1033 results about "Information integration" patented technology

A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.

The invention relates to the technical field of machine vision, and discloses a machine vision-oriented cross-modal image-text analysis method, which comprises the following steps of: partitioning an input image to generate an image block sequence; inputting the image block sequence into a visual converter for multi-scale feature extraction, and generating target visual features; encoding the input text to generate a target text feature; inputting the target visual features and the target text features into a deep reconstruction bottleneck network for compression alignment, and generating a cross-modal compression vector; and inputting the cross-modal compression vector into a large language model to generate cross-modal decoding information, so that cross-modal redundant information can be effectively filtered, compact shared semantic representation can be learned, the information integrity of the compression process is ensured through bidirectional reconstruction verification, cross-modal semantic alignment is realized, and the method has the advantages of high efficiency and high reliability. Omnibearing cross-modal content generation from the whole to details is achieved, and the requirements of different application scenes are met.

The invention discloses an intelligent bidding and tendering information identification method, which relates to the technical field of engineering management, and comprises the following steps: standardizing and preprocessing a bidding and tendering information document format; constructing a natural language processing model special for the bidding and tendering field; training a multi-level information extraction model based on deep learning; carrying out cross-document information integration and correlation analysis; performing anomaly detection and verification based on rules; semantic understanding and sentiment analysis; automatic subject matter description and technical specification extraction; a real-time dynamic monitoring and information updating mechanism; the invention discloses a graph-based decision support and recommendation system. According to the intelligent bidding and tendering information identification method, efficient and intelligent identification of bidding and tendering information is realized by constructing a natural language processing model specially aiming at the field of bidding and tendering. The bidirectional coding capability of BERT and the sequence modeling capability of LTM are combined, and a hierarchical attention mechanism is introduced, so that the model can accurately identify various key information in bidding and tendering documents.

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention relates to the field of standard comparison analysis, in particular to a method for automatically identifying differences of domestic and overseas standard files. A method for automatically identifying differences of standard files at home and abroad comprises the following four steps of S1, standard file analysis and information extraction, S2, semantic understanding and information integration, S3, standard difference comparison, S4, compliance analysis and suggestion, and in combination with a rule engine and a natural language generation technology, evaluating difference influences and outputting adjustment suggestions. According to the method, comprehensive analysis of the standard file is realized through multi-modal integration and structured processing, and a knowledge base with cross-language and format is constructed; clause association reasoning is realized through deep semantic analysis and knowledge graph construction; according to the method, accurate difference recognition is achieved through hybrid embedding and a dynamic threshold strategy, the accuracy and efficiency of standard comparison are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for standard system analysis in international trade and technical cooperation.

The invention relates to the field of enterprise process automation, in particular to a multi-mode Action Transform model and an intelligent task execution method thereof. The technical problems that in an existing Web page operation automation technology, flexibility and adaptability are insufficient, generalization ability is weak, context understanding ability is limited, multi-modal information integration is insufficient, and data utilization efficiency is low are solved. According to the technical scheme, the multi-modal Action Transform model and the intelligent task execution method thereof comprise a multi-modal input processing module, a multi-modal fusion module, a Transform encoder and an operation prediction module; according to the method, various inputs such as images, texts and DOM structures are processed at the same time, the fault-tolerant ability and adaptability to page changes are improved, the long-range dependency relationship in the operation sequence can be effectively captured and utilized through the attention mechanism, and deep fusion of various modal information such as the images, the texts and the DOM structures is achieved through the attention mechanism and the feature fusion layer.

The invention belongs to the technical field of computers, and particularly relates to an information integration management method and system supporting real-time updating, and the method comprises the steps: accessing a heterogeneous data source through a distributed gateway, carrying out the adaptive analysis of a protocol, and standardizing a structure; adding a millisecond timestamp and a unique identifier, and generating a semantic data cluster through streaming engine sliding window association analysis; after multi-dimensional consistency verification is executed, writing and storing are carried out by adopting a double-buffering and snapshot isolation mechanism; and dynamically assembling the customized view according to the user role and the context and safely returning the customized view. The system correspondingly comprises a protocol analysis module, a time sequence marking module, a streaming association module, a semantic verification module, a double-buffer control module, an index updating module, a view assembling module and a secure transmission module. According to the invention, millisecond-level real-time convergence, conflict-free fusion and on-demand view output are realized, and the data access flexibility, the quality guarantee capability and the service adaptation flexibility are comprehensively improved.

The invention provides a water conservancy construction progress intelligent management method and system. Wherein hydraulic engineering project plans are collected, dynamic changes in a life cycle are obtained through an integrated building information model technology, and a hydraulic engineering construction progress management framework is generated; extracting multi-source heterogeneous data, performing cross-modal information integration by applying a multi-modal fusion network algorithm, evaluating specific influence of external environment factors by adopting a causal forest technology, and generating a water conservancy construction progress and risk early warning report; establishing a kinetic equation by applying a graph neural differential equation algorithm, performing dynamic system simulation, and constructing a virtual water conservancy construction environment by adopting an enhanced digital twinborn simulation technology to capture construction efficiency and resource utilization conditions and generate a water conservancy construction resource allocation optimization report; and a lean six-sigma methodology is introduced to set a lean improvement plan, project feedback is collected, and a water conservancy construction progress intelligent management scheme is generated. According to the technical scheme provided by the invention, the intelligent level of water conservancy construction progress management is remarkably improved.

The invention relates to the technical field of equipment health assessment, in particular to a power plant equipment health assessment model construction method and system. The method comprises the following steps: acquiring station information data of power plant equipment; site equipment data transmission channel configuration is carried out on the power plant equipment site information data, and a site equipment data transmission channel is generated; carrying out information integration on the power plant equipment station information data by utilizing the station equipment data transmission channel to generate a power plant equipment operation data integration platform; performing power plant equipment operation monitoring on the power plant equipment operation data integration platform to obtain power plant equipment operation monitoring data; and performing equipment multi-working-condition trend fluctuation calculation on the power plant equipment operation monitoring data to obtain equipment working condition trend fluctuation data. Through data integration, refined working condition analysis, unsteady state abnormal state transition analysis and dynamic loss assessment, the precision of the power plant equipment health assessment model is improved.

The invention provides a GraphRAG-based large model question and answer method, system and device, a medium and a product, and the method comprises the steps: determining an available data source, and carrying out the preprocessing of data; performing knowledge extraction, knowledge fusion and knowledge calculation on the preprocessed data to construct a knowledge graph; key entities are extracted from user query through a large language model of GraphRAG; retrieving related sub-graphs in the knowledge graph according to the extracted key entities to form context information; and sending the context information into a large language model of the GraphRAG to perform a generative answer. According to the invention, more related knowledge points and information sources can be covered, and more comprehensive answers can be provided; for complex queries needing multi-step reasoning or multi-document information integration, the accuracy is high; when a large-scale data set is processed, the computing power resource demand is relatively low; cross-domain integration and reasoning can be realized; the diversity of answers is improved, and the weighting of the answers is remarkably enhanced.

The invention discloses a multi-literature intelligent classification and automatic review generation method based on a large language model, which is characterized by comprising the following steps of: retrieving literatures or uploading local literatures, and extracting text contents; identifying keywords by utilizing an algorithm or a large language model; converting the keyword into a vector form by using a vectorization model; summarizing the research direction of the clustering result by using a large language model; comprehensively summarizing the single literature and literatures in different research directions by using a large language model; and executing the steps of reviewing, quotation insertion and the like through a large language model and a vectorization semantic matching technology. Compared with the prior art, the method has the advantages that the efficiency and quality of document classification and review generation are remarkably improved, technologies such as text vectorization, topic clustering and large language model generation are well fused, efficient and accurate multi-document review automatic generation is achieved, the method is suitable for interdisciplinary academic document analysis and information integration application, and the method is suitable for popularization and application. Wide application potential is shown.

The invention relates to the technical field of building informatization, and discloses a construction collaborative management method and system based on BIM and Internet of Things. The method comprises the steps of building a BIM model, collecting field real-time data, generating a dynamic construction state through association, performing collaborative analysis and forming a scheduling plan, and finally realizing closed-loop management and control through feedback of the Internet of Things. The system comprises a BIM model construction module, a field data acquisition module and a collaborative analysis and scheduling module which are in collaborative linkage to realize information integration, state perception and dynamic optimization control in the whole process of building construction. The method can effectively improve the precision and efficiency of the engineering construction process, enhances the intelligent management and control capability of the construction site, and has good engineering practicability and popularization value.

The invention discloses an intelligent question answering system and method based on question decomposition and community semantic search. According to the method, firstly, a complex question is decomposed into a plurality of single-hop sub-questions through an original loop question decomposition technology, then information needed for answering each sub-question is obtained based on communities in a vector retrieval knowledge graph, and the sub-questions are answered by using a large language model. And finally, generating a final question and answer for the original complex question by combining the LLM with the answers of all the sub-questions. Compared with other methods in the industry, the method has the advantages that the information integration capability is remarkably improved, information fragmentation is avoided through community-level search and abstract generation, and high-quality and logically consistent answers are provided. When a complex query is processed, the effect of the method is superior to that of a general method in the industry and is improved by 29% at most; meanwhile, on the basis of ensuring a better question and answer effect, the efficiency is remarkably improved, and on the premise that the effect exceeds that of an existing optimal method GraphRAG, the token consumption is only 3.2% of that of the existing optimal method GraphRAG.

The invention relates to the technical field of police service management, discloses a police service management system and method based on an image recognition technology, and aims to improve the police service management efficiency and accuracy. The system comprises a data receiving module, a data preprocessing module, a target detection module, a behavior analysis module and an information integration module. A monitoring video stream is received, data is preprocessed to form an image data set, and then a specific target is detected by using a deep convolutional neural network and a track sequence is output. And carrying out behavior analysis on the target track by adopting a long-short-term memory network, and identifying an abnormal mode. And finally, integrating the detection result and the behavior analysis data, and generating a police service management report. According to the invention, intelligent processing of monitoring videos is realized, police service departments are assisted to respond to abnormal conditions in time, and urban public security is maintained. Through the deep learning technology, the accuracy of target detection and behavior recognition is improved, and powerful support is provided for modern police service management.

The invention relates to the technical field of construction management, in particular to a tunnel construction progress real-time tracking system which comprises an environment monitoring module, a progress acquisition module, an anomaly recognition module, a risk assessment module and a resource allocation module. According to the method, through identification of the time sequence and the change amplitude of the tunnel excavation face environment parameters, pre-screening of potential progress interference factors, enhancement of influence factor perception ability and dynamic comparison of environment data and process progress are achieved, the process disjunction phenomenon can be captured in time, and the aging control precision is improved; through linkage analysis of operation time and equipment operation records, association analysis of abnormal positioning, scheduling response and progress offset section resource data is facilitated, operation node-level dynamic evaluation is achieved, allocation imbalance and high-frequency mismatched resources are avoided, operation time sequences are adjusted preferentially, field bottlenecks are relieved, and operation efficiency is improved. And real-time closed-loop linkage between the construction progress and resource configuration is enhanced through information integration and correlation analysis.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention relates to the technical field of machine tool cutter wear monitoring, and particularly provides an integrated multi-dimensional data monitoring system and a monitoring method.The integrated multi-dimensional data monitoring system comprises a multi-dimensional data acquisition module which can acquire multi-dimensional data of the working state of a cutter in real time; the feature information layer and the intelligent analysis layer comprise a multi-source data fusion module, an abnormal state recognition module, a residual life prediction module and a self-adaptive learning module; the decision execution layer comprises an early warning feedback module, a parameter optimization suggestion module and an intervention control module; the knowledge base system comprises a case base module, a rule base module and a model base module, different physical quantities are collected through the multi-dimensional data collection module, full-dimensional sensing of the state of the cutter is achieved in combination with machining features, material information and environment data, multi-sensor collection, physical model construction, feature information integration and intelligent algorithm analysis are combined, and the machining accuracy of the cutter is improved. And comprehensive, real-time and accurate monitoring of various abnormal states of the machine tool cutter is realized.

The invention discloses a sports video commentary generation method and system based on a multi-modal large language model, and the method comprises the steps: obtaining a multi-modal data set which comprises a sports video, and an audio and a commentary text corresponding to the sports video; constructing a multi-modal large language model, encoding the sports video, the audio and the explanation text so as to project corresponding video frames, audio waveforms and metadata to a shared embedding space, and determining a multi-modal embedding vector; a multi-modal clustering memory unit is set, multi-modal embedded vectors are grouped, and feature alignment between modals is optimized through comparative learning and information entropy regularization; based on a retrieval enhanced context learning mechanism, a historical instance is retrieved through sparse regularization distance measurement to serve as reference input of a current input multi-mode embedded vector; and jointly inputting the current multi-modal embedded vector and the reference input into the multi-modal large language model to obtain a sports video commentary. According to the method and the device, the problems of insufficient multi-modal information integration and insufficient context utilization are solved.

The invention relates to the field of industrial automation, in particular to an industrial robot glass processing system based on large model intelligent decision, and relates to an industrial robot key technology based on multi-source information integration and intelligent decision. Glass cutting is optimized through multi-physics field monitoring, the edge defects are reduced by 70%, and the rejection rate is reduced by 50%. And the edge grinding of the special-shaped glass is assisted by three-dimensional reconstruction, and the precision deviation is controlled within 0.1 mm. And multi-modal data improves the coating uniformity, the thickness deviation is within 5 [mu] m, and the film layer shedding is reduced by 80%. Multi-robot cooperation is achieved through reinforcement learning, the machining efficiency is improved by 60%, and the cost is reduced by 40%. And a digital twinning optimization process is adopted, so that the equipment failure rate is reduced by 60%.

The invention relates to the field of intelligent contract vulnerability detection, in particular to an intelligent contract vulnerability detection method based on code graph fusion analysis and a graph matching network. The method comprises the steps of constructing an intelligent contract source code data set; through static analysis, extracting three different code feature information including a control flow diagram, a data flow diagram and an abstract syntax tree from the source code of the smart contract; carrying out information integration on the three extracted code features, and constructing a code graph covering wider features; on the basis of the generated code graph, a GraphCodeBerT model is used for pre-training; vectorizing a code graph which is output by the pre-training model and contains structural and semantic features through a graph embedding network; and learning the feature representation of the contract pair by using a cross-graph attention mechanism of a graph matching network to realize specific vulnerability detection of the smart contract source code. According to the invention, potential vulnerabilities can be accurately identified.

The invention belongs to the technical field of computers, and particularly discloses a knowledge graph construction method and device for target culture resources and electronic device.The method comprises the steps that knowledge graphs of multiple different fields are obtained, and a big language model is trained based on the knowledge graphs to obtain a big language model; integrating the structured information of the knowledge graph into the language representation of a large language model; obtaining target culture resource related text data, and performing fine tuning on the large language model based on the target culture resource related text data; performing knowledge extraction on the target cultural resource related text data through the fine-tuned large language model to obtain related knowledge in a triple form; and constructing the knowledge graph based on the related knowledge in the triple form, so that the problem that the knowledge graph construction presentation result is relatively poor due to the sparse target culture resource data sample in the prior art can be solved.

The invention provides an emotional dialogue generation method fusing knowledge and an emotional reason context graph, and relates to the technical field of information processing. The invention provides a common emotional dialogue generation model fusing knowledge and an emotional reason context graph, and the model comprises six modules: an utterance coding module, a dialogue context graph fusing knowledge, a knowledge-enhanced KDCG network module, an emotional classification module, an emotional reason recognition module and a reply generation module. The problem of contextual information integration in multiple rounds of dialogues is effectively solved by introducing an emotion reason contextual graph, the understanding ability of the system on implicit expression emotions and intentions is improved by introducing background knowledge in a knowledge base and combining the emotion reason contextual graph, and finally the understanding depth of the dialogue system on user emotions is comprehensively improved. Through joint modeling of emotions and reasons, the system can more clearly understand the emotion source of the user, so that a more targeted and humanized emotional response is generated.

The invention discloses a runoff prediction model based on multi-source information integration of an Informer model, and belongs to the technical field of hydropower station runoff prediction. The invention aims to solve the problem of poor accuracy of medium and long term runoff prediction, especially for runoff sequences with multi-source, non-linear and non-stationary characteristics. The monitoring power station range is selected, and historical runoff data of the hydropower station and hydrological information such as rainfall and temperature of a surrounding area are obtained; adopting a Pearson correlation analysis method to select strong correlation features as multi-source time sequence feature input of an Informer model; preprocessing the data by using an STL seasonal decomposition method, and extracting characteristics of a period term and a trend term; runoff prediction is carried out by adopting a sparse attention mechanism and a self-attention distillation mechanism of an Informer model; according to the method, the prediction speed and feature capture can be enhanced, the runoff prediction accuracy is improved, and powerful support is provided for water resource management and scheduling of the hydropower station.

The invention discloses an AI workflow processing method and system for intelligent hardware, and the method comprises the steps: obtaining multi-person cooperation input information, including voice, text and image information, through an edge device; the multi-user cooperation input information is processed, workflow related data is generated, and the workflow related data comprises a multi-user cooperation input information integration result, a work priority processing result, workflow distribution processing information, flow and computing power distribution information, plug flow interruption emergency processing information, live broadcast effect data and live broadcast setting optimization information; sending the workflow related data to cloud service equipment; receiving target format instruction information sent by the cloud service equipment; and determining a target application program based on the target format instruction information, and sending the target format instruction information to the target application program for the target application program to process.

The invention provides a feature extraction method and system for a three-dimensional CAD model and electronic equipment, and the method comprises the steps: a data processing step: generating a corresponding geometric attribute adjacency graph, an extended adjacency matrix, a spatial position matrix and a surface-surface shortest path side chain matrix based on the data of the three-dimensional CAD model; and a feature extraction step: performing semantic segmentation and instance segmentation on the processing features of the three-dimensional CAD model through a processing feature recognition neural network based on the geometric attribute adjacency graph, the extended adjacency matrix, the spatial position matrix and the surface-surface shortest path side chain matrix so as to perform feature extraction. By means of the mode, the geometric attribute adjacency graph and the novel descriptor are generated after the three-dimensional CAD model is recoded, information of the three-dimensional CAD model is prevented from being lost, and information integrity during data conversion is guaranteed. Semantic segmentation and instance segmentation are carried out on a geometric attribute adjacency graph and a novel descriptor through a specific neural network model, and the accuracy of feature extraction is improved.

The invention discloses a hierarchical retrieval enhancement generation method and system. The method comprises the following steps: determining question information to be retrieved according to a hierarchical retrieval enhancement generation task; performing document-level retrieval on an external knowledge base based on the problem information to obtain a most relevant document set; slicing the most relevant document set according to paragraphs and performing paragraph-level retrieval to obtain a most relevant paragraph set; slicing the most relevant paragraph set according to sentences and performing sentence-level retrieval to obtain a most relevant sentence set; performing fragment generation operation on each sentence in the most relevant sentence set by utilizing a sliding window, and then performing fragment-level retrieval to obtain a most relevant fragment set; and carrying out information integration on the most relevant fragment set and knowledge in the model by utilizing a large language model, and carrying out context information supplement by utilizing factual knowledge obtained based on an external knowledge graph to obtain a context answer text of question information. According to the scheme, the accuracy and the reliability of a retrieval result can be remarkably improved.

The invention discloses a method for realizing tobacco field yield prediction based on multi-source data analysis, and particularly relates to the technical field of tobacco yield prediction. Firstly, a tobacco planting area is divided into monitoring areas, multi-source data such as weather, soil, remote sensing images and actual yield are obtained in real time, the collected data are preprocessed, space-time consistency characteristics and information integrity characteristics are extracted, a comprehensive matching degree index is generated through calculation and weighting, on the basis, a yield prediction model is established, and a tobacco yield prediction result is obtained. The prediction value and the actual yield are compared and analyzed, if the deviation is large, model parameters are dynamically adjusted through fuzzy logic based on the comprehensive matching degree index and the actual yield data, and a data processing and fusion algorithm is optimized, so that the prediction precision is remarkably improved, and the field management risk caused by data isomerism is reduced.

The invention provides a multi-modal perception-driven vegetable knowledge graph construction method and device, and relates to the technical field of agricultural artificial intelligence, and the method constructs a vegetable knowledge graph by obtaining the multi-modal information of each vegetable, taking a vegetable entity as a node, and taking the relationship between the multi-modal information of different vegetables as an edge. According to the multi-modal perception-driven vegetable knowledge graph construction method provided by the invention, information integration and intelligent decision support in a vegetable production process are realized, and the efficiency and quality of agricultural production are improved.

The invention provides a large model agent interactive question and answer task decision-making method and system, and relates to the technical field of natural language process.The method comprises the steps that 1, user input is received through multiple rounds of dialogues, key information is extracted to form an initial memory node set, meanwhile, fuzziness and information integrity of a query intention are analyzed, and an initial memory node set is obtained; generating an uncertainty evaluation result; and step 2, based on the uncertainty evaluation result, evaluating the timeliness and importance degree of each node, generating an enhanced memory node set, and constructing a dynamic memory structure according to a semantic and sequential relationship to form a structured memory network. According to the method, the understanding accuracy of the intelligent agent on multi-round dialogue contexts, the logic continuity of decision response and the continuous adaptability of task processing capacity are improved.

The invention relates to the technical field of multi-view clustering, in particular to a multi-view clustering method and device, and can solve the problem that the overall effect of an existing method in a large-scale clustering task is limited due to the fact that the existing method has problems in the aspects of calculation efficiency, robustness and multi-view information integration to a certain extent. The method comprises the following steps: dynamically learning an anchor matrix and a projection matrix for each view, and constructing a bipartite graph to generate a similarity matrix; calculating a graph Laplacian matrix based on the similarity matrix of each view, and extracting spectrum embedding; the spectrums of multiple views are embedded and stacked into a third-order tensor, and cross-view shared information is extracted by using a low-rank tensor constraint; multi-view atlas embedding is aligned through a spectrum rotation technology, and a discrete clustering indication matrix is directly output.