185 results about "Multiple linear regression model" patented technology

The invention discloses a switch cabinet discharge detection method and system, and relates to the technical field of power equipment state monitoring and fault diagnose.The light intensity attenuation rate and the vibration frequency are collected through a photonic crystal fiber, the concentration of decomposed gas is detected in combination with a Raman spectrum, and a space-time joint feature matrix is generated; performing space weight distribution on the space-time joint feature matrix based on the switch cabinet structure, and inputting the space weight to an RBF-SVM (Radial Basis Function-Support Vector Machine) to generate discharge type probability distribution; based on a high-frequency energy parameter and a gas decomposition concentration parameter in the spatio-temporal joint characteristic matrix, inputting the parameters into a multiple linear regression model, calculating an insulation aging index, and judging an insulation degradation grade according to a threshold value; the photonic crystal fiber is used for collecting the light intensity attenuation rate and the vibration frequency, the Raman spectrum is combined for detecting the decomposition gas concentration to generate the space-time joint characteristic matrix, synchronous monitoring of various physical and chemical parameters in the switch cabinet is achieved, and the accuracy and comprehensiveness of recognition of different types of discharge phenomena are improved.

The invention relates to electroencephalogram signal acquisition, in particular to an electroencephalogram signal acquisition and monitoring system, which comprises a mobile terminal, a signal acquisition device, an EEG (electroencephalogram) signal, an fNIRS hemodynamic signal and IMU (inertial measurement unit) motion data, a physical information neural network method of a self-adaptive sampling strategy is adopted to identify a BCG vascular pulse pseudo-film segment in an EEG signal by using an fNIRS hemodynamic signal, and then a multiple linear regression model of motion artifacts is established based on the fNIRS hemodynamic signal and IMU motion data so as to identify a motion pseudo-film segment in the EEG signal. Performing interpolation restoration on the detected pseudo-film segments to eliminate artifacts in the EEG electroencephalogram signals, and drawing and displaying an electroencephalogram in real time according to the EEG electroencephalogram signals after the artifacts are eliminated; according to the technical scheme provided by the invention, the defect that BCG vascular pulsation artifacts and motion artifacts in the EEG signals are difficult to effectively eliminate can be effectively overcome.

The invention discloses a disastrous big wave occurrence probability and wave height combined prediction method, device and equipment, a medium and a product, and relates to the technical field of meteorological ocean forecasting. The method comprises the following steps: acquiring an effective wave height measured value and environmental factor data of a to-be-predicted region in a historical time period; key environment factors are screened through spatial correlation analysis and linear fitting verification, a first training data set and a second training data set are constructed, and a multiple linear regression model and a logic regression model are trained respectively to obtain a wave height prediction model and a big wave occurrence probability prediction model; and inputting the key environmental factors of the current time period of the to-be-predicted region into the model to obtain the predicted occurrence probability of the disastrous waves and the predicted value of the significant wave height in the predicted time period. According to the method, the key environmental factors are screened and the two models are used for respective prediction, the influence of the various environmental factors on the big waves is considered, the occurrence probability and the wave height of the disastrous big waves can be predicted more accurately, and the ocean safety is effectively guaranteed.

The invention discloses an LED energy storage street lamp management method and system combined with Internet of Things perception, relates to the technical field of Internet of Things and intelligent illumination, and solves the problem that it is difficult to use the Internet of Things to monitor, collect and process various data in real time so as to predict maximum brightness demand values of street lamps in different time periods. The influence of shelters on the illumination intensity of the street lamp is difficult to analyze; the battery state and the energy storage efficiency of the LED energy storage street lamp are difficult to evaluate; a charging and discharging control strategy is difficult to generate; the energy consumption under the charge and discharge control instruction is difficult to optimize by using digital twinning, and the optimized charge and discharge control instruction is difficult to execute. Various sensing data are collected and processed through the Internet of Things, the brightness requirement of the street lamp is predicted by using a multiple linear regression model, a dynamic shielding coefficient is analyzed, and the battery state and energy storage efficiency of the LED energy storage street lamp are evaluated, so that an intelligent charging and discharging control strategy and a digital twin model simulation energy consumption optimization strategy are generated; and the energy consumption of the LED energy storage street lamp is reduced.

The invention discloses a pillow regulation and control method self-adaptive to cervical vertebra flexion and shoulder width and application of the pillow regulation and control method to a pillow, and relates to the technical field of sleeping posture regulation. The method comprises the steps of registering a three-dimensional contour point cloud through an ICP algorithm, calculating a neck curvature value, establishing a multiple linear regression model in combination with a pressure gradient change curve and a pressure peak offset, and realizing quantitative correlation between pressure data and a cervical vertebra inclination angle; based on the curvature change amplitude and shoulder breadth classification, a personalized model is constructed; and through attitude monitoring and pressure feedback, a real-time optimization instruction is generated. A target displacement error is set, and correction is stopped after the bending degree reaches the standard continuously for three times; pre-storing a medical-level support parameter library; multi-user switching is achieved through pressure feature and head contour double verification, and physical compression emergency stop is set. According to the method, through multi-source data fusion, scientific model construction and dynamic closed-loop control, the personalization and safety of pillow supporting are improved, and the method is suitable for people with sub-health cervical vertebrae and multi-user scenes.

The invention discloses a clinical examination equipment remote calibration method and system based on edge calculation, and relates to the field of instrument calibration, the system is composed of a plurality of functional modules, and the system comprises: a multi-modal data acquisition module for acquiring historical calibration data of absorbance, including fluorescence images, cell fluorescence signal intensity and morphological characteristics; the edge calculation module is used for establishing a multiple linear regression model according to historical calibration data of the absorbance and predicting an attenuation curve of the optical detection unit; on the basis of a federated learning framework, a fluorescence signal intensity compensation matrix is optimized by utilizing shared parameters among edge nodes, and light path gain parameters are automatically adjusted; performing outlier analysis on each batch of detection data through a built-in Westgard rule engine, and triggering automatic redetection of morphological characteristics; analyzing absorbance time sequence data in combination with a 1D-CNN model, and identifying reagent pollution and optical assembly aging; and the drifting calibration module adopts a PID controller to dynamically adjust the power of the laser.

The invention provides a soil comprehensive nutrient evaluation method and device based on a satellite remote sensing image, equipment and a medium. The method comprises the following steps: acquiring a Sentinel-2 multispectral image, cultivated land vector boundary data and actually measured soil nutrient data of a target area; establishing a multiple linear regression model by utilizing the wave band reflectivity of the Sentinel-2 multispectral image and actually measured soil nutrient data, generating a region-level nutrient content grid map, and extracting a nutrient content grid map of soil in a cultivated land range of a research region; classifying the grid map by adopting a spatial constraint natural breakpoint method clustering target function; a three-digit decimal code is adopted to represent a grading result, a soil comprehensive nutrient spatial distribution diagram is generated, coordinated color matching is set, various areas and proportions of soil comprehensive nutrients are counted, and fertilization suggestions are given, so that the defects of single sensing dimension and disjunction of decision support during remote sensing monitoring of the soil nutrients are overcome; and a closed loop serving precision agricultural practice is difficult to form.

The invention discloses a six-dimensional force sensor calibration method based on an intelligent algorithm and ensemble learning, which improves calibration precision and system adaptability by combining data anomaly detection, the intelligent algorithm and the ensemble learning. The calibration method comprises the following steps: S1, building a six-dimensional force sensor calibration system; s2, loading and unloading experiments of force and torque are carried out on the six-dimensional force sensor on the standard calibration table, and multi-channel analog signal data output by the six-dimensional force sensor are obtained; s3, repeating the operation in the step S2 for a plurality of times; s4, data anomaly detection; s5, performing preliminary calibration by adopting a multiple linear regression model to obtain a preliminary decoupling matrix of the six-dimensional force sensor; s6, calculating error data after multiple linear regression calibration, and taking the error data as input characteristics of subsequent error compensation; s7, performing error compensation based on the residual neural network; and S8, determining calibration precision and outputting a final model.

The invention discloses a coal preparation plant production personnel positioning method and system based on video analysis, and the method comprises the steps: processing a video stream through employing an improved OfficientDet model, and obtaining an initial target personnel detection frame; processing the initial target person detection frame by using an improved FairMOT model and a dynamic motion model to obtain a continuous tracking trajectory of the target person; processing the continuous tracking trajectory of the target person by using a multiple linear regression model, predicting the position of the next frame, and allocating weights by using an entropy weight method to obtain a predicted motion trajectory; and processing the continuous tracking trajectory of the target person by using a ResNet-50 model to obtain a depth feature, performing identity confirmation of the target person based on the feature, and associating the identity with the predicted motion trajectory to complete person positioning. According to the method, tracking errors caused by environmental changes are reduced, and effective application in different environments is ensured.

The invention discloses a method for increasing the sidewall turn-up height in a building process of an all-steel radial tire, which comprises the following steps of: acquiring multi-source data such as temperature, pressure and angle in a turn-up process through an infrared temperature sensor, a pressure sensor and the like; and constructing a multiple linear regression model, a machine learning model and a hybrid model fused with the physical principle, analyzing the relationship between the process parameters and the anti-package height, and performing verification and optimization. According to model output parameters, a servo motor is used for accurately controlling the downward pressing angle and pressure of a reverse wrapping pressing roller, and production is executed in cooperation with an auxiliary supporting ring and a sectional type reverse wrapping technology; meanwhile, the turn-up height is detected in real time through an intelligent monitoring system, and parameters are automatically adjusted when the turn-up height does not reach the standard. According to the method, precise regulation and control of the tire sidewall turn-up height are achieved, compared with a traditional process, the product percent of pass is increased to 95% or above from 85%, the production efficiency is improved by 20%-30%, the tire structure stability is effectively enhanced, the rolling resistance and the tire burst risk are reduced, and intelligent upgrading of tire manufacturing is promoted.

The invention relates to the field of coal mine safety engineering, and provides a coal mine gas control extraction effect dynamic evaluation and closed-loop control method, which comprises the following steps: S1, based on a basic threshold value in an extraction design scheme, periodically and dynamically calibrating the threshold value through a multiple linear regression model, and generating a dynamic threshold value matrix of a core index; s2, collecting multi-dimensional data to construct a fusion data matrix; s3, a dynamic multi-scale convolutional neural network model is constructed and trained, and the model comprises a variable-length filter generator which is used for adaptively generating a variable-length filter according to the input data so as to extract multi-scale time sequence features; outputting a standard state judgment result of the current extraction effect and a core index prediction value of a future preset time period in parallel; and S4, starting a three-level linkage feedback management and control flow coordinated by the AI model and the expert rule base according to the standard reaching state judgment result and the predicted value output in the step S3. Accurate evaluation and short-term prediction of core indexes such as the extraction flow, the gas concentration and the attenuation coefficient are achieved.

The invention provides a hot blast stove system energy-saving control method based on multi-parameter cooperative control. Firstly, parameters such as waste gas temperature, furnace top temperature, waste gas oxygen content and coal gas flow are collected through sensors, real-time data are input into a cooperative control module, an optimal waste gas temperature rise curve is fitted through a pre-trained deep neural network model, and an initial opening instruction of an air and coal gas regulating valve is calculated in combination with a multiple linear regression model. And then a parameter self-adaptive PID controller is used for dynamically adjusting parameters according to the real-time temperature deviation, a final control instruction is generated and executed, and effective regulation and control of waste gas and the vault temperature are achieved. The system can intelligently decide to increase or decrease the air or gas amount, optimize the combustion efficiency, reduce the gas consumption and prolong the service life of equipment. The method breaks through the limitation that control only depends on the optimal air-fuel ratio traditionally, a gas energy-saving scheme based on the waste gas temperature rise curve is provided, the method is suitable for a hot blast stove system in the metallurgical industry, and it is expected that gas consumption can be reduced by 3%-15%.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The invention discloses an intelligent pipeline data monitoring method and related equipment, and the method comprises the steps: collecting the multi-source original data of a target pipeline through a distributed sensor and intelligent inspection equipment; preprocessing the multi-source original data to obtain preprocessed data; performing feature extraction operation on the preprocessed data to obtain target feature data; acquiring historical operation data of the target pipeline; according to the historical operation data, constructing a target multiple linear regression model; constructing a target long-short-term memory network model according to the target feature data; and performing anomaly detection on the target pipeline according to the target multiple linear regression model and the target long-short-term memory network model to obtain a detection result. The method can improve the precision of pipeline data monitoring, and can be widely applied to the technical field of pipeline engineering.

The invention discloses a configuration modeling system for parameters of a vehicle-mounted head-up display under big data of Internet of Vehicles, and the system comprises a data preprocessing module which carries out the preprocessing of vehicle operation state data, vehicle-mounted equipment log data, environment information data and key arrangement parameter data of the vehicle-mounted head-up display, and obtains the preprocessed data; the multiple linear regression model training module divides a training set from the preprocessed data, and randomly sets initial model parameters of a multiple linear regression model; iteratively updating the initial model parameters of the multiple linear regression model by using the training set through a gradient descent algorithm to obtain the multiple linear regression model after model parameter optimization so as to obtain a trained multiple linear regression model; and predicting arrangement parameters of the vehicle-mounted head-up display in different driving scenes of the vehicle through the trained multiple linear regression model. The visual interference risk in the driving process is reduced, and the driving safety and the man-machine interaction comfort are improved.

According to the precise variable fertilization and pesticide spraying integrated collaborative method for the mountain rape unmanned aerial vehicle, heuristic function layered weighting and node gradient continuity inspection in the improved A star algorithm can generate a flight path highly adaptive to mountain topographic features, the path has the advantages of low energy consumption and full coverage, and the flight path has the advantages of being high in adaptability to the mountain topographic features. Operation hindrance caused by complex terrain can be effectively avoided; in an operation parameter calculation link, an improved weighted multiple linear regression model is constructed, a dynamic space-time weight item is utilized to optimize a characteristic variable, a residual error correction mechanism is combined to improve calculation precision, and accurate quantification of fertilization and pesticide spraying parameters is realized. On the basis, collaborative linkage of path planning and parameter calculation is established through a regional weight association mechanism, the adaptability of the path planning and the parameter calculation is remarkably enhanced, the efficiency and accuracy of unmanned aerial vehicle operation in the complex mountain environment are effectively improved, waste of resources such as fertilizer and chemicals is reduced, and the economic benefit is improved. Meanwhile, the dual adaptability to the rape operation growth dynamic state and the mountain topographic feature is enhanced.

The invention relates to the technical field of disease risk prediction and precision medical treatment, in particular to an establishment method and application of a late-onset psoriasis risk prediction model, and the method comprises the following steps: (1) collecting lifestyle data, serum metabolite data, clinical characteristics and polygene risk scores of a subject; (2) constructing a healthy lifestyle score according to the lifestyle data; (3) screening metabolites significantly related to the healthy lifestyle by using a multiple linear regression model; (4) screening metabolites significantly related to the risk of late psoriasis by using a Cox regression model; (5) screening a key metabolite set by adopting an elastic network regression model; and (6) inputting the sample features into a machine learning model to obtain a late-onset psoriasis risk prediction model. According to the method, the prediction accuracy is high, the AUC can reach 0.86 by combining the lifestyle, serum metabolites and genetic risks, and the method is obviously superior to a prediction method only depending on clinical characteristics or genetic information.

This application discloses an elevator energy-saving control method and system, relating to the field of elevator control technology, aiming to solve the problem that the energy consumption of elevators remains high in practical applications under existing technologies. The method is used in a carrier frequency adjustment device and includes the following steps: acquiring elevator operating status information; acquiring a set of influencing factors based on the elevator operating status information; performing trend analysis based on the set of influencing factors to obtain the influence coefficient value corresponding to each influencing factor; inputting the influence coefficient values ​​corresponding to each influencing factor into a trained target multiple linear regression model, so that the target multiple linear regression model outputs a target carrier frequency value; adjusting the current carrier frequency value of the elevator to the target carrier frequency value to reduce the energy consumption of the elevator during operation. Based on the method described in this application, the energy consumption of the elevator under different operating states can be reduced, and the smooth operation of the elevator can be ensured.

The invention discloses a method for predicting cement strength. The method comprises the following steps: S1, collecting multi-source key data; s2, first-layer prediction-hydration kinetic model construction and 28d strength preliminary prediction are carried out; s3, second-layer prediction-fusion algorithm training and intensity prediction correction are carried out; and S4, verifying a prediction result and optimizing the system. According to the method, a multiple linear regression model of 28d strength and hydration kinetic parameters is established, the coupling influence of multiple factors such as cement chemical components, specific surface area, hydration rate constant and reaction activation energy is considered, and the limitation that an existing method only pays attention to a single index is overcome; a fusion algorithm combining a gradient boosting tree and a deep neural network is adopted to comprehensively analyze multi-time-point strength data and a preliminary predicted value, and the precision and reliability of cement 28d strength prediction are remarkably improved. The actually measured value and the predicted value are compared and analyzed, a self-learning historical database is established, the adaptability of the prediction model is effectively optimized, and the prediction result is closer to the actual situation.

The invention relates to an intersection group collaborative optimization method and device in a man-machine mixed driving scene, and a storage medium. The method comprises the following steps: dividing a to-be-optimized urban area into a plurality of traffic zones; predicting the ownership of automatic driving vehicles and human driving vehicles in the traffic zone according to the vehicle type purchase intention data and a pre-constructed multiple linear regression model; according to a binomial Logit model, calculating a human driving travel ratio and an automatic driving travel ratio of the traffic zone, according to the travel ratio and the inventory, calculating the travel occurrence amount of human driving vehicles and automatic driving vehicles of the traffic zone, and obtaining a preset attraction amount of the traffic zone; calculating traffic demands of human-driven vehicles and automatic-driven vehicles between the starting point and the ending point of the traffic zone according to the travel occurrence amount and the attraction amount; constructing a flow distribution model according to the road network of the traffic zone; and solving the intersection group collaborative optimization model according to the flow distribution model to obtain a final signal optimization scheme.

This invention belongs to the field of transformer insulation drying treatment and intelligent monitoring technology. Specifically, it provides an intelligent prediction system for the drying endpoint of transformers, comprising: dividing the drying process into multiple stages based on historical data and physical mechanisms, outputting feature baselines using the K-means algorithm, constructing a multiple linear regression model with basic thresholds and adjustment variables to achieve personalized threshold adaptation, accurately identifying the current stage by matching real-time features and clustering features with Euclidean distance, calculating dynamic personalized thresholds by combining real-time time-series features and adjustment variables, embedding a feature-time dual-dimensional attention layer in the LSTM model, guiding the model to focus on key information based on personalized thresholds, and outputting the endpoint prediction result. This invention can achieve phased intelligent control and accurate endpoint prediction of the drying process, reduce the risk of under-drying or over-drying, and is adaptable to different equipment and operating conditions.

The application discloses a dandelion medicinal material effective component rapid determination method based on color chroma values, and relates to the technical field of traditional Chinese medicinal material quality detection. * , a * , b * Chroma values of dandelion medicinal material powder are collected, and the contents of chicoric acid, monocaffeoyl tartaric acid and chlorogenic acid are determined by using high performance liquid chromatography as true values; subsequently, a neural network prediction model with chroma values as input and effective component contents as output is constructed and trained, preferably a single hidden layer structure; finally, the model is used for fast and nondestructive content prediction of unknown samples; compared with a traditional multiple linear regression model, the neural network model used in the application has significantly higher prediction accuracy, and the test set R² is all more than 0.86; the application realizes fast, nondestructive, low-cost and high-precision detection of dandelion medicinal material effective components, and is suitable for multiple scenes such as raw material quality control, market supervision, optimal harvest period judgment and fast pricing grading.

The invention relates to a novel prediction method for the residual life of a power transmission tower, and belongs to the field of power system equipment monitoring and maintenance. The method comprises the following steps: firstly, arranging a permanent magnet on the surface of a power transmission tower body to magnetize the power transmission tower body by utilizing a magnetic flux leakage signal detection technology, collecting and normalizing magnetic induction intensity and extracting magnetic signal characteristic quantity by combining a Hall sensor, and establishing a function relationship with angle steel corrosion depth and width, so as to determine a corroded quantity; then, based on environmental factors, namely temperature, humidity, sulfur dioxide concentration and chloride ion concentration, predicting the corrosion rate through a multiple linear regression model; analyzing the relationship between the corrosion degree and the residual bearing capacity to obtain the corrosion degree of the power transmission tower under the ultimate bearing capacity; and finally, calculating the residual life of the iron tower by calculating the amount to be corroded and combining the corrosion rate. The residual life of the power transmission tower can be accurately predicted, and a reliable basis is provided for regular maintenance and reinforcement of the tower.

The invention discloses a method for forecasting anthracnose of wine grapes, and particularly belongs to the technical field of plant disease and insect pest forecasting. The method comprises the following steps: carrying out intra-group correlation analysis on climatic factors of threshold number of days before the wine grape anthracnose attack through SPSS to obtain meteorological factors significantly related to the wine grape anthracnose; through stepwise regression analysis, redundant climate factors in the meteorological factors significantly related to the wine grape anthracnose are removed, a multiple linear regression model is constructed, the meteorological factors significantly related to the wine grape anthracnose are screened out, and multicollinearity is avoided; secondly, inputting meteorological data to be measured into the multiple linear regression model, and outputting a prediction result of the disease index of anthracnose; and finally, forecasting the anthracnose of the wine grapes based on the prediction result of the disease index of the anthracnose. The method can prevent and control anthracnose, improve wine grape quality and reduce economic loss.

The invention relates to the technical field of down-the-hole drill ball tooth drill bit wear measurement, in particular to a down-the-hole drill ball tooth drill bit wear value real-time measurement method and system, and the method comprises the steps: carrying out the optimization and registration of an initial three-dimensional model, and obtaining an overall wear quantized value of a down-the-hole drill ball tooth drill bit; analyzing a mapping relation between the while-drilling parameters and the overall wear quantized value, and constructing a database between the while-drilling parameters and the drill bit wear value; deriving a dimensionless variable according to a dimensional analysis method and a database, and performing combined screening on the dimensionless variable, a second-order polynomial and a cross term to determine a dimensionless variable combination influenced by drill bit wear; and substituting the dimensionless variable combination into the multiple linear regression model to establish a mathematical relationship model between the drill bit wear and the while-drilling parameters, thereby realizing the real-time measurement of the wear value of the ball tooth drill bit of the down-the-hole drill. According to the method, non-dimensional variables of the while-drilling parameters are obtained based on dimensional analysis, the model is built in combination with multiple linear regression, the wear value of the ball tooth drill bit of the down-the-hole drill is measured in real time, and construction efficiency and safety are improved.

This invention discloses a method for controlling the addition of supernatant from kitchen waste as a carbon source for denitrification based on three-dimensional fluorescence spectroscopy. The method includes steps such as sample collection and detection, three-dimensional fluorescence spectral processing, establishment of a multiple linear regression model, and control of the carbon source dosage. By acquiring real-time online data on the changes in three-dimensional fluorescence spectra during the denitrification process of nitrate-containing wastewater and combining this with a multiple linear regression model to predict carbon source consumption, this invention achieves precise control of the carbon source dosage, ensuring a sufficient supply of carbon source during the denitrification stage while avoiding waste caused by excessive carbon source addition. This significantly improves the denitrification rate, reduces energy consumption and operating costs, and ensures that the effluent quality meets standards.

The invention discloses a Dst index monitoring method, system and equipment based on a Zhang-Heng-1 satellite, a medium and a program product. The method comprises the following steps: acquiring magnetic field data acquired by the Zhang-Heng-1 satellite; performing data preprocessing on the magnetic field data to obtain satellite-borne observation data; carrying out background magnetic field deduction processing on the satellite-borne observation data by utilizing a global geomagnetic field model to obtain a satellite-borne magnetic field residual error; and finally, the satellite-borne magnetic field residual error is input into a ground-based Dst index mapping model, a corrected satellite-borne Dst index is obtained, and the ground-based Dst index mapping model is a multiple linear regression model obtained through segmented fitting according to different magnetic storm grades based on historical Dst index data and solar radiation indexes. According to the method, high-timeliness and high-precision monitoring of the global geomagnetic storm index is realized through high-precision mapping from the satellite-borne original observation residual error to the foundation standard Dst index.

The application discloses a tobacco leaf baking management method and system, and the method comprises the following steps: pre-entering and storing baking stage judgment threshold values corresponding to different regions, varieties, parts and maturity tobacco leaves, and completing threshold value library initialization; in the baking process, multi-source data fusion judgment is realized by synchronously collecting multi-dimensional tobacco leaf state data in combination with a multiple linear regression model, and a judgment result of the current baking stage is output. The application effectively solves the problems of low accuracy and high misjudgment rate of the existing tobacco leaf baking stage judgment scheme, and realizes the effects of improving the judgment accuracy and reducing the baking quality loss.

The embodiment of the application provides a database capacity management method and device, and relates to the technical field of databases; a prediction model is established to realize automatic monitoring and early warning of a data growth trend in a database space. The method comprises the following steps: collecting original data of a plurality of associated data related to database capacity; grouping the plurality of original data to obtain a plurality of groups of combined data; performing standardization processing on the plurality of groups of combined data to obtain a plurality of groups of sample data sets; training the plurality of groups of sample data sets by using a multiple linear regression model to obtain a plurality of undetermined prediction models of database capacity; loading the plurality of groups of sample data into corresponding undetermined prediction models to obtain a plurality of output values, comparing the plurality of output values with corresponding database usage values, and selecting a capacity prediction model of the database from the plurality of undetermined prediction models; and loading associated data of a to-be-tested database into the capacity prediction model to obtain a capacity prediction value of the to-be-tested database.

The invention provides a power transformer thermal aging health state estimation method and system. The method comprises the steps of obtaining health index monitoring data of transformer thermal aging; inputting the health state monitoring data into a multiple linear regression model, and outputting a classification label of the health state of the transformer based on the multiple linear regression model; inputting the health state monitoring data into a Bayesian deep neural network model, and outputting probability prediction of transformer health state classification labels and uncertainty estimation of prediction results based on the Bayesian deep neural network model; and simulating and calculating the combined weight of the multiple linear regression model and the Bayesian deep neural network model through a Monte Carlo Markov chain method, and generating a final health state prediction value of the transformer according to the combined weight, the output of the multiple linear regression model and the output of the Bayesian deep neural network model. According to the scheme, the interpretability of the thermal aging health state evaluation process can be guaranteed, and the accuracy and reliability of evaluation can be improved.