1164 results about "Data association" patented technology

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

The invention discloses a human resource data natural language query and SQL generation method and system, and relates to the technical field of SQL generation, and the method comprises the steps: preprocessing a natural language query text input by a user, and constructing a query semantic tree; traversing the query semantic tree to generate a candidate entity set, determining an unambiguous entity through a three-layer ambiguity resolution mechanism of semantic scoring, service verification and cross validation, identifying a composite intention, disassembling the composite intention into a sub-intention sequence, and generating an SQL operation instruction set in combination with a preset SQL operation mapping rule; based on the SQL operation instruction set and the human resource data association model, determining a data table set and an association path, generating a dynamic calculation logic and structured condition expression, and constructing a structured query skeleton; according to the method, the personalized SQL template is dynamically generated according to the query skeleton, after filling, grammar verification, strategy optimization and logic consistency verification are performed, and the optimized SQL statement is output, so that the query accuracy and performance are remarkably improved.

Systems and methods for embedding-based LLM tuning include generating a first embedding of received user input data and utilizing a translation model trained to associate user input with device names to generate a second embedding that differs at least in part from the first embedding. Reference embeddings corresponding to devices associated with user account data may be generated and a subset of the reference embeddings that satisfy a threshold similarity to the second embedding may be determined. A large language model (LLM) configured to determine a response to the user input data may utilize data for a subset of devices that correspond to the subset of the reference embeddings for performing speech processing.

The invention relates to the field of semiconductors, in particular to an answer generation method and device based on a semiconductor knowledge base and a medium. The method comprises the steps of performing data association on structured knowledge according to a semiconductor domain knowledge structure to construct a class of knowledge bases; performing data recombination on the unstructured knowledge according to the layout so as to construct a second-class knowledge base; a query question is received, retrieval is executed in the first-class knowledge base and the second-class knowledge base based on a preset retrieval model, and a plurality of related knowledge fragments are obtained; determining a first fusion weight between the knowledge fragments of the same knowledge type based on the relevancy between each knowledge fragment and the query question and the update time of each knowledge fragment; determining an optimal knowledge type according to the group feature information, and determining a second fusion weight between different knowledge types according to the optimal knowledge type; and based on the first fusion weight and the second fusion weight, obtaining a reference answer according to the plurality of knowledge fragments. And the requirements of the semiconductor field on information comprehensiveness and answer accuracy are considered.

The invention provides a manufacturing abnormality diagnosis method and system based on a production data stream, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting an initial multi-source heterogeneous data stream of a production site, and implanting virtual data anchor points corresponding to key production equipment, process nodes and quality monitoring links in the initial multi-source heterogeneous data stream; performing spatial point set construction and tetrahedron inclusion detection based on the virtual data anchor points to perform data relevance quantitative evaluation and pruning on the initial multi-source heterogeneous data stream to generate an optimized multi-source heterogeneous data stream; and based on a pre-established unified semantic metadata dictionary, performing semantic analysis and normalization processing on the optimized multi-source heterogeneous data stream, and generating a standardized production event stream with unified semantics and time sequence and product identification. According to the invention, accurate diagnosis and root cause positioning of manufacturing abnormality can be realized.

The invention discloses an airborne radar ground moving target stable tracking method, and belongs to the technical field of avionics, and the method comprises the following steps: S1, obtaining and fusing multi-modal measurement data; s2, clutter suppression and pretreatment; s3, target state estimation; s4, data association and track repair; s5, group target collaborative tracking; and S6, outputting a tracking result. According to the airborne radar ground moving target stable tracking method, multi-mode fusion and track adhesion are used for supplementing a single measurement short plate and repairing a broken track; by means of clutter map and optimization filtering strong clutter suppression, the state precision is improved; the multi-target tracking is optimized by using the graph model and the group target modeling, the complexity is reduced, the prior is fused, and the tracking stability of the complex scene is enhanced.

The invention discloses a mutual cooperative door lock complementary acquisition method and system, and the method comprises the steps: extracting the structural feature data of a target person according to an event that a self door lock recognizes a non-white list person, generating an assistance request containing a target ID and the feature data, and transmitting the assistance request to an adjacent door lock; according to the assistance request received by the adjacent door lock, target feature matching is carried out, and after matching succeeds, a target video stream is collected and selectively coded; according to the time points when the target appears and disappears, generating an association starting pointer and an association ending pointer, and synchronously storing the pointers between the door locks; and analyzing the association pointer according to the playback request, and automatically starting stream playing of the association video streams of the plurality of door locks to realize multi-view synchronous display. By utilizing the embodiment of the invention, continuous automatic tracking and multi-view synchronous playback of cross-equipment target activities can be realized through active cooperation and data association between door locks, and the continuity and efficiency of security monitoring are improved.

Retrieval of structured tabular data in a retrieval-augmented generation (RAG) platform is described. An example method includes receiving a tabular file including structured data, extracting short and long metadata from the file, and generating a dataset agent object using the extracted metadata. A human-readable description is associated with the structured data, and the dataset agent object and metadata are stored in a metadata index. A natural language query is received through a query agent object rendered within a canvas including interconnected agent objects. A dataset agent object is identified based at least in part on metadata filtering using the natural language query and short metadata. A structured query conforming to structured query language syntax is generated using a large language model (LLM) and executed on the structured data. A response including the result set and dataset agent identifier is generated and transmitted to a downstream agent object for further processing.

The invention discloses an emergency rescue resource scheduling method based on the Internet of Things, and the method comprises the steps: collecting disaster area environment parameters, images and personnel distribution data through Internet of Things equipment, and associating historical disaster cases to construct a multi-source heterogeneous data set; extracting disaster characteristics through preprocessing, dynamically distributing data source weights, and generating a disaster assessment matrix; outputting a resource demand peak value based on the time sequence prediction model and performing dynamic correction; constructing a deep reinforcement learning model optimization decision strategy, and generating a scheduling scheme in combination with priority matching and an improved A * algorithm; according to the method, the disaster sensing precision and the demand prediction accuracy are improved, the scheduling decision adaptability is enhanced, the response time of a high-priority region is shortened, and the efficient demand of emergency rescue in a complex disaster is met.

The invention discloses a DCS operation and maintenance method based on fusion dynamic weight distribution and fault chain deduction, and relates to the technical field of operation and maintenance of a distributed control system, and the method comprises the steps: obtaining a plurality of groups of source data of a monitoring region; corresponding monitoring periods are determined according to the multiple groups of source data, and differential monitoring period information is obtained after combination; according to the technical key points, a multi-source data fusion module is utilized to associate data of a DCS system, an associated production system and a cross-plant cooperative system, and fragmented data such as equipment operation data, production load fluctuation and cross-plant interaction information are woven into a complete data network through adjustment of space-time alignment and multi-dimensional verification; the global sensing capability not only solves the one-sidedness of traditional single data judgment, for example, environmental interference is easily misjudged by only depending on equipment temperature data, but also can capture cross-system implicit associated faults; and secondly, when the multi-source data are mutually verified, an evidence chain is formed through logic consistency verification, so that the misjudgment rate of suspected faults is greatly reduced.

The invention relates to the technical field of intelligent traffic and road maintenance, and provides a road disease intelligent identification system and method based on artificial intelligence and Beidou positioning, and the method comprises the steps: carrying out the edge calculation preprocessing of noise reduction, image enhancement and region-of-interest segmentation of collected road image data, and extracting initial image features; inputting the initial image features into a deep learning disease recognition model based on transfer learning optimization, outputting a disease type and a disease grading result, and forming multi-source fusion data; performing spatio-temporal data association analysis on the multi-source fusion data to realize disease environmental impact assessment; the method comprises the following steps: predicting future development conditions of road diseases, sending out early warning information, dynamically evaluating road health condition grades according to road disease data, making an optimal maintenance plan according to positions, types and grades of the diseases and environmental influence evaluation results, and optimizing traffic dispersion and route recommendation according to the road disease conditions.

The invention discloses a polymorphic data asset context analysis and industry chain collaboration method, and particularly relates to the technical field of data intelligence and industry collaboration, comprising the following steps: accessing multi-source data through a heterogeneous adapter group to generate a four-dimensional intermediate representation containing entities, relationships, timestamps and space coordinates; inputting the representation into a space-time diagram neural network to construct a data association dynamic matrix carrying a dynamic attenuation weight; performing space-time interpolation restoration on the matrix missing link to form a global connected data asset topology network; generating a cross-enterprise security collaboration map based on the differential privacy chain architecture hierarchical encryption topology network; activating the four-dimensional relation chain in response to the collaborative request, and outputting a Nash equilibrium strategy through multi-agent reinforcement learning; an asset value report is generated after the strategy is executed, data distribution drift is detected based on KL divergence, and incremental updating of the data association dynamic matrix is triggered. According to the method, the problems of dynamic modeling and security collaboration of multi-source heterogeneous data are solved, and the decision-making accuracy of an industrial chain is remarkably improved.

The invention belongs to the technical field of cross-border logistics information credibility management, and discloses a cross-border logistics information credibility management method based on block chain and dynamic hash chain evidence storage, and the method comprises the steps: deploying a multi-modal data adapter, integrating a lightweight MobileViT model and a category exclusive feature template, extracting a key frame through a video, and generating a feature package; after sensor data is subjected to Kalman filtering processing, a timestamp and a numerical sequence are output; a historical feature library is compared through an LSH algorithm, repeated features are filtered, evidence storage redundancy is avoided, unstructured data features are associated with structured data such as customs clearance numbers, accurate data correspondence during traceability is ensured, and a verification result is real and reliable; for a temporary checking scene, a temporary node generates a temporary permission containing a 24-hour validity period, and a corresponding Hash unit can only be verified and cannot be accessed after expiration, so that an illegal data access risk is completely eradicated, subsequent verification is prevented from being influenced by excessive permission recovery, and data security and credible verification requirements are balanced.

A join decision manager (JDM) generates a data processing pipeline. The data processing pipeline includes at least one join operation associated with build-side row data and probe-side row data. The JDM determines the maximum cardinality associated with the probe-side row data. The JDM determines size of the build-side row data at a decision node of the at least one join operation. The JDM configures execution of the at least one join operation as one of a broadcast join or a hash-hash join based on the size of the build-side row data and the maximum cardinality.

The invention discloses a tunnel precise drilling and blasting design method integrating multi-source intelligence and closed-loop optimization. The method comprises the steps that geological parameters are collected through geological detection equipment; based on fracture mechanics and rock mass damage theories, constructing a quantitative relation model reflecting joint network distribution, stress wave propagation characteristics and a rock mass crushing effect coupling mechanism; inputting the geological parameters into a pre-constructed multi-dimensional knowledge base, integrating historical engineering data association rules by the multi-dimensional knowledge base by using a graph database, and extracting geological features; based on a deep learning model, analyzing the mapping relation between the geological features and the blasting parameters, and generating a preliminary blasting scheme including the blast hole distance, the charging structure and the detonating mode; according to the method, a closed-loop optimization system of geologic features-blasting parameters-effect evaluation is constructed, so that dynamic intelligent decision-making and construction self-adaptive regulation and control of tunnel drilling and blasting design are realized, and the contour forming precision and the blasting efficiency are remarkably improved.

The invention relates to the technical field of electronic data digital processing, and discloses a data maintenance management method and system based on artificial intelligence. The method comprises the following steps: acquiring multi-dimensional features such as an access time sequence, metadata, an association relationship and a service context of data; constructing a hybrid model fusing a long-short-term memory network and a graph attention network, and predicting a future value score of the data; dynamically generating a hierarchical storage, archiving or deleting strategy based on the score and storage cost-performance model; and through the access feedback after the execution of the monitoring strategy, the prediction model is optimized in a closed-loop manner by using a reinforcement learning mechanism. The system comprises a data feature acquisition module, a value prediction module, a strategy generation module and an execution feedback module. According to the method, self-adaptive, refined and intelligent management of the full life cycle of the data is realized, the utilization efficiency of storage resources is remarkably improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.

The invention relates to the technical field of digital manufacturing and precision machining, and discloses a part machining method and system based on inspection planning and feature driving, and the method comprises the steps: reading a full three-dimensional model, analyzing a tolerance mark, instantiating the tolerance mark into a procedure inspection item node, and building a persistent reference; identifying geometric features and matching the geometric features with the inspection items to generate a to-be-tested feature set; measuring point distribution is calculated in a self-adaptive mode according to the feature types, and a collision-free measuring path is generated; converting the path into a numerical control code containing a radius compensation algorithm and embedding the numerical control code into a machining program; after the numerical control center executes measurement, abnormal data calculation deviation is eliminated, and a compensation value is written into a register to correct parameters. By establishing full three-dimensional model data association and applying curvature adaptive sampling and statistical data cleaning strategies, data faults of design and detection are eliminated, the problem of environmental noise interference is solved, and the precision control level of complex feature processing is improved.

The invention relates to the technical field of data annotation, and discloses a data annotation system and method for an automatic driving scene, and the method comprises the steps: constructing a scene database and a data set for managing to-be-annotated data, working condition data and accepted data; creating an annotation item and obtaining to-be-annotated data; the to-be-labeled data and the corresponding working condition data are associated, scene criticality evaluation is carried out, a scene criticality evaluation driving mechanism is introduced to carry out dynamic strategy generation, and a task strategy and a quality inspection strategy are included; splitting the to-be-labeled data according to a task strategy and performing task allocation; performing intelligent labeling model training based on the data set corresponding to the to-be-labeled data category; pre-labeling by utilizing the trained intelligent labeling model, carrying out manual labeling by utilizing a plurality of built-in labeling tools, and outputting labeled data; performing quality inspection on the labeled data according to a quality inspection strategy so as to output checked and accepted data; and performing intelligent labeling model training and updating based on the checked and accepted data. According to the invention, a high-expansibility system is constructed, and man-machine cooperation high-quality labeling is realized.

The invention discloses a business document design and user-defined field modeling method supporting dynamic expansion. The method relates to the technical field of custom field modeling, and comprises the following steps: constructing a core foundation and a metadata system; a non-intrusive expansion and data association model; performing high-performance query and index optimization; and performing dynamic verification and visual application. Structural stability and flexible expansion are realized through separate design of the core table and the expansion table, field definition, storage, verification and form rendering are driven by metadata, complex query and cross-receipt field multiplexing are supported, data consistency and full-link connection are guaranteed, business receipt design and self-defined field modeling expandability are improved, and the method is suitable for large-scale popularization and application. The problem of low expandability of business document design and custom field modeling in the prior art is solved.

A high-performance loosely coupled multi-modal data fusion system for an intelligent driving environment perception system, and on-board equipment are disclosed in the present disclosure. The data fusion system includes a fusion detection model based on a modal-specific feature interaction strategy, configured to convert LiDAR point clouds, camera images, and millimeter-wave radar point clouds into unified bird's-eye view (BEV) features and perform multi-modal fusion; and a fusion tracking model based on a cascade coupling data association strategy of motion-appearance features, configured to perform subsequent trajectory tracking and matching based on feature information of the multi-modal fusion. A VoD dataset and a K-Radar dataset are selected for training, validating, and testing comprehensive performance of the models. An inference model is accelerated by applying TensorRT, to be quantified and deployed on an on-board computing test platform.

The invention provides an e-commerce digital optimization method and system based on reinforcement learning, and relates to the technical field of data processing, and the method comprises the steps: 2, building an analysis framework based on a time sequence data unit set, and recognizing a data association mode; for the sales trend data of each warehouse, analyzing a sales change trend and positioning a key turning point by calculating a slope and an angle of a sales line segment in adjacent time periods; internal evaluation nodes and external evaluation nodes are determined in the association mode to construct a dynamic evolution path, strategy optimization parameters are calculated in combination with sales trend geometric features, and state features are formed. The strategy is dynamically adjusted, and the e-commerce operation efficiency is improved.

The invention discloses a method and system for generating a financial information graph of a user through a financing platform, and relates to the technical field of financial information. The method is used for solving the problems of weak user operation data association and inaccurate risk assessment in a traditional financing platform. Firstly, multi-dimensional analysis based on transaction semantic recognition is performed on a user bank flow, transaction scene features are extracted, and a supply chain core enterprise fund flow path is recognized; then, a cash flow-supply chain cooperation efficiency coefficient is calculated in combination with order account period information, and enterprise fund turnover efficiency is quantified; then, constructing a dynamic weighted supply chain network topology based on the coefficient, and identifying key fragile nodes through a graph neural network; and finally, fusing cooperation efficiency and risk characteristics, predicting enterprise performance capability through a dynamic attention mechanism, generating an interactive dynamic information graph of a fund flow path, a supply chain state and a risk result, and realizing intelligent analysis and visual presentation of a financing platform on an enterprise operation state.

The invention provides a full-link data tracing processing method and system for a cross-border home supply chain, and relates to the technical field of supply chain management, and the method comprises the steps: 3, dividing a plurality of data unit sets which are correlated with each other based on the process data of the supply chain, determining a reference data unit set, and obtaining two groups of different data correlation rules; analyzing an interaction relationship between the two groups of data association rules, establishing a data state identification range, and selecting two data verification reference points in the data state identification range; and according to the evolution relationship of the data verification reference point on the time sequence, the evolution span is quantified by calculating the comprehensive distance between two points on the time and data feature dimensions, a data state evolution path is constructed to generate a data calibration coefficient, and a data link track comprising a timestamp and link information is formed. According to the invention, full-link data tracing of the cross-border home supply chain is realized, and the collaboration efficiency of each link of the supply chain is improved.

Methods, apparatus, systems, and articles of manufacture are disclosed for sequence of event generation for a process control system. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to at least one of execute or instantiate the machine readable instructions to obtain a first digital signal from a first field device representative of a first sensor data value labeled with a first timestamp generated by the first field device, obtain a second digital signal from a second field device representative of a second sensor data value labeled with a second timestamp generated by the second field device, and store a data association of the first and second sensor data values in a datastore, the data association representative of a sequence of events including an ordering of the first sensor data value and the second sensor data value based on the first and second timestamps.

The invention provides a vehicle-mounted radar target course angle filtering tracking method and system. The method comprises the steps that S1, the radial speed of clustered point cloud is converted into the absolute radial speed relative to the ground; s2, when a new track is created, an independent course angle extended Kalman filtering tracking module is synchronously initialized; s3, setting a projection direction based on a course angle estimated value output by the module, and completing data association between the point cloud and the track and track state updating through projection; and S4, extracting a position variable quantity based on the updated track state, forming an observed quantity by combining the absolute radial speed of the associated point cloud, inputting the observed quantity to a course angle module for extended Kalman filtering updating, outputting an updated course angle estimated value, and feeding back the updated course angle estimated value to the step S3 of the next frame. According to the invention, by constructing a cooperative closed loop of course angle estimation and data association, the stability of course angle estimation and the overall tracking robustness are significantly improved.

The invention discloses an intelligent analysis system for the running state of a 3C vehicle-mounted contact network, and belongs to the technical field of crossing of intelligent operation and maintenance of rail transit and industrial artificial intelligence. And the system associates the multi-source heterogeneous observation data to a specific equipment unit through an equipment centralized data binding module. And the multi-modal feature extraction and state management unit processes the data and maintains a multi-dimensional state vector of the equipment by using the Kalman filtering updating unit. And the physical-data hybrid decision maker fuses the data driving rule and the simplified physical model to output a diagnosis result. The topology analyzer performs global verification based on a mechanical transfer rule. The system further comprises a long-term health state prediction and feedback module, early failure risks are predicted through a hidden Markov model, Kalman filtering process noise is dynamically fed back and adjusted, and cooperation of long-term prediction and short-term estimation is achieved. According to the method, the technical problems of multi-source data splitting, lack of physical basis in diagnosis and incapability of predictive maintenance are solved.

The invention discloses a numerical control machine tool part machining data storage method and system. The numerical control machine tool part machining data storage method comprises the steps that multi-source data in the machining process is collected and synchronized in real time; performing process-entropy-based process section segmentation on the data stream, extracting multi-modal features, evaluating data values through a classifier fusing rules and machine learning, and generating feature data packets according to a hierarchical storage strategy; a data gene code is bound for each part, a processing process knowledge graph is constructed, and full-chain data association and accurate tracing are achieved; a sample is generated based on the feature data packet, a graph neural network and Bayesian optimization are utilized to train a process optimization model at a cloud end, optimization parameters are fed back to a machine tool to be executed, and a closed loop is formed. Process entropy segmentation, knowledge graph and GNN closed loop optimization are adopted, the problems that data storage cost is high, tracing is difficult, and data values are not mined are solved, and the accuracy of data tracing is improved. And intelligent data compression, full-life-cycle tracing and process adaptive optimization are realized.

The present invention extends to methods, systems, and computer program products for data retrieval and validation for asset onboarding and deriving asset characteristics. A first set of data associated with an asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the asset are derived from the first set of data using one or more AI agents.

The invention relates to the technical field of data analysis, and discloses a low-code data analysis platform and method based on multi-source data real-time access, and the platform comprises a data access module, a modeling module, a rendering engine and an early warning module. The method corresponds to the platform. According to the method and the device, the problems of real-time integration and standardization of the multi-source heterogeneous data are solved by obtaining the standardized data entity, and a high-quality data basis is provided for subsequent analysis; a dynamically constructed knowledge graph and a graph embedding technology are deeply fused into a data analysis process, so that semantic association among data entities is intelligently understood, an optimized query statement is automatically generated, intelligent recommendation and rationality verification are provided for establishing a data association relationship for a user, and the technical threshold of data modeling is reduced; a second-level response closed loop from data access to visual presentation is formed based on intelligent chart recommendation of data features and early warning rules and instant calculation and triggering in a real-time data stream.