3930results about "Other databases querying" patented technology

The invention discloses a data import and field intelligent matching method for a low-code platform, and particularly relates to the technical field of low-code platform data processing. Constructing a field initial feature vector, extracting a standard field and historical matching features thereof from the target data model, and calculating a semantic matching score matrix by using a multi-source feature fusion model; screening the initial mapping table based on a dynamic threshold value, performing statistical deviation analysis in combination with a field value distribution difference measurement feature delta, and when delta exceeds a preset threshold value, calling a value mapping correction model to complete rematching to obtain an optimized mapping table; if it is detected that user history adjustment exists in the same source file type, re-correction is carried out based on a user behavior log, and a final field mapping table MP3 is output; finally, the MP3 is applied to data import, and automatic matching and binding are achieved. The method improves the accuracy and automation level of field matching, and is suitable for a multi-source heterogeneous data integration scene.

Methods, systems, and non-transitory computer readable storage media are disclosed for controlling access to artificial intelligence systems based on determined intent of queries. The disclosed system utilizes one or more digital content analysis models to determine an intent of one or more queries to an artificial intelligence system. The disclosed system utilizes the one or more digital content analysis models to determine an intended use of the artificial intelligence system. Additionally, the disclosed system determines whether the intent of the one or more queries aligns with the intended use of the artificial intelligence system by generating a similarity score and comparing the similarity score to a similarity threshold. Based on whether the intent aligns with the intended use, the disclosed system executes computing instructions to enable or block the one or more queries from being processed by the artificial intelligence system.

The invention discloses a spacecraft intelligent design method and system capable of autonomously learning, and belongs to the technical field of spacecraft design and simulation. The problems that in spacecraft design, natural language requirements are fuzzy and difficult to process, instruction generation is prone to ambiguity, linkage between simulation and design is not smooth, a design scheme lacks an effective iterative optimization mechanism, and traditional design is low in efficiency are solved. The method comprises the following steps: firstly, receiving a natural language demand, extracting key parameters to generate prompt words, inputting the prompt words into Design.AI, and generating a simulation instruction sequence; simulation is started through MCP Server conversion, if the result does not reach the standard, feedback correction is carried out, and iterative optimization is carried out. And repeating the steps until a design target is met, and generating a design report containing multiple items of content. Through natural language processing, real-time simulation, iterative optimization, knowledge accumulation and other technologies, the spacecraft design efficiency is greatly improved, the accuracy is enhanced, the threshold is lowered, and the flexibility is improved.

The invention discloses a method for reducing a large model illusion problem based on an RAG technology, and the method comprises the steps: extracting semantic entities, relation phrases and context features in a natural language query, and constructing a multi-source heterogeneous hypergraph; fusing the graph structure and sequence context information by using a graph attention network and a sequence perception network to form unified semantic representation; evaluating the illusion risk based on the confidence score, the evidence coverage rate and the semantic deviation index, and triggering reverse retrieval and fusion reinforcement; and the content is generated through causal consistency discrimination feedback control. According to the method, the illusion phenomenon of the generation result is remarkably reduced, and the method is widely applied to the field of intelligent question answering and information retrieval.

The embodiment of the invention provides a multi-modal knowledge graph construction method in cross-media retrieval. The method comprises the steps that extracted multi-modal features are mapped to a multi-modal feature space through a linear transformation layer; in the multi-modal feature space, the intra-modal attention weight of each modal feature is calculated according to a self-attention mechanism, the cross-modal attention weight of different modal features is calculated according to a cross attention mechanism, the two weights are fused to obtain a final fusion weight, each modal feature is weighted and input into a graph attention network, and the multi-modal feature is obtained. Obtaining a multi-modal fusion graph structure; performing semantic analysis on each modal feature, matching with a preset multi-modal semantic knowledge base, determining potential semantic association, performing semantic alignment on the multi-modal fusion graph structure according to a preset graph matching algorithm and the potential semantic association to obtain a multi-modal knowledge graph, performing cross-modal data retrieval according to the multi-modal knowledge graph, and performing cross-modal data retrieval according to the multi-modal knowledge graph. The multi-modal data cross-media retrieval method and device can improve the efficiency and accuracy of multi-modal data cross-media retrieval.

The invention provides a settlement monitoring method and system based on data traceability, and the method comprises the steps: firstly obtaining the time sequence settlement observation data of each monitoring point in a settlement monitoring region in a continuous observation period, including the settlement amount and the environment interference information of a corresponding observation moment, and then constructing a settlement data traceability association network, the method comprises the following steps: associating time sequence settlement observation data with geological base information of a monitoring point, an observation equipment traceability identifier and data processing node information, then tracing a generation link of settlement data of each monitoring point, extracting an equipment calibration record and the like, generating traceability link characteristics, then carrying out coupling analysis on the traceability link characteristics and the settlement amount, determining a traceability reliability index, and finally determining a traceability result. And finally, data are screened according to indexes, a dynamic settlement trend model and a settlement monitoring analysis report containing traceability link characteristics are generated in combination with the spatial distribution characteristics of the monitoring points, and the accuracy and reliability of settlement monitoring are improved.

The invention provides a data query method and system for converting a natural language into a database query language, and the method comprises the steps: analyzing the natural language input of a user through a multi-modal understanding agent on the basis of constructing a dynamic knowledge graph based on metadata, combining a historical session with a business term table, eliminating ambiguity, and generating a standardized Query, a retrieval routing agent selects a query strategy according to Query complexity, simple query directly matches a cache template, complex query traverses a knowledge graph, and related tables, fields and service constraints are returned; then, an expert committee agent generates an SQL (Structured Query Language) by adopting multi-stage collaboration, executes plan pre-evaluation, and selects a version with the highest comprehensive score; the test agent simulates and executes the SQL in the isolation environment, and verifies the grammar legality and the field permission; and finally, executing the detected SQL, and processing a result. According to the method, the accuracy of converting the natural language into the SQL (NL2SQL) in a complex database scene can be effectively improved.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The invention discloses an SQL statement structure verification system based on large model and knowledge graph fusion enhancement. The SQL statement structure verification system comprises a knowledge graph construction module, an input design module, a verifier design module and an output design module. According to the method, a knowledge graph is introduced as a structured semantic support, and a closed-loop enhancement processing flow of SQL statements from generation, verification to optimization is realized in combination with a structure perception cue word design, a rule verification mechanism and a Function Call feedback interface; the defects that an existing Text2SQL method is insufficient in structural understanding, lack of semantic reasoning, weak in generalization ability, dependent on computing power and the like are overcome. According to the method, triple guarantee of semantic perception, rule driving and model assistance is realized, the accuracy, robustness and interpretability of SQL generation are remarkably improved, and the cognitive load and operation risk of non-professional users in database interaction are effectively reduced.

The invention discloses a multi-modal data retrieval method and device, a storage medium and computer equipment. The method comprises the following steps: collecting multi-modal original data; on the basis of description information of metadata of original data, all metadata belonging to the same associated items and logic relations among all the metadata are obtained, a metadata chain is constructed, a distributed graph database is constructed on the basis of the metadata chain, the metadata chain is expressed in the distributed graph database in the form of a graph, the graph comprises nodes and edges, the nodes represent the metadata, and the edges represent the metadata. The edge represents a logical relationship between the metadata; and when a data retrieval instruction is received, traversing each node in the metadata chain along the logical relationship of the metadata chain in the distributed graph database, obtaining a target node matched with a data retrieval requirement corresponding to the data retrieval instruction, and returning original data corresponding to metadata represented by the target node. Multi-modal data dynamic association retrieval can be realized, and cross-modal information mining efficiency and accuracy are improved.

A method, an apparatus, and a computer-readable storage medium for generating a document graph. A plurality of electronic documents is received. Each electronic document has a predetermined document type. A machine learning model is selected from the plurality of machine learning models based on the predetermined document type. The selected machine learning model is instructed to extract a plurality of document portions from each electronic document in the plurality of electronic documents in accordance with the predetermined document type. A relationship between two or more document portions is defined based on the content of each document portion, and the document portions are associated based on the relationship. A graph structure having a plurality of nodes is generated. Each node includes at least one document portion. Each node is connected to another node in accordance with the relationship between document portions included in the nodes. The graph structure is stored.

The invention discloses an agent reasoning method based on a knowledge graph, and belongs to the technical field of knowledge graphs, and the method comprises the steps: integrating user query, the knowledge graph and multi-modal data through an NLP model and an entity linking technology, and extracting a target entity, a relation constraint and a structured feature vector. The LLM can synthesize more information to generate a more comprehensive conclusion, the knowledge embedding model maps an entity relationship into a geometric relationship in a vector space, the LLM is assisted to verify reasonability of reasoning, the system can comprehensively generate confidence through the LLM output probability, knowledge embedding similarity and data quality, and the reliability of a result can be explained through confidence score. The LLM generation capability is combined with the vector reasoning capability of knowledge embedding, and the limitation of a single model is made up.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

The invention discloses an open source software risk detection method and device based on an SBOM list, equipment and a medium, and relates to the technical field of software detection. The method comprises the following steps: acquiring an SBOM of target software, and analyzing component information of the SBOM; generating a feature character string according to the component information, and calculating a feature hash value through a hash algorithm; based on a known open source component, analyzing component information and vulnerability information of the component, calculating a feature hash value of the component, and storing the feature hash value to form a feature database; searching a matching item in a feature database based on the feature hash value of the target software component, and if the matching item is found, obtaining a license and vulnerability information of the target software component; if not, marking, manually judging and supplementing the data to the feature database; constructing a license compatibility matrix, and checking the compatibility of the obtained license and the overall license of the project; and comparing the acquired vulnerability information with a feature database, calculating vulnerability severity by combining a CVSS scoring model, and generating a priority repair report. According to the method, the software security detection speed and accuracy are improved.

The invention discloses a Multi-agent-based psychological counseling platform and method. The counseling platform comprises an evaluation agent used for dynamically extracting psychological features based on dialogue behaviors of a visitor agent and updating results to a traits library module; the planning agent is used for constructing a personalized psychological counseling scheme based on the traits library module and dynamically adjusting a counseling process and a strategy according to counseling feedback; the consultant agent is used for carrying out dialogue interaction with the visitor agent according to the personalized consultation scheme and implementing emotion pacification and psychological intervention; the visitor agent is used for expressing psychological troubles, feeding back psychological state changes and promoting the consultation process; the historical dialogue long and short-term memory module is used for storing multiple rounds of psychological counseling interaction contents and comprises historical dialogue short-term memory and historical dialogue long-term memory; and the dynamic probability memory retrieval module is used for generating probability distribution based on the current consultation context and dynamically calling memory nodes from the historical dialogue long and short term memory module or the traits library module.

The invention provides a large model intelligent reasoning method combining reinforcement learning and retrieval enhancement generation, and belongs to the technical field of artificial intelligence. Comprising the following steps: data input: data preprocessing; constructing a reinforcement learning environment; an RAG mechanism is integrated; training the model; and evaluating and iteratively optimizing. A reinforcement learning framework based on rules is introduced to guide a model to develop advanced reasoning skills such as reflection, verification and summarization. And in combination with a retrieval enhancement generation mechanism, the model can access and utilize a wide background knowledge base before answering questions. The synergistic effect between information retrieval and text generation is optimized. According to the method, the deficiency of knowledge of the model can be made up by introducing the external knowledge base, and the synergistic effect between information retrieval and text generation can be optimized in the reinforcement learning process, so that the capability of the model for processing complex reasoning tasks is remarkably improved, and formation of a more generalization reasoning strategy is promoted.

The invention relates to the technical field of industrial waste heat recovery optimization, in particular to an industrial circulating cooling water waste heat multi-source coupling system based on data driving, and the system comprises a waste heat collection unit, a data processing unit and an intelligent coupling regulation and control unit. After the data processing unit preprocesses data, high, medium and low temperature layers are divided according to temperature, a clustering algorithm is combined to subdivide a calorific value sub-layer, a dynamic characteristic file containing a parameter change curve, fluctuation amplitude and duration is established, the intelligent coupling regulation and control unit compares the file with pre-stored user heat demand characteristics, the matching degree is calculated preferentially according to the temperature contact ratio, and the matching degree is calculated preferentially. A single or multi-source cooperative coupling path is generated, target thermal parameters are synthesized through thermal capacity weighting in a multi-source mode, the supply and demand matching precision and the utilization efficiency of waste heat are improved, diversified energy consumption requirements of industrial scenes are met, and energy conservation and emission reduction are facilitated.

The invention discloses a code fingerprint-based open source component identification and vulnerability detection method, which comprises the following steps of: receiving a local or remote code, extracting a difference file, generating an AST and constructing a code attribute graph; sHA-256 Hash fingerprints and GNN semantic fingerprints are calculated for the function level sub-graphs to form mixed fingerprints, accurate matching is conducted through a Bloom filter, semantic matching is completed through nearest neighbor, and a component version is determined through a distribution difference algorithm. The method comprises the steps that firstly, a component identifier is mapped into a PURL or an SWID, an OSV / NVD library is inquired to obtain a CVE, comprehensive risks are calculated in combination with CVSS, EPSS and dependency depth, and an SBOM and a vulnerability report conforming to CycloneDX or SPDX are output. The method is high in speed and high in accuracy, and the open source risk can be automatically treated in continuous integration.

A system and method for structuring and analyzing multimodal, unstructured data, as well as an article of manufacture and processor-readable storage medium implementing the same, may be used to provide a content creator with a tool to understand what actionable steps they could take to make a video perform well or go viral. Embodiments include a user interface simultaneously including a region for accepting a user query and a region that displays insight cards. The insight cards are generated based on retrieving a plurality of streaming contents, sorting the plurality of streaming contents into a viral set using one or more virality metrics, generating at least one token from the viral set, and performing a search of the viral set as a function of the at least one token to produce one or more results. The results are presented through the one or more insight cards in the user interface.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The invention relates to the technical field of information retrieval, and provides a multi-dimensional heterogeneous knowledge recall enhanced hybrid retrieval method and system.The method comprises the steps that texts recalled through keyword retrieval, sparse vector retrieval and dense vector retrieval are screened through a reciprocal sorting fusion algorithm, and a text type candidate knowledge list is obtained; based on user query, generating and checking a query statement through a large language model, and retrieving an entity-relationship-attribute triple from the knowledge graph library; based on user query, generating enhanced knowledge through a knowledge graph enhanced retrieval method fusing keyword retrieval, vector retrieval and community retrieval; and carrying out format alignment and duplicate removal on the text type candidate knowledge list, the triple result and the enhanced knowledge to form a multi-modal candidate pool, and carrying out reordering score calculation and ordering on each piece of recall knowledge in the multi-modal candidate pool through a reordering model and a business rule to obtain a final retrieval result. And the coverage blind area of single retrieval on heterogeneous knowledge is solved.

The invention provides an occupational development recommendation method based on a school friend relation chain and dynamic employment data, and the method comprises the following steps: integrating the academic data of a school student database and the dynamic employment data of an external recruitment platform, and achieving the credible storage and updating of a school friend occupational trajectory through a block chain technology, analyzing the unstructured text by using a natural language processing technology and extracting a standardized field; a dynamic time window model is constructed, employment information is graded and weighted according to data timeliness, a path clustering algorithm is adopted to analyze occupational transition trajectories of the same professional school friends, and three types of typical development mode libraries of a technical deep ploughing type, a management transition type and a cross-industry transition type are generated; constructing a school friend relation graph from hierarchy, industry and geography dimensions, analyzing position promotion rate and regional aggregation characteristics, identifying key inward-pushing nodes through an improved PageRank algorithm, and labeling associated enterprise resources; and constructing a four-dimensional target model based on the post matching degree, the school friend association strength, the development potential and the salary growth space.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

The invention provides a data intelligent question and answer ChatBI method and device based on a large model. The method comprises the following steps of 1, constructing a database knowledge vector library; and step 2, question semantic transliteration and retrieval. According to problem transcription and data retrieval supplementation, a large language model is utilized to generate a corresponding visual chart according to the characteristics and problems of data, automatic visualization of any data is achieved, meanwhile, a more detailed and clearer expression mode is provided in a dialogue mode, and the method has the advantages of being high in practicability and easy to popularize. The flexible, intelligent and automatic process is realized, and the trend and relationship of the data are more intuitively displayed for customers. By combining a big language model and a data visualization technology, a proper visual chart is automatically generated according to natural language cue words and data, so that the data analysis and presentation efficiency is greatly improved, the learning cost of a user is reduced, and a personalized data display solution is provided for users in different fields.

The invention relates to a multi-agent search enhancement generation method and system. The method comprises the following steps: analyzing a task instruction through a first agent to generate a search feature set; matching a plurality of corresponding second intelligent agents and performing directional semantic conversion on the original semantic vector to generate a query statement set; screening a plurality of third intelligent agents, and executing query operation through the third intelligent agents to obtain a preliminary retrieval set; inputting the preliminary retrieval set and the retrieval feature set into a fourth agent for verification, and generating an optimized retrieval result; in conclusion, according to the multi-agent search enhancement generation method and system provided by the invention, intelligent analysis, cross-modal semantic conversion, dynamic database partition scheduling and search result optimization verification of task instructions are realized through a multi-agent cooperation mechanism; the method has the effects of improving the retrieval precision and efficiency, enhancing the adaptability to complex query intentions and optimizing the collaborative retrieval capability of the heterogeneous database.

The invention relates to the technical field of commodity trade management, and discloses a commodity trade business management system based on a block chain. A block chain data acquisition module of the system obtains commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizes the information to a block chain distributed account book; the trade risk quantitative analysis module generates a trade link real-time risk coefficient set based on account book real-time data in combination with a trade compliance rule base, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes when the risk coefficient exceeds a preset threshold value, calculates a collaborative execution cost factor when the enterprise qualification state is changed in combination with logistics node information, and obtains an interference influence value set of each scheme on the trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to the optimal risk control strategy scheme, and facilitates efficient management and control of commodity trade business.

The invention relates to the technical field of computers, and discloses a legal case strategy analysis method and system, electronic equipment and a storage medium, and the method comprises the steps: analyzing a to-be-analyzed legal case, extracting key information, and constructing a dynamic affair graph based on the key information; matching with a historical case database by adopting a multi-dimensional class case retrieval method on the basis of the dynamic event atlas, and retrieving similar historical cases; based on the judgment result information of the similar historical cases, performing analysis in combination with key information or a dynamic affair map of the to-be-analyzed legal case, and generating judgment result prediction of the to-be-analyzed legal case; and fusing the dynamic affair atlas, similar historical cases and judgment result prediction, calling a large language model adjusted by a law field instruction through a retrieval enhancement generation architecture, and generating a litigation policy analysis report with a preset structure part. According to the method, the automation degree and depth of case information processing are improved, and closed-loop generation from analysis to strategy is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.