502 results about "Data filling" patented technology

The invention provides a test risk digital twinning early warning method based on multi-domain cooperative monitoring, and belongs to the technical field of virtual-real fusion test and digital twinning, and the method comprises the steps: firstly, building a fine finite element simulation model which comprises a digital tool system, a digital sensor and a test piece and considers nonlinearity; secondly, performing nonlinear finite element simulation analysis, and constructing a multi-level mechanical response field inversion reduced-order model; thirdly, completing the construction of a complete sensor data set through a data filling algorithm, and carrying out the failure judgment of the first hierarchical structure based on the complete sensor data set; and finally, carrying out future loading level sensor data prediction and completing failure judgment of a second hierarchical structure. Carrying out full-field mechanical response inversion and online real-time correction; and performing response inversion of the region of interest to realize failure judgment of the third hierarchical structure. According to the invention, real-time dynamic monitoring and early warning of the structure test risk can be realized, the real-time performance, the robustness and the accuracy are high, and a powerful guarantee is provided for the safety and the reliability of the structure test.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention provides an LSTM (Long Short Term Memory)-based intelligent fusion terminal station transformer data working condition dividing filling method. The method comprises the steps of data acquisition and working condition division: carrying out working condition refining and pile dividing on station area data uploaded by an intelligent fusion terminal according to a photovoltaic equipment operation state OpSt; data preprocessing: carrying out abnormal value detection, missing value preliminary filling, filtering denoising and normalization processing on the data after working condition division; sub-working-condition model training: aiming at each type of working conditions, respectively constructing and adopting the preprocessed data to train a corresponding LSTM data filling model; and missing value classification and filling: calculating the working condition category of to-be-filled data according to the similarity, calling the trained LSTM data filling model under the corresponding working condition, and performing prediction filling on the missing value. The method can solve the problems of high dimension, non-stationarity and complex missing modes in the monitoring data of the photovoltaic transformer area.

The invention discloses an unsupervised time sequence missing data filling method based on a dynamic cause and effect graph structure, which comprises the following steps: S1, collecting a data sample, and processing to obtain an initial filling complete data table; s2, clustering all the initial filling complete data tables, and selecting representative data tables; s3, constructing a sub-causal graph of each representative data table, and further constructing a total causal graph; s4, on the basis of a causal relationship in the total causal graph, constructing and training a corresponding weighted expandable depth time sequence convolutional network for the to-be-filled target feature column to obtain a missing value filling model, and further performing coarse filling on missing values; and S5, performing refined secondary filling on the target feature column by using the obtained missing value filling model to obtain a complete data table. The invention provides a filling method without real missing labels, optimization is carried out through a self-consistency or structure maintenance principle, the dynamic dependence and real causal relationship between the time series data can still be captured in a dynamic complex scene, and a better data filling effect is achieved.

In some embodiments, a method may include determining hierarchical agents including a first agent and a second agent based on a document defining a UI, the hierarchical agents having access to initial context data. The method may include delegating a task related to a UI element to the first agent based on information associated with the UI element and restricting portions of the initial context data available to the first agent to a propagated subset based on input data types mapped to the first agent and portions of the initial context data available to the second agent. The method may include generating interaction data by providing a machine learning model with the propagated subset and updating the document (e.g., by populating or interacting with the UI element based on the interaction data).

The invention discloses an underground engineering on-demand ventilation knowledge graph retrieval enhancement generation method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps of data cleaning and ontology layer modeling, knowledge extraction based on prompt engineering, knowledge graph data filling and storage, storage of an extraction result in a Neo4j graph database, and retrieval enhancement generation of an inference engine. Developing an intelligent question and answer platform, and providing knowledge graph visualization and interactive question and answer functions; the system comprises a data acquisition and preprocessing layer, an ontology and knowledge extraction layer, a graph storage layer, a semantic index and entity link layer, a sub-graph arrangement and cue word generation layer, a generation and reasoning layer and an application and interaction layer. According to the invention, the acquisition and management efficiency of underground engineering ventilation knowledge is improved, the professionality and accuracy of the question-answering system are enhanced, the interface is friendly, knowledge tracing is supported, and convenient experience is provided for users.

The invention discloses a laser wind-finding radar missing measurement data filling method based on a DFA scale index, and the method comprises the following steps: (1) carrying out the standardization processing and quality control of the original data of a laser wind-finding radar, and obtaining the wind speed standardization data; step (2), performing detrending fluctuation analysis on the wind speed standardized data and calculating a DFA scale index; combining the wind speed standardized data with the DFA scale index to construct wind speed data containing the DFA index; (3) dividing the wind speed data containing the DFA index into a training set and a test set; performing machine learning model training by using the training set to generate a missing data filling model; and step (4), filling the missing measurement data of the laser wind finding radar by using the evaluated missing measurement data filling model. The method can solve the problem that the existing laser wind-finding radar missing measurement data filling method depends on artificial experience and cannot fully utilize the internal physical mechanism of the wind speed, so that the data restoration precision is not ideal.

The invention discloses an early warning method and device for multi-device time sequence data and electronic equipment. The method comprises the steps of determining a time sequence data stream; dividing the time sequence data stream according to the model identifier to obtain a plurality of sub-time sequence data streams, sliding on the sub-time sequence data streams by using the time windows according to a preset step length from the starting timestamps of the sub-time sequence data streams, and determining the time sequence data under the time window which slides each time as a target time sequence data stream; performing timestamp alignment and data filling on time sequence data in the target time sequence data stream to obtain an aligned target time sequence data stream, and predicting a time sequence data predicted value corresponding to the aligned target time sequence data stream in a future time period through a prediction model corresponding to the aligned target time sequence data stream; and determining early warning information corresponding to the target time sequence data stream according to the time sequence data predicted value. The technical problem that the accuracy of an early warning result is affected by data time sequence dislocation caused by inconsistent acquisition time of time sequence data between devices is solved.

The invention provides a power battery passport management method and device, electronic equipment and a storage medium. A data system covering the whole life cycle of a battery is constructed; obtaining power battery multi-source data reported by suppliers at all levels, binding the power battery multi-source data with a unique identifier of the power battery, and realizing evidence storage and verification through a block chain consensus mechanism; constructing a digital twin engine based on a preset battery electrochemical model and a thermal runaway model, performing working condition prediction on the battery operation state, and generating working condition early warning threshold data; and filling a preset passport template with the multi-source data and the working condition early warning threshold data, generating a power battery electronic passport, and performing versioning maintenance and uplink release. Unified management, credible evidence storage and traceability of power battery full life cycle data are realized, normalization and dynamism of passport generation are improved, prospective early warning of battery operation risks is realized in combination with digital twinborn prediction, and integrity, safety and compliance of the electronic passport are guaranteed.

The invention relates to the crossing field of artificial intelligence technology and e-government, and discloses an e-government enterprise service intelligent response method based on AI cue word engineering, comprising: constructing and maintaining a government knowledge graph and a scenarized cue word template library; receiving and analyzing a service request input by an enterprise, and extracting an enterprise feature tag and a business intention; selecting a target cue word template based on the matching degree of the enterprise feature tag and the scene tag of the template in the scene cue word template library, and generating an adapted cue word template; retrieving associated knowledge data from the government affair knowledge graph according to the service request, and filling the adapted cue word template with the retrieved associated knowledge data to generate a structured cue word; inputting the structured cue word into an AI model to drive the AI model to generate a government affair service response; and collecting multi-dimensional evaluation feedback of government affair service response, and carrying out iterative optimization. The demand understanding precision is improved, and the risk of core information misjudgment is reduced.

Methods, systems, and computer-readable storage media for receiving use case data descriptive of a use case that includes tabular data matching, the use case data including process data, task data, table schema data, and a set of few-shot examples, populating a CoT extraction prompt template using the use case data to provide a CoT extraction prompt, prompting a LLM using the CoT extraction prompt, receiving, from the LLM, a CoT script responsive to the CoT extraction prompt, generating an inference prompt template using the CoT script, and deploying the inference prompt template for production inference.

The invention provides a traffic real-time road condition prediction method and system based on knowledge engineering, and the method comprises the steps: collecting multi-source traffic data in real time, carrying out the missing data filling and data space-time alignment processing of the multi-source traffic data, and obtaining the processed multi-source traffic data; fusing the processed multi-source traffic data by adopting a method based on dynamic space-time tensor modeling to obtain fused traffic data; based on the fused traffic data, constructing a self-evolution traffic knowledge graph with time-space attributes; on the basis of the self-evolution traffic knowledge graph, constructing a road condition hybrid prediction model by adopting a knowledge-guided meta-learning framework; and performing traffic real-time road condition prediction based on the road condition hybrid prediction model. According to the invention, the robustness of the system is obviously enhanced, and more accurate road condition prediction is realized.

The present invention provides a method and system for automatically and seamlessly processing clinical trial source data into electronic data capture (EDC) systems. In one embodiment, a file structure is defined for an electronic binder system (eBinder); source data is uploaded to the eBinder; the source data is encrypted, Patient Identifiable Information in the source data is masked; the source data is converted into machine readable plain text in the JavaScript Object Notation (JSON) format using Natural Language Processing (NPL) technologies; the JSON data is converted into tabulated machine readable data in the HyperText Markup Language (HTML) format using NPL technologies; the HTML data is converted into machine understandable data using NPL technologies; the machine understandable data is populated into EDC datasets using NPL technologies; the source data and converted data are displayed side-by-side for source data verification; and a platform is provided for regulatory data verification or auditing.

The invention provides an irrigation area sediment deposition prediction method and system, computer equipment and a storage medium, and belongs to the field of water conservancy project intellectualization, and the method comprises the steps: collecting historical hydrological data, real-time monitoring data and remote sensing observation data of a to-be-predicted irrigation area; the method comprises the following steps: resampling historical hydrological data and real-time monitoring data, carrying out forward filling completion on missing data, filling remote sensing observation data into a unified time axis, and filling missing spatial features based on spatial feature values of an adjacent time reference to obtain multi-source data with time-space alignment; extracting features of the multi-source data to obtain four features; and combining the four features and calculating correlation weights among the features so as to determine the sediment flow, the sediment concentration and the sediment flow velocity of the to-be-predicted irrigation area. According to the method, aiming at the defect of non-uniform spatial and temporal distribution among multi-source data sources, spatial and temporal alignment is realized through time re-adoption and spatial feature completion, the input information can accurately reflect the sediment transportation process of an irrigation area, and the prediction accuracy is improved.

The invention belongs to the technical field of health information processing and management, and discloses a personalized health management service system based on multi-source data fusion, which comprises a data acquisition device access wearable device, a health application, a medical information system and a user terminal entry data, the fusion processing platform carries out missing completion, abnormity elimination, time alignment and weighted fusion on the data to form a weighted fusion sequence, the cross-domain correlation builder generates a correlation map of lifestyles and clinical indexes, and the risk modeling engine outputs individual health risks under the constraint of the map. The management scheme generator generates personalized diet, exercise, sleep and disease prevention schemes under the constraint of the rule base. The compliance evaluation system combines subjective feedback and objective monitoring to form scores, the scheme adjustment mechanism executes minimum disturbance update based on scores and atlas trends, and the auditing unit realizes whole-process data tracing and compliance guarantee.

A system for securely updating an electronic transaction card held by an account holder with an additional account and / or account data is described. A dynamic transaction card may be securely updated with an additional account by using pre-stored shell data and / or inactive data, whereby the pre-stored shell data may be populated using data received from an issuer system and / or the inactive data may be activated via an activation signal received from a mobile device.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice-driven information recording and checking method, device, equipment and medium. Key fields are extracted by applying a semantic analysis strategy to form preliminary structured data, an independent data system is connected to verify authenticity and generate data conflict information, and automatic error correction is executed based on the conflict information to generate updated structured data; and according to the user role matching output template, filling the template with the updated structured data to generate display content, and pushing the display content to target terminal equipment. Structured data generation is realized through voice input and semantic analysis, the data accuracy is improved by combining cross-system verification and automatic error correction, personalized display and real-time pushing are realized by utilizing a role matching template, and a closed loop of acquisition, verification and display is formed, so that the efficiency of information recording and management is improved.

The invention discloses a robust lightweight time sequence prediction method based on bidirectional filling and geometric attention, and belongs to the field of multivariate time sequence data missing filling and prediction. The problem of monitoring data missing caused by frequent failure of the sensor in a complex geological environment is solved, and the accuracy and reliability of geological disaster prediction are improved. The invention provides a co-processing framework which is used for fusing a bidirectional cyclic interpolation time sequence (BRITS) and a SimpleTM light-weight prediction model, and is characterized in that the BRITS and the SimpleTM light-weight prediction model are combined with each other, and the BRITS and the SimpleTM light-weight prediction model are combined with each other. According to the framework, firstly, forward and backward time sequence dependence and multivariate variable relevance of sensor data are captured by utilizing a BRITS model, a failure mode is accurately recognized, and efficient data filling is realized; and then, in combination with a specific stable wavelet transform multi-scale decomposition and geometric product attention mechanism of the SimpleTM model, fine-grained time sequence characteristics of the filled data are deeply mined under low calculation overhead, and reliable support is provided for subsequent disaster prediction. According to the method, the prediction precision and robustness in a data missing scene are remarkably improved, meanwhile, the lightweight deployment requirement in a resource-limited tunnel construction environment is considered, and the railway tunnel construction safety is effectively guaranteed.

The invention relates to the technical field of data processing, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a table generation method, device and equipment based on image data and a medium, the method comprises the following steps: obtaining an initial document image, and carrying out image enhancement processing on the initial document image to obtain a to-be-identified document image; performing visual table detection on the to-be-identified document image to obtain a target table area in the to-be-identified document image, and performing horizontal projection and vertical projection on the target table area to obtain a target table layout; extracting target table data of each table unit in the target table layout, and constructing a mapping relationship between the table units and the target table data; and generating a corresponding structured spreadsheet according to the target table layout, and filling the target table data into the structured spreadsheet according to the mapping relationship. According to the invention, the image table data identification accuracy can be improved.

The invention discloses a power grid multi-source data fusion missing value filling method, a storage medium and electronic equipment. The method comprises the steps of obtaining three-dimensional load tensor original data of a source domain and a target domain, and executing preprocessing; constructing a source domain condition generative adversarial network model which comprises a generator and a discriminator; establishing loss functions of the discriminator and the generator; training a source domain condition generative adversarial network model; calling a generator of the source domain conditional generative adversarial network model to carry out filling training on training three-dimensional load tensor data of the target domain, and completing model parameter migration adaptation at the same time; in the migration process, shallow layer parameters of the source domain condition generation adversarial network model are frozen, and a staged progressive fine tuning strategy is adopted to train a target domain data filling model; and storing the trained target domain data filling model, inputting the missing three-dimensional load tensor data of the target domain, and outputting the three-dimensional load tensor filling data of the target domain. According to the invention, the problem of poor data filling effect caused by lack of sample data volume is solved.

The invention provides a real-time data processing method and system for multiple devices of a regional wind power plant, and the method comprises the steps: obtaining the monitoring operation data of the multiple devices of the regional wind power plant, and carrying out the data preprocessing of the monitoring operation data, so as to obtain processed data; performing first exception identification on the processed data to obtain a first exception set; performing second exception identification on the processed data to obtain a second exception set; determining a comprehensive exception set based on the first exception set and the second exception set, and determining rejected data based on the comprehensive exception set; according to the method, the accuracy, the recall rate and the precision rate of identification can be effectively improved by performing two times of anomaly identification on the data, then the condition diffusion filling is performed on the data, the long-term relationship in the time series data can be effectively captured, and the accuracy of the data processing is improved. The missing value is estimated by selecting the normal value as the observed value, so that the validity and accuracy of data filling can be improved.

The invention discloses a method and a system for code-free definition of a contract form based on a WORD template, and belongs to the field of contract or protocol content filling and reporting. According to the method, a back end obtains a Word template file, analyzes and generates a control object list and parameter mapping, generates an unfilled PDF template through template rendering, automatically generates a new control list by analyzing text field attributes and coordinate information, and a front end combines a page picture and the control list to generate a suspended WEB form through rendering. In the operation period, multi-page contract calling is achieved through page navigation, data filling and interaction are completed based on two-way binding, data verification is executed, legal data are stored and serialized, a Word template is filled according to the data, and finally a complete contract PDF is generated. According to the method, automatic conversion and accurate positioning from the Word template to the WEB form are realized, the form generation efficiency, the configurability and the system flexibility are remarkably improved, and the method is particularly suitable for rapid business adaptation of documents such as contracts and protocols.

The application discloses a kind of forest scene voxel model construction method and system, the method includes: based on laser radar obtains the point cloud data of reference tree of the region to be measured and the point cloud data of canopy structure;According to the point cloud data of canopy structure, the point cloud data of reference tree is carried out point cloud conversion, and the point cloud conversion data of reference tree is obtained;According to the point cloud conversion data of reference tree, the blank of the point cloud data of canopy structure is filled with reference tree, and high-density point cloud data is generated;High-density point cloud data is voxelized, and forest scene voxel model is constructed.The system includes: data acquisition module, point cloud conversion module, data filling module and construction module.Through using the application, it can be realized in large space range to reconstruct the fine scale three-dimensional structure of forest scene and improve resolution.The application is a kind of forest scene voxel model construction method and system, and can be widely applied to laser radar data three-dimensional modeling technical field.

The invention provides a measurement data reconstruction method based on edge computing, which takes an edge node as an execution main body, the edge node is in communication connection with a plurality of devices, and the method comprises the following steps: obtaining device data sequences collected by the plurality of devices in a preset sampling time period, and obtaining a cleaning data set based on the plurality of device data sequences; performing spatial difference degree analysis based on the cleaning data set to obtain an equipment spatial difference degree data set; based on the equipment space difference degree data set and the cleaning data set, carrying out elimination processing to obtain a preliminary data set; and based on the preliminary data set and the equipment space difference degree data set, performing data reconstruction processing under a preset missing data filling strategy to obtain a reconstructed data set. According to the technical scheme, data reconstruction is carried out based on the spatial difference degree, and the accuracy of data reconstruction is improved.

The invention discloses a report generation method and device, equipment and a storage medium, and relates to the technical field of document processing, the method comprises the following steps: performing fine-grained splitting on a multi-source heterogeneous environmental assessment document, and generating a target environmental assessment knowledge base, the multi-source heterogeneous environmental assessment document comprising a historical environmental assessment document, an environmental protection regulation document and an environmental monitoring document; the environmental assessment project basic information is input to a preset environmental assessment report generation model, a target report generation template is set in the preset environmental assessment report generation model, and the target report generation template is generated based on environmental assessment expert writing thoughts and a standard example template; and performing data filling on the target report generation template based on the target environmental assessment knowledge base and the environmental assessment project basic information through a preset environmental assessment report generation model to generate a target environmental assessment report. By applying the technical scheme, the technical problem that a report generation method in the prior art depends on manual operation and a fixed template and lacks flexibility and pertinence is solved.

The invention discloses a graph database query throughput optimization method based on a client multiplexing object, and belongs to the technical field of graph database query optimization. The graph database query throughput optimization method comprises the following steps: receiving serialized response data from a graph database service; analyzing the column number of the result set from the metadata, and creating an object list containing a corresponding number of value objects; parsing the first row of data, including parsing data structure information from the metadata and caching the data structure information, and filling the parsed first row of data into a corresponding value object in an object list; and analyzing the data of the second row and the subsequent rows in the result set, multiplexing the cached data structure information and the created object list, and directly filling the analyzed data of each column of the current row into the corresponding value object in the object list. Through object multiplexing, metadata cache multiplexing and lazy loading optimization, the dynamic memory allocation frequency in the deserialization process is remarkably reduced, and the processing throughput of query response is improved.

The invention discloses an electronic archive management method for a business document generation layout file, and belongs to the technical field of electronic archive management. Comprising the following steps: constructing a multi-dimensional feature vector by collecting business document scene features, screening candidate templates from a template pool by adopting a multi-level similarity matching algorithm, and adjusting and determining a target template in combination with a dynamic structure; the basic metadata and the associated system data are integrated, and qualified metadata is output through multi-level rule verification and abnormal data correction; based on the target template, executing data filling typesetting and format resource preprocessing by adopting a double-thread asynchronous processing mechanism, and generating a layout file conforming to PDF / OFD double standards through an adaptive algorithm; calculating a hash value of the layout file and writing the hash value into a block chain to realize tamper-proof evidence storage, and realizing secure storage and quick retrieval by adopting a distributed architecture in which a main file is separated from an index file. According to the method, the problems of low template matching precision, incomplete data verification, low processing efficiency, insufficient file safety guarantee and the like in the prior art are solved.

The invention, which relates to the technical field of data fusion, discloses a multi-source data fusion cooperative completion method and system for a digital intelligent material yard, and the method comprises the steps: collecting a multi-source data set of a target material yard, and obtaining material yard source data through spatial three-dimensional alignment; performing neighborhood point density detection, splicing data missing boundaries, and performing triangular mesh segmentation and marking; multi-modal data is used as input, semantic description constraints and implicit physical constraints are combined, and a diffusion supplementing unit is built; the marked data are imported into the unit, then random noise of the triangular mesh is taken as a starting point, a data supplementing result is generated through multi-round iterative denoising under constraint guidance, and after the triangular mesh is correspondingly filled, stock ground supplementing data are determined. The technical problems that data acquired by multiple devices in an existing digital intelligent stock ground are mutually separated and data missing exists due to the influence of environmental factors are solved, and the technical effects that multi-source data are integrated, missing data are accurately complemented, and complete and reliable stock ground data are formed are achieved.

The invention belongs to the technical field of aluminum wheel production quality inspection, and provides an aluminum wheel quality index intelligent management method and system and a storage medium, based on real-time data acquisition and field intelligent terminal manual data filling and reporting, the rate of finished products, the rework rate, the number of waste products and other data of each process are calculated, and through a preset threshold value and color marking, the quality index of the aluminum wheel is obtained. According to the method, the index state is fed back in real time, multi-dimensional visual analysis of data is realized through dynamic graph display and data drilling, response adjustment of an optimal process is carried out on a production machine in combination with an analysis result and processing suggestions, performance appraisal of field production personnel is associated based on each shift quality index, and the enthusiasm of the field production personnel is aroused. AI modeling is formed through data accumulation to gradually improve a quality index management system, the method is suitable for multi-link production scenes such as casting, machining and coating in the aluminum wheel manufacturing process, the stability of the production quality of aluminum wheel products is improved, and the quality abnormity response time is shortened.