77 results about "Coefficient of determination" patented technology

Methods and systems for performing comprehensive predictive analysis and modeling in a wager gaming environment are provided. Statistical calculations for deriving predictive values for a patron behavior in a casino or gaming environment may include various criteria, including but not limited to wager gaming activities, resort / hotel usage, dining / meals, non-gaming point of sale transactions, entertainment expenditures, coupon / prize redemption, “comps” provided, etc. In some implementations, a plurality of predictive models may be used. One or more criteria may be used to evaluate and / or rank the predictive models. For example, such an evaluation and / or ranking may be based, at least in part, on a correlation coefficient or a function thereof. In some such implementations, an evaluation and / or ranking may be based, at least in part, on a comparison of the coefficient of determination, R2, corresponding to each predictive model.

The invention discloses a method for inverting remote sensing forest biomass. The method comprises the following steps: on the basis of remote sensing data pretreatment, extracting characteristic variables of a vegetation canopy from a LiDAR point cloud (comprising canopy three-dimensional space information) and multispectrum (comprising spectrum information on the upper surface of the canopy) data respectively; screening the characteristic variables of the LiDAR point cloud and the multispectrum through correlation analysis, and inverting overground and underground biomass by combining the ground actually measured biomass information through a stepwise regression model. Through the adoption of the optimized inverting model of northern subtropical forest biomass, constructed by method, the 'determination coefficient' R<2> of the model can be increase by 3-24%; the forest biomass can be estimated in high precision, and the 'relative root-mean-square error' (rRMSE) can be reduced by 2-10%. The method can be applied to the fields of forestry investigation, forest resource monitoring, forest carbon reserve evaluation, forest ecosystem research and the like, and provides quantitative data support for forest sustainable management and forest resource comprehensive utilization.

The invention belongs to the technical field of vibration monitor and control of steam turbine sets, in particular relates to a method for searching and analyzing the abnormality of signals during vibration and process of a steam turbine set. The method comprises the following steps of forming time sequences by history data with the preset group number; dividing each group of the time sequences into time subsequences, calculating mode characteristic values of the time subsequences and executing normalization processing; forming a mode characteristic value aggregation by the processed mode characteristic values; extracting a maximum value of all mode characteristics from the mode characteristic value aggregation and ranging in order from the largest to the smallest to form a mode characteristic extreme value sequence; gaining an abnormal characteristic boundary of the mode characteristic extreme value sequence; determining abnormal time subsequences; storing the abnormal time subsequences over the abnormal characteristic boundary in an abnormal change sequence; and executing optimal regression analysis on the abnormal change sequence based on coefficients of determination, and finding out a matched early warning grade according to an early warning mechanism rule. The invention increases the early warning precision and depth of vibration and process parameters.

The invention discloses a power transmission line lightning stroke interference and fault identification method based on transient waveform characteristics. The identification method comprises the following steps of: S1: after a transient quantity protection starting criterion is actuated, acquiring an additional component current of each phase at a line protection installation position, and calculating ground mold and line mold components according to a Karenbauer transformation matrix; S2: according to an energy ratio criterion of the transient ground mold and line mold components, identifying lightning stroke interference, and if the criterion is smaller than a setting, carrying out next identification; and S3: carrying out mean sliding denoising on the additional component current of each phase to acquire a waveform of a main wave, carrying out fitting on the waveform of the main wave, and identifying lightning stroke interference and a fault of each phase line according to a fitting decision coefficient. The power transmission line lightning stroke interference and fault identification method can accurately identifying different types of lightning stroke interference and faults, is not influenced by a fault initial angle, a transition resistance, a line compensation device and the like, has high sensitivity and reliability, is applicable to different types of power transmission lines, and provides beneficial bases for the practical process of transient quantity protection.

The invention discloses a method for rapid detection and authenticity identification of fatty acid of racy camellia oil by near infrared transmission spectroscopy (NITS). The method comprises the steps of: screening high quality varieties with oil content of 30%-60% from more than 130 camellia seed varieties, wherein in the camellia oil, the content of oleinic acid is 65%-85%, the content of linoleic acid is 5%-20%, the content of palmitic acid is 3%-15%, and the content of stearic acid is 1%-5%; establishing a near infrared transmission spectroscopy (NITS) model of the main fatty acids (oleinic acid, linoleic acid, palmitic acid, stearic acid and total unsaturated fatty acid) with a partial least squares (PLS) method by adopting the Fourier near infrared transmission spectroscopy (NITS), wherein the determination coefficients of the correction models of the oleinic acid, the linoleic acid and the palmitic acid respectively are 0.93446, 0.96538 and 0.88789, and the determination coefficients of cross validation respectively are 0.91987, 0.95755 and 0.84447; further verifying accuracy and reliability of five NIRS (Near Infrared Reflectance Spectroscopy) models by external verification so as to obtain related coefficients of the external verification, which respectively are 0.9424, 0.9682, 0.8862, 0.6834 and 0.7587; and evaluating doped camellia oil by establishing the NIRS model of binary system miscella of the camellia oil. The method is applicable to rapid measurement of the content of main fatty acids of the camellia oil and the doped camellia oil, has the characteristics of simplicity, convenience, rapidness and economy, and is suitable for industrial application.

The invention relates to a leaf surface dust fall quantity testing method based on a hyperspectral technique. The method comprises the following steps: firstly, collecting healthy leaves with different leaf surface dust fall quantities, quickly testing spectral information of a single leaf, putting the leaf subjected to the spectral information testing into a room, acquiring leaf surface dust fall quantity data of the leaf by virtue of a leaf area instrument and an electronic scale, and determining a sensitive spectral band subjected to leaf surface dust fall by analyzing the correlation between hyperspectral information and the leaf surface dust fall quantity data; and carrying out modeling by virtue of the data of the sensitive spectral band subjected to the leaf surface dust fall, selecting a model with a minimal root-mean-square error and maximal errors between a determination coefficient and a predicted root-mean-square error and between a sample standard deviation and the predicted root-mean-square error, and predicting the leaf surface dust fall quantity of the model only by virtue of the hyperspectral information of the leaf. Compared with a traditional determination method, the leaf surface dust fall quantity testing method has the beneficial effects of reducing the tedious experimental steps of indoor leaf area testing, cleaning, weighing and the like, being simple, convenient and rapid, and meanwhile, providing references for monitoring of the sand storm strength and environment quality of a dust fall region by virtue of astronautic hyperspectral remote sensing.

The invention belongs to the technical field of power system direct-current power transmission, and particularly relates to a bipolar short circuit fault current calculation method of an MMC-based HVDC system. The method comprises the following steps of: establishing an operation system of double-end MMC-HVDC, and obtaining element parameters of a power transmission system; setting direct-current system bipolar short circuit faults at different moments and fault points; carrying out equivalence on the discharging process according to a fault current component and a discharging current generation mechanism, importing a regulatory factor to an equivalence parameter, and simplifying a discharging current calculation formula; and firstly importing the regulatory factor to fit a curve of the discharging current on the basis of an Lsqcurvefit fitting function of MATLAB, so as to obtain a final discharging current calculation manner. Through a coefficient of determination, the superiority of the fitting effect is explained, and the calculation formula can provide reference basis for equipment selection, fault principle design and fault fixed value setting.

The invention relates to a cucumber whole-course photosynthetic rate predicting model based on a support vector machine. Photosynthetic rate test data of cucumber seedlings is obtained by utilizing a multi-factor nested experiment; model training is carried out by adopting a LM training method; a cucumber whole-course photosynthetic rate model fused with multiple growing periods is established; the cucumber whole-course photosynthetic rate model is subjected to comparison verification with a single-growing-period photosynthetic rate model and a whole-growing-period cucumber photosynthetic rate model respectively by adopting an abnormal check manner; the result shows that the whole-course photosynthetic rate model, which is established by adding the growing period as the one-dimensional input quantity, can effectively cross a local flat region, have obvious superiority, and satisfy training requirements that the error is less than 0.0001; the determination coefficient of a predicted value and a practically measured value of the model is 0.993; the error is less than 6.253%; both the training effect and the model-fitting degree of the model are superior to the mixed-growing period model; the precision of the model is similar to that of the single-growing-period photosynthetic rate model; and thus, the model disclosed by the invention can provide theoretical basis and technical support for light environment adjustment and control of facility crops.

The invention discloses a nonlinear fitting based intelligent detection method for a running exception state of a contact network. The method is implemented by the specific steps of (1) initializing a system; (2) preprocessing an image; (3) performing machine learning and region extraction; (4) performing edge detection; (5) extracting an exception state feature edge and judging whether a pixel value reaches a threshold or not; (6) extracting a to-be-fitted curve and selecting a pixel point; (7) performing nonlinear fitting; (8) calculating whether a coefficient of determination reaches a threshold or not; and (9) manually judging whether an exception exists or not. According to the method, the problem of incapability of accurately detecting and quickly identifying an exception state by an existing safety inspection apparatus is solved; the working efficiency and quality of the contact network are improved; and new and effective scientific technical support is provided for improving information automation level and working efficiency of power supply management.

The invention provides a modeling method for a cucumber GA-RBF photosynthetic rate prediction model integrated with stomatal conductance. The method comprises the steps: utilizing a multi-factor nested test to obtain the experimental data taking the stomatal conductance, the temperature, the CO2 concentration, the illumination intensity and the relative humidity as input and taking the apparent photosynthesis rate as output; utilizing a genetic algorithm to perform initialization of population; according to the given initial range of the spread parameters, calculating a fitness function; and through selection, intersection and variation, finding out the optimal spread parameter in an RBF neural network, and constructing a cucumber GA-RBF photosynthetic rate prediction model integrated with stomatal conductance. The test result shows that the predicted value and actual measurement value determination coefficient is 0.99878; the linear gradient is 0.99781; the error is less than 6%; compared with the photosynthetic rate prediction model which does not use the genetic algorithm to optimize the RBF algorithm and does not integrated with stomatal conductance and the cucumber GA-RBF photosynthetic rate prediction model which is not integrated with stomatal conductance, the training result has better prediction effect.

The invention discloses an optimal light quality and photon flux density-based light requirement real-time dynamic obtaining method. The method comprises the steps of firstly performing modeling based on a photosynthetic rate of a GA-GRNN, and optimizing an expansion speed of a GRNN by utilizing a GA algorithm, wherein related analysis of a predicted value and an actually measured value of a photosynthetic rate prediction model of the GA-GRNN is remarkably superior to that of a GRNN model; and then based on the photosynthetic rate prediction model of the GA-GRNN, performing photosynthetic rate optimization by using a quantum genetic algorithm to obtain corresponding optimal light quality and photon flux density, and building a light environment control target value model by adopting multiple linear regression fitting, wherein determination coefficients of optimal light quality model and photon flux density model are 0.992 and 0.9893 respectively. The photosynthetic rate at each temperature is taken as the actually measured value, and the photosynthetic rate corresponding to the optimal light quality and the optimal photon flux density is taken as the predicted value; a related analysis method is adopted; the determination coefficient is 0.936, the fitting straight slope is 1.012, and the intercept is 0.054; and a result shows that the built coupled light quality and photon flux density control target value model is good in performance.

The invention discloses a multiple linear regression model-based belt weigher main error factor analysis method. The method includes the following steps that: a belt weigher experiment platform is set up, main error factors such as tensions, temperatures and equivalent flow rates are changed according to actual situations, the tensions, temperatures, equivalent flow rates, calibration values and hopper weighers of various test points are recorded, and the relative errors of the calibration values and hopper weighers are calculated; the correlation coefficients of the main error factors of a belt weigher are calculated, and the correlation of the main error factors is judged; the relations of the factors are determined according to the correlation, and a multiple linear model is set and solved; residual sum of squares, determination coefficients and MS variance of residuals are adopted as check indexes, and the regression effect of the established model is evaluated; and the test values of the test points are predicted through using a fitting model, and the test values are compared with practical values, so that prediction errors can be calculated, and the accuracy of a fitting result is determined. With the method of the invention adopted, a theoretical basis is provided for quantitative analysis on the degree of influence of the main error factors on the accuracy of the belt weigher.

The invention discloses a cucumber whole-course photosynthetic rate prediction model based on a neural network. A multifactor nesting experiment is utilized for obtaining cucumber seedling photosynthetic rate test data, an LM (Levenberg-Marquardt) training method is adopted to carry out model training, and a cucumber whole-course photosynthetic rate model which combines a growing stage is established and is subjected to model performance parameter comparison and accuracy verification with a photosynthetic rate model of a single growing period and a whole-course photosynthetic rate model which does not combines a growing period stage parameter. A training result indicates that the whole-course photosynthetic rate model established in a way that the growing period is added to serve as a one-dimensional input quantity can effectively pass over local flat areas, and the whole-course photosynthetic rate model has an obvious superiority, meets a training requirement that errors are smaller than 0.0001, and is verified in an xor checkout way, so that a determination coefficient of a model prediction value and an actual measurement value is 0.9897, an error is smaller than 6.559%, and a theoretical basis and technical support can be provided for facility and crop luminous environment regulation and control.

The invention provides an intelligent predicting method for time sequence based on trend and periodic fluctuation, which comprises the steps of: computing an original time sequence to be predicated, separating a fluctuation component and a trend component of the sequence; for the trend component, automatically selecting a trend model with a maximal R2 from a trend model database through regression computing; converting the fluctuation component to a periodic variable according to an and-angle formula, adding the periodic variable into an optimal trend model; automatically selecting z periodical lengths with optimal influence functions according to a principle of maximizing a coefficient of determination, substituting z periodical lengths into the optical trend model with the periodical variable, and obtaining a final whole model; and determining a specific model parameter through computing the original time sequence by the final whole model, thereby predicting change trend of the original time sequence. The intelligent predicting method has advantages of high prediction intelligence and high precision through prediction processing automation and combining the trend component and the fluctuation component of a predicting model.

The present invention discloses a hyperspectral inversion method for the content of chlorophyll in apple leaves based on an SVR (support vector regression) algorithm. In order to realize the hyperspectral inversion of the content of chlorophyll in the apple leaves, the correlation between various spectral parameters and measured SPAD (soil and plant analyzer development) values is analyzed, normalized spectral parameter values and SPAD values are subjected to polynomial regression and SVR, determination coefficients of an SVR inversion model adopting a normalized differential vegetation index as a variable in modeling and model detection are 0.7410 and 0.8914 respectively, root-mean-square errors are 0.1332 and 0.1256 respectively, and higher precision and good prediction capacity are realized. Compared with polynomial regression, SVR has a better inversion effect and can be used as an optimal algorithm for hyperspectral inversion of chlorophyll.

The invention discloses a method for inverting an effective leaf area indexes of man-made forest by a UAV (Unmanned Aerial Vehicle) lidar, and belongs to the forest resource survey, forest site quality assessment and forest productivity estimation research field. The method for inverting the effective leaf area indexes of man-made forest by the UAV lidar normalizes the original point cloud data ofthe UAV lidar, extracts canopy structure characteristic variables from the normalized point cloud data, combines the ground actual measured data and the extracted characteristic variables, and estimates the effective leaf area index of the sample area scale in the study area based on the screened characteristic variables by means of a statistical model method. By obtaining the UAV lidar point cloud and extracting the canopy characteristic variables, by combining the ground actual measured data, the method for inverting effective leaf area indexes of man-made forest by the UAV lidar is relatively higher in efficiency and accuracy of the obtained continuously distributed effective leaf area indexes on the "face" of the specific range. Compared with other similar remote sensing methods, whenthe method for inverting effective leaf area indexes of man-made forest by the UAV lidar is used to extract the effective leaf area indexes of the man-made forest, the coefficient of determination isincreased by more than 5%.

The invention relates to a fish swarm algorithm-based cucumber seedling stage carbon dioxide optimization regulation and control model. A model formula is put forwards with temperature T and photon flux density PFD adopted as independent variables and with carbon dioxide concentration corresponding to a maximum photosynthetic rate adopted as a dependent variable; the establishment and application of the model are also disclosed. Multi-dimensional data are acquired by using a photosynthetic rate multiple-factor nested experiment; a photosynthetic rate multivariable nonlinear regression model is constructed; a fish swarm algorithm-based carbon dioxide model optimization method is designed; carbon dioxide saturation points under different temperature and different photon flux density are obtained; and the cucumber carbon dioxide optimization regulation and control model with the carbon dioxide saturation points adopted as target values can be built. As indicated by model verification test results, the fish swarm algorithm-based cucumber seedling stage carbon dioxide optimization regulation and control model of the invention can dynamically obtain the carbon dioxide saturation points under different temperature and different photon flux density, the determination coefficient of the actually-measure values and calculated values of the carbon dioxide saturation points is 0.98, and the maximum relative error of the actually-measure values and calculated values of the carbon dioxide saturation points is lower than 3%, and the model has high precision and is of great significance for the improvement of the carbon dioxide environmental regulation and control efficiency of facilities.

The invention discloses an application of an improved partial least squares regression method to microorganism culture medium optimization. According to the application, a cross validation technologyand a variable screening technology are integrated to reduce potential overfitting problem of PLSR, namely, a proper number of potential variables are extracted by carrying out cross validation through a leave-one-out method, and a steady principle of coefficients is decided by utilizing a variable projection importance technology-based variable screening method and a regression model so as to establish simple and steady models with high prediction precision. According to the application, the VIP regression model is capable of effectively establishing the simple and steady models which are high in prediction precision and can be further popularized.

The invention discloses a method for detecting the quality of Brassica campestris based on the near infrared spectrum, which comprises the steps of fitting collected near infrared spectrum data of a Brassica campestris sample and chemical values measured by a standard method, and optimally establishing a model by applying partial least squares (PLS); selecting an optimal spectrum pretreatment method, and measuring the quality of the model by comparing a determination coefficient (R2) and a root mean square error (RMSECV) so as to construct a high-quality quantitative analysis model of the nearinfrared spectrum of Brassica campestris. According to the method, the surface color, the mass loss rate, the hardness and the VC content of Brassica campestris can be rapidly and accurately predicted, the grade of Brassica campestris can be judged, thereby laying a foundation for rapid and nondestructive quality detection research of Brassica campestris, and having very high practicability and wide applicability.

The invention discloses a Marshal test piece stability and flow value test method. The method comprises the steps of selecting asphalt Marshal test pieces as a sample population, and recording volumeparameters and physical indexes of samples; removing abnormal values in data, performing normalization, and judging data correlation; selecting the data serving as a neural network training set and atest set; building BP and RBF neural network models; training the built neural network models by using the training set; simulating test set data by using the trained neural network model, performingprediction for multiple times continuously, and taking an average value as a final result; obtaining a relative error and a determination coefficient of a predicted numerical value; and finally, selecting and using the model according to the determination coefficient. By utilizing the built neural network model, the flow values and stability of the Marshal test pieces can be calculated out efficiently and accurately, so that the damage of a loading device to the Marshal test pieces is avoided, materials are saved, the test efficiency is greatly improved on the premise of ensuring the accuracy,and the possible danger in a test process is avoided.

The invention discloses a method for measuring moisture content in rice through near infrared spectroscopy based on SiPLS. The method comprises the following steps: rejecting an abnormal sample by adopting a X-Y residual analysis method, selecting a correction set sample and a prediction set sample by applying a classical Kennard-Stone method, performing preprocessing optimization on the near infrared spectrum by utilizing a second-order derivative, dividing the spectrum into 25 sub-intervals by adopting the SiPLS method, jointly establishing a moisture model in the rice by utilizing the 3rd,the 9th, the 18th and the 20th sub-intervals, wherein the correction set determination coefficient is 0.9573, and the mean-square root error of the correction set is 0.3886. 40 verification set samples are used for verifying a calibration model, the prediction correlation coefficient achieves 0.9625, and the average prediction recovery rate is 100.27%, so that the model has good prediction capacity. Compared with the full-spectrum modelling, the model established adopting the SiPLS method is not only simple, but also the prediction precision of the model is improved. The method disclosed by the invention can be used for quickly measuring the moisture content in the rice as an accurate, nondestructive and environment-friendly detection measure.

The invention discloses a method for estimating leaf area indexes of Ginkgo biloba plantation based on a unmanned aerial vehicle (UAV) laser radar porosity model. The method comprises steps: 11) filtering and interpolating the discrete point cloud data of the lidar on the unmanned aerial vehicle to generate a digital terrain model, and normalizing the lidar point cloud data by using the generateddigital terrain model to obtain the normalized lidar point cloud data; 2) the Leaf area index (LAI) being estimated at plot scale by using a porosity model. The method for estimating the leaf area index of typical subtropical plain plantation by combining unmanned aerial vehicle (UAV) laser radar data with the porosity model method has the advantages of flexibility, rapidity, low cost, high quality and the like, and can better record the canopy structure characteristic of the forest and effectively enhance the inversion capability and precision of the leaf area index. The validation results show that the determination coefficient of LAI is increased by more than 5% compared with other similar remote sensing methods.

Provided is a technique to improve both the image quality of a radiation image and the operability of a radiation imaging apparatus. In the present invention, three or more unirradiated images are obtained before capturing a radiation image, and a temporal change in the amount of electric charges in each pixel is approximated using a non-linear function. At this time, the amount of residual electric charges is determined by evaluating a coefficient of determination of an approximate equation, and the most appropriate offset correction processing method is selected in accordance with the status of the determination.

A silver salt photothermographic dry imaging material wherein said material has photographic speeds (1) and (2) determined based on the predetermined conditions and the photographic speed (2) is not more than {fraction (1 / 10)} of the photographic speed (1), and the coefficient of determination value R2 of the linear regression line is 0.998-1.000, which is obtained from the predetermined density points having a* and b* arranged in two-dimensional coordinates in which a* is used as the abscissa and b* is used as the coordinate of the CIE 1076 (L*a*b*) color space, b* of the intersection point of the linear regression line with the ordinate is −5-5, and gradient (a* / b*) is 0.7-2.5.

The invention discloses a neural network based rock destroy strength determination method. The method is characterized in that a neural network is used to research the rock strength criterion under a uniaxial or triaxial loading condition, various collected rock related data are randomly divided into training and verifying subsets, the compressive strength and the smallest principal stress are used as input values and the largest principal stress value is used as an output value to train the neural network, and the trained neutral network is used to predict largest principal stress value during the test rock destroy. The method mainly comprises a step of experiment data analysis and a step of ANN based largest principal stress value prediction. The method shows the mean square deviation of the neutral network prediction result is reduced by 30-40%, and the determination coefficient is increased by 0.05-0.08 and is more close to 1. The use of the ANN to predict the rock strength has a wide adaptive loading range, so the method is suitable for rock-kind-changeable complex non-linear conditions, and is flexible and accurate.

The invention provides a near-infrared-band hyperspectral diagnosis method for detecting the content of nitrogen in rubber tree leaves. The method comprises the following steps: 1) selecting rubber tree sample leaves; 2) collecting hyperspectral data of the rubber tree sample leaves at the near-infrared band; 3) carrying out pretreatment on the rubber tree leaf samples and then, measuring the content of nitrogen in the pretreated rubber tree leaf samples; 4) obtaining average spectrum of different hyperspectral data point selection spectroscopic data of the rubber tree leaf hyperspectral databy combining a PCA (principal component analysis) method and a K-means clustering method; 5) carrying out modeling on the average spectrum of the different hyperspectral data point selection spectroscopic data by utilizing a partial least-squares regression algorithm, and selecting an optimal hyperspectral data point selection method according to a decision coefficient of a training set adopted bymodeling; and 6) predicting the content of nitrogen in the rubber tree leaves by utilizing the established model.

The invention discloses a chlorophyll fluorescence imaging based transgenic maize glyphosate tolerance phenotype detection method. The method includes: (1) preparing experimental samples; (2) acquiring shikimic acid contents of canopy leaves of transgenic maize and non-transgenic maize plants subjected to glyphosate stress in different day numbers; (3) acquiring canopy chlorophyll fluorescence parameters of the transgenic maize and the non-transgenic maize plants subjected to glyphosate stress in different day numbers through a chlorophyll fluorescence imaging system; (4) establishing a PLSR model to reject abnormal samples, and selecting a modeling set and a prediction set through K-Means; (5) selecting the chlorophyll fluorescence parameters most sensitive in glyphosate stress response by means of principal component analysis; (6) establishing a PLSR regression analysis model on the basis of the selected chlorophyll fluorescence parameters and the practical shikimic acid content of the transgenic maize plants, wherein determination coefficients R<2> of the modeling set and the prediction set are 0.75 and 0.63 respectively. The method is high in identification precision and simple in operation, and quick nondestructive testing of transgenic maize glyphosate tolerance phenotypes can be realized.

The invention discloses a subarea optimization method for a crop irrigation system in an underground water shallow-buried area. The method comprises the steps of obtaining local historical data; based on the local historical data, utilizing an Aquacrop model to obtain first simulation yield and simulation water consumption of each growth stage of crop wheat in different areas under different set irrigation systems, wherein different areas take set groundwater burial depths as partition boundaries; according to all the first simulated yields and the simulated water consumption of the corresponding growth stages, acquiring second simulated yields of all the regional crops under the water production function models and stage sensitivity coefficients or indexes of the different growth stages by adopting the set multiple water production function models; calculating a decision coefficient between the first simulated yield and the second simulated yield; determining an optimal moisture production function model corresponding to each region by adopting a phase sensitive coefficient or an index and a decision coefficient; and taking the stage sensitive coefficient or index under the optimal moisture production function model as an irrigation decision variable, and obtaining an irrigation system by using Aquacrop in combination with a set irrigation quota.

The invention discloses a fatigue performance prediction method based on machine learning. The method comprises the steps: firstly collecting material data to form initial sample data, employing a Sobol global sensitivity analysis method to evaluate the contribution degree of each feature variable to an output variable variance, carrying out the sorting of the importance of the feature variables,screening out key feature variables, and forming training sample data; dividing the training sample data into a training set, a verification set and a test set for training the model; performing modeltraining learning based on the training set and verification set data, obtaining corresponding model parameters, outputting a model trained based on existing training set / verification set learning, quantitatively evaluating model prediction performance by adopting a determination coefficient R2, completing establishment of the model, and performing fatigue performance prediction by utilizing thetrained model. According to the method, the nonlinear corresponding relation between the material information and the fatigue performance is established by utilizing the more comprehensive material information, and the method is applied to fatigue performance prediction of a higher-precision and generalized environment.

The invention relates to a kiln muddy water optimal prediction model establishment method based on a hyperspectral image technology; the method comprises the steps of acquiring hyperspectral image information of near-infrared and visible light wave bands, performing black-and-white correction to eliminate errors caused by uneven illumination, extracting the average spectral information of the region of interest, estimating the average spectral reflectivity of each pixel point in the image, and separately establishing quantitative prediction models based on the full-wave band and the characteristic spectrum through unprocessed and SNV and different modeling methods. According to the method, the optimal model for the kiln muddy water quantitative prediction is screened out through a comparison training set, a test set quantitative prediction and the value of the root-mean-square error; the quality of the kiln mud is quickly evaluated for each large liquor brewing enterprise, a theoretical basis and technical support are provided for manual kiln protection and kiln protection, and technical guarantee is provided for transformation upgrading and digital and intelligent on-line real-time monitoring of the fermentation state of the kiln mud for wine brewing industrialization.