567 results about "Evaluated data" patented technology

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

The invention belongs to the technical field of agricultural Internet of Things, and discloses an Internet of Things-based intelligent agricultural cloud platform data acquisition and management system, which comprises the following steps of: acquiring plant rhizosphere microenvironment information, generating rhizosphere original data and performing correlation analysis to obtain rhizosphere electrochemical interaction data; collecting canopy microclimate information, and performing temperature and humidity gradient quantification processing and pore dynamic response characteristic extraction to obtain pore response characteristic data; collecting field illumination information, and carrying out multispectral distribution analysis and photoperiod sensitivity differentiation analysis to obtain a crop photoperiod sensitivity spectrum; performing multi-dimensional association fusion to generate crop physiological status comprehensive evaluation data; agricultural measure analysis and resource allocation optimization are carried out, and finally a resource allocation instruction is generated and agricultural operation scheduling is carried out; according to the invention, intelligent acquisition, analysis and management of data in each link of agricultural production are realized, and the utilization efficiency of agricultural resources and the production accuracy are improved.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a test evaluation method and device for an industrial park integrated management system. The method comprises the following steps: acquiring a performance index data set of the industrial park integrated management system; the performance index data set comprises a performance index test data subset of each subsystem; the performance index test data subset comprises a test data sequence of each performance index; pre-processing the performance index data set to obtain a to-be-evaluated data set; and performing test evaluation processing on the to-be-evaluated data set to obtain a performance evaluation result value of the industrial park integrated management system.

The invention discloses an automatic software function testing and evaluating method based on a multi-modal large model, and particularly relates to the field of non-embedded software function testing and evaluating, which comprises the following steps: collecting software texts, images and structured data, generating corresponding feature vectors, aligning through a cross-modal comparison algorithm, and constructing a multi-modal testing and evaluating data set; deep features of all modes are extracted through a mode exclusive encoder, and a unified multi-mode function vector is output based on information entropy dynamic weighted fusion; constructing triple training data, and optimizing the multi-modal large model by combining a joint loss function with an enhanced training strategy; and finally, deploying the model at a cloud end and an edge node, carrying out real-time triggering test by docking a CI / CD process, and realizing functional integrity verification, interactive compliance detection and accurate defect positioning through semantic vector comparison, graph isomorphism rate calculation and cross-modal attention backtracking. According to the scheme, the test comprehensiveness and precision are improved, and the rapid iteration requirement of software is met.

Systems and methods for dynamically evaluating metadata consistency and data reliability in a data management system are disclosed herein. The system may retrieve first metadata and second metadata. The system may retrieve a metadata ruleset. Based on the metadata ruleset, the system may generate a first metadata consistency metric indicating a first measure of consistency. The system may determine to process each record of the first metadata as a batch. The system may generate a second metadata consistency metric indicating a second measure of consistency. The system may determine to process each record of the second metadata independently.

The invention discloses a construction monitoring system for a horizontal warehouse arched roof based on multi-modal data fusion, relates to the technical field of construction monitoring, and is used for solving the problem of poor structural construction anomaly recognition. The method comprises the steps of collecting multi-modal data and completing synchronous preprocessing, setting a sampling frequency according to modal characteristics, dividing construction stages, constructing a stage response function, dynamically updating during stage switching, fitting an actual observation value and a prediction value, extracting a modal response deviation degree, constructing a credibility function and evaluating data reliability. Further analyzing a residual trend, adjusting an abnormal score in combination with credibility, identifying a collaborative abnormal behavior between modals, systematically constructing a modal causal graph, identifying an abnormal propagation path, extracting modal conduction characteristic information, and triggering a risk response strategy according to the modal causal graph, so that dynamic tracking of the structural abnormality in the construction process of the horizontal warehouse arched roof is realized; and the accuracy of construction process monitoring identification is improved.

Various embodiments include methods and systems for automatically evaluating data structures and / or data management documentation for compliance with relevant standards, requirements, and policies. Various embodiments provide a framework for automating compliance reviews of data structures and / or data management against the latest version of standards, requirements, and policies. Methods may include transforming input requests for analysis and data structures and / or data management documents into formats suitable for processing by a large language model (LLM), prompting the LLM to identify concepts and / or elements that are likely subject to a standard, requirement, or policy, submitting a query to a vector database to identify standards, requirements, or policies relevant to the requested analysis and documents, prompting the LLM to assess compliance the data structures and / or data management documents with standards, requirements, or policy, and producing a report on compliance of the data structures and / or data management documents with the relevant standards, requirements, or policies.

The invention discloses a multi-level dynamic isolation data processing system and method, and relates to the technical field of data security, the system comprises a data access module supporting a cloud native environment and a hybrid deployment mode, a strategy matching module, a sandbox execution module and an audit analysis module; the data access module receives the data, evaluates the sensitivity level and the purpose of the data, and labels the data based on an evaluation result; the strategy matching module reads the data label, searches an isolation strategy and an auditing rule which are matched with the data label from a strategy library, and automatically loads the strategies to the sandbox execution module once the matched strategies are found; the sandbox execution module selects an isolation mode according to the sensitivity level of the data, and processes and analyzes the data in an isolation environment according to a preset rule and algorithm; and the auditing analysis module records all operations on the data in real time, and analyzes the operation compliance based on a preset auditing rule. The problems of large resource consumption, insufficient strategy flexibility, coarse behavior audit granularity and the like in the existing data sandbox technology can be solved.

The invention provides an identification detection method and system for a radio frequency connector, and the method comprises the steps: obtaining the initial appearance image data of the surface of the radio frequency connector, and carrying out the processing of the image data, and obtaining a first appearance image; extracting an appearance feature set and an identification area image from the first appearance image; performing character segmentation and feature extraction on the identification area image, obtaining a preliminary manufacturing information text in combination with a pre-established text recognition model, verifying the preliminary manufacturing information text and associating the preliminary manufacturing information text with the appearance feature set to generate an associated data set; comparing the appearance feature set with a preset standard appearance database to generate defect marking data, fusing the associated data set with the defect marking data to generate comprehensive analysis data, and generating a visual chart according to the comprehensive analysis data; and further analyzing performance index change and defect severity according to the visual chart, generating risk assessment data, and generating a final detection analysis file according to the risk assessment data.

The invention belongs to the technical field of new energy electric power, and discloses a new energy bearing intelligent evaluation and regulation system and method, the system is composed of a data acquisition module, an intelligent evaluation module, a prediction module, a regulation strategy module and an execution and feedback module, and the data acquisition module acquires meteorological, historical operation, load and power grid state data in real time; the time sequence of the prediction module and a deep learning algorithm are combined, short-term to medium-and-long-term prediction is carried out on new energy output and load change, output change is captured in real time, regulation and control lag is avoided, power grid risks are quantified through a multi-model fusion algorithm, and node accessible capacity and a risk assessment result are output; and the regulation and control strategy module generates a regulation and control instruction by utilizing optimization algorithms such as a genetic algorithm based on the evaluation result and the prediction data, and realizes closed-loop control through the execution and feedback module, so that the evaluation data directly guides regulation and control actions.

The invention provides an internet right product agent management system, and relates to the technical field of product agent management. The system comprises a product management module, an order management module, an agent management module, a database and a data analysis module. The system processes on-off and stock of products through the product management module, the order management module calculates commission, and the agent management module achieves credit evaluation. The data analysis module predicts requirements through a multi-layer architecture; a data input layer integrates internal and external data; the analysis engine layer adopts feature coding and a time attention mechanism to model space-time correlation, and outputs a demand prediction formula; and the decision output layer generates an agent personalized report through inventory calibration, credit grading and regional weighting strategies, recommends products and early warns risks. The system realizes intelligent acquisition and sales decision making, and improves the operation capability and market influence of the system.

The invention discloses a raw coal quality data optimization and reconstruction method based on clean coal ash content feedback, and belongs to the technical field of computers. Data completion is achieved through the generative adversarial network; a generator predicts missing raw coal data according to the high-sampling-frequency clean coal ash content, and a discriminator evaluates the authenticity of the data. According to the method, the characteristic that the clean coal ash sampling rate is higher in the dense medium separation process is used for guiding data reconstruction; meanwhile, a composite loss function including adversarial loss, reconstruction loss and regression loss is designed, and the generation quality is improved. The method effectively solves the problem of raw coal quality data missing in the coal dressing process, and has high engineering application value.

The invention provides a financial risk prediction method based on a multi-objective ensemble learning algorithm, and aims to solve the problems of multi-risk type comprehensive evaluation, data complexity and nonlinear relation processing, dynamic change adaptability and the like in risk prediction in the financial field. According to the method, a financial time sequence is divided into a plurality of time blocks through a space-time block data preprocessing technology, and a heterogeneous model library is independently trained, so that the adaptability and prediction precision of a model are improved. Meanwhile, an NSGA-II multi-objective optimization algorithm is combined with Gibbs distribution to dynamically distribute weights, the prediction precision and the engineering practicability are balanced, and dynamic optimization of economic indexes and engineering indexes is achieved. In addition, a time decay and mixed attention mechanism is designed, multi-model prediction results are fused, market dynamic changes are further captured, and the accuracy and real-time performance of risk prediction are improved. According to the method, the precision and the real-time response capability of financial risk prediction are effectively improved.

The invention discloses a method for training a large model in the field of satellite communication, and aims to solve the problem that a general large model is insufficient in processing capability in the professional field of satellite communication. The method comprises the specific steps that multi-modal professional corpora (structured and unstructured data) in the satellite communication field are collected; generating more than ten thousands of incremental pre-training and fine-tuning corpora through data preprocessing, performing data enhancement on the corpora by adopting a plurality of mixing strategies, and dividing a training set and a test set according to a general proportion; based on low-rank self-adaptive fine tuning technologies such as LoRA, incremental pre-training and supervised fine tuning are carried out on the general large model; a professional evaluation data set is constructed manually, a self-established evaluation data set is used for comparing semantic comprehension and generation capabilities of the satellite communication large model and the general model in the satellite communication field, and model training hyper-parameters are iterated according to an evaluation result; an RAG framework is fused, a self-built private database, a self-defined text partitioning method and a knowledge base calling strategy are integrated, knowledge base data are accurately called, and question and answer content conforming to rules and specifications is generated.

The invention discloses a silica sol preparation process optimization method and system based on data analysis, and belongs to the field of data processing.The method comprises the steps that operation data of an ion exchange column are obtained, meanwhile, quality parameters of silica sol are obtained, and evaluation data about the health state of the ion exchange column are calculated based on operation data of an inlet and an outlet of the ion exchange column; and then evaluating the health state index of the ion exchange column, predicting the quality parameters of the silica sol based on the operation data of the ion exchange column, estimating the influence of the column state on the quality of the silica sol, calculating the index relative error based on the comparison of the prediction index value and the actual measurement value of the silica sol, and adjusting the collection frequency of the silica sol. Meanwhile, the residual effective operation time of the ion exchange column is evaluated by combining the column saturation and the health state index, so that the stability of silica sol production is improved, and the production efficiency is improved.

The invention provides a flow battery energy storage power station full life cycle health degree assessment management method. The method comprises the following steps: constructing an initial health reference library, collecting electrochemical parameters through a reference electrode, collecting electrolyte physical parameters and environmental parameters through a sensor, synchronously recording operation and maintenance data, and carrying out time-space synchronization processing; introducing a hierarchical fusion assessment mechanism, assessing data integrity and constructing an attention mechanism model, calculating a dynamic weight according to a parameter relative change rate and a health degree correlation coefficient, generating a weighted feature vector, and predicting a short-term health degree index by adopting a bidirectional long-short-term memory network Bi-LSTM; a particle filter (PF) algorithm is introduced to be combined with a physical parameter drift distance to correct a long-term health degree index, and then a real-time health degree index is output through fuzzy comprehensive evaluation. The method can achieve the linkage evaluation of the health state in the operation period and the full life cycle trend, corrects the long-term SOH through the PF algorithm in combination with the physical parameter drift distance, and reduces the error of long-term prediction.

The invention discloses a yarn uniformity analysis method based on image processing, and relates to the technical field of image data processing, and the method comprises the steps: employing a convolutional neural network model to carry out the deep learning analysis of the surface features of a yarn through a preliminary feature distribution diagram, carrying out the adaptive feature weighting processing of a complex texture region, and obtaining a feature distribution diagram; obtaining a more accurate third characteristic distribution diagram; if the characteristic values of some areas in the third characteristic distribution diagram exceed a preset abnormal threshold value, performing local amplification analysis on the areas, and further extracting fine defect boundaries in combination with an edge detection algorithm to obtain a defect candidate area set; and generating a visual analysis report according to the multi-dimensional evaluation data, visually presenting yarn uniformity distribution by applying a thermodynamic diagram technology, marking specific position information for a defect high-incidence area, and outputting a final detection analysis result. The problems of yarn image acquisition and defect detection under complex illumination can be effectively solved, and the accuracy and efficiency of yarn uniformity detection are improved.

Systems and methods for dynamically evaluating metadata consistency and data reliability in a data management system are disclosed herein. The system may retrieve first metadata and second metadata. The system may retrieve a metadata ruleset. Based on the metadata ruleset, the system may generate a first metadata consistency metric indicating a first measure of consistency. The system may determine to process each record of the first metadata as a batch. The system may generate a second metadata consistency metric indicating a second measure of consistency. The system may determine to process each record of the second metadata independently.

The invention discloses a vegetation and soil coupling effect evaluation method and system based on big data, and the method comprises the steps: collecting experiment data and monitoring data of a preset region, and carrying out the preprocessing of the experiment data and the monitoring data; performing multi-dimensional coupling correlation feature extraction on the experimental data and the monitoring data to obtain a coupling feature set, and performing space-air-ground collaborative coupling evaluation on the monitoring data through the coupling feature set to obtain an original coupling degree; performing disturbance evaluation on the soil data according to a soil dynamic adjustment decision to obtain a disturbance coupling degree, and performing coupling mechanism decomposition based on deep learning according to the original coupling degree and the disturbance coupling degree to obtain a coupling difference mechanism; and constructing a vegetation and soil coupling effect evaluation model according to the disturbance coupling effect and the coupling difference mechanism, inputting to-be-evaluated data into the vegetation and soil coupling effect evaluation model, and outputting an evaluation result.

The invention discloses a leasing user credit risk assessment method and system based on a block chain, and relates to the technical field of block chain management. The leasing user credit risk assessment method based on the block chain comprises the following steps: carrying out block chain calling delay assessment; evaluating data write-in delay; and performing off-chain transmission delay evaluation. According to the method, the stability delay evaluation is performed based on the link transmission data volume, meanwhile, according to the stability delay evaluation result, whether to perform block parameter optimization or not is judged, data write-in delay evaluation is performed based on write-in delay data, whether to perform block chain data call optimization or not is judged, and under-chain transmission delay evaluation is performed based on transmission delay data; and meanwhile, whether data byte number optimization is carried out or not is judged, so that the effect of improving the reliability of the lease user credit rating data transmission risk assessment is achieved, and the problem of low reliability of the lease user credit rating data transmission risk assessment caused by corresponding transmission delay in the data chaining process of the block chain in the prior art is solved.

The invention discloses a flow field adaptive calculation method and system based on an acoustic Doppler flow meter. Comprising the following steps: acquiring original frequency spectrum data and synchronous attitude, position and water level information of a Doppler current meter; performing preprocessing and quality marking on the original data; multi-dimensional features are extracted, and a current dominant flow field type is identified through a pre-trained flow field classification model; adaptively selecting a flow velocity profile reconstruction algorithm according to the flow field type, and dynamically adjusting key algorithm parameters according to flow field stability or turbulence intensity parameters in the reconstruction process; a calculation grid is dynamically generated based on a flow velocity field of a reconstruction strategy to perform flow integral calculation, and data, a model and an integral error are comprehensively evaluated to quantify uncertainty of an output flow result; and finally performing flow field abnormity diagnosis, short-term flow trend prediction and equipment health state early warning. According to the method, the problem of insufficient precision of a traditional fixed algorithm in a complex flow field is solved, and the reliability and the intelligent level of flow monitoring are remarkably improved.

The invention relates to the technical field of oil and gas field data management, and discloses an oil and gas field interval time sequence data management method based on a swan mongolian system. The method comprises the following steps: acquiring open time sequence data in real time through a distributed data acquisition module of a swan gap system, calculating data point density of a target area and comparing the data point density with a safety threshold; when the density exceeds the limit, evaluating data source interaction by using a statistical analysis model, and outputting a data coupling effect hidden danger state; analyzing and processing the environmental factor data by adopting a time sequence, and generating potential interference degree evaluation; determining whether to trigger an overall data optimization process or not according to the hidden danger state and interference evaluation; and during triggering, individual path complexity evaluation is executed on each data source, a priority sequence is calculated and adjusted, oil and gas field equipment is controlled according to the sequence, and dynamic management of open time sequence data is realized. According to the method, the real-time performance, accuracy and flexibility of data management are improved, and the production requirements of modern oil and gas fields are met.

The invention discloses a proxy model auxiliary evolution method based on two-stage adaptive switching and Voronoi niche. The method comprises the steps of generating an initial sample by adopting optimal Latin hypercube sampling, obtaining a real fitness value through simulation, constructing an initial training data set, and setting an initial iteration counter k and an evaluation budget threshold value; training a proxy model based on the current data set, and executing evolutionary algorithm optimization by using the proxy model to obtain a current optimal individual; judging two stages according to an iteration counter k and a distance threshold value; the development execution stage comprises the following steps: evaluating the real fitness and updating a data set; the exploration execution stage comprises the steps of multi-modal approximate detection, Voronoi niche division, multi-modal collaborative search, real fitness evaluation and data set updating; judging a termination condition and outputting a result; according to the method, three core technologies of an adaptive stage switching mechanism, multi-modal approximate detection and Voronoi niche division and multi-extremum collaborative filling are creatively integrated, so that the defects of insufficient global exploration, low local development precision and poor convergence efficiency of a traditional method in a high-dimensional multi-modal expensive optimization problem are effectively overcome; and the solving efficiency and reliability of a complex and expensive optimization problem are remarkably improved.

The invention discloses a multi-drug-resistant bacterium infection risk assessment method, system and device and a storage medium, and belongs to the technical field of medical data analysis, and the method comprises the steps: collecting a clinical related data set of an ICU patient, and carrying out the preprocessing of the clinical related data set to obtain a to-be-assessed data set; performing feature extraction on the to-be-evaluated data set in combination with a long short-term memory network and a time sequence attention mechanism to obtain a high-risk feature set; inputting the high-risk feature set into a pre-constructed dynamic scoring model, and calculating a corresponding risk score through the dynamic scoring model; and grading the risk score based on a preset grading rule to obtain a current risk level, and outputting a corresponding intervention strategy through a preset intervention decision mechanism based on the current risk level. According to the scheme, the high-risk feature set is extracted by combining the long-short-term memory network with the time sequence attention mechanism, then the risk score is calculated through the dynamic scoring model, the risk level is determined according to the preset grading rule, the intervention strategy is output, and the accuracy of MDRO infection assessment is improved.

A system for generating a bill of materials comprising one or more product recommendations aligned with customer requirements, comprises a bill of materials processing device. The bill of materials processing device is configured to execute an AI-based recommendation engine. The recommendation engine is configured to receive customer data from one or more user inputs, retrieve product data from a product data source, and retrieve transaction data from a transaction data source. The recommendation engine includes a pre-processing module, training module, and inference module. The pre-processing module executes one or more pre-processing techniques to translate the data into a program-compatible format. The training module executes training and inference processes to generate the bill of materials. The training processes put the data in better condition for the inference module. The inference module uses two or more product recommendation processes to evaluate the data and to generate the BOM in a machine-readable format.

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

The invention discloses an input-output method and system realized based on a calculation engine in the coal chemical industry, and relates to the technical field of coal chemical industry, and the method comprises the steps: building a molecular model based on a reaction network of a coal molecular structure, and updating the parameters of the molecular model in real time through extended Kalman filtering; constructing a data fusion layer through a hybrid modeling technology, and constructing a coal chemical industry residual learning architecture by using a mechanism model and a neural network series structure; evaluating a data-driven model prediction confidence interval according to a Monte Carlo method; a digital twin verification platform is constructed based on hardware-in-the-loop test, and a data-driven adaptive iteration mechanism is constructed through an online and offline collaborative updating mechanism. The method has the beneficial effects that the medium component fluctuation is accurately captured, the output accuracy is improved, reliable evaluation dependence is provided, and the real-time response force and long-term stability of the system in a complex environment are improved.

Techniques and systems for generating synthetic data 200 are described. The described techniques and systems provide realistic synthetic data by preserving observable and missing data distributions. A method of generating synthetic data includes receiving data having missing elements having missing elements; evaluating the data for patterns with respect to the missing elements; labeling the data according to the patterns; generating labeled synthetic data using the labeled data; and inserting blanks into the labeled synthetic data according to associated labels of the labeled data to generate synthetic data with corresponding missing elements.