2372results about "Metadata based other databases retrieval" patented technology

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention relates to the technical field of AIGC content generation, discloses an AIGC content generation method and system based on multi-modal fusion, and aims to solve the problems of decentralization, low efficiency and insufficient originality of a traditional content generation tool. Multi-modal data such as texts, images, videos and audios are integrated, user intentions are analyzed in combination with intelligent retrieval and a domain knowledge base, automatic generation from multi-modal input to high-quality creative content is achieved, a cross-modal collaborative generation technology is adopted, semantic features are dynamically aligned, and logically coherent content is generated. The content emotional value is enhanced through an emotional analysis and dynamic optimization strategy, the homogenization bottleneck is broken through, meanwhile, an automatic quality evaluation and format adaptation mechanism is integrated, deep application of scenes such as text travel, advertisement, e-commerce and interactive network television service is supported, marketing copywriting, short videos and cross-platform distribution schemes can be efficiently generated, and the market competitiveness is improved. And the content production efficiency and the creativity transmission are obviously improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating content-item-specific large language model responses from content items by segmenting a content item and selecting relevant sections of the content item to provide to a large language model to generate a corresponding output. In particular, in one or more embodiments, the disclosed systems can generate a text representation that includes a plurality of text segments each comprising a number of tokens of the text representation. Further, the systems can extract, from the plurality of text segments, segment-specific text embeddings that correspond to respective portions of the text representation of the content item. Additionally, the systems can determine a segment-specific text embedding corresponding to a model output request. Moreover, the systems can generate a model output by passing a text segment corresponding to the segment-specific text embedding to a large language model together with the model output request.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating personal responses through retrieval-augmented generation. In particular, the disclosed systems can generate a query embedding from a query generated by an entity and determine data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity. The disclosed systems can provide the data context to a large language model and generate a personalized response informed by the data context. Subsequently, the disclosed systems can provide the personalized response for display on a client device associated with the entity.

The invention provides a landscape garden planting planning management method and system based on terrain environment analysis, and the method comprises the steps: generating a comprehensive terrain environment database through collecting comprehensive environment and biodiversity data; generating a plant ecological adaptability prediction model by using a genetic algorithm to generate an ecological adaptability intelligent evaluation result; obtaining a first target planting combination and a layout scheme through the constructed three-dimensional virtual ecological system model in combination with a user interactive function; optimizing in combination with a style migration algorithm and an emotion perception interactive design method to obtain a final target planting combination and layout scheme; monitoring key monitoring points and indexes to obtain existing planting ecological data, and performing landscape garden planting planning management based on the target planting combination and layout scheme and the existing planting ecological data; according to the invention, intelligentization, individuation and sustainability of landscape garden planting planning are realized, and powerful support is provided for design and management of landscape gardens.

The invention relates to the technical field of data processing, in particular to a modular task management system based on dynamic process configuration, which comprises a dynamic process configuration module, a task state circulation engine, a visual interaction interface module, a historical track tracing module and a modular expansion interface. The dynamic process configuration module dynamically binds enterprise configuration data with regional process original data through a process template ID to generate an enterprise exclusive process topological structure comprising a regional identifier, a node association relationship and a process classification label; and the task state circulation engine updates the task state data based on an event-driven and atomized transaction lock mechanism, and synchronizes the task state data to the change management general table. According to the method, flexible configuration of process logic, visual display of task states and cross-enterprise rapid adaptation are realized, and system expansibility and operation efficiency are improved.

The invention discloses a large model enhanced cross-modal retrieval method and system and related equipment. The cross-modal retrieval method comprises the following steps: acquiring an image-text pair; based on the image-text pair, obtaining large model enhancement information of the image-text pair, combining an original image, a text and the enhancement information, adopting a multi-branch encoder of a pre-training CLIP model to obtain a plurality of feature vectors, constructing a multi-stage collaborative alignment loss function, and performing collaborative alignment on an image mode and a text mode in a public semantic space; and training a model through the multi-stage collaborative alignment loss function and a pre-constructed training database, and performing retrieval through the trained model. According to the method, collaborative learning is carried out on the image-text features obtained by the encoder, auxiliary semantic enhancement information is added, and collaborative alignment is carried out on the image modality and the text modality in the public semantic space, so that a better retrieval network is trained, and the accuracy of image-text retrieval is improved.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a dialogue processing method, device, equipment and medium based on historical dialogue recognition, which comprises the following steps: receiving an interaction request, judging whether a stored historical dialogue identifier is associated, directly generating first response data when the stored historical dialogue identifier is not associated, and sending the first response data to a server; and extracting historical session data from the database when the data are associated, stopping extraction after a capacity threshold value is reached, integrating the interaction request and the extracted data into context parameters, submitting the context parameters to the intelligent processing module to obtain second response data, and outputting the first response data or the second response data. According to the method, on the basis of judging the session relevance, the historical session data is subjected to limited extraction, and the context parameters are constructed, so that the capability of keeping session continuity under the condition that computing resources are limited is realized, and the processing efficiency of multiple rounds of sessions and the system expandability are improved.

This disclosure describes techniques relating to development of data flow pipelines. In one example, this disclosure describes a method that includes accessing, by a computing system, metadata identifying characteristics of a data flow pipeline; generating, by the computing system and based on the metadata, the data flow pipeline, wherein the data flow pipeline includes a plurality of stages, and wherein the plurality of stages includes a multi-sourced stage in which data output by each of a subset of stages in the plurality of stages are used as input to the multi-sourced stage; executing, by the computing system, the data flow pipeline; detecting, by the computing system, modifications to the metadata; generating, based on the modifications, an updated data flow pipeline; and executing, by the computing system, the updated data flow pipeline.

Implementations relate to generating and using multimodal embeddings. In various implementations, first modality data may be obtained and encoded into first modality embedding(s) using a trained first modality encoder that is stored in memory of edge-based client device(s). Second modality data may be obtained and encoded into second modality embedding(s) using a trained second modality encoder that is also stored in the memory of the edge-based client device(s). The first and second modality embeddings may be processed using an edge-based multimodal LLM that is also stored locally in memory of the edge-based client device(s) to generate a multimodal contextual embedding, which may be provided to a remote server that hosts a central LLM, e.g., in conjunction with a natural language input provided by the user. Information generated using the central LLM, responsive to the natural language input, may be received from the remote server.

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.

Techniques are disclosed relating to a multi-agent search client. In an example method, a search agent application receives a query. The search agent application determines, using a search agent, one or more agents to each execute a portion of the query. The search agent application outputs, to a domain agent, a portion of the query. The search agent application determines, using the domain agent, an execution step based on the portion of the query and information about an executor. The executor generates instructions based on the execution step that are configured to generate a response to the execution step and outputs a command to execute the instructions. The executor then outputs, to the first domain agent, the response to the execution step. The search agent application outputs, using the search agent, a response to the query based on the response to the execution step.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

A computer system uses a machine-learned language model to generate an SQL query for a user query. The system receives a user query comprising a task for performing a database query. The system identifies an embedding for the user query to represent the user query. The system generates a prompt for input to a machine-learned language model, and the prompt specifies the user query, metadata associated with the identified data table and a request to generate one or more SQL statements for performing the database query on the data table. The system provides the prompt to a model serving system and receives an output generated that includes the requested SQL statements for performing the database query. The system presents a response to the user query using the received SQL statements.

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 relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent document processing method, device, equipment and medium, comprising: receiving a document processing request and generating a task identifier, creating and sending a document processing task, constructing an intermediate document structure and generating a plurality of subtasks, and processing the sub-tasks, writing states and page contents into a cache, regularly querying task states, updating accessible identifiers, responding to an access request, loading the page contents, and integrating pages to generate a complete document after all the sub-tasks are completed. According to the method, the document generation task is divided into a plurality of sub-tasks which can be independently processed, and page-level asynchronous loading is realized in combination with a state cache and a regular query mechanism, so that the response speed and the system processing efficiency are remarkably improved while the generation accuracy is ensured; the problems that in the prior art, document generation delay is high, loading is slow, and user waiting time is long are effectively solved.

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.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

The invention discloses a distribution transformer monitoring system and method, and relates to the technical field of electric power intelligent monitoring, and the method comprises the steps: obtaining the three-dimensional temperature gradient field distribution and mechanical stress distribution of a distribution transformer based on a standardized multi-source data set, and outputting a comprehensive field matrix through a field coupling analysis method; performing spatial analysis on the comprehensive field matrix by adopting spatial hot spot analysis, identifying and marking temperature and mechanical stress abnormal areas, performing spatial aggregation and noise filtering through an OPTICS density clustering algorithm, and outputting abnormal feature vectors; inputting the abnormal feature vector into a pre-trained intelligent diagnosis model, outputting a comprehensive fault risk score and performing preliminary diagnosis; searching fault data in a historical case library based on the preliminary diagnosis result for comparison verification, and optimizing the preliminary diagnosis result through a machine learning algorithm to generate a final diagnosis result; according to the method, a field coupling analysis method is adopted to establish a heat-force bidirectional action model, and the spatial distribution characteristics of the temperature gradient and the stress tensor are accurately reflected.

The invention relates to the technical field of forest fire prediction and simulation, in particular to a forest fire spreading prediction simulation system under complex mountain terrain conditions, which comprises a data acquisition module, a data preprocessing module, a terrain analysis generation module, a vegetation analysis module, a meteorological analysis module, a fire spreading simulation module and a result display module. According to the forest fire spreading prediction simulation system under the complex mountainous terrain condition, the influence of multiple factors such as the complex mountainous terrain, vegetation and weather on forest fire spreading is comprehensively considered, the prediction accuracy and reliability are improved, a user can visually know the forest fire spreading condition through visual result display, and the forest fire spreading prediction simulation system has the advantages of being convenient to use and high in practicability. According to the invention, scientific and reasonable fire prevention and extinguishing strategies can be formulated conveniently, latest data can be acquired and processed in time, powerful support is provided for monitoring and early warning of forest fire, scientific decision basis can be provided for forest management departments, fire departments and the like, and improvement of the prevention and control capability of forest fire is facilitated.

The invention provides a data knitting system and method based on dynamic metadata identification and DAG optimization, and relates to the technical field of data knitting, the data knitting system comprises a metadata management layer, a data processing layer and an execution layer, the metadata management layer comprises a dynamic metadata identification module and a labeling storage engine module, the data processing layer comprises an adaptive DAG optimization engine module and a dynamic task distributor module, and the execution layer comprises a data computing node cluster module and a multi-source data connector module; the method comprises the steps that a DAG task scheduling strategy is established, and dynamic optimization is achieved; according to the method, the service labels and DAG task scheduling are deeply bound to form a'data-calculation-service 'closed loop, the execution sequence and resource allocation of the tasks can be adjusted according to the service labels changing in real time, so that the task scheduling not only considers the use of calculation resources, but also can optimize the tasks in real time according to service requirements, and the task scheduling efficiency is improved. Therefore, the overall treatment efficiency is improved.

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.

A method and an apparatus for generating a motion of a virtual character, and a method and an apparatus for constructing a motion library of a virtual character are provided, and belong to the field of computer technologies. The method for generating a motion of a virtual character includes: obtaining audio and text of a virtual character, the text indicating semantic information of the audio (201); determining a semantic tag of the text based on the text (202); retrieving a motion category matching the semantic tag and motion data belonging to the motion category from a preset motion library (203); and generating a motion sequence of the virtual character based on the motion data (204).

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 invention provides an electric power image-text cross-modal retrieval method based on Transform and a related device. The method comprises the steps of obtaining a target text; performing text feature extraction based on the target text to obtain target text features; retrieving in a to-be-processed image data set based on the target text features, and determining a target image conforming to the target text semantics, including: performing image feature extraction on the to-be-processed image in the to-be-processed image data set based on a trained retrieval model to obtain image features; performing matching based on the target text features and the image features to obtain target image features; and determining the target image based on the target image feature.

Systems and techniques are provided for processing streaming data. For example, a process can include obtaining, by an edge device, a plurality of data streams corresponding to objects, activities, or events registered in an edge environment associated with the edge device. One or more machine learning networks implemented on the edge device can generate a plurality of features and / or embeddings corresponding to each respective data stream of the plurality of data streams. The edge device can determine a subset of salient content from the plurality of data streams, based on analyzing the plurality of features and / or embeddings generated for each respective data stream. The edge device can generate index information corresponding to the determined subset of salient content. The subset of salient content and the generated index information can be stored locally at the edge device for search and retrieval based on local queries received at the edge device.

The invention discloses a large-span continuous rigid frame construction line shape intelligent monitoring method, and relates to the technical field of large-span continuous rigid frame construction, and the method comprises the following steps: S1, data collection; s2, data processing; s3, performing multi-stage intelligent early warning; s4, construction regulation and control; and S5, outputting a result. According to the method, multi-source equipment is used for synchronously collecting data, the real-time performance and integrity of the data are guaranteed, deep learning is used for eliminating anomalies, a neural network is used for constructing an environment influence model and correcting the data, the influence of environment factors on linear data is corrected, digital twinning is combined with a finite element and a neural network to predict a deformation trend, and the prediction precision is improved. A linear deviation trend is pre-judged in advance, a construction scheme is optimized, multi-level intelligent early warning dynamically adjusts a threshold value according to multiple factors, early warning information is accurately issued, risk response timeliness is enhanced, decision suggestions can be automatically generated through construction regulation and control, an evaluation effect is fed back, and the construction process is optimized.

The invention discloses a heat preservation and thermal insulation project whole industrial chain quality tracing method and system, and the method comprises the steps: obtaining industrial chain data and monitoring data of a heat preservation and thermal insulation project, carrying out the tracing coding based on the industrial chain data and the monitoring data, and constructing a heat preservation and thermal insulation project whole industrial chain quality tracing network, determining industrial chain data corresponding to the to-be-traced monitoring data through the traceability code, and performing correction and anomaly screening on the to-be-traced monitoring data to obtain to-be-traced abnormal monitoring data, and according to the to-be-traced abnormal monitoring data and the thermal insulation project whole industry chain quality traceability network, determining a thermal insulation project whole industry chain quality hidden danger source. The method not only can improve the efficiency and accuracy of quality traceability of the whole industrial chain of the thermal insulation project, but also has good interpretability, and can be directly applied to a quality traceability system of the whole industrial chain of the thermal insulation project.

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.