764results about "Metadata based other databases retrieval" patented technology

The invention relates to the technical field of archive information management, and discloses an archive metadata intelligent extraction and indexing system, which comprises a multi-modal preprocessing module for obtaining and preprocessing original multi-modal archive data; the context entity recognition module is used for performing entity recognition and standardization according to the context vector; the cross-modal fusion module is used for carrying out confidence weighted multi-modal fusion and logic verification; the archive association module is used for carrying out association identification and consistency detection between archives; the intelligent indexing module is used for carrying out hierarchical intelligent indexing and quality feedback on the consistency constrained file metadata set; the quality evaluation module is used for carrying out metadata quality evaluation and active repair on the standardized indexing result; the knowledge graph module is used for constructing a time sequence knowledge graph and intelligent retrieval service; according to the method, collaborative extraction of cross-modal information is realized by constructing a confidence-weighted multi-modal fusion model and a bidirectional attention mechanism.

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of function calls at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of function calls at a second level of granularity, the second level of granularity being finer than the first level of granularity.

Disclosed are a data query method and system, and a related device, relating to the field of artificial intelligence. The method comprises the following steps: a data query system acquires input data; obtains, on the basis of the input data, metrics to be queried as requested by a user; uses, on the basis of said metrics, a multi-agent collaborative processing strategy to confirm a data query result, the multi-agent collaborative processing strategy being used for indicating a manner of implementing data retrieval on the basis of at least one of a graph model and a vector database; and presents the data query result, wherein the graph model expresses, in a graph-based form, correspondences between the metrics and between the metrics and database table data, and the vector database expresses, in a vector-based form, descriptive texts and computational formulas of the metrics. In this way, even if the expression of input data differs from the expression of fields in the database, or if metrics to be read are not present in the database, corresponding SQL statements can still be obtained by means of the graph model or the vector database, thereby reducing the error rate of natural language query, and meeting the query requirements of users.

The invention provides a multi-modal retrieval enhancement generation method and device oriented to a large model, and the method comprises the steps: coding multi-modal information, uniformly mapping all modal codes to a shared representation space, obtaining each modal vector in the shared representation space, and obtaining a multi-modal retrieval enhancement generation result based on query information and context information inputted by a user; identifying a task intention of the modal vectors sharing the representation space, outputting a modal weight vector, dynamically selecting a fusion mode based on the task intention, fusing the modal vectors based on a gating fusion network and the fusion mode, and dynamically adjusting the proportion of different modal vectors in fusion according to the modal weight vector. And in response to the semantic consistency conflict of each modal vector, selecting a target conflict mediation strategy based on a strategy selection rule, carrying out conflict mediation on each modal vector based on the target conflict mediation strategy, generating a retrieval answer based on each fused modal vector and context information, and sending the retrieval answer to a server. And executing a text generation task based on the retrieval answer by adopting an auxiliary model.

The invention discloses a user participation degree prediction method based on distillation multi-modal retrieval enhancement, and belongs to the technical field of social media data mining and user behavior analysis. According to the method, the correlation of the UGC is evaluated by introducing the self-enhanced distillation module, and the top-K related UGC is selected in combination with the selected retriever, so that the interference of irrelevant information on the related UGC is effectively avoided, and the noise caused by irrelevant artifacts can be filtered out when the related UGC is retrieved, thereby keeping the original feature representation of the related UGC. The heterogeneous graph construction module enhances the interactive representation capability between UGCs through multi-relation modeling, and can more accurately optimize a prediction result in user participation prediction. According to the method, information retrieval between the related UGC and the unrelated UGC can be better balanced, excessive diffusion of unrelated information is avoided, and the performance of the model in user participation degree prediction is improved. Particularly, when complex multi-modal data containing a large number of irrelevant UGCs is processed, the mechanism can effectively enhance the distinction degree of the relevant UGCs, and the prediction accuracy is improved.

The invention provides a multi-modal material retrieval method, electronic equipment, a storage medium and a program product. The multi-modal material retrieval method comprises the following steps: receiving query demand information described by at least one modal; the query demand information is rewritten, structured query information is obtained, and the structured query information comprises query conditions described according to all modalities of the query demand information and meta-information of a target material expected to be queried; converting the query condition into a preset representation space, and obtaining a conversion material which corresponds to the multi-modal material library and is converted into the preset representation space; based on the similarity between the converted query condition and the converted material, retrieving from a multi-modal material library to obtain a first batch of candidate materials; based on the meta-information of the target material and pre-extracted meta-information of a multi-modal material library, retrieving from the multi-modal material library to obtain a second batch of candidate materials; and determining a target material from the first batch of candidate materials and the second batch of candidate materials and returning the target material.

The invention provides a course dynamic optimization method and system, an electronic device and a storage medium, and relates to the technical field of online education, and the method achieves the quantitative association of a course unit-knowledge fragment through a soft mapping matrix, enables the perplexity to be bidirectionally transmitted between the course unit and the knowledge fragment, and achieves the quantitative traceability. The comprehensive confusion degree is obtained by fusing the confusion degrees of the course unit layer and the knowledge fragment layer, so that the high-confusion course unit can be positioned more accurately and explainably, and fine optimization of the course can be realized; through a course optimization decision with cost constraint, automatic generation of a course optimization scheme with controllable cost is realized; besides, personalized learning path optimization of a course structure layer is realized, and a course optimization strategy continuously acts on subsequent learners, so that dynamic evolution of course contents and personalized learning paths is realized.

The invention discloses an RAG knowledge base construction method and device and a medium, and the method comprises the steps: carrying out the analysis of the format, structure and semantic features of input data through a multi-level priority judgment logic, and automatically recognizing the specific data type. And calling a corresponding adaptive segmentation strategy for processing according to the identified data type, and generating data slices with semantic coherence. And asynchronously calling the large language model to generate a keyword set for each data slice, and uniformly storing the data slices, the keyword sets and associated multi-modal resource links by adopting a mixed storage mode to construct an enhanced RAG knowledge base. In the retrieval stage, query keywords are generated for user query according to the same logic, and related results are accurately retrieved from the knowledge base through a keyword matching mechanism. According to the method, the semantic segmentation problem is effectively avoided through intelligent segmentation with adaptive types, multi-modal data access is supported, and unified management and efficient retrieval of heterogeneous resources are realized.

The invention discloses a university education personalized learning path generation method and system based on artificial intelligence, and relates to the technical field of personalized learning path generation, and the method comprises the steps: collecting the multi-modal data of students, obtaining an input vector, and constructing a pre-constructed knowledge graph; performing knowledge state modeling through TransformerEncoder on the basis of the input vector, performing knowledge state updating through graph attention based on a pre-constructed knowledge graph to generate a knowledge state containing a knowledge point relationship, and predicting a mastering probability of each knowledge point; using CNN to extract behavior characteristics of students, generating a cognitive load state through Transform, and finally outputting a cognitive load value; on the basis of the knowledge state and the cognitive load value, constructing double-target learning path optimization, and searching and solving through a Monte Carlo tree to generate a next personalized learning task; the learning effect and the learning experience are remarkably improved.

The invention discloses an intelligent agent memory management method and system. According to the method, deep semantic organization of memory content is realized by defining a structured memory unit containing ternary metadata formed by a multi-label set, a multi-dimensional attribute set and a multi-level path; and receiving and processing a memory operation from any trigger event through a unified memory management framework. According to the method, the problems of low retrieval precision, poor data isolation between instances and tight coupling with service logic caused by flattening of a memory structure of an existing agent system are effectively solved, and the performance, the safety and the expandability of the agent system in enterprise-level application are remarkably improved.

The invention relates to the field of power transmission line operation and maintenance, in particular to a power transmission line tree obstacle intelligent supervision system and method based on the Internet of Things, and the system comprises an intelligent sensing module, a data cloud transmission module, a fusion prediction module, an analysis decision module and a linkage execution module. According to the coupling prediction, historical data are processed through an algorithm model, future tree growth and conductor wind deviation are predicted, and the scientificity and accuracy of prediction are ensured through cooperation of a state space model and a Kalman filtering algorithm, so that the prediction accuracy is improved through a sensor deployed at the front end and an intelligent algorithm at the cloud end. The device can continuously and automatically monitor for 7 * 24 hours, not only measures the current static distance, but also predicts the future growth trend of the tree based on historical data, and calculates the dynamic windage yaw of the lead by combining the real-time wind speed and wind direction, thereby being convenient to give out early warning before the tree actually approaches the safe distance, realizing the process from post-event disposal to pre-event prediction, and improving the working efficiency. And the preventive safety level of the power grid is improved.

The invention belongs to the technical field of integrated circuit simulation verification, particularly relates to an RISC-V simulation resource dynamic generation method and system, and solves the problems of instruction consistency and variable-length instruction truncation through a metadata double-table structure and a cross-boundary instruction splicing mechanism. Virtualized two-stage translation support and abnormal injection are realized through a recursive multiple hit detection and dynamic attribute bit modification mechanism. According to the method, complete storage is replaced with lightweight metadata, memory occupation is remarkably reduced, the problem that memory occupation is linearly increased along with time is solved, simulation efficiency and consistency are improved, and the method is suitable for full-system verification of a high-performance RISC-V processor.

The invention relates to the technical field of industrial robot data acquisition and transmission, in particular to a welding robot behavior recording system integrating edge acquisition and cloud management, which comprises the following modules: an edge end data acquisition module for acquiring the state of a welding robot and sensor data in real time, and performing time alignment and validity processing, generating edge end data; the data preprocessing module is used for optimizing the edge end data and unifying the data format to form preprocessed data; and the local storage management module is used for carrying out hierarchical storage and index management on the preprocessed data according to the data access frequency and importance. According to the invention, through a breakpoint continuous transmission mechanism of cooperation of the edge end and the cloud end, fragmentation and recording of the transmission progress in the data transmission process are realized, continuous transmission is only performed on the uncompleted fragmentation after the network is interrupted, meanwhile, sorting and integrity verification are performed on the received fragmentation at the cloud end, and reliable uploading of large-capacity data in a complex network environment is realized.

The invention relates to the technical field of ocean observation, in particular to an intelligent quality control system and method for ocean survey data, which takes whole-process data quality closed-loop management and control as a core, proposes an element-standard-process three-dimensional quality control framework, constructs a real-time and timing dual-mode architecture, and integrates a whole-chain visualization and non-intervention automation technology. By extracting more than 40 differentiated quality control elements of more than 10 types of data, a four-layer quantitative standard of equipment state-parameter setting-data integrity-operation compliance is established, and closed-loop control of the whole process is standardized. The error condition detection rate of the system reaches 89% or above, the manual quality control time cost is reduced by 90% or above, the quality control time efficiency is compressed to the real-time / timing hour level, paradigm innovation from post-event error correction to real-time intelligent management and control is achieved, core support is provided for high-quality collection and high-reliability application of ocean survey data, and the method is suitable for various ocean comprehensive scientific investigation scenes.

The objective of the present disclosure is to search for content in a content streaming system, and an operating method of a server may comprise the steps of: acquiring a search word; using a language model trained on the basis of synopsis information included in metadata of content items, so as to determine a first vector corresponding to the search word; determining similarity between the search word and a first content item on the basis of the first vector corresponding to the search word and a second vector of the first content item; and providing a content search list including information about at least one content item including the first content item selected on the basis of the similarity.

The invention discloses a question and answer interaction method, device and equipment based on artificial intelligence and a medium, and relates to the technical field of artificial intelligence in the professional service fields of finance, insurance, medical treatment, banking and the like, and the method comprises the steps: segmenting a historical dialogue into segments with coherent themes, and constructing the segments into memory units; generating a semantic embedding vector and a time decay weight for each unit to form a memory bank; retrieving related memory units from a memory bank based on user query, comprehensive semantic relevance, theme consistency and time decay weight; the query and related units are combined into a context input generative language model, and a topic consistency constraint is applied during the generation process to generate a topic consistent reply. By optimizing the memory granularity and enhancing the memory representation and timeliness, multi-dimensional accurate retrieval and constraint generation are realized, and the system improves the accuracy, coherence and service consistency of question and answer interaction.

The invention provides a power distribution network operation state sensing method and device based on multi-source heterogeneous data fusion, and belongs to the field of power distribution network monitoring. The method comprises the following steps: acquiring multi-source fusion measurement data of each power distribution network substation and constructing a database; sequentially performing first compression processing and second compression processing on the multi-source fusion measurement data in the database to obtain a compressed database; extracting data features of the multi-source fusion measurement data at each bus node by using the compressed database according to the bus node of each power distribution network substation; quantifying similarity measurement between any two bus nodes by using the data features, and converting the similarity measurement into difference measurement between each bus node and the whole power distribution network system; and monitoring the operation state of the power distribution network based on the difference degree measurement of each bus node. The method achieves the comprehensive and accurate perception of the operation state of the power distribution network, can find potential equipment faults and potential safety hazards in advance, and improves the operation safety and reliability of the power distribution network.

Systems and methods for an active-active mirrored artificial intelligence architecture are disclosed. In some implementations, the system identifies, for an inference request from a user, at least two geographically separate compute clusters configured to handle the inference request, each compute cluster storing a version of an artificial intelligence model for processing the inference request. The system determines, from the inference request, a data consistency requirement mapping the inference request to one or more data consistency types. The system determines, for each compute cluster, a composite metric based on, among other things, a consistency freshness score of the compute cluster. The system routes the inference request to a selected compute cluster having a lowest composite metric subject to a constraint that the consistency freshness score for the compute cluster satisfies the data consistency requirement for the inference request. The system returns an inference response for the inference request to the user.

An example method performed via a computing device for providing support for a mass spectrometry (MS) system includes obtaining from a mass spectrometry library (i) a ranked hit list of reference spectra corresponding to a set of fragmented spectra of an analyte acquired with the MS system; and (ii) a first set of metadata corresponding to the list of sorted hit items for the reference spectrum. The method also includes obtaining, from the MS system, a second set of metadata corresponding to the set of fragmented spectra. The method further includes evaluating an order of entries in the ranked hit item list with a machine learning model based on the first set of metadata and the second set of metadata.

The invention relates to the technical field of data processing, and discloses a multi-source data difference attribution system and method based on tensor decomposition, and the method comprises the steps: obtaining an original engineering quantity list file, and then carrying out decoupling to generate a standardized list item text sequence and an initial value set, constructing unit prices to form a tetrahedral geometric model set, mapping the tetrahedral geometric model set to a holographic feature vector space, and generating a multi-dimensional list feature vector set; constructing a standard cost reference vector field, identifying a difference vector set and generating a visual cost difference topological data model; a natural language query instruction is responded, a target difference attribution tensor is locked, a logic verification chain is constructed, and an intelligent diagnosis report is output; according to the method, a computable logic distance is established in multi-source heterogeneous data by reversely reconstructing a high-dimensional price-rate feature space, so that the problem of attribution blocking caused by dimension collapse in traditional cost auditing is effectively solved, and accurate traceability and compliance automatic diagnosis of cost differences are realized.

Technology is disclosed for dynamically creating and / or validating new URLs based on a user's query and / or contextual data. This enables the discovery of relevant content that may not be pre-existing in the search engine's index. This process enhances the search engine's ability to deliver the most current and relevant results by generating potential content sources in real-time, validating them, and incorporating the discovered content into the index for immediate query execution. Various embodiments are additionally or alternatively directed to a process that involves iteratively discovering, crawling, and indexing new content by following links from seed URLs, which may be either pre-existing or generated. This process deepens the search engine's index by continuously exploring outlinks, fetching new content, and updating the index in real-time.

The invention discloses an intelligent archive digital processing method and system, and the method comprises the steps: carrying out the synchronous scanning of a paper archive through a multi-spectral linear array scanner, and collecting the archive image data of a visible light wave band and a near-infrared wave band; performing non-uniform illumination correction and perspective distortion correction on the archive image data to generate a geometric and color standardized dual-channel fusion archive image; based on the dual-channel fusion file image, outputting structured layout description information including a text area, a table area, a signature area and an illustration area; generating a preliminary archive information atlas according to the structured layout description information; and based on the archive information atlas and a preset archive metadata specification, finally outputting a fully structured digital archive object conforming to an archive standard. According to the embodiment of the invention, the fully-structured digital archive object can be automatically generated in a high-fidelity manner, and the accuracy, integrity and knowledge degree of digital processing are improved.

The invention discloses a literature comprehensive retrieval system and a retrieval enhancement generation method thereof, and relates to the technical field of literature synthesis, and the method comprises the following steps: a demand anchoring layer carries out the retrieval of an initial retrieval request or a recursive feedback follow-up problem and a learning key point according to breadth and depth parameters; generating a corresponding number of query statements meeting a preset retrieval rule according to the query quantity; the resource acquisition layer retrieves and collects literatures and metadata in multiple channels according to query statements; the organization processing layer extracts a set number of documents, performs integration and structured extraction on contents of the documents, generates follow-up questions and learning key points, feeds back the follow-up questions and the learning key points to the demand anchoring layer, and synchronously retains the learning key points and the documents; and the result output layer converts the retained content into a target format. In this way, through combination of hierarchical cooperation and a recursion mechanism, traditional single retrieval limitation is broken, deviation caused by fuzzy requirements is avoided, retrieval accuracy, processing efficiency, information integrity and achievement usability are taken into consideration, and literature retrieval efficiency is improved.

Method for resolving a conflict in a set of information (200), comprising the steps identifying and storing existing information sources (210) with existing source confidence metrics (232); existing topics (220); and existing pieces of content (240) with topic confidence metrics (251);receiving a first piece of content (241) having a first information source (211); prompting large language model, LLM (150) to provide identified topics (222) addressed in the first piece of content (241); andfor each of the one or several identified topics (222) performing the following steps: identify an existing pieces of content (240) that is related to the identified topic (222); prompt a second LLM (160) to provide information regarding if the first piece of content (241) supports a particular existing piece of content (242); anddetermine the first topic confidence metric (252) based on a source confidence metric (253) between the first information source (211) and the identified topic (222) as well as an existing topic confidence metric (254) for the identified topic (222) and the particular piece of content (242).

The invention provides a zero-trust gateway encrypted traffic non-inductive filtering method and device based on an eBPF multi-level mapping architecture and a computer program product. The method comprises the steps that a data packet is intercepted in a network driving layer through an XDP mode of an eBPF, and quintuple and encryption protocol visible meta information are extracted; performing hierarchical strategy matching on the meta-information by adopting a multi-stage mapping architecture, and dynamically optimizing a search path through a performance model; and executing real-time verification and access decisions based on a zero-trust policy engine to realize non-perceptual filtering of encrypted traffic. According to the method, the problems of weak detection capability and high matching delay of a traditional scheme under encrypted traffic are solved, the method has the advantages of high speed, low delay and high precision, and meanwhile, data privacy and compliance are guaranteed.

The invention relates to a metadata-driven data consanguinity analysis and influence evaluation method, system and device and a medium. The method comprises the following steps: performing parameter analysis on an original query language script, and constructing a parameter function model in combination with a parameter dimension table; performing parameter replacement on an original query language script according to the model, and analyzing the replaced script to obtain a table-level blood relationship, a field-level blood relationship and the like; constructing a record-level query template based on the field-level blood relationship and the conversion logic, and constructing a dynamic blood relationship map; and executing a record level query template, carrying out record node mounting on the atlas, obtaining and responding to a data change event, carrying out hierarchical influence propagation calculation, obtaining and responding to a data exception event, carrying out root cause node positioning based on the mounted dynamic blood relationship atlas, and generating a repair suggestion. According to the method, through dynamic parameter analysis and record-level blood relationship analysis, the accuracy and automation level of data blood relationship analysis are improved, and the efficiency of data governance is improved.

The invention provides a multi-source solid waste comprehensive road system and method for road reconstruction and expansion, and relates to the technical field of multi-source solid waste distribution. The system comprises a solid waste classification and coding module, a standard solid waste three-level classification system is established, and a unique digital identity code is generated for each kind of solid waste; the multi-source solid waste database construction module is used for storing and managing digital identity codes, attribute data, image information and geographic position information of the solid wastes and constructing a multi-source solid waste database; the solid waste image recognition and search module determines a solid waste image code and inputs the solid waste image code into a multi-source solid waste database for search matching to obtain a target solid waste attribute file corresponding to the solid waste image code; the intelligent matching module for the solid waste road receives project requirements of road reconstruction and extension, performs multi-target optimization matching on the project requirements and the target solid waste attribute file through a built-in intelligent algorithm, and outputs a recommended solid waste recycling scheme; and optimal utilization of solid wastes in road use is selected, so that high value of resources is realized.

The invention discloses an interactive response method, device and equipment for multi-modal sensing data and a storage medium, and relates to the technical field of intelligent interaction.The interactive response method for the multi-modal sensing data comprises the steps that the multi-modal sensing data is obtained, and a user mental evaluation result is constructed based on the multi-modal sensing data; identifying user identity information according to the multi-mode sensing data, and determining short-term memory information and long-term memory information according to the user identity information; when a demand instruction of a user is received, performing retrieval in the short-term memory information and the long-term memory information according to the demand instruction to obtain a retrieval result; and generating interaction response content according to the retrieval result and the user mental evaluation result, and performing interaction. Through multi-modal perception and dynamic memory management, long-term continuous personalized interaction is realized, and the user interaction experience is improved.

A Hypothesis Generation and Testing System (HGTS) includes a framework for enabling artificial intelligence (AI) systems to autonomously formulate, test, refine, and store hypotheses using structured experimental workflows. HGTS may integrate with, but operates independently from, traditional databases, model repositories, context-aware AI databases and the like to provide a persistent, traceable, and interpretable record of hypothesis lifecycles. The system incorporates a probationary hypothesis database for unverified ideas, a validation engine for controlled experimentation, a confidence scoring and lifecycle management agentic subsystem for hypothesis evaluation, and recursive learning agents that iteratively refine models and experimental methods. Modular agents autonomously propose, test, and document hypotheses using statistical, symbolic, and deep learning techniques. The system ranks outcomes and retains full contextual metadata, enabling reproducible discovery. HGTS thereby provides a foundational mechanism for reproducible, interpretable, and self-directed intelligence, forming a cornerstone technology for artificial general intelligence (AGI).

The invention belongs to the field of natural language processing, and relates to an intelligent memory dynamic evolution method and system based on metadata and two channels, and the method comprises the steps: extracting an entity in an instruction input by a user, and obtaining an instruction entity; querying a metadata index database based on the instruction entity to obtain an entity state; when the entity state is a known state or a non-input state, updating the relational vector database and the graph database through a two-channel mechanism; a user input instruction is matched to the new relational vector database and the new graph database for mixed retrieval, and a preference relation and a hard constraint relation are obtained; constructing a context vector based on the user core portrait, the preference relationship and the hard constraint relationship; processing the user input instruction and the context vector to obtain generated content, and outputting feedback; and the interaction response capability and the engineering landing effect of the large language model intelligent agent in a long-period and complex interaction scene are greatly improved.