27results about How to "Reduce forecast error" patented technology

This invention belongs to the interdisciplinary field of artificial intelligence and geological resource assessment, specifically relating to a method and system for predicting the energy storage potential of salt caverns based on multi-source geological information. It aims to address the problems of low prediction accuracy and difficulty in balancing breadth and precision in existing technologies due to single-source data, static models, and lack of adaptive capabilities. The method includes: acquiring multi-source data such as seismic, well logging, core, and geostress data; constructing a geological feature fusion vector containing seven core parameters; establishing a high-dimensional nonlinear mapping model driven by a deep neural network; dynamically adjusting the prediction range based on data coverage density and confidence level; and achieving continuous model iteration through online incremental learning. The system integrates data quality assessment, anomaly handling, and distributed computing modules. By adopting the above technical solutions, this application can achieve a significant improvement in prediction accuracy and adaptive intelligent adjustment of the prediction range.

The present application relates to the technical field of financial risk assessment, in particular to a kind of agricultural loan risk assessment analysis method based on big data, comprising the following steps: obtaining multi-dimensional associated data sequence of peasant household, analyzing node relationship and screening main path, according to main path logic vector judging path angle and screening collaborative fragment, extracting node coordinates to calculate spatial offset, sample path projection screening and marking, sequentially comparing path structure to identify time progression relationship, obtain agricultural loan risk assessment analysis data list.In the present application, by constructing the multi-dimensional data atlas of agricultural production cycle, the synergistic effect and spatial offset in the path are revealed, the risk assessment accuracy is improved, the evaluation can adapt to the dynamic changes of agricultural production, the prediction error caused by natural environment and periodic factors is reduced, the timeliness and accuracy are enhanced, the defects of traditional methods ignoring agricultural environment are effectively avoided, and the operability and decision support capability of loan risk assessment are improved.

This invention discloses a method for predicting gate switch oscillation in GaN-HEMT devices, belonging to the field of semiconductors and power devices. The invention trains a BPNN network model to obtain a preliminary mapping relationship between parasitic capacitance and gate-source voltage based on the BPNN algorithm model. Then, a Cascode-based two-port network function for GaN HEMT is established. The parasitic capacitance value of the GaN HEMT device under all S-parameters is obtained experimentally. This extracted parasitic capacitance value is then input into the BPNN network model as a dataset to obtain Vo. GS_m V under all S parameters DS C GS C GD The relationship between the surface and the curve is shown. Simulation results show that the standard error of the prediction method of the present invention is smaller than that of ANN and PSO, proving that the detection method for GaN-HEMT gate switch oscillation based on the BPNN algorithm model of the present invention has high prediction accuracy.

This invention relates to a regional generalization method for an adaptive graph spatiotemporal prediction model for wind power clusters, belonging to the field of wind power prediction and new energy power system dispatching technology. The technical solution is as follows: first, a regional generalization method for the adaptive graph spatiotemporal prediction model of wind power clusters is used to solve the adaptability and accuracy problems of cross-regional prediction; then, the transparency and credibility of prediction decisions are improved by relying on the model structure interpretability analysis method. This includes the following two steps: first, achieving effective generalization prediction; second, endowing the prediction with interpretability. The beneficial effects of this invention are: significantly improved generalization performance, reducing computational costs and deployment cycle; optimized prediction accuracy, enabling the adaptive graph spatiotemporal prediction model of wind power clusters to more accurately capture the spatiotemporal correlation between wind farms, reducing short-term wind power prediction errors by 10%-20%, and providing more reliable data support for grid dispatching; and enhanced interpretability, meeting the requirements of the dispatching system for model auditability and reducing operation and maintenance decision risks.

This invention provides a method for predicting the dominant frequency of blasting vibration by integrating parameter fluctuation processing and the Dream Optimization Algorithm (DOA), belonging to the field of underground engineering safety control technology. The system includes a data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal hyperparameter combination is selected by combining a robustness fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.

This invention belongs to the field of marine engineering antifouling material technology and artificial intelligence optimization technology. It discloses a collaborative optimization method for the antifouling stability and safety of antifouling materials used in debris-blocking nets, comprising the following steps: Step 1, collecting multi-dimensional data and preparing samples of the antifouling materials for the debris-blocking nets; Step 2, constructing and training an AI prediction model: selecting key feature parameters from the multi-dimensional data, using these key feature parameters as core feature vectors to construct a dual-objective prediction model, which outputs predicted values ​​for the material's antifouling stability index and environmental safety index; Step 3, collaborative optimization under multiple constraints: setting multiple constraints for the material; using the hybrid meta-heuristic algorithm ALO-KHO to solve the multi-objective optimization problem; selecting the optimal solution from the Pareto optimal solution to obtain the parameter combination of the antifouling materials for the debris-blocking nets with the highest score. The prediction model of this invention has high accuracy, strong generalization ability, and significant multi-objective collaborative optimization effect.

The present application relates to the field of aero-engine health management and residual life prediction, and discloses a residual life prediction method of aero-engine fusing machine learning and improved GM(1, N) model. The method faces aero-engine multi-source monitoring data, firstly, the working condition and sensor time sequence are preprocessed and feature construction is carried out; then, the adaptive window division is carried out on the operation sequence of a single engine, so that different stages are allowed to present different dynamic characteristics; further, the total life is introduced as a unified unknown quantity to construct the main variable of residual life, the grey modeling is established in each window, and the robust solution is carried out according to the relative error criterion, the total life estimation is obtained by searching, and the residual life prediction value is output; when the training data is available, the life estimation and fitting error output by the grey model are taken as additional features, which are fused with the original features and input into the machine learning model for integrated prediction, so as to form a solution for the residual life prediction of aero-engine.

The application relates to a cloud service workload prediction method and system based on a convolution enhanced Transformer, which comprises the following steps: collecting cloud server workload data and preprocessing the cloud server workload data to decompose the cloud server workload data into a trend component and a residual component; constructing a workload prediction model, training the workload prediction model by using the trend component and the residual component, and obtaining a trained workload prediction model; predicting the cloud server workload by using the trained workload prediction model to obtain a workload prediction value; predicting the trend component by using a trend information capturing module to obtain a prediction result of the trend component; predicting the residual component by using a convolution enhanced Transformer encoder module to obtain a prediction result of the residual component; and fusing the prediction result of the trend component and the prediction result of the residual component by using a feature fusion module to obtain a final workload prediction value.

This invention relates to the field of cable operation and maintenance, aiming to solve the problems of traditional cable insulation assessment neglecting multi-factor coupling and low accuracy of lifespan prediction, and to provide a precise early warning system. This invention includes modules for data acquisition, improved multi-dimensional coupled aging assessment, optimized lifespan decay prediction, early warning output, and data storage. It collects multi-dimensional electrical and environmental parameters through sensors, quantifies the aging level through coupled modeling and dynamic weight calculation, predicts the remaining lifespan based on an improved Arrhenius model and time-series residual correction, and outputs three levels of early warning through multiple channels. This invention improves the accuracy of aging assessment and lifespan prediction, supports cable operation and maintenance decisions, reduces fault risks, and is applicable to power system cable insulation condition monitoring scenarios.

This invention provides a method for predicting VAT tax burden rate fluctuations based on Long Short-Term Memory (LSTM) networks and attention mechanisms. The method includes: Step S1, data acquisition and preprocessing, which involves acquiring tax-related time-series data of the target object within a continuous time window, and cleaning, imputing missing values, aligning the time series, and standardizing the data; Step S2, feature engineering and sequence construction, which involves constructing tax-related features based on the preprocessed data, and constructing supervised learning samples using a sliding window method, where each sample contains a feature sequence within a historical window and a corresponding tax burden rate fluctuation label; Step S3, constructing an LSTM-attention hybrid model, which involves constructing a deep learning model containing LSTM layers and attention layers; and Step S4, model training and optimization. This invention can effectively capture long-range temporal dependencies in tax burden rate fluctuations, automatically extract deep feature interactions, and is interpretable.

This invention belongs to the interdisciplinary field of sensor design and artificial intelligence, specifically a sensor design method that proactively adapts to the application environment. This invention employs an active learning approach, actively selecting the most informative samples from unlabeled data for labeling, thereby maximizing model performance with less data acquisition. Compared to traditional methods of multiple sensor fabrications, active learning dynamically assesses the uncertainty or representativeness of data samples during model training, prioritizing data points that have the greatest impact on the model's decision boundaries and the strongest performance improvement potential. This method is not only applicable to a single type of sensor chip, but its database design and algorithm model are compatible with various material systems (including organic / inorganic, flexible / rigid, etc.), and can achieve rapid adaptive modeling and parameter recommendation for different application scenarios through transfer learning, demonstrating good scalability and versatility.

This invention discloses a microgrid lifecycle planning method based on a physically decoupled surrogate model in the field of microgrid planning and optimized operation technology. This method includes: constructing a two-layer microgrid planning model covering the entire equipment lifecycle; establishing a physically decoupled surrogate model architecture; decomposing the objective vector into a smooth continuous subspace and a sparse abrupt subspace, and designing corresponding loss functions for each; adaptive active learning sampling during the execution phase, utilizing an uncertainty quantification method based on committee queries, and introducing a dynamic decay weighting mechanism; and employing a hybrid oracle-based online correction integer-encoded multi-objective evolutionary algorithm to solve for the optimal configuration scheme. This invention can eliminate the feature masking effect between heterogeneous objectives, improve the accuracy of identifying physical hard constraint boundaries, and achieve efficient and high-fidelity planning scheme solutions under limited computing resources.

The present application relates to a kind of data-driven mode under the method and system for predicting net pressure in fractured well fracture, comprising: collecting oilfield field data, and calculating fracture net pressure;Correlation between well logging parameters and fracture net pressure is calculated and screened, and the well logging parameters after screening are pretreated;Fracture net pressure prediction model is constructed, part of data set is input into fracture net pressure prediction model for training, and the hyperparameters of fracture net pressure prediction model are optimized;Optimized fracture net pressure prediction model is trained using all data sets;When obtaining incremental data, the trained fracture net pressure prediction model is used as submodel, the structure and parameters of submodel are replicated to generate new submodel, and submodel is trained to form submodel for different well sections;The comprehensive prediction value of all submodels is calculated.The present application effectively overcomes the limitation of traditional prediction method, significantly improves the prediction accuracy and model adaptability, and has important significance for oil and gas exploitation engineering.

This invention relates to the field of electricity meter technology, specifically to a method and system for predicting the aging state of electricity meters based on LSTM. The method includes: calculating the aging driving force index for each field vector and the aging driving force index for each simulated vector; taking any field vector as the target vector; calculating the absolute difference between the aging driving force index of the target vector and the aging driving force index of each simulated vector; using the simulated vector corresponding to the minimum absolute difference as the matching vector of the target vector; constructing a mapping vector for the target vector by combining the aging driving force index of the target vector, the aging driving force index of the matching vector, and the matching vector; iterating through the mapping vectors of each field vector to obtain the mapping vector; fine-tuning the prediction model based on all mapping vectors; and predicting the aging state of the electricity meter to be predicted based on the fine-tuned prediction model. This invention can reduce the error of the LSTM model when actually predicting the aging state of electricity meters.

This invention provides a method for predicting the lifespan of lithium batteries based on physical neural networks, comprising: constructing a coupled ordinary differential equation of lithium battery charge state and thermodynamic evolution, and establishing a continuous-time dynamic model of lithium battery charge change; constructing a PI-LSTM hybrid analysis framework based on physical information neural networks, and incorporating the coupled ordinary differential equation as a physical constraint term into the loss function of the PI-LSTM hybrid analysis framework; collecting time-series sensing data of lithium battery operation, training the PI-LSTM hybrid analysis framework, and completing the inversion identification of lithium battery physical parameters; based on the trained PI-LSTM hybrid analysis framework, outputting the state of charge and time to depletion of the lithium battery under the target operating condition, and completing the prediction of lithium battery lifespan; this invention aims to achieve accurate and interpretable prediction of lithium battery SOC and TTE in multiple devices, providing support for battery management of electrical equipment such as drones.

The application discloses a kind of aluminum alloy variable polarity TIG deep penetration welding penetration prediction method and system, comprising: collecting welding pool image, input pre-trained type convolutional neural network to carry out pool state identification;If it is positive polarity stage, then the image is input to pre-trained semantic segmentation type convolutional neural network, the shadow area caused by pool area and small hole is marked, and the segmentation result image is obtained;Approximate depth of pool width and small hole is calculated based on the segmentation result image;The pool width and the approximate depth of small hole are input into the pre-trained integrated learning prediction model to predict the current penetration;The system includes image acquisition device, pool state identification module, feature region segmentation module, feature size calculation module and penetration prediction module;The application can realize the real-time prediction of aluminum alloy blind hole type deep penetration welding weld penetration, and provide support for energy control and process optimization of welding process.

The application discloses an eye age difference index construction method and system based on fundus images and electronic equipment, and relates to the technical field of image processing. The application trains the constructed double-channel fusion attention network model by adopting a training set and a pairing matrix loss function, can reduce the prediction error between samples in addition to the mean square error, and makes the prediction accuracy of the trained double-channel fusion attention network model further improved. The trained network model is used as a fundus age prediction model to analyze the left and right eye retina fundus images of a to-be-measured object, and the characteristics are fused, so that more accurate fundus age can be obtained, so as to accurately construct the eye age difference index, and then provide a favorable basis for judging the aging track degree of human health.

This invention relates to the field of meteorological forecasting technology, specifically a meteorological forecasting adaptive optimization method and system based on multi-algorithm fusion. This method constructs a parameter optimization framework based on multi-algorithm fusion, embedding physical constraints, and possessing adaptive capabilities, aiming to systematically solve key problems in the parameter tuning process of traditional meteorological forecasting systems. This framework achieves efficient and intelligent optimization through a series of tightly linked steps, ensuring the effectiveness and stability of the method in operational scenarios. This process integrates preceding modules into a unified platform through a microservice architecture, automating data and control flows. The evaluation system includes historical backtesting, real-time forecast testing, and extreme weather-specific testing, quantifying forecast accuracy improvement, resource consumption optimization, and cross-scenario generalization capabilities. Results are fed back to a knowledge base to form a self-optimization loop, ultimately verifying the comprehensive advantages of this method in improving forecast efficiency, ensuring physical rationality, and operational applicability.

This invention relates to a method for predicting the performance of marine turbocharger compressors via a performance map. It addresses the problems of existing methods, such as multiple constraint conflicts, local distortion, and the inability to simultaneously satisfy multiple physical consistency requirements, and belongs to the field of marine power. The invention includes: acquiring sparse data point sets containing speed, flow rate, pressure ratio, and efficiency at multiple speeds, and grouping them by speed. For a target speed, two adjacent subsets of speed lines are selected, and feature point data for the target speed are constructed using linear interpolation. The similarity between each known speed line and the target speed is calculated, including speed difference, peak position flow rate difference, peak amplitude difference, surge, stagnation endpoint flow rate difference, local slope difference, and curvature difference. Adaptive weights are generated based on the similarity, and the pressure ratio and efficiency curves of all known speed lines are weighted for prediction to obtain the target speed prediction curve. Joint constraint solving is performed to construct an objective function and apply multiple types of constraints to form a prediction result that satisfies all constraints, ultimately generating a complete compressor performance map.

This invention discloses a method for predicting urban flood disasters based on satellite infrared brightness temperature and precipitation data. The method includes: combining satellite infrared brightness temperature and precipitation data to identify relevant data of complete lifecycle MCSs (Multi-Category System Components); extracting and filtering CCS area evolution data; determining the optimal value of the sensitivity coefficient; fitting the relationship function between CCS area and time, determining the most vigorous development time as the center point, determining the slope critical threshold based on the sensitivity coefficient, and finding boundary points to divide the development, maturity, and dissipation stages; finally, integrating urban underlying surface characteristics and a waterlogging model to quantitatively predict the level of urban flood disasters and push emergency response measures, optimizing the prediction results and response plans through a dynamic feedback mechanism. This invention can accurately capture the essential characteristics of each stage of MCSs, and based on the capture results, achieve full-process automation from meteorological monitoring to flood prediction and emergency response.

The invention relates to a data prediction method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring historical sequence data of a to-be-predicted object; the sequence length of the historical sequence data is matched with the target sequence length; the target sequence length is obtained by performing adaptive iterative updating on a plurality of initial sequence lengths; inputting the historical sequence data into a pre-trained data prediction model for multi-step prediction processing to obtain prediction data of the to-be-predicted object in a plurality of continuous time steps in the future; the pre-trained data prediction model is obtained by training sample sequence data of the to-be-predicted object; the sequence length of the sample sequence data is matched with the target sequence length; and obtaining a data prediction result of the to-be-predicted object based on the prediction data of the to-be-predicted object in the plurality of continuous time steps in the future. By adopting the method, the data prediction accuracy can be improved.

The application relates to the technical field of power generation power prediction, in particular to a mountainous area distributed photovoltaic power generation power prediction method and system. The method comprises the following steps: collecting photovoltaic power generation power of a target mountainous area photovoltaic power station at each time and various types of environmental data; wherein the environmental data comprises weather states, the environmental contribution change degree of each time period is determined, and the noise interference degree of each time period is obtained; according to the correlation between the photovoltaic power generation power and various types of environmental data, the possibility that various types of data collected at each time is noise data is evaluated, the correlation improvement coefficient of each time data point is calculated, the noise interference degree of the time period to which each time belongs is combined, and the sliding window size when the collected data at each time is subjected to smoothing processing is determined; and the photovoltaic power generation power is predicted by using all types of data subjected to smoothing processing. Therefore, the accuracy of mountainous area photovoltaic power generation power prediction is improved.

This invention relates to the field of mine safety technology, specifically to a three-dimensional blasting pressure relief and anti-impact method and system in dynamic mining process, including the following steps: Step 1, mine data acquisition and analysis: (1) Collect geological data: burial depth, coal seam thickness, roof lithology, mining parameters and historical mine pressure manifestation records; (2) Analyze energy event distribution through microseismic monitoring system; This invention constructs a coupled algorithm based on LSTM neural network and FLAC3D / RFPA numerical simulation to realize real-time optimization of blasting parameters, and trains the model through multi-source data such as microseismic spectrum and roof subsidence rate, so that the stress field control error is greatly reduced and the vertical stress peak transfer distance is greatly improved, which completely solves the problem of lag in manual adjustment. Combined with seismic wave CT dual-mode scanning, the response time for solving the problem of lag in manual adjustment is greatly shortened, and the quantitative evaluation of high frequency low energy conversion and stress peak transfer amount is realized.

The invention discloses a man-hour management system and method for AI confidence evaluation and adaptive approval. Comprising a data acquisition module for acquiring a demand text and a code submission record; the confidence evaluation module is used for outputting predicted working hours and confidence by adopting Monte Carlo Dropout; the grading examination and approval module is used for executing automatic passing, manual rechecking or automatic rejection according to the confidence coefficient, and allocating rechecking persons according to dominant uncertainty; the deviation monitoring module calculates the deviation between the actual working hours and the predicted working hours; the threshold value self-adaptive module is used for performing Pareto optimal threshold value search by adopting an NSGA-II algorithm and taking an automatic passing rate and a re-checking accuracy rate as double targets; and the model self-optimization module analyzes a positioning deviation root cause through an anti-fact root cause, and performs directional increment updating by adopting an elastic weight consolidation algorithm. Through five-layer closed-loop cooperative control, the problems of low prediction credibility, poor examination and approval efficiency and incapability of continuously optimizing the model are solved, and intelligent closed loop of man-hour management is realized.

This application relates to a data processing method, system, electronic device, and storage medium for wind turbine generator sets. The method includes: real-time acquisition and uploading of multi-dimensional operational data from the wind turbine generator set; preprocessing and normalizing the multi-dimensional operational data, and extracting key features from the processed data using a convolutional neural network to obtain key feature data; acquiring historical datasets and a Logistic regression model, training the Logistic regression model based on the historical dataset to obtain the fault early warning model; calculating the probability of fault occurrence using the fault early warning model on the key feature data, and issuing an early warning signal when the probability of fault occurrence exceeds a preset threshold. Through precise data processing and model training, higher model fit and smaller prediction errors are ensured, enabling more accurate prediction of faults in the wind turbine generator set's drivetrain, achieving early warning, and improving the accuracy and reliability of fault early warning.

This invention discloses a few-sample dynamic modeling method for composite material composite structures of electric aircraft based on attention meta-learning, including the following steps: Step 1: The dataset used comes from vibration experiments conducted on composite laminated wing box structures, composite laminated air beam structures, and composite conical-cylindrical composite shells under different temperature conditions. Given limited data, various composite material structures and ambient temperatures are divided into independent task samples. Each task includes vibration test data of the structure at its respective temperature. The data is divided into a meta-training set and a meta-test set. This invention relates to the field of dynamic modeling technology, innovatively integrating meta-learning with dynamic modeling of composite material composite structures of electric aircraft. The AMLT model can utilize MAML's rapid task adaptability and multi-task learning capabilities to quickly transfer and apply knowledge to new tasks, resulting in higher adaptability and generalization ability when facing new tasks.

This invention relates to the field of spectroscopic detection technology, and more particularly to a method for detecting adulterated glycine in glutamine supplements based on tapered fiber-FTIR. The method includes: preparing a chalcogenide glass tapered fiber, coupling it to the external optical path of a Fourier transform infrared spectrometer, and constructing a liquid sample detection platform; preparing a pure glutamine solution, a mixed solution with gradient glycine doping, a low-concentration adulterated sample, and a blind sample; contacting the sample with the tapered fiber; and collecting data at 4000~400 cm⁻¹. ‑1 The infrared absorption spectrum within the specified range was analyzed to identify characteristic spectral markers of glycine adulteration. The spectral data were preprocessed to construct qualitative and quantitative prediction models. Blind sample detection was performed using the quantitative model, and the detection limit and quantitation limit of the method were calculated. The advantages are: utilizing the strong evanescent field effect of tapered optical fibers significantly improves detection sensitivity; and combining this with chemometric methods to construct a stable model enables rapid and accurate detection of glycine adulteration in glutamine.