932 results about "Data filling" patented technology

The invention relates to the technical field of automatic report generation, and provides a test analysis and report generation method and system based on large model retrieval enhancement, and the method comprises the steps: firstly carrying out the semantic task decomposition of user query, and meanwhile, achieving the multi-dimensional information extraction through the butt joint of a knowledge vector library and a structured image-text knowledge system established by a private knowledge base module. And three strategies of RAG, Self-RAG and Graph-RAG are combined to enhance the generation capability of the large model so as to accurately obtain background knowledge. And constructing a standard SQL statement according to a structured query requirement, and performing data filling according to template prompt by an output result fusion module in combination with a query background and an SQL execution result to form a test report. By optimizing a retrieval enhancement generation method, professional knowledge supplement related to query is realized, and the reasoning ability of a large model in a professional scene is effectively enhanced, so that the accuracy and the intelligent level of data analysis are improved.

The invention relates to the technical field of computer data processing, in particular to an automatic Excel template data backfilling method based on an LLM semantic understanding technology, which is based on the application of a large language model LLM, a natural language processing NLP, a natural language to NL2SQL and an automatic data filling technology. Through semantic understanding and the NL2SQL technology, efficient and accurate extraction and filling of template data are achieved, the problems that an existing method is low in efficiency and prone to making mistakes are solved, the working efficiency and data quality under the scenes of grassroots social governance and the like are improved, and the burden of workers is relieved.

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention discloses an energy management system data filling method and architecture based on time sequence analysis, and the method achieves the intelligent prediction and filling of a data missing segment in an energy management system through constructing a filling model based on a naive Bayes algorithm, and improves the data integrity and continuity. The system firstly dynamically manages historical data and new data through a sliding window mechanism to ensure that the model can fully consider the change trend of time sequence data in the filling process. The filling model combines the distribution characteristics of historical data and current data during initialization, and endows the data in different time periods with weights, so that the model can adapt to the equipment state and environment change. And after the data missing segment is detected, the system immediately calls the filling model to carry out prediction filling, replaces the original missing data with the result and writes the result into the database, so that the accuracy of subsequent analysis and anomaly detection is ensured.

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 relates to the technical field of artificial intelligence chips, and provides an operator fusion method and device, electronic equipment and a storage medium, and the method comprises the steps: determining an input tensor, an output tensor and a coverage range of a to-be-fused operator, and the to-be-fused operator comprises a plurality of view operators; on the basis of the metadata of the input tensor, the metadata of the output tensor and the logic relation between the view operators in the coverage range, determining the mapping relation between the dimensions of the input tensor and the output tensor; and performing data filling on the output tensor based on the mapping relationship and each data in the input tensor, and taking the filled output tensor as a fusion result of the operator to be fused. According to the method, multiple continuous view operators are efficiently and quickly fused, so that the problem of performance reduction caused by frequent data handling in the model is effectively solved.

The invention discloses a financial data automatic filling method and device, equipment and a storage medium, and belongs to the technical field of big data. The method comprises the following steps: determining a data source corresponding to original financial data by using a pre-trained data classification model; acquiring a data processing rule engine and a target data filling template matched with the data source; performing data cleaning on the original financial data based on a data cleaning rule; performing data analysis on the standardized data by using a data analysis rule; performing association relation extraction on the target data source field and the target filling field by using a data mapping rule, and performing data field mapping according to the field association relation; and according to a data field mapping result, filling the target data source field into the target data filling template to generate a financial data report. The invention further relates to the technical field of block chains, and financial data are stored on the block chain nodes. According to the method and the device, the applicability and the operation convenience of automatic filling and reporting of financial data in a multi-source data scene are improved.

The invention relates to a dam deformation prediction method with embedded feature selection, and belongs to the field of dam safety monitoring and artificial intelligence, and the method comprises the following steps: obtaining the original monitoring data of an environment quantity and a dam deformation quantity; performing preprocessing of data cleaning, data filling and data normalization on the original data; evaluating the importance of environmental features based on a random forest algorithm, and selecting an optimal feature set in combination with a recursive feature method; constructing a dam deformation prediction model based on a convolutional neural network, a long and short term memory neural network and an attention mechanism deep learning algorithm; and the optimal feature is used as the input of CNN-LSTM-Attention to predict the deformation of the dam. Through the method in the application, the environmental characteristics having significant influence on dam deformation can be effectively determined, the complex mapping relation between the deformation and the environmental characteristics is fully excavated, and accurate dam deformation prediction is realized.

The invention relates to the technical field of iron and steel smelting, in particular to a continuous casting quality data processing method based on multi-modal cognitive reasoning, which comprises the following steps of: synchronously acquiring multi-source sensor data, performing time alignment, format standardization, noise filtering and normalization processing, extracting image / thermal image spatial features through CNN (Convolutional Neural Network), and calculating the continuous casting quality data according to the image / thermal image spatial features. The LSTM is combined with frequency domain analysis to extract vibration / sound time sequence features, cross-modal alignment fusion is realized through feature vectorization, quality prediction, anomaly detection and real-time deployment are realized based on a deep neural network and an attention mechanism by fusing process knowledge, and then slab defect causes are accurately identified by integrating a multi-modal reasoning result. Through time synchronization, format unification, noise filtering and data filling, the problem of heterogeneity of multi-source sensor data is solved, and time dislocation and noise interference are eliminated; abnormal data are cleaned by an outlier removal technology, the reliability of input data and the model robustness are improved, and efficient cooperative processing of multi-modal data is ensured.

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 provides a GNSS virtual observation value generation method and system based on real-time precise point positioning, and belongs to the technical field of GNSS positioning, and the method comprises the steps: obtaining ephemeris clock error data and observation data received by a reference station in real time, filling a UCUD PPP observation equation, and solving a non-difference floating point solution of a to-be-estimated parameter of each parameter station; through UPD estimation, non-difference floating point solution ambiguity parameters recover the whole cycle characteristic, and a fixed solution STEC and a fixed solution ZTD are obtained; the STEC is locally fitted into a function about the position, and a regional non-difference ionosphere model is established; the ZTD is locally fitted into a function about the position, and a regional non-difference troposphere model is established; and generating a double-difference atmospheric delay between the main reference station and the virtual reference station based on the non-difference ionosphere model and the regional non-difference troposphere model to obtain a virtual observation value. According to the method, the reliability of GNSS signal atmospheric delay is effectively improved, so that the quality of a virtual observation value is improved.

The invention provides a presentation file generation method and system and electronic equipment, and the method comprises the steps: inputting input data into a pre-trained prompt template generation model in response to the input data and an initial presentation file template sent by a user, so as to enable the prompt template generation model to output a prompt template corresponding to the input data; sending a text data generation instruction to a large language model based on the prompt template and the to-be-sorted document, so that the large language model generates text data in a preset format based on the to-be-sorted document; determining a target node corresponding to the text data based on a preset node division rule; and based on the initial node and the target node of each reserved position in the initial PowerPoint template, filling the text data into the corresponding reserved position to generate a target PowerPoint. In the mode, the prompt template and the text data meeting the task requirements can be generated according to the input data, so that the target presentation file is quickly generated, the generation efficiency is improved, and the manual editing cost is reduced.

The invention relates to a wind resource prediction method based on quality control assimilation of multi-source observation data, and the method comprises the steps: integrating the multi-source observation data, including a multi-band radar reflectivity factor, a radial speed, a ground observation station, and wind profile radar data; quality control processing such as mottle removal, data filling, speed folding removal and discontinuous data removal is carried out, so that the accuracy and continuity of the data are improved; then, through lattice point interpolation and three-dimensional variation assimilation technologies, the processed observation data are fused into an initial background field, and an analysis field closer to the actual atmospheric state is generated, so that the reliability of severe convective weather risk assessment is remarkably improved, and the accuracy of wind energy prediction is also enhanced; and more comprehensive, detailed and stable information support is provided for weather forecast and energy management.

The invention provides a data filling method and device based on a time sequence continuous deletion filling model, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a training data set; respectively inputting the forward and backward input sequences into forward and backward prediction networks of a long-term filling module to obtain network output; a linear function is introduced to carry out weighted integration on network output to obtain long-term filling module output; inputting the true value of the to-be-predicted sequence and a randomly generated continuous missing mask into a short-term filling module to obtain a short-term filling module output; and outputting and inputting the long-term and short-term filling modules into the element weighting module to obtain a filling result. The invention provides the LSTI, and relates to a time sequence filling model which can automatically combine the output of the long-term filling module and the short-term filling module aiming at the continuous missing data of the time sequence. According to the method, long-term dependence and short-term dependence are respectively modeled through two specially designed expert models, so that continuous missing data can be effectively filled.

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 belongs to the technical field of power system application, and particularly relates to an intelligent power flow data filling method based on space-time coupling. The invention provides an intelligent power flow data filling method based on space-time coupling, which comprises the following steps: firstly, carrying out data preprocessing on original data through Hampel filtering, phase alignment and normalization, constructing a space-time encoder through joint coding of adaptive graph convolution (Adaptive GCN) and attention LSTM, capturing space-time coupling characteristics, and carrying out data processing on the space-time coupling characteristics; a multi-physical constraint generative adversarial network (Pry-GAN) is designed through generator physical constraints and a multi-scale discriminator to realize data filling, and it is ensured that a filling result has both numerical reasonability and physical feasibility.

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 a financial reimbursement method and device based on artificial intelligence, and electronic equipment. The method comprises the following steps: obtaining target reimbursement data input by a target user; inputting the target reimbursement data into a semantic analysis model to obtain filling intention data output by the semantic analysis model; the semantic analysis model is obtained by training sample reimbursement data and a corresponding order filling intention label result; matching the form filling intention data with a form template library to obtain a target reimbursement template matched with the form filling intention data; performing data filling operation on the target reimbursement template based on the filling intention data to obtain a target reimbursement form; and auditing the target reimbursement form, and carrying out financial reporting based on an auditing confirmation result. According to the method, the problem that mistakes are easy to make when bills are input manually is avoided, and the trouble caused by the fact that new and old employees are not familiar with reimbursement processes and form differences can be avoided.

The collection, communication, storage, monitoring, and analysis of environmental, vehicle, charger, and charging session data over a single EV or over a fleet of EVs is used to enhance or optimize existing services as well as deliver new services based on analysis of the collected data. One such service is reduction in total cost of travel. The cost minimization is achieved with near-real-time data, models populated using past collected data, and knowledge of energy costs and of chargers located in different geographic areas with potentially limited power for charging available at each charger site or station. A fleet management application used for cost minimization optimizes cost of travel for a single EV or for an entire fleet. Using individual EVs cost optimization as a goal, each EV may be optimized for potential local minimum cost of travel based on predictions using data collected from the fleet or from multiple fleets.

The invention discloses a debris flow geological disaster monitoring and early warning method, and belongs to the field of intelligent early warning, and the method comprises the following steps: carrying out the preprocessing of debris flow disaster impact factor data collected by a multi-source heterogeneous sensor, and obtaining a fusion data set under a unified spatial-temporal scale; performing abnormal value detection and data filling on the fused data set to obtain a high-quality data set; performing feature extraction on the high-quality data set by adopting a multi-scale convolutional neural network to obtain multi-scale features; establishing a debris flow disaster occurrence probability prediction model; and inputting the multi-scale features into the debris flow disaster occurrence probability prediction model to obtain debris flow geological disaster early warning. According to the invention, accurate early warning of debris flow geological disasters is realized, and powerful support is provided for disaster prevention and reduction.

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.

The invention relates to a database test case generation and optimization method, system and device and a storage medium. Knowledge information related to a target database is obtained, and after vectorization is conducted on the knowledge information, the knowledge information is stored in a vector database supporting vector storage and vector retrieval; obtaining test demand information, retrieving vector data related to the test demand information from the vector database, and extracting original text data corresponding to the vector data through a mapping relation of the vector database; obtaining a pre-constructed cue word template, filling the test demand information and the original text data into the cue word template to obtain cue words, analyzing the cue words through a preset large language model, and generating a test case of the target database; and sending the test case to a database server for running to obtain a running result, optimizing the cue word according to the running result, and re-inputting the cue word into the large language model until the test case generated by the large language model meets user requirements.

The invention discloses a model visualization method based on an improved offshore wind turbine BIM model lightweight algorithm, and the method comprises the following steps: Step 1, extracting data from an uploaded BIM model through a C-end tool and a corresponding analysis plug-in; 2, geometric data information and material information of the fan model are extracted through Revit secondary development, geometric data are written into a. Obj file, the material information is written into a. Mtl file, and the material information comprises colors, chartlets and transparency; and step 3, the optimized QEM algorithm is adopted to simplify the fan model. In the model generation stage, a model simplification algorithm is used, on the premise that model details are guaranteed, the model is further simplified, and the size of the model is compressed; in a data filling process, performing model simplification by using an improved QEM algorithm; in order to enable the model simplification algorithm to have a better simplification effect, concepts of boundary processing, patch overturning, texture processing and averaging field triangles are added on the basis of an original algorithm, and the simplification effect of the model simplification algorithm is optimized.

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 embodiment of the invention provides a pollution emission enterprise filling data quality control method and equipment. The pollution emission enterprise filling data quality control method comprises the following steps: mining and analyzing production technology and production process data of each industry, and constructing a raw material-output product association database; under the condition that the data filled by the pollution emission enterprise is received, querying the raw material-output product association database based on the filled data, and determining possible unfilled data; and generating a corresponding filling data frame and / or filling prompt information based on the possibly unfilled data. According to the method, a raw material-product association database is constructed in advance, and under the condition that part of filling data filled by a pollution emission enterprise is obtained, whether the filling data has possible missing data or not is inquired through the part of filling data. And when it is determined that the data is possibly missed to be filled, prompting can be carried out by generating a corresponding filling data frame or filling prompt information, and a pollution emission enterprise is guided to realize filling of the data which is possibly missed to be filled.

The invention provides a data filling and abnormity monitoring method and system based on multi-task learning, and relates to the technical field of sensor data processing.The method comprises the steps that multiple types of sensor data are integrated and preprocessed, correlation among the multiple types of sensor data is comprehensively analyzed, and a correlation matrix is obtained; the method comprises the following steps: grouping multiple types of sensors, constructing a multi-task learning prediction model based on a correlation matrix, inputting monitoring data of related sensors at corresponding time of a certain measuring point in the trained model to obtain prediction data and simulation data, performing data filling based on the prediction data, and performing anomaly detection based on the simulation data. Through the multi-task learning prediction model, the inherent correlation among multiple types of monitoring data and the correlation of measuring points in time and space can be fully utilized, and the monitoring data prediction and filling precision is improved.

The present invention relates to the field of tunnel deformation monitoring data filling. Disclosed are a method for performing tunnel deformation data filling on the basis of an improved GAIN. The method comprises: an auto-encoder performing an initial imputation method Autoencoders-LSGAIN on an LSGAIN, wherein Autoencoders is composed of two parts, that is, an encoder and a decoder, the encoder is a function which converts original data into a code, and the encoder comprises a full-connection neural network and a convolutional neural network; and the decoder reversely restores the code into an input, so as to generate an output most similar to the original data. The tunnel deformation data filling method performed on the basis of an improved GAIN that is used in the present invention improves the convergence speed of a GAIN method, and shortens a calculation time required for filling, such that a filling process can be completed within a relatively short time, and thus the overall efficiency of data processing can be improved, and subsequent analysis and modeling work can be developed more quickly, and the method has good performance in tunnel deformation monitoring data filling.

The invention provides an industrial process operation data filling and repairing method and system, and relates to the technical field of data filling. The global long-term trend of data is captured through cyclic matrix nuclear norm minimization, local short-term fluctuation characteristics of the data are described in combination with time-adaptive Laplacian regularization, and correlation among variables is represented by graph structure regularization of adaptive weight. Balance parameters are dynamically adjusted through a self-adaptive weight mechanism, different time sequence characteristics are adapted, and the interpolation effect is optimized. And finally, solving the optimization model by adopting an alternating direction multiplier method, and generating filled and repaired data. Meanwhile, the global trend, the local fluctuation and the relation between variables of the data are considered, and the accuracy and the reliability of data filling are remarkably improved.

The invention relates to a method for quickly generating a report, which comprises the following steps of: 1, creating an initialized Word document template, and marking a data position needing to be replaced in a Word document by using an occupant; step 2, creating a configuration table in the database, and defining a data source table and a processing rule required by each field of the report in the Word document; step 3, reading a Word document template, querying a database based on report fields, and obtaining a data source table and a corresponding processing rule in the configuration table; step 4, querying required data from the database table based on a data source table in the configuration table, and executing a corresponding processing rule; 5, replacing an occupant in the Word document template with the queried and processed data to realize data filling; and step 6, generating and exporting a final Word document filled with all data. According to the method, the technical problems in the prior art when a complex report is generated in the field of geological engineering investigation are effectively solved, and efficient, accurate and low-cost automatic report generation is realized.

The invention relates to the field of image processing, optical character recognition and natural language processing, and discloses a Mongolian power table data extraction method and system based on image recognition, and the method comprises the following steps: carrying out the image preprocessing of an input power table image sample, and improving the image quality; extracting a table frame structure, optimizing table line segment positions, and segmenting table cells; identifying a power business field corresponding to each cell and establishing a mapping relationship between the cells and the power business fields; and based on the mapping relationship, generating structured spreadsheet data, and filling the structured spreadsheet data with the power business data. According to the method, by introducing table structure optimization, context associated character extraction and industry term correction technologies, the efficiency and accuracy of Mongolian table data processing are effectively improved, and technical support is provided for digital management of the power industry and intelligent application of minority languages.