85 results about "Fuzzy query" patented technology

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

The invention discloses a multi-modal design work intelligent classification and retrieval system based on deep learning, and relates to the technical field of multi-modal deep learning retrieval. Image, text and audio features are extracted through MS-CNN and Transform; realizing cross-modal semantic fusion by using a knowledge graph and GNN; the hierarchical cascade classifier is combined with incremental learning to realize rapid and accurate classification; the semantic enhancement retrieval supports fuzzy query and cross-modal retrieval; in addition, the system also has the functions of design collaboration, quality evaluation and safety operation and maintenance, and the design resource management efficiency is improved. According to the method, multi-modal data are fused, design features are accurately extracted, cross-modal deep correlation analysis is achieved, through intelligent classification, semantic retrieval and personalized recommendation, the design resource retrieval efficiency and accuracy are improved, meanwhile, data safety is guaranteed, design cooperation is supported, a designer is assisted to rapidly obtain inspiration and grasp the design trend, and the design quality is improved. And the intelligent development of the design industry is promoted.

The invention relates to the crossing field of an artificial intelligence technology and an automotive electronic technology, and discloses a voice interaction question answering method and system based on a vehicle-mounted large model knowledge base, and the method comprises the following steps: receiving a user query text, and executing intention recognition and structured analysis; based on an analysis result, constructing and executing a mixed retrieval request including structured filtering, full-text keyword retrieval and semantic vector retrieval; fusing and reordering the plurality of retrieval result sets obtained respectively to obtain a high-quality final retrieval result set; and finally, constructing a context based on the final retrieval result set and a user query text, and calling a large language model to generate and output a final answer. By introducing a hybrid retrieval and fusion reordering mechanism, comprehensive context information is provided for a large language model, the accuracy and reliability of questions and answers are improved, and the ability of a system to understand and process complex fuzzy queries is enhanced.

The invention relates to the technical field of natural language processing, in particular to a verifiable large model retrieval enhancement generation system and method based on an evidence chain, and the method comprises the steps: receiving an initial query, recognizing the fuzziness and information gap of the initial query in combination with an associated retrieval document, and generating a supplementary query set; based on the initial query, the supplementary query and the corresponding retrieval document, generating candidate answers with references and verifying the information supportability of the candidate answers; for the candidate answers passing the verification, extracting support information and constructing a hierarchical attribution mapping relation; and integrating the information to form to-be-evaluated information, and if the current verified to-be-evaluated information meets a preset sufficiency condition, integrating the generated preliminary answer and the to-be-evaluated information to synthesize a target answer. The method effectively overcomes the defects that a traditional RAG system is fragmented in information integration, has one-sided fuzzy query and answer, is low in attribution efficiency and excessively depends on retrieval content, and has the advantages of answer comprehensiveness, verifiability and deployment lightweighting.

The invention relates to the technical field of government affair services, in particular to an NLP entity recognition method based on semantic extension, which comprises the following steps: collecting multi-source time sequence data and generating an entrepreneurship ecological data graph through a dynamic time warping algorithm; dynamic evolution of a term library is realized by using a generative adversarial network, and a term semantic association graph is constructed in combination with a graph convolutional network; a double-path attention mechanism is deployed in a Transform encoder, and global semantics and local attention guided by terms are fused through a gating loop unit; modeling fuzzy query analysis into a Markov decision process, and optimizing a semantic extension strategy by adopting a reinforcement learning agent; multi-granularity features are extracted through a feature pyramid network, and model parameters are optimized in combination with a bidirectional long-short-term memory network and an online learning feedback loop; and finally, calculating entity service association strength by using a graph attention network, and generating an interpretable report based on a Shapley value attribution algorithm. A closed-loop learning system is formed, and the business starting policy matching accuracy is improved.

The invention provides a satellite image fuzzy query method and system based on multi-dimensional vectorization indexing, and relates to the technical field of satellite image retrieval, the method comprises the following steps: mapping a structured query condition generated based on natural language query into a three-dimensional hierarchical index condition structured database to retrieve and output three-dimensional index metadata; and after the three-dimensional index result set is loaded according to the three-dimensional index metadata, space-time interaction visual output for generating the three-dimensional index result set is carried out by taking dynamic weight distribution as a sorting basis. The technical problems that in the prior art, satellite image high-response query cannot be carried out based on user fuzzy expression, so that the satellite image data query and calling efficiency is low, and the query result practicability is insufficient are solved. The technical effects that full-link closed-loop retrieval driven by natural languages is achieved, query flexibility is improved through intelligent mapping, efficient positioning is guaranteed through multi-dimensional indexing, the accuracy of dynamic sorting optimization results is guaranteed, and interaction friendliness is visually enhanced are achieved.

The invention provides an entity enhancement and context-aware paragraph retrieval method for an RAG system for solving the problems of fuzzy query intention and insufficient paragraph context modeling in RAG retrieval. The method comprises the following steps: firstly, identifying and extracting a key entity by using a named entity, carrying out weighted fusion on the key entity and question representation obtained by a pre-training model to form an enhanced query vector, and accurately describing a core semantic intention of the question; secondly, performing semantic modeling on paragraphs in a document library, mining a potential semantic association relationship between the paragraphs, and constructing a context interaction model between the paragraphs based on a graph neural network and a gating loop unit mechanism, so as to obtain paragraph vector representation with complete semantics and clear hierarchy; and finally, calculating the similarity between the enhanced query and the paragraph vector, and completing high-precision paragraph-level retrieval. According to the method, the correlation and the recall rate are remarkably improved, insufficient entity utilization and weak context modeling are relieved, and good expansibility and cross-domain applicability are achieved.

This invention belongs to the field of natural language processing and information extraction technology, and provides a method and system for extracting parameters from boiler design documents based on multi-round self-feedback RAG. The method includes: preprocessing the boiler design document and the original query, converting them into several text segments and expected inputs respectively; encoding the text segments and expected inputs into high-dimensional vectors and query vectors respectively, storing the high-dimensional vectors and constructing a vector index; generating a response based on retrieved external knowledge to assist a large language model, and iteratively optimizing the generated results through multiple rounds. This invention effectively solves the problems of incorrect and missing parameter extraction in complex or fuzzy query scenarios, significantly improving the reliability of the final results; it overcomes the limitations of traditional methods and standard large language models in handling multimodal data, enhancing the processing capability for complex multimodal documents; and it improves the accuracy of information retrieval, laying a solid foundation for subsequent accurate parameter extraction.

The application relates to the technical field of natural language processing, in particular to an evidence chain-based verifiable large model retrieval enhancement generation system and method, which comprises the following steps: receiving an initial query, identifying the ambiguity and information gap of the initial query in combination with associated retrieval documents, and generating a supplementary query set; generating a cited candidate answer based on the initial query, the supplementary query and the corresponding retrieval document, and verifying the information supportability of the cited candidate answer; extracting supportable information from the verified candidate answer and constructing a hierarchical attribution mapping relationship; integrating the above information to form to-be-evaluated information; if the to-be-evaluated information currently verified satisfies a preset sufficiency condition, integrating a generated preliminary answer and the to-be-evaluated information to synthesize a target answer. The application effectively solves the defects of the traditional RAG system, such as information integration fragmentation, one-sided query answering, inefficient attribution and excessive dependence on retrieval content, and has the advantages of comprehensive answer, verifiability and lightweight deployment.

The invention provides an anti-fraud early warning system based on multi-source cross-platform fusion, belongs to the field of network security and privacy computing, and aims to solve the problems of privacy set intersection matching failure and early warning missing report caused by data noise. According to the scheme, data are cleaned through a multi-modal normalization engine, and the cleaning distortion degree is calculated; generating a fuzzy feature vector containing a plurality of variants through a fuzzy feature mapper to avoid the Hash avalanche effect; performing privacy set intersection in a ciphertext domain through an encryption calculation unit; and finally, a risk decision-making module combines the variant weight and the time decay factor to carry out weighted scoring and generate an early warning. According to the method, accurate matching is upgraded into fuzzy query, so that the early warning robustness and accuracy are remarkably improved, and risk quantification and privacy security are realized at the same time.

This invention discloses a method and system for intelligent chart querying based on a large model of multimodal data. The method includes acquiring fuzzy query information input by the user and real-time monitoring data of a machine tool; performing intent recognition and entity extraction on the fuzzy query information, parsing it into structured query conditions; establishing a multimodal data association library based on a large model, inputting the real-time monitoring data of the machine tool into the multimodal data association library, and storing it according to preset association requirements; inputting the structured query conditions into the established multimodal data association library; and generating interactive charts based on the output information of the multimodal data association library, embedding key conclusion summaries. This application utilizes a large model to understand user intent and dynamically recommend related charts, breaking through the traditional fixed menu navigation mode and realizing intelligent interaction through joint analysis of data and charts.

A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: generating a first ambiguity score for a first query; propagating the first ambiguity score for the first query to generate a second ambiguity score for a second query; training a machine-learning classifier at least based on the first query and the second query; and generating, using the machine-learning classifier, a third ambiguity score for a third query. Other embodiments are described.

The present application provides a data verification method, device and computer readable storage medium, comprising: obtaining a received key encrypted to-be-verified user identifier, and constructing a fuzzy query condition based on the encrypted to-be-verified user identifier to query the serial number corresponding to the user identifier from a receiver; receiving the query result fed back by the receiver, wherein the query result comprises a plurality of receiver user identifier ciphertext values and corresponding serial numbers matched with the fuzzy query condition; matching the encrypted to-be-verified user identifier with the receiver user identifier ciphertext values in the query result, and if the matching is successful, obtaining the user attribute encrypted by a homomorphic encryption algorithm corresponding to the to-be-verified user identifier based on the corresponding serial number; calculating the difference between the homomorphic encryption value of the user attribute of the requester and the homomorphic encryption value of the user attribute obtained from the receiver through ciphertext calculation, and sending the difference to the receiver; receiving the verification result fed back by the receiver after decrypting and analyzing the difference.

This application provides a method, apparatus, electronic device, and storage medium for generating network topology maps. The method includes: acquiring XDR data, core network configuration data, and B-domain data corresponding to a target enterprise; performing data filtering processing on the XDR data, core network configuration data, and B-domain data to generate a network data field table; constructing a network device service path table corresponding to the network devices within the target enterprise based on the network data field table; and drawing a network topology map corresponding to the target enterprise based on the core network node configuration conversion table and the network device service path table. This application can realize real-time updated full-path visualization applications, fuzzy queries by slice, enterprise name, or DNN, presentation of key indicators and alarms, and achieve functions such as visibility, manageability, and evaluation of private network services, adapting to the needs of refined management of private network resources.

An exemplary system and method are disclosed that provide the private querying of a biometric scan, such as a person's photo, against a private database such that the client issuing the private query learns only the identity of the query if it is in the database while the server performing the processing learns nothing of the biometric scan. The exemplary system and method conduct privacy-preserving searches over fuzzy databases via a fuzzy labeled set intersection (FLPSI) protocol. FLPSI protocol computes an intersection of noisy input sets to assess for closeness / similarity metrics in an efficient and optimized search.

The invention relates to a data processing technology, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a data visualization configuration method, device and equipment and a storage medium, the method comprises the following steps: carrying out word segmentation on a preset target text to obtain a word segmentation text; performing keyword extraction on the word segmentation text according to a preset part-of-speech rule to obtain a target keyword; performing fuzzy query in a preset database according to the target keyword to obtain a query field; performing data query in the database according to the query field to obtain target data; and displaying the target data returned by the database on a visual interface. According to the method, the data visualization efficiency and the data query accuracy can be effectively improved.

The invention discloses a building physical fuzzy query method based on a large language model and a medium. The method comprises the following steps: acquiring a current to-be-queried user problem in real time in a building physical fuzzy query system, and analyzing the current to-be-queried user problem to obtain an analysis result of the current to-be-queried user problem; through a multi-type hierarchical fuzzy matching method, comparing the problem analysis result of the current to-be-queried user to generate current matching query key information; inputting the current matching query key information into a pre-constructed building data dictionary knowledge graph and a building physical vector database, and generating a current information fusion candidate answer; and verifying the current information fusion candidate answer through a large language model answer matching degree verification method, and if the verification is passed, obtaining a target fuzzy query answer. The problems of low query efficiency and incompleteness caused by lack of a fuzzy query mechanism for building physical system characteristics are solved, and the building physical fuzzy query efficiency and accuracy are improved.

The invention discloses a BIM shield monitoring system based on fuzzy data query and a nearest neighbor algorithm. According to the technical scheme, the BIM shield monitoring system is characterized by comprising a data acquisition module, a data preprocessing module, a fuzzy data query module, a nearest neighbor algorithm module, a path prediction module and a BIM platform integration module; the data acquisition module is used for acquiring tunneling data, including tunneling speed, position and direction, of the shield tunneling machine in real time; the data preprocessing module is used for preprocessing the collected tunneling data, including de-noising processing and abnormal value processing, so as to improve the accuracy and reliability of the data; according to the system, through combination of modular design and an advanced algorithm, comprehensive monitoring of the tunneling process of the shield tunneling machine is realized. All the modules are in close cooperation, accurate and efficient monitoring and decision support is provided for shield construction from data acquisition and preprocessing to fuzzy query, prediction and visual display, and the safety, efficiency and intelligent level of shield engineering are improved.

The present invention discloses a data retrieval method and system based on data columns, which includes: extracting and distinguishing each field data of each row in the accessed source system database table, and placing the data in a newly added data column of Elasticsearch; filtering out index data from multiple fields of the data column; and utilizing the inverted index algorithm of Elasticsearch to fuzzily match the query content input by the user to the data column according to the index data to obtain the corresponding field data. By implementing the present invention, search performance is improved, and the exploration and innovation of the word segmentation application of fuzzy query is realized, supporting the demand conditions when searching with one click, meeting the business needs of users when performing queries, conforming to the search habits of users, achieving accurate hits in billions of data, no missed searches, and short time consumption, while achieving a second-level response to billions of data.

The invention belongs to the technical field of artificial intelligence, and discloses a knowledge graph question answering method combined with a semantic large model and a related device, and the method comprises the steps: obtaining a user query question, inputting the user query question into a knowledge graph role generation type large model, and obtaining an initial graph query statement; performing fuzzy query on the initial graph query statement through a preset fuzzy retrieval field to obtain a final graph query statement, and inputting the final graph query statement into a knowledge graph system for query to obtain a graph query result; and inputting a graph query result into the summarized role generation type large model to obtain a user query answer. The large generative model can understand complex and fuzzy problems and return answers easy to understand in a natural language form, so that the satisfaction degree and the use experience of the user are improved. Compared with a traditional fixed mode query, the method allows a more flexible and intelligent query processing mode, can better deal with problems in different fields and types, supports a wider application scene, and provides an efficient, accurate and easy-to-use knowledge graph question and answer platform.

The present application belongs to the technical field of data retrieval, and particularly relates to a method for improving data retrieval speed, comprising the following steps: S1, generating a multi-table fuzzy query request; S2, building a business physical view service; S3, dimension reduction of the multi-table fuzzy query request, and single-table query of the business physical view; wherein the business physical view is classified into a single physical view for the same type of business function. The present application establishes a business physical view service, creates and updates the business physical view to be queried in real time, and performs full-text indexing on the business physical view, so as to provide single-physical-view fast fuzzy query for business query, make the fuzzy query not limited by left and right fuzzy queries, and greatly improve the query efficiency from tens of seconds or even hundreds of seconds to tens of milliseconds, thereby greatly improving the user experience, especially for multi-table joint query and multi-field simultaneous query, which more embodies the query advantage.

The invention discloses an engineering material price intelligent retrieval method based on fine-grained field perception fusion. The method comprises the steps that an original query set is received, and a material library is provided; performing semantic alignment and structure perception enhancement processing on the original queries in the original query set to generate standardized enhanced queries; calculating the global similarity of normalized enhanced query and records in a material library through a dense semantic channel and a sparse lexical item channel; performing field-level analysis on each record in the standardized enhanced query and material library, and calculating field-level fine-grained similarity of the dense semantic channel and the sparse lexical item channel on a predefined key field set; carrying out normalization processing on the global similarity and the field-level fine-grained similarity, and carrying out weighted fusion to generate a final fusion similarity; and sorting the records in the material library according to the final fusion similarity, and outputting the record with the highest rank as target material price information. The method effectively relieves the influence of noise and fuzzy query on the matching precision.

The invention provides a vector database construction method and system, and relates to the technical field of databases. The method comprises the steps of performing normalization processing on an original vector so as to limit a sudden change value and zoom the sudden change value to a unified range; recursive dichotomy is carried out on the value range of each dimension, binary codes are allocated to each interval, and mapping of the intervals and the codes is dynamically recorded through a skip list; alternately combining codes of all dimensions of the normalized vector to generate a global binary string; and finally, taking the binary string and the original vector as a joint primary key, and constructing a B + tree index. According to the method, a high-dimensional proximity problem is converted into a prefix matching problem of a one-dimensional space, and excellent characteristics of a B + tree and a skip list are utilized, so that efficient insertion, deletion, modification, accurate query and fuzzy query of high-dimensional vector data are realized; the problems of complicated calculation, high storage overhead, difficulty in dynamic updating and low query speed of the traditional method on high-dimensional data are effectively solved.

The invention relates to the technical field of rail transit inspection, and discloses a rail transit inspection platform based on man-machine interaction, which comprises a data acquisition module, a fuzzy label module, a label association module, an intelligent calling module, a user feedback module and a fuzzy query module. Polling information is labeled through a fuzzy label system, a correlation model is constructed to realize deep reasoning between labels, and a data processing flow is optimized in combination with intelligent calling and user feedback. According to the invention, the organization efficiency and the intelligent level of the inspection data can be improved, the manual operation complexity is reduced, and the defect identification accuracy and the inspection work reliability are improved.

The invention discloses a large model dialogue information collection method and system fused with business query. The method comprises the steps that initial input of a user is received, a session variable is initialized, a predefined cue word template is loaded and combined with a session state filling placeholder to generate a calling cue word, and a large model is called to extract a candidate structured variable; when it is judged that service query needs to be executed, multi-level fuzzy query is executed according to accurate, fuzzy and auxiliary matching priorities, a dialogue strategy is adjusted according to a result, variables are confirmed, and a clear list or a question is generated; and when the session variables are updated, converting the user anaphora and calculating the confidence of the active list to determine a target item, and generating response iteration until all target variables are extracted. Based on the construction of the system, the problems of insufficient fusion of information extraction and business verification, fuzzy reference and multi-list confusion in the prior art are solved, the variable extraction accuracy and the dialogue efficiency are improved, and the system is adaptive to complex multi-round business dialogue scenes.

This invention relates to the field of data processing technology and discloses a data query method, apparatus, computer equipment, and storage medium. The method includes: receiving text content input to a platform front-end page to obtain target text; when the target text includes filtering parameters, passing the filtering parameters to a target mapping file through annotations; constructing a fuzzy query statement in the target mapping file and performing a fuzzy query to obtain a first list of standard document information; during a repeal operation, receiving the standard number of the repealed standard document, repeating the above operation to obtain the first list of standard document information, and then changing its status to a questionable state. The repeal operation is used to uniformly change the status of standard documents referencing the repealed state to a questionable state. This invention achieves rapid retrieval and query of terminology and normative reference documents, as well as the function of associating standard status with normative reference documents, improving retrieval efficiency and accuracy.

The invention discloses a vector node optimization method and system for enhancing RAG retrieval accuracy, and particularly relates to the technical field of information retrieval and natural language process.The method comprises the steps that scenarized understanding and reconstruction are conducted on an original query of a user, and a target query is generated; based on the target query, calculating scene adaptive dynamic weights fusing semantic relevancy, timeliness and authority for the knowledge base document nodes; multi-channel retrieval is triggered in a self-adaptive mode according to the scene and complexity, and preliminary sorting is optimized through dynamic weight; performing multi-dimensional verification and reordering on the primary result, and outputting a high-order result; and continuously optimizing the system through feedback learning. According to the method, the problems of fuzzy query, static weight and single strategy in traditional retrieval are solved, the spanning from semantic matching to scenarized accurate matching is realized, and the accuracy, adaptability and result reliability of retrieval are remarkably improved.

The embodiment of the invention provides a tobacco business information query rewriting method and system based on direct preference optimization, and belongs to the technical field of data processing. The method comprises the following steps: constructing a tobacco business fuzzy question preference data set; constructing a query rewriting task model according to the tobacco business fuzzy question preference data set; training the query rewriting task model by using an algorithm based on direct preference optimization to obtain a query rewriting optimization model; according to the query rewriting optimization model, tobacco business information query rewriting is completed. According to the method, the problem that a traditional question answering system is poor in adaptability to non-standard expression can be effectively solved, the understanding ability and response accuracy of the system to natural language input are remarkably improved, the processing effect of tobacco business information is improved, and the tobacco business information conforming to specifications can be accurately generated for user fuzzy query.

An exemplary system and method are disclosed that provide the private querying of a biometric scan, such as a person's photo, against a private database such that the client issuing the private query learns only the identity of the query if it is in the database while the server performing the processing learns nothing of the biometric scan. The exemplary system and method conduct privacy-preserving searches over fuzzy databases via a fuzzy labeled set intersection (FLPSI) protocol. FLPSI protocol computes an intersection of noisy input sets to assess for closeness / similarity metrics in an efficient and optimized search.