140 results about "Nonlinear regression" patented technology

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention provides an intelligent scheduling method and device for a multi-hole gate and a server, and relates to the technical field of hydraulic engineering automation and artificial intelligence optimization scheduling, and the method comprises the steps: determining target input characteristics through the historical operation data and simulation data of the multi-hole gate, and improving the target input characteristics through a lightweight gradient lifting tree model; performing nonlinear regression training processing on the target input features, and determining a target traffic prediction model at the current moment; performing hierarchical expansion processing on the gate opening degree combination through a multi-constraint search model to obtain a gate expansion scheduling scheme, and performing batch prediction processing and scoring processing on the gate expansion scheduling scheme by using a target flow prediction model to obtain a gate candidate scheduling scheme set; and performing upper limit detection screening processing and error tolerance screening processing on the gate candidate scheduling scheme set, and determining a target scheduling scheme of the porous gate. The method can significantly improve the prediction precision of the outlet water of the gate and the scheduling efficiency of the multi-hole gate.

The invention relates to the technical field of fluid mechanics modeling and aerodynamic acoustic analysis, in particular to a spectral orthogonal decomposition-based separated flow wall surface pressure pulsation dominant structure modeling method, which comprises the following steps of: acquiring a whole flow field and / or wall surface unsteady pressure data of a separated flow under a preset working condition; performing spectral orthogonal decomposition on the unsteady pressure data, performing frequency domain decoupling on multi-scale features, and extracting feature values and spatial dominant feature modes under each feature frequency; constructing a complex wave packet physical parameterized model, fitting the spatial dominant feature modals, and compressing the spatial dominant feature modals into sparse physical parameter vectors after nonlinear regression solution; and reconstructing a wall surface pressure pulsation dominant component based on the sparse physical parameter vector and the characteristic value, and carrying out at least one of flow mechanism analysis and pneumatic order reduction modeling according to the dominant component. Therefore, the problems of low reconstruction precision and the like caused by the fact that asymmetric evolution and variable acceleration convection of a large-scale structure in separation flow wall surface pressure pulsation cannot be accurately represented in a frequency domain in related technologies are solved.

The invention discloses a multi-model fusion space-time water quality prediction method. The method comprises the following steps: S1, collecting multi-source time sequence data of a key monitoring unit of a sewage plant; s2, cleaning the collected original time sequence data; s3, extracting long and short term dependence characteristics of the water quality indexes by adopting an LSTM module; a Transform module is adopted to extract global attention features; an XGBoost model is adopted to extract nonlinear regression features and prediction results; s4, constructing a plurality of model fusion strategies, and performing integrated evaluation on prediction results of the prediction models, including static weighted fusion, adaptive attention weight fusion and integrated learner fusion; and S5, performing performance evaluation on each fusion strategy by adopting the same evaluation index, automatically selecting the fusion strategy which shows the optimal on the verification set, and performing prediction output on the key water quality index at the future moment. The prediction precision of the key water quality indexes and the intelligent level of system operation are improved.

The invention relates to the technical field of deformation measurement, in particular to a road bridge settlement deformation monitoring device and method.The method comprises the steps that firstly, a bridge temperature state vector representing the temperature state more comprehensively is constructed, sub-time periods representing the thermodynamic time period are divided in each period time period, and a temperature-to-deformation driving mechanism is introduced; a theoretical prediction displacement value is determined based on nonlinear regression analysis in combination with a bridge temperature state vector; then, diagnosis of a structure response mechanism is introduced, and screening of normal time periods representing the healthy bridge structure response mechanism is carried out based on a combined difference cost matrix constructed on two dimensions of morphological difference and deformation correlation difference; and finally, a more accurate settlement deformation monitoring value is determined based on the deviation between the theoretical prediction displacement value and the vertical displacement value of the bridge in a normal time period, so that the accuracy of road and bridge settlement deformation monitoring according to the settlement deformation monitoring value is higher.

The invention discloses a high polymer material performance prediction method and system based on deep learning, and the method comprises the steps: constructing an automatic encoder, and carrying out the dimension reduction of various data of a high polymer material through unsupervised learning; optimizing a multi-mode encoder structure by adopting an automatic design mechanism; extracting a fusion characteristic value of the high polymer material by using a multi-modal data encoder; dynamic attention fusion: introducing a dynamic gating weight to adaptively distribute modal weights to input data; introducing physical constraint, and embedding molecular dynamics into back propagation; performing quantum circuit acceleration graph convolution; and predicting and outputting, mapping the fusion characteristic value to the tensile strength and elastic modulus performance indexes of the high polymer material, and realizing nonlinear regression through a multi-layer perceptron. According to the method, a material molecular dynamics equation is converted into a forward propagation kernel from a posterior constraint, the dynamic behaviors of molecules can be simulated and predicted more accurately, and the calculation efficiency is improved while the precision is ensured.

The invention discloses a cascade reservoir backwater jacking influence quantification and tail water level prediction method based on hydrodynamic simulation and regression analysis. The method comprises the following steps: step 1, data collection and preprocessing; 2, constructing a one-dimensional hydrodynamic model, simulating water surface lines under different constant flow conditions by taking the upstream reservoir outlet flow as an upper boundary and the downstream reservoir dam front water level as a lower boundary, and calculating the upstream reservoir tail water level; 3, multiple groups of upstream reservoir outlet flow and downstream reservoir dam front water level combinations are set, the corresponding tail water level is calculated through a hydrodynamic model, and a data table is formed; 4, establishing a function relation model among the tail water level, the reservoir outlet flow and the dam front water level by using a multivariate nonlinear regression method; step 5, performing significance test and error analysis on the regression model; 6, a calculation page rule of the influence of the downstream reservoir backwater jacking on the upstream reservoir tail water level under different working conditions is developed; the method is beneficial for improving the refined scheduling level of the cascade reservoirs.

This invention discloses a method for locating epileptogenic zones based on high-frequency oscillations and connectivity, comprising the following steps: 1. Acquiring SEEG data and selecting representative channels; 2. Filtering to acquire 54-200Hz signals and selecting baseline and target data; 3. Acquiring normalized high-frequency energy; 4. Calculating time and energy coefficients to obtain a high-frequency epileptogenicity index; 5. Filtering to acquire 12-45Hz signals; 6. Calculating nonlinear regression analysis; 7. Calculating total intensity; 8. Defining and calculating the connectivity high-frequency epileptogenicity index and evaluating performance. This invention can accurately locate epileptogenic zones in patients with different seizure patterns, and has potential application value in epilepsy treatment.

The application discloses a kind of live cell dynamic monitoring method, comprising the following steps: obtaining the cell scanning picture group that live cell grown in culture vessel is photographed under specified condition, and the picture is preprocessed;Cell scanning picture group after pre-processing is input into trained cell identification model, and output cell growth state chart group;Cell growth state chart group is input into trained survival rate calculation model, and the survival rate of cell in each cell growth state chart is output;The cell survival rate numerical value obtained is drawn into cell survival rate-time curve chart;The cell survival rate-time curve chart generated is combined with nonlinear regression statistical method, and the half inhibitory concentration value of the efficacy of specified parameter calculation is selected.The growth state of cell under specified condition is monitored, and the survival rate of cell and the half inhibitory concentration value of the efficacy are calculated based on these data;It can significantly improve the efficiency and accuracy in the field of drug research and development, drug screening, treatment scheme effect, etc.

This invention discloses a method for predicting concrete performance based on multivariate nonlinear regression analysis, comprising the following steps: obtaining a real-valued dataset of concrete performance under different combinations of conditional parameters using an orthogonal strategy; defining the type of regression equation and solving the parameters in the regression model using the least squares method based on the aforementioned dataset; verifying the accuracy of the regression model using the aforementioned dataset and additional datasets; and predicting the concrete performance of different parameters using the validated regression model. The results show that this performance prediction method can establish an excellent regression prediction model based on a small amount of real data and achieve rapid and accurate prediction of concrete performance with different parameters.

The invention discloses a lithium battery health state estimation method and system, electronic equipment and a storage medium, and belongs to the field of battery state prediction.The method comprises the steps that probability modeling is conducted on a small amount of lithium battery charging and discharging time sequence data through a variational self-encoding network, an expansion sample with degradation consistency is generated, and an expansion data set is formed; key health indexes reflecting battery aging characteristics in the extended data set are constructed into a characteristic matrix as input; a kernel extreme learning machine is adopted as a nonlinear regression prediction model, global optimization is carried out on kernel function parameters and regularization coefficients of the nonlinear regression prediction model through an artificial bee colony optimization algorithm, and finally an SOH prediction model which is obtained through training and represents the relation between the battery health state and the characteristics is obtained; and outputting a battery health state estimation result based on the SOH prediction model. According to the method, the health state prediction precision and efficiency can be improved.

This invention discloses a method for monitoring the cold chain environment of fruits and vegetables based on multi-sensor fusion, belonging to the field of intelligent sensing and agricultural product logistics technology. It includes: using a flexible multimodal sensor array attached to the surface of fruits and vegetables to acquire the temperature, humidity, and original level signals of the target gas in the surface microenvironment; performing a cross-drift compensation step based on local temperature and humidity physical feedback, using a multivariate nonlinear regression model to use temperature and humidity as dynamic compensation factors, calibrating the baseline drift of the gas sensor in real time, and obtaining high-confidence gas concentration values; calculating the rate of change of the gas concentration values ​​in real time, and automatically triggering an edge interruption when the rate of change exceeds a preset jump threshold, dynamically increasing the sampling frequency and adjusting the radio frequency duty cycle. This invention solves the technical problems of large measurement errors caused by the inability of traditional rigid sensors to conformally fit, severe sensor baseline drift in high humidity environments, and the mutual constraint between high-frequency monitoring and node energy consumption.

The invention discloses a regional resident load meteorological sensitivity analysis method, device and equipment based on fused meteorological indexes, and a medium, and relates to the field of power load characteristics, and the method comprises the steps: constructing a multi-factor fused meteorological index model of a single meteorological point, determining a multi-meteorological point fusion meteorological index of the target area according to the resident load proportion corresponding to the target area and a multi-factor fusion meteorological index model, and then constructing a target data set according to the multi-meteorological point fusion meteorological index and the resident load data; and segmenting the target data set to perform piecewise nonlinear regression analysis on the obtained target first data set and the target second data set, and determining the current residential load meteorological sensitivity of the target area through a target piecewise regression analysis function obtained through analysis and the target data set. Therefore, nonlinear segmented regression modeling and sensitivity analysis of the resident load and the fused meteorological indexes in the target area can be realized, and support is provided for refined analysis and management of the resident load.

The application provides a kind of intelligent scheduling method, device and server of multi-hole gate, it is related to the technical field of water conservancy engineering automation and artificial intelligence optimization scheduling, comprising: using the historical operation data and simulation data of multi-hole gate, determine target input characteristics, and through light gradient boosting tree model, target input characteristics are carried out nonlinear regression training processing, determine the target flow prediction model of current time;Through multi-constraint search model, the gate opening combination is carried out hierarchical expansion processing, and the gate expansion scheduling scheme is obtained, and the target flow prediction model is used to carry out batch prediction processing and scoring processing on the gate expansion scheduling scheme, to obtain the gate candidate scheduling scheme set;Upper limit detection screening processing and error tolerance screening processing are carried out for the gate candidate scheduling scheme set, to determine the target scheduling scheme of multi-hole gate.The application can significantly improve the prediction accuracy of gate water outlet and the scheduling efficiency of multi-hole gate.

A method for measuring slurry concentration in a tailings thickening device includes the following steps: obtaining the pressure values ​​at each monitoring point, which are equidistantly located at different heights within the tailings thickening device; calculating the slurry density at each monitoring point; calculating the slurry concentration at each monitoring point; constructing a distribution model of the slurry concentration within the tailings thickening device using nonlinear regression based on the slurry concentration and distance from the liquid surface at each monitoring point; and substituting the distance from the liquid surface at the point to be measured into the distribution model to obtain the slurry concentration at the point to be measured. Compared with existing technologies, this invention offers a simple and rapid measurement method that avoids direct contact with the slurry, preventing interference from the slurry to the monitoring instrument and ensuring the reliability of the monitoring data. It is highly operable, requiring only a pressure sensor to complete the monitoring without adding other auxiliary equipment, thus reducing the complexity of the monitoring system. It is also highly timely and can measure the slurry concentration at any height, providing a basis for subsequent fine-tuning of the slurry concentration.

This invention discloses a closed-loop control system and method for the internal temperature of a rotating component based on temperature calibration. The method includes: collecting the operating parameters, structural property parameters, and actual internal temperature values ​​of the rotating component under laboratory conditions, constructing a dataset and dividing it into a training set and a test set; constructing a multivariate nonlinear regression model based on the first law of thermodynamics and Fourier's law of thermal conduction, fusing the operating parameters and structural property parameters; optimizing the model parameters using the training set and evaluating the model accuracy using the test set; in actual operation, collecting the operating parameters and structural property parameters in real time, inputting the optimized model to calibrate the internal temperature in real time; using the calibrated temperature as a feedback signal, dynamically adjusting the PID control parameters according to the rotational speed to regulate the cooling power, thus achieving closed-loop control. This invention achieves non-invasive real-time calibration of the internal temperature by constructing a multivariate nonlinear regression model and dynamically adjusting the PID parameters according to the rotational speed conditions, realizing precise temperature control and energy efficiency optimization.

The invention provides a dynamic modeling simulation method and system for the energy efficiency ratio of a solar seawater desalination system, and belongs to the technical field of seawater desalination and system modelling. Membrane surface resistance, selective permeability and direct-current bus voltage ripple data in the photovoltaic electrodialysis process are firstly obtained; secondly, Fourier transform is carried out on ripple data, then the ripple data and membrane parameters are spliced to generate an input matrix, the matrix is imported into a deep belief network, and a restricted Boltzmann machine is used for extracting an unsteady-state ion impedance vector reflecting the influence of voltage fluctuation; a nonlinear regression model of the vector and unit water production energy consumption is established through a least square support vector machine; finally, the water production rate and the energy efficiency ratio are calculated according to the predicted energy consumption and the photovoltaic power, and a dynamic simulation curve is generated. According to the method, the nonlinear influence of the photovoltaic voltage ripples on the membrane impedance can be quantified through deep learning, and the precision of predicting the energy efficiency ratio of the seawater desalination system under the fluctuating power supply working condition is remarkably improved.

The invention provides a Nusselt number experimental calculation method for a special flow channel structure, which comprises the following steps: determining structural parameters and basic input parameters of the special flow channel structure PCHE, and constructing a thermodynamic model of the special flow channel structure PCHE; based on a thermodynamic model, obtaining a total heat transfer coefficient and total heat transfer resistance through a thermal resistance method, and then determining an undetermined coefficient; a heat exchange experiment between the first working media is carried out, nonlinear regression fitting is carried out on the undetermined coefficient through an intelligent algorithm, and a first Nusselt number is obtained; and carrying out a heat exchange experiment between the first working medium and the second working medium, and carrying out nonlinear regression fitting on the undetermined coefficient by using the same intelligent algorithm in combination with the first Nusselt number to obtain a second Nusselt number. According to the method, step-by-step nonlinear regression is carried out on the undetermined coefficient in the thermal resistance model through two groups of heat exchange experiments in combination with an intelligent algorithm, so that the accuracy of measuring the wall temperature is improved, and the fitting precision of the Nusselt number is improved.

The invention discloses a slurry formula performance prediction and grade optimization method for horizontal directional drilling. The method comprises the following steps: S1, slurry experiment and data acquisition based on an HDD process; s2, establishing an HDD slurry viscosity nonlinear regression model; s3, defining a slurry performance grade for HDD construction; s4, performing Monte Carlo simulation and formula space global performance scanning; s5, visualizing the result of the step S4; S6, performing HDD field formula optimization and decision making; the method provides a standardized process and decision basis for slurry preparation. The initiative'performance grade distribution cloud picture 'enables an engineer to see all possible performance results of the formula clearly at one eye, and blind trial and error are thoroughly avoided. The lowest-cost formula meeting the requirement can be quickly locked, and the material cost is saved; meanwhile, field debugging time is shortened, and the project progress is accelerated.

The application provides a geopolymer preparation optimization method and system based on machine learning. Applied to the field of material science and machine learning technology, the method comprises the following steps: collecting geopolymer preparation experimental data and performing pretreatment; performing nonlinear regression modeling on the geopolymer performance based on four machine learning regression algorithms to construct a geopolymer performance prediction model; calculating and assigning weights according to the mean square error of each machine learning model on the validation set to obtain a performance prediction result; receiving target performance parameters and initial raw material ratio range input by a user, performing performance prediction by using the trained geopolymer performance prediction model, and searching for an optimal ratio combination satisfying the target performance constraint through an optimization algorithm; and preparing a coal gangue-slag-fly ash geopolymer grouting material according to the optimal ratio combination. The application effectively improves the prediction accuracy and model generalization ability, and realizes intelligent recommendation and accurate performance prediction of raw material ratios.

The invention relates to the technical field of Internet advertisement services, and discloses an integrated marketing advertisement publishing and management system based on the Internet of Things, and the system comprises a state monitoring unit which monitors a terminal input stream in real time to obtain the interaction frequency and the interface depth; a load calculation unit which generates a load increment based on the weighted product, accumulates the load increment to a load counter, and periodically deducts a count value at a preset rate; the threshold value generation unit is used for mapping the real-time count value into a dynamic admission base price; according to the invention, a dynamic pricing mechanism based on leaky bucket points is constructed, and the inertial attenuation characteristic of a load counter is utilized to quantify the nonlinear regression process of the cognitive load of a user, so that the problems of mistaken advertisement touch and thinking flow interruption caused by instantaneous state detection are solved, and the accuracy of advertisement display is improved. And intelligent dynamic balance between commercial resource configuration and user experience protection is realized.

This application belongs to the field of image processing and relates to a skin extraction method and system. The method includes: performing nonlinear regression fitting on the prior anatomical features of skin tissue in historical oral images to obtain a skin cutting threshold; acquiring a CBCT image of the oral cavity to be processed; extracting boundary features from the cross-sectional image layers of the CBCT image to be processed based on the skin cutting threshold to obtain a dynamic candidate masking region feature map; applying inter-layer geometric consistency constraints to the dynamic candidate masking region feature map to generate a target skin masking region; extracting the original pixel values ​​within the target skin masking region and suppressing their background values; performing isosurface evolution processing on the suppressed original pixel values ​​to generate an isosurface grid; and performing patch validity screening on the isosurface grid to generate the skin extraction result. This application can reduce the interference of irrelevant data on the extraction result during the skin extraction process and ensure the quality of skin tissue extraction as much as possible.

The invention relates to a method, a device and a non-transitory storage medium for predicting the cumulative elongation of the length of a steel wire rope in an elevator system. The method comprises the steps that the accumulated elongation of the length of a steel wire rope under different accumulated starting times of an elevator is obtained; dividing different elevator accumulative starting times into at least two elevator accumulative starting time intervals; estimating to-be-fitted parameters in a layered nonlinear regression model according to the accumulated starting times of each elevator in at least two accumulated starting times intervals of the elevators and the accumulated elongation of the length of the steel wire rope corresponding to the accumulated starting times of each elevator, wherein the layered nonlinear regression model is provided with a plurality of interval sub-models; based on the given elevator accumulative starting times, an elevator accumulative starting times interval corresponding to the given elevator accumulative starting times is determined; and predicting the accumulative elongation of the length of the steel wire rope under the given accumulative starting times of the elevator through a corresponding interval sub-model in the layered nonlinear regression model.

The invention belongs to the field of data processing and water resources, and particularly relates to a running water ecological landscape water demand calculation method based on a mountainous area. The method comprises the following steps: identifying characteristics such as karst landforms and ancient terraces through remote sensing data; constructing a nonlinear regulation and storage model of the underground water storage reservoir to simulate a base flow hysteresis release effect; calculating a landscape fragmentation degree and a connectivity index, and establishing a mapping relation between a space structure and a hydrological process; constructing an ecological water demand quota correction matrix based on nonlinear regression; and outputting a final water demand calculation result in combination with the base flow supply sequence and the dynamic landscape parameters. According to the method, by introducing the nonlinear regulation and storage theory and the landscape pattern index, deep coupling and dynamic correction of the ecological water demand and the space structure are achieved, and the precision and scientificity of water demand calculation in the complex environment are improved.

The application provides a system capacity prediction method, device and equipment of an Internet of Things platform and a storage medium. The method comprises the following steps: obtaining running indexes of a plurality of service layers of the Internet of Things platform and the business volumes of the running indexes at different time points in a set time period, the set time being a continuous time period before the current time point; generating a data pair set of the service health degrees and the business volumes at each time point in the set time period according to the running indexes and the business volumes of the running indexes at different time points; training a nonlinear regression prediction model of the business volumes and the health degrees according to the data pair set; and predicting the system capacity of the Internet of Things platform according to the nonlinear regression prediction model of the business volumes and the health degrees. The method improves the accuracy of the system capacity prediction of the Internet of Things platform.

A method, computer program product, and computer system for assessing N machine learning models that predict a dependent variable. For n=1, . . . , N, a machine learning model n is trained using training data n comprising In data points, wherein data point Pni in the training data n includes an observed dependent variable yni (i=1, . . . , In), wherein the training includes: (i) tuning the machine learning model n to generate a predicted dependent variable f(xn)ni that is fitted, using a regression algorithm n, to the observed dependent variable yni (i=1, . . . , In), (ii) determining a universal R-squared (UR2n) for the predicted dependent variables f(xn)ni (i=1, . . . , In), and (iii) outputting f(xn)ni (i=1, . . . , In) and UR2n, wherein xn is a feature vector of features used by machine learning model n to predict the dependent variable f(xn)ni (i=1, . . . , In).

The invention relates to the technical field of building acoustic environment monitoring, regulation and control, and discloses a real-time monitoring method for a building noise reduction and sound absorption part based on an acoustic sensor. According to the method, acoustic signals of a building target area are collected through a multi-channel acoustic sensor array, and a sound pressure level distribution map is generated; performing time-frequency domain decomposition on the atlas, separating low-frequency standing waves from high-frequency scattering components, and generating a sound energy absorption efficiency reference value by combining impedance characteristic parameters of a sound absorption component material; according to the reference value and a preset noise reduction target threshold value, dynamically dividing an acoustic sensitive area and marking the position of a sound absorption part needing to be optimized; based on the real-time sound pressure level change of the sensitive area, a nonlinear regression model is adopted to predict a performance attenuation curve of the sound absorption component, and an acoustic regulation and control parameter adjustment instruction is output; and the instruction is converted into the physical structure adjustment amount of the sound absorption component, and the monitoring frequency range of the sensor array is synchronously updated to adapt to the adjusted state. According to the method, dynamic linkage of monitoring and regulation is realized, and various building environment noise reduction requirements are met.

This invention discloses a method for determining the threshold for rapid drought-flood transitions to disasters targeting sensitive objects. It innovatively incorporates society, water resources, and the ecosystem as three core objectives into the threshold determination framework, breaking through the limitations of traditional single-factor fixed thresholds. By treating the disaster threshold as a dynamic multidimensional surface, it can reveal the nonlinear variation law of the critical conditions for disasters under different characteristics of disaster-bearing bodies. This invention achieves quantitative analysis from the description of disaster-loss relationships to the determination of critical conditions through the construction of a nonlinear regression model and its inverse solution. Through comparative analysis of subsystem and comprehensive thresholds, this invention can identify the contribution of each system to the overall threshold, providing a basis for identifying the dominant system and key vulnerable factors. The final threshold surface output by this invention is presented in the form of a three-dimensional graph, which is intuitive and easy to apply, providing scientific support for early warning of rapid drought-flood transition disaster risks, land spatial planning, and disaster prevention and mitigation decision-making.

The present application provides a film formation condition output device, output method, output program, and film formation device, which can output more appropriate film formation conditions. The film formation condition output device has: a first calculation section that calculates a first film formation condition for achieving a target film formation result using a linear model that models film formation processing by a film formation device; a second calculation section that calculates a second film formation condition for achieving the target film formation result using a nonlinear regression model that models the film formation processing by the film formation device and can calculate a confidence interval of a predicted film formation result; and a selection section that selects and outputs either the first film formation condition or the second film formation condition based on a determination result of whether the confidence interval of the predicted film formation result when the second film formation condition is calculated satisfies a prescribed condition.

Provided is a system, method, and device for automated energy remediation. The system includes at least one processor programmed or configured to: store energy usage data for a plurality of households, store environmental data associated with the plurality of households, the environmental data including outdoor temperature measurements, determine an inflection temperature for each household of the plurality of households based on a nonlinear regression model, determine a gap metric value based on a maximum median inflection temperature and a minimum inflection temperature from the plurality of households, form a plurality of groups based on the plurality of households and household data associated with each household, each group including a subset of households of the plurality of households, determine at least one group of the plurality of groups, and automatically initiate at least one energy protocol for households in the at least one group.