1360results about "Metadata based other databases retrieval" patented technology

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

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.

The invention discloses a robot interactive question-answering method and system based on a large service model, and belongs to the technical field of intelligent robots, and the method comprises the steps: carrying out the feature extraction and fusion of the original input of a user, obtaining a multi-modal vector, building a recognition model, outputting an emotion recognition tag, an intention recognition tag, a user state vector and a session history, fusing the user state vector with a domain knowledge graph, outputting a structure reasoning vector and a joint condition vector, and splicing a knowledge abstract vector with a condition fusion vector to obtain the user state vector as the input of a text generator; and outputting a natural language answer and a user state vector, mapping the natural language answer into a multi-modal behavior action, and finally outputting a voice answer signal and the multi-modal behavior action. According to the method, the answer strategy of the robot is dynamically adjusted according to the emotional state of the user by combining context awareness and knowledge reasoning technologies, so that intelligent interaction of emotional resonance and professional depth coexistence is realized.

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.

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.

The invention discloses a material weighing and conveying control method and system, and relates to the technical field of self-adaptive control systems. The method comprises the following steps: setting a visual positioning identifier in an unloading area, and configuring an identification code for a material; establishing an information management database, associating the identification codes of the materials with material attributes, and generating a material association attribute table; collecting an identification code of the material, and calling a material association attribute table based on the identification code; and based on the information management database, obtaining a production line state table of each production line and a warehouse state table of the current warehouse, generating a candidate transportation list, determining a comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as a final transportation target point. Compared with the prior art in which fixed rules are used for scheduling, the material transportation targets can be dynamically distributed according to real-time production requirements, manual intervention is avoided, the logistics efficiency is improved, high-requirement materials are preferentially distributed, and production interruption caused by material shortage is prevented.

The embodiment of the invention discloses a dynamic metadata sensing and self-adaptive mapping method and system, and the method comprises the following steps: deploying a lightweight probe to be agented to a plurality of heterogeneous source database systems, and building a trusted communication channel between the probe and a coordination center through equipment fingerprint generation and security authentication; a database log event is captured in real time, log analysis is carried out through an FPGA module, the change of a field structure or a semantic level is identified, and structured event data is generated; performing context embedding representation on the captured event based on a semantic understanding model, performing strategy reasoning in combination with a historical strategy and a knowledge graph, and generating a matched mapping strategy; loading and executing the mapping strategy in a trusted execution environment (TEE) to realize data conversion, cleaning and complementation, and performing desensitization processing on sensitive data through a differential privacy mechanism; and collecting performance indexes and abnormal logs of a strategy execution result, continuously training the model through a closed-loop optimization mechanism, and correcting an unreasonable strategy or updating a rule base.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

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.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

Systems and methods are provided to improve data retrieval from a cache memory by using semantic matching to retrieve data from the cache memory. The system includes a two-tiered cache system, with a first tier implementing “key-value” pairs, and a second tier that includes a table that is configured as an artificial intelligence (AI) search indexed source. When a new input does not have a matching “key” at the first tier, the system performs a semantic search at the second tier of the cache to determine if relevant data is stored in the cache. The current systems and methods increase the likelihood of obtaining data for queries from the cache memory, reduce the response time to the queries, improve search consistency, reduce computing resource utilization, improve system performance, and reduce costs.

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 relates to the technical field of intelligent retrieval, in particular to an engineering knowledge base construction and deep retrieval method, which comprises the following steps of: in a knowledge base construction stage, performing knowledge level identification on an engineering knowledge document to form a multi-level tree structure taking chapter titles and corresponding contents as nodes; performing analysis processing on the heterogeneous content of each node to generate knowledge fragments; and generating embedded vectors of knowledge fragments by adopting a way of combining path embedding and content embedding, and storing association relationships among knowledge, vector data and original information to complete knowledge base construction. In the deep retrieval stage, after a user question is received, a knowledge blank question list is generated through query and rewriting, a knowledge base is retrieved through traversal of the list, the retrieval process is optimized through knowledge grouping, correlation sorting and loop termination judgment, and finally reply content subjected to traceable verification is generated and output based on existing knowledge accumulated in a circulation mode. According to the method, the reuse efficiency and the retrieval accuracy of the engineering knowledge can be improved.

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 an urban reserve land intelligent classification method and system based on spatial data fusion, and relates to the technical field of data processing, and the method comprises the steps: carrying out the formalized expression of a land use rule of a preset urban region, and constructing a land domain knowledge graph; collecting multi-source spatial data, performing feature extraction and alignment, and generating a high-dimensional feature vector of each plot; and performing hybrid classification evaluation based on the high-dimensional feature vector of each land parcel, synchronously outputting land use types, development potential levels and land reference value intervals of the land parcels, and calling a land domain knowledge graph to perform compliance verification and correction. The technical problems that an existing urban reserve land classification method depends on artificial experience and lacks accurate and multi-dimensional data fusion and compliance check, so that the accuracy and practicability of a classification result are low are solved, and the purpose that the classification accuracy and practicability of the urban reserve land are improved through multi-source spatial data fusion and an intelligent classification framework is achieved. And the accuracy and practicability of urban reserve land classification are improved.

The invention relates to a customer service scene-oriented collaboration system and method, and belongs to the technical field of artificial intelligence. In the system, a shared memory storage center stores knowledge entries from a plurality of customer service agents in a predefined data structure; the shared memory storage center is provided with an access control mechanism associated with a visible domain identifier, and an access range is limited during retrieval through a logic partition; the memory management module receives candidate knowledge entries from each customer service agent based on a trigger event, determines visible domain identifiers for the candidate knowledge entries, associates the candidate knowledge entries with the visible domain identifiers, and stores the associated candidate knowledge entries and the visible domain identifiers in the shared memory storage center; the problem processing and decision-making module responds to an access request from any customer service agent and determines a corresponding visible domain identifier according to the identifier of the customer service agent; and executing access based on the visible domain identification and the input question, and retrieving knowledge entries with access from a shared memory storage center to realize cross-agent multiplexing.

An AI-powered music registry, collaboration, and workflow management platform that addresses the challenges faced by music industry participants in the digital age. The system comprises a segmentation and hashing subsystem for musical pieces, segments, and isolated elements, enabling the evaluation of uniqueness and the consideration of individual creators' contributions. An artificial intelligence (AI) and machine learning (ML) subsystem is employed for extracting and isolating individual instruments, vocals, and performer contributions, while a component-level tracking module enables enhanced crediting and royalty distribution.

The invention provides a multi-language cross-modal information retrieval method, device and equipment. The method comprises the following steps: acquiring first to-be-retrieved data of a first modal; preprocessing the first to-be-retrieved data to obtain first data; inputting the first data into a corresponding type of encoder in a multi-modal information retrieval model for encoding to obtain a first type of encoded data; the encoder comprises a Chinese encoding unit; inputting the first type of coding data into a projection layer of a multi-modal retrieval model for projection processing to obtain a first target coding vector; and according to the first target coding vector, performing at least one type of other modal information retrieval in a vector database to obtain at least one type of other modal retrieval result data with the same semantics as the first data. According to the method, the multi-modal information retrieval efficiency, the semantic interpretation capability and the multi-modal alignment capability can be improved.

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 discloses an image-text retrieval method based on feature collaboration and adaptive attention adjustment, which comprises the following steps of: firstly, generating a regional feature set of an image and a text feature set corresponding to a sentence, and generating an enhanced regional feature set by adopting a global-local feature collaboration enhancement module; performing interactive matching on word features in the text feature set and the enhanced regional feature set to obtain a comprehensive image feature vector concerned by each word, and performing similarity calculation to obtain a similarity score between an image and a sentence; and meanwhile, an adaptive cross-modal attention regulator module is adopted to update a comprehensive image feature vector concerned by each word, and triple loss based on the most difficult negative sample is applied to training of a target function. According to the method, through cooperation of local and global features of the image, region features are enhanced, channel weights and attention distribution of image regions and word pairs are optimized, and then the cross-modal semantic alignment capability between the image and the text 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 discloses a database query statement generation method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a user input statement and a knowledge graph; performing information extraction on the user input statement to obtain key information; screening the knowledge graph based on the key information to obtain a candidate entity set and a mode sub-graph; the prompt information, the user input statement, the mode subgraph and the candidate entity set are input into a large language model, and a plurality of candidate database query statements are obtained; and according to the mode subgraph and the execution result corresponding to each candidate database query statement, screening the plurality of candidate database query statements to obtain a target database query statement, through the technical scheme of the invention, the database query statement meeting semantic requirements can be automatically generated.

The invention discloses a meteorological equipment maintenance knowledge base construction method and device and a medium, and the method comprises the steps: obtaining multi-modal data related to meteorological equipment maintenance, and synchronizing structured data in the multi-modal data to a knowledge base management platform through a Restful API interface; the multi-modal data further comprises unstructured data; analyzing the unstructured data to extract image-text features, and asynchronously transmitting the crawled webpage content to a knowledge base management platform through a message communication protocol; constructing a vector index and knowledge graph association relationship on a knowledge base management platform, and labeling a multi-modal semantic tag of an equipment type and a fault phenomenon; a natural language query request of a user is received, knowledge base fragments are retrieved through keyword retrieval and vector similarity calculation, retrieval results are input into a large language model to generate equipment maintenance suggestions, and associated data sources are output.

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 embodiment of the invention discloses a data-space-oriented private domain data federal retrieval method and system, relates to the technical field of computers, and can realize effective and accurate query of dispersed private domain data in a data space on the premise of meeting the requirement of a data owner for locally controlling and managing the private domain data of the data owner. According to the method, the local retrieval capability of a federated private domain data source is called, so that effective retrieval of dispersed private domain data in a data space is realized on the premise that a centralized index does not need to be established; on the basis of the probability locality sensitive hash table, the target data source with the stored private domain data conforming to the query intention of the user can be selected in a fuzzy mode on the premise that the original data in the private domain data source does not need to be known, and the retrieval request is sent to the target data source, so that the leakage risk of the original data in the private domain data source can be reduced; and accurate query of dispersed private domain data in the data space is realized.

Methods and systems for improved natural language processing and generation of insights are described herein. Aspects of machine learning and natural language processing may be employed to interpret user queries and provide insights, recommendations, and visualizations. The system employs large language models and machine learning services to enhance the natural language interaction between users and their analytics.

The invention provides a cross-modal heterogeneous data retrieval method and system based on semantic information, and relates to the technical field of cross-modal learning.The cross-modal heterogeneous data retrieval method comprises the steps that cross-modal heterogeneous data and user role information are obtained, and the cross-modal heterogeneous data comprise document type data, code type data and structured data; based on a preset permission semantic matching rule, determining a fine granularity access permission range corresponding to the user role information, and screening out a target cross-modal heterogeneous data set conforming to the fine granularity access permission range from the cross-modal heterogeneous data; performing semantic feature extraction and fusion processing on the target cross-modal heterogeneous data set on the basis of index performance requirements in a high-concurrency scene to generate a unified semantic index; according to the cross-modal heterogeneous data retrieval method, cross-modal retrieval is executed on the basis of the unified semantic index and the user query requirement, the cross-modal heterogeneous data retrieval result is obtained, and safe, efficient and semantic-accurate cross-modal heterogeneous data retrieval is achieved.

A computer-implemented method for creating and utilizing a multi-aspect vector search index is disclosed. The method involves generating multi-dimensional vectors representing digital documents within a document repository, enabling advanced content retrieval through comprehensive vector-based indexing. The approach allows for sophisticated search capabilities by mapping documents across multiple aspects, facilitating precise and efficient content identification and extraction from extensive document collections.