718 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 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 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 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 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 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.

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.

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 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 discloses a transformer substation GIS equipment LCC prediction method based on an improved Attention-LSTM algorithm. The method comprises the following steps: S1, collecting and counting life cycle cost data of transformer substation GIS equipment in operation; s2, establishing a data screening correction model according to the data characteristics of the full-life-cycle cost data of the GIS equipment of the transformer substation, judging abnormal data, and performing data filling; s3, performing dimensionless and uniformization processing on the processed LCC data of the GIS equipment according to types; s4, based on the LSTM algorithm, introducing an Attention algorithm, and constructing an LCC data accurate prediction model based on the improved Attention-LSTM; s5, dividing LCC data predicted by the GIS equipment of the transformer substation into a prediction set and a verification set, and substituting the prediction set to start data prediction; and S6, importing the predicted LCC data into the step S2 to carry out validity evaluation, if a requirement is met, completing prediction, otherwise, carrying out re-prediction. By means of the method, the full-life-cycle cost of the GIS equipment of the transformer substation can be accurately measured and calculated.

The invention provides a tunnel extrusion deformation prediction method and device. Comprising the steps of obtaining case data of tunnel extrusion deformation, and generating an initial data set; performing data filling on the initial data set by adopting a plurality of data filling methods to generate a plurality of complete data sets; training a plurality of first machine learning models according to the complete data set, and determining a model training data set according to model performance indexes of the plurality of first machine learning models; training a plurality of second machine learning models according to a training set in the model training data set, and optimizing hyper-parameters in the training process of the second machine learning models by adopting an improved black wing plinergic algorithm in the model training process; and determining a tunnel extrusion deformation prediction model according to the model performance indexes of the plurality of second machine learning models obtained by training. Acquiring detection data of the tunnel; according to the detection data of the tunnel and the tunnel extrusion deformation prediction model, the model performance of the tunnel extrusion deformation prediction model obtained through training can be improved by predicting whether the tunnel is deformed or not.

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 an AI auxiliary test case generation and verification method and a terminal, and the method comprises the steps: obtaining and analyzing game commodity data, filling a script template with the analyzed game commodity data, verifying and adjusting the script template through a trained AI model, recognizing a test scene according to the adjusted script template, and carrying out the verification of the test scene. A corresponding test case is generated; and executing the test case, verifying the execution result of the test case through database query and log analysis, and generating a test report, so that the method can adapt to tests of different scenes and requirements, and the reliability and execution efficiency of the test are improved.

The invention discloses a life cycle evaluation missing data completion method, system and device and a medium, and the method comprises the steps: defining a data template, and determining a target range of life cycle evaluation in the data template; obtaining input data, and filling the data template with the input data; determining missing data in the data template; according to the method, the system, the equipment and the medium, the missing data in the data template is predicted by utilizing the large model, the predicted missing data is written into the data template, and the missing data in life cycle evaluation can be complemented based on the large model.

The invention discloses an ocean buoy data filling method based on a space-time neural network. In order to solve the problems of insufficient spatial-temporal dynamic correlation modeling and low filling precision in the prior art, high-precision reconstruction of ocean buoy missing data is realized by coupling a graph convolutional network GCN and a gating cycle unit GRU in combination with spatial-temporal feature modeling. The method comprises the steps of data preprocessing; analyzing a data missing mode, and constructing a data sample by utilizing double mask matrix construction and a time window division strategy; based on multi-scale spatial feature extraction of GCN and long-term time-dependent modeling of GRU, model performance is improved through hyper-parameter optimization and Bayesian search; and carrying out model training, and evaluating the filling effect by adopting indexes such as mean square error. According to the method, the reconstruction precision of the space-time correlation missing value is remarkably improved, the generalization ability for a real missing mode is enhanced, and the method is suitable for real-time data processing of a large-scale ocean monitoring network and has high engineering application value.

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 belongs to the technical field of variable report data processing, and provides a variable report filling method, a storage medium, equipment, a program product and a device. The change report filling method comprises the following steps: determining a change area according to a predefined format of a change report; according to a preset filling requirement, a data storage table in a corresponding format is created for the change area of each category in a matched mode, and a corresponding access model is constructed for each cell in the change area in a matched mode; executing the access operation of each access model, storing the extracted report data into the data storage table matched with the corresponding change area, and further determining the total amount of the change row / column data; and dynamically expanding the total row / column number of the corresponding change report according to the total amount of the change row / column data, and filling the report data in the data storage table into the row / column of the corresponding change area to obtain the filled change report. According to the method, data in different sub-reports are compared and analyzed from a cross multi-dimensional angle.

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.