhanced.

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention discloses a person and post matching method, system and equipment based on Transform and a medium, belongs to the technical field of cross application of natural language processing and human resource management, and aims to solve the technical problem of how to reduce subjective errors in a person and post matching process, improve matching accuracy, fairness and objectivity and improve manual matching efficiency at the same time. According to the technical scheme, the method comprises the following steps: data collection: collecting post information data and job seeker resume data from a plurality of data sources, and cleaning and sorting the collected data to obtain a post description text and a resume text; text preprocessing: preprocessing the acquired and sorted post description text and resume text to obtain the preprocessed post description text and resume text; the method comprises the following steps of: fusing a Transform model and a BERT model; constructing an encoder-decoder structure based on the Transform model; optimizing the model; multi-label text classification: constructing a multi-label classification system; and outputting and evaluating a matching result.

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

The invention provides an intelligent retrieval matching method and system for multidimensional data of image-text features, and relates to the technical field of icon image retrieval. Comprising the following steps: extracting image features, text content and semantic features of an icon image by using a convolutional neural network, an image segmentation attention mechanism network, a converter optical character recognition model and a bidirectional semantic understanding model, constructing the extracted features into heterogeneous feature tensors, and performing singular value decomposition to obtain icon feature fingerprint vectors; and constructing a multi-level index based on locality sensitive hashing, realizing rapid retrieval, calculating visual, text and semantic similarities in combination with a deep metric learning model, weighting according to variances and discrimination coefficients of similarity features to obtain a comprehensive similarity score, and outputting a retrieval result with the highest similarity.

The invention discloses an intelligent information screening method and system for a human resource system, and relates to the technical field of human resource systems. The problem that a traditional matching method is difficult to deeply mine semantic association and cannot accurately measure the matching degree of candidates and posts is solved. According to the method, through multi-step intelligent data processing and optimization, the candidate screening efficiency and accuracy are remarkably improved, a semantic analysis technology is adopted to construct a multi-layer topic tree and an emotion analysis model, and deep semantic matching of candidate resumes and position requirements is achieved; meanwhile, candidates are intelligently screened by using a bidirectional semantic matching network, the screening effect is continuously optimized by mining potential high-matching candidates and historical data feedback, bidirectional recommendation is realized through a block chain communication link, the security and efficiency of data transmission are guaranteed, complex enterprise recruitment requirements can be effectively adapted, the recruitment period is shortened, and the recruitment efficiency is improved. The man-post matching accuracy is improved, and the human resource management cost is greatly reduced.

The invention provides a pattern multi-row matching method and device for ATE equipment, and the method comprises the steps: determining a cyclic pattern sequence comprising a plurality of rows of target patterns based on the number of matched rows when a first to-be-tested pattern carrying a LOOPMATCH instruction is recognized after a test is started; lOOPMATCH circulation is executed on the basis of the circulation Pattern sequence, and in each LOOPMATCH circulation, the Pattern matching result of the target Pattern in each row is determined; when all Pattern matching results pass in one cycle, jumping out of the LOOPMATCH cycle and determining that the cycle Pattern sequence test passes, otherwise, continuing to circulate to the maximum cycle number and then determining that the cycle Pattern sequence test does not pass; according to the technical scheme of the embodiment of the invention, when the first to-be-tested Pattern carrying the LOOPMATCH instruction is detected, multiple rows of target Patterns are constructed for loop testing, and LOOPMATCH loop is jumped out after any complete matching succeeds, so that the depth of the Pattern sequence can be effectively reduced, multi-row synchronous multi-time level matching is realized, the test resource overhead is reduced, and the test efficiency is improved.

The invention discloses a deep learning intelligent person and post matching method and system based on a large language model and multiple Prompts, and relates to the technical field of big data, a resume data set and a post data set are collected, and a resume embedding vector and a post embedding vector are constructed by taking the resume data set and the post data set as input and taking a resume embedding vector and a post embedding vector as output; taking the matching similarity of the embedded vector as a double-tower model of a prediction target, training the double-tower model, constructing a matching model taking the resume data set and the post data set as input and taking the matching degree as output, training the matching model, collecting man-post matching requests, and providing a primary screening set for the man-post matching requests through the double-tower model; and screening out a final matching result from the preliminary screening set by using a matching model. Through cooperative work of the two models, efficient and accurate man-post matching is realized.

The invention discloses a BIM-based two-dimensional data and three-dimensional model verification matching method and system. The system comprises a 2D drawing processing module, a 3D model processing module, a difference detection module and a report generation module. In the 2D drawing processing module, a user imports a process pipeline plan and a profile map, and through preprocessing and image semantic analysis, pipeline elevation labeling information is extracted and numbered. And the 3D model processing module loads a target BIM model, locates a process pipeline area, analyzes pipeline parameters and a space state, analyzes a pipeline topological relation and performs conflict verification. And the difference detection module binds the 2D drawing with a corresponding pipeline object in the 3D model, establishes a mapping relation, compares differences, and performs labeling and classification. And the report generation module is used for carrying out visual annotation in the 2D drawing and the 3D model based on the annotation difference, automatically generating a difference comparison table and outputting a report, so that efficient and accurate verification and matching of the two-dimensional data and the three-dimensional data are realized, the examination period is shortened, and the rapid collaborative correction efficiency of the construction team is improved.

The invention relates to the technical field of task matching, and provides a task matching method based on user information and task characteristics, which comprises the following steps: step 1, collecting and analyzing user information which comprises but is not limited to employment information, skilled direction, experience and price; 2, analyzing task information, wherein the task information comprises but is not limited to basic information and additional information; the basic information collection includes but is not limited to task titles, task description, task categories, budget ranges, time requirements, task importance and geographic position requirements; 3, establishing a multi-dimensional matching model, and mapping the user information and the task features; step 4, optimizing and sequencing matching results by utilizing a machine learning algorithm; and 5, dynamically adjusting the matching weight, and continuously improving the matching precision according to historical data. According to the task matching method based on the user information and the task features, the task allocation efficiency and stability are improved, and personalized task recommendation is provided.

Methods, systems, and computer-readable storage media for receiving, by an entity matching ML model, a query and target pair including a query entity and a target entity, providing, by the entity matching ML model, a query-target prediction by processing the query entity and the target entity, the query-target prediction indicating a match type between the query entity and the target entity, generating a prompt by populating a prompt template with at least a portion of the query-target prediction, inputting the prompt into a large language model (LLM), and receiving, from the LLM, an explanation that is responsive to the prompt and that describes one or more reasons for the query-target prediction output by the entity matching ML model.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

The invention discloses an employment information matching method and system based on data analysis, and particularly relates to the field of employment matching, and the method comprises the steps: collecting structured and unstructured data, and carrying out the semantic feature extraction through employing a BERT model and BiLSTM-CRF; in the preprocessing stage, entity standardization is realized through a knowledge graph, and a job seeker portrait and post model including a skill matrix and an occupational development trajectory is constructed; in the feature engineering stage, extracting four core features of skill matching degree, salary expectation integrating degree, commuting tolerance and occupational development goodness of fit; the salary integrating degree is quantitatively evaluated through a bidirectional tolerance model, a random forest model with time decay is used for dynamic weight, feature weight is generated based on historical successful cases, and a real-time feedback mechanism is introduced to adjust a weight coefficient; finally, the matching degree function fuses the weighted features and the industry trend factors, and the model is continuously optimized through a three-level updating mechanism.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a segmentation and matching method, device, equipment and medium for an image background region. Multi-scale features are obtained through cavity convolution branch, fusion features are generated through weighted fusion, the fusion features are input into a decoder network to obtain a background segmentation mask, a background area image is obtained by combining the preprocessed image, and a visual converter model is input to extract background feature vectors; and performing similarity matching with a target feature vector in a preset feature database to retrieve a target image. According to the method, the segmentation accuracy and robustness are improved through combination of multi-scale feature fusion and the visual converter, meanwhile, the retrieval discrimination and reliability are enhanced through feature matching, the problems that an existing method is insufficient in segmentation precision and limited in retrieval capacity are solved, and precise segmentation and efficient retrieval of the background in a complex scene are achieved.

The invention provides a cross-hardware platform AI algorithm model automatic matching migration method and system based on low-code development, and the method comprises the steps: firstly receiving an AI algorithm model uploaded by a user, obtaining the information of a target hardware platform needing to be migrated, carrying out the optimization adjustment of the AI algorithm model according to the obtained information of the target hardware platform, and carrying out the automatic matching migration of the AI algorithm model. The optimization and adjustment process comprises the following steps: analyzing the AI algorithm model, selecting a corresponding model type according to the function of the algorithm model, carrying out parameter quantification, model conversion, model deployment and evaluation on the AI algorithm model according to the model type, classifying and downloading the converted model, and carrying out optimization and adjustment on the model. An optimized AI algorithm model is obtained; and finally, deploying the obtained AI algorithm model to a target hardware platform. According to the method, the defects of a traditional matching method can be overcome, the model performance and universality are improved, and a unified adaptive solution is provided for different hardware platforms.

The invention provides a self-adaptive semantic-driven data set field matching method and system, and the system comprises a data preprocessing module which is used for carrying out the cleaning, standardization and preliminary analysis of an input data set, and extracting a field name, a data type, a field description and a data sample; the deep semantic representation modeling module is used for constructing a field-level semantic representation vector; the multi-level similarity calculation module is used for comprehensively calculating the grammatical similarity, the semantic similarity and the statistical similarity among the fields, dynamically adjusting the weight of the similarity of each level by adopting a weighted fusion algorithm, and generating a comprehensive similarity matrix; and the matching result management and application module is used for generating a field matching mapping table and a fusion suggestion according to the comprehensive similarity matrix. According to the method, high-precision automatic matching of data set fields is realized by fusing deep semantic understanding, multi-dimensional similarity calculation and incremental adaptive learning, and the efficiency and accuracy of data set fusion are remarkably improved.

The invention discloses a person and post matching method and device, equipment, a medium and a product, and relates to the technical field of information processing, and the method comprises the steps: receiving recruitment post data and resume data; and performing data matching on the recruitment post data and the resume data through a pre-constructed person-post matching model to obtain a matching result, the person-post matching model being constructed based on a recruitment vector model, an information extraction model, a weight evaluation model and a matching degree scoring model. Therefore, data matching is carried out on the recruitment post data and the resume data through the human-post matching model constructed by the recruitment vector model, the information extraction model, the weight evaluation model and the matching degree scoring model to obtain the matching result, and the problem that post requirements and resume advantages are not aligned in the prior art is solved. Therefore, the recruitment quality of the enterprise is not high and the talents are lost can be solved, and the accuracy of man-post matching can be improved.

The invention discloses a big language model tool matching method and system based on a knowledge graph, belongs to the technical field of artificial intelligence, and solves the problem that an API (Application Program Interface) tool is difficult to efficiently manage and accurately select in a real-time information processing process of an existing big language model. According to the method, a multi-source data fusion technology is introduced to collect and sort information of external API tools of different data sources, and a dynamic relation weight adjustment mechanism is utilized to dynamically adjust the weight of the relation between different API nodes according to the actual use condition of the API tools and user feedback; representing the characteristics of each API node through a graph embedding technology based on an intelligent matching algorithm of deep learning, and calculating the matching degree of each candidate API and a request by using a sequence processing model; according to the method, the knowledge graph and the intelligent matching algorithm are combined, an external API tool used by a large language model can be dynamically managed and optimized, and the information processing accuracy and the system response speed are improved.

The invention relates to the technical field of industry optimization, and discloses an enterprise value-added service intelligent matching method and system based on multi-modal learning, and the method comprises the following steps: dynamically evaluating the strong and weak chain relationship between enterprises, constructing a time-varying adjacency matrix, and updating the chain strength weight through a propagation correction mechanism; time sequence features of enterprise operation efficiency and innovation ability are fused, and a dual-channel fusion feature is generated through a cross-modal attention mechanism; and constructing a three-dimensional tensor based on the strong and weak chain scores, efficiency and innovation features, and generating final fusion features through CP decomposition and multi-head attention enhancement. According to the method, by extracting service features and modeling enterprise demands, combining similarity calculation, recommending the most suitable service and adopting a clustering algorithm to identify an enterprise demand mode, a recommendation strategy can be dynamically adjusted according to real-time data, the demand of manual intervention is reduced, the decision-making speed and efficiency are improved, and the method is suitable for popularization and application. And enterprises can quickly respond to market changes, and resource allocation is optimized.

The invention relates to the technical field of natural language processing and deep learning matching recommendation, and discloses a supply and demand matching method for digital science and technology personalized services, which comprises the following steps: carrying out semantic understanding and analysis on a submitted technical long text by adopting a natural language large model, effectively extracting a core text of technical contents, and carrying out semantic analysis on the core text; and based on the text classification model, accurately delimiting industry field labels, thereby solving matching obstacles caused by cross-industry term differences. Precise extraction of clear numerical parameters and performance indexes in technical texts is realized by using a natural language large model, a refined demand text set is established, and deep semantic vectorization representation is performed through a word embedding model; through cosine similarity calculation and a screening rule based on industry labels, the matching accuracy and reliability of cross-industry technical services are greatly improved; based on an association weight mechanism of performance indexes, semantic similarity and performance index association degree are comprehensively considered, and the accuracy of matching recommendation results is optimized.

The invention relates to the field of power question answering, in particular to a power knowledge question-answer matching method and system based on an improved retrieval enhancement generation technology, and the method comprises the steps: employing a training set to carry out the decomposition of a pre-constructed Embedding model, and carrying out the training of the Embedding model, and obtaining an Embedding fine tuning model; the method comprises the following steps: vectorizing a power field file by adopting an Embedding fine tuning model to obtain a power knowledge base, and constructing a regular expression rule base; the user consultation content is converted into a query vector, the query vector is matched with the regular expression rule base to obtain an accurate matching result, and meanwhile the query vector is matched with the power knowledge base to obtain a fuzzy matching result; semantic correlation sorting is carried out on the accurate matching result and the fuzzy matching result, and then resorting is carried out by adopting a resorter in combination with a service rule; and inputting the reordering result into a large language model to obtain a result corresponding to the user consultation content. The method can effectively solve the problem of inaccuracy during retrieval of a specific ID, and improves the retrieval precision of a retrieval enhancement generation system.

The invention discloses a similar law judgment document matching method based on prompt engineering and judgment prediction, belongs to the field of text matching, aims to solve the problem of inaccurate case matching in case retrieval of law case handling personnel, and is characterized by comprising the following steps: S10, acquiring a training data set; wherein the data set comprises case serial numbers, judgment texts and legal elements; s20, training a class case matching model by using the training data set, wherein the class case matching model comprises a plurality of Bert models sharing weights; wherein the class case matching model is trained by adopting a joint learning method; and S30, performing similarity prediction on the judgment text contents of a plurality of input judgment documents by using the trained class case matching model.

The invention discloses a Token level cache matching method and system of a large language model and a storage medium. The method comprises the following steps: constructing a local context fragment; generating a context embedding vector of the current Token, and calculating a context entropy value, a semantic consistency index and a semantic change gradient; for the target Token, determining a semantic category of the target Token; inputting the semantic category to which the target Token belongs into a Hash decision maker to obtain a Hash granularity level; if the Hash is the first-level Hash, executing fixed-length Hash and mapping the Hash to a semantic cache bucket based on a semantic theme or a context range of the first-level Hash; if the first-level hash is the second-level hash, dynamically adjusting the hash length according to the semantic similarity with the adjacent Token; if the Hash granularity level of the target Token is a third-level Hash, constructing a high-dimension context representation and executing a fine Hash operation; and matching the hash result with the KV in the pre-stored cache, and executing corresponding operation according to the matching result.

The invention discloses a CAD drawing intelligent matching method and system based on multi-stage deep learning, and belongs to the field of image comparison, and the method mainly comprises the steps: S1, loading and preprocessing a CAD drawing, and constructing a triple data set through a data enhancement and sample balance technology; s2, constructing a target detection model and a target matching model, and training the target detection model and the target matching model by using the triple data set; wherein the target detection model adopts a YOLOv5 model, and the CAD drawing is detected in combination with sliding window detection and a non-maximum suppression technology; the target matching model adopts a double-end network structure; and S3, matching the CAD drawings to be identified by using the trained target detection model and target matching model. According to the invention, through a plurality of advanced technologies, intelligent matching and judgment of two or more CAD images in any different formats are realized.

The invention discloses a component identifier matching method and system based on NLP semantic segmentation and a multi-level word library, and relates to the field of constructional engineering informatization, and the method comprises the steps: building a standard main word library and a preset compensation word library, carrying out the semantic segmentation of an input component identifier, and obtaining a segmented lexical element sequence, calculating semantic similarity and morphological similarity between the segmented lexical element sequence and entries in a standard main word bank, and performing standard term mapping based on the semantic similarity and the morphological similarity to obtain a standard term mapping result; carrying out structured data conversion on the segmented lexical element sequence which does not complete the standard term mapping by utilizing a regular expression mode to obtain structured data, and carrying out AI extension library matching on the segmented lexical element sequence which does not complete the structured data conversion to obtain a synonym matching result; and combining the standard term mapping result, the structured data and the synonym matching result to obtain component identification description. By implementing the method, the matching accuracy of the component identifier names in the engineering project can be improved.

The invention relates to the technical field of ship manufacturing, and discloses a ship block assembly man-hour matching method based on multi-modal process feature recognition. Multi-modal process data including structured process parameters, unstructured process documents and the like are collected, each modal process feature vector is obtained through feature extraction, and then a fusion feature vector is generated through a multi-modal feature fusion algorithm. And constructing a man-hour matching knowledge base according to historical assembly order data, and determining an assembly process sequence and a man-hour allocation scheme by using a dynamic matching algorithm. According to the method, a process set can be optimized through a graph traversal algorithm, real-time sensor data are analyzed to deal with abnormal fluctuation, and process compliance is verified by means of a process rule knowledge graph. According to the method, multi-modal data can be effectively fused, the man-hour matching precision is improved, the assembly process is optimized, the ship block assembly efficiency and quality are improved, and the cost is reduced.

A point cloud matching method and apparatus, an electronic apparatus, and a storage medium. The point cloud matching method comprises: dividing a point cloud to be matched into a plurality of point cloud blocks, inputting the plurality of point cloud blocks into a pre-trained point cloud feature encoding module, and obtaining a feature vector of the plurality of point cloud blocks; on the basis of the feature vector of the plurality of point cloud blocks, obtaining a global descriptor vector of said point cloud, matching the global descriptor vector of said point cloud with a global descriptor vector of a point cloud frame in a preset historical database, and determining a point cloud frame in a historical database within a preset matching threshold range to be a point cloud matching result.

The invention relates to the field of artificial intelligence, in particular to an answer matching method and system for an artificial intelligence large model, and the method comprises the steps: obtaining a question text of the nth question asked by a user, carrying out the feature extraction of the question text through a natural language processing technology, and constructing a question feature set; inputting the question feature set into a difficulty evaluation model, and outputting a difficulty score of the question text; based on the difficulty score of the question text, performing matching degree calculation on all the artificial intelligence large models in the large model library by adopting a large model matching degree formula to obtain the matching degree of each artificial intelligence large model; and analyzing the matching degree of each artificial intelligence large model according to a preset large model matching strategy to obtain a selection scheme. By adopting the method, the matching degree between the question and various large models can be accurately and quantitatively measured, so that the professionalism and accuracy of question answering are improved; and meanwhile, the continuous dialogue processing capability of the large model can be improved.

The invention discloses a fusion semantic vector space mapping-based translation ambiguity term accurate matching method, which comprises the following steps of: S1, obtaining source language ambiguity terms, context texts and a target language candidate translation list, and extracting domain tags and term matching features to form a multi-modal data set; s2, using improved XLM-R model coding to generate term-level, sentence-level and translation-level semantic vectors; s3, training a dynamic mapping matrix based on a bilingual parallel corpus, and aligning source side vectors to a shared semantic space; s4, fusing the source-side basic vector and the multi-dimensional features through a double-channel attention fusion network, and generating source-side and translation-side comprehensive semantic vectors; s5, introducing term-context attention weight to correct cosine similarity; and S6, outputting an optimal translation through normalized sorting and part-of-speech secondary judgment. According to the method, multi-field ambiguous term accurate matching is realized, the term translation precision and efficiency in professional fields are improved, and the requirements of high reliability of term translation in the fields of medicine, machinery, computers and the like are met.

Embodiments of this application provide a model matching method and a communication apparatus. The method includes: receiving an identifier of a first artificial intelligence AI model from a first communication apparatus; and determining, based on the identifier of the first AI model, whether a second AI model is deployed on a second communication apparatus, where the second AI model is an AI model that matches the first AI model. In this way, an identifier of an AI model can be exchanged to determine, based on the identifier of the AI model, an AI model that matches the AI model.