10 results about "Multiple regression equation" patented technology

The invention belongs to the technical field of personnel efficiency prediction and selection, and particularly relates to a behavior efficiency prediction method based on psychological characteristics. According to the method, corresponding psychological indexes and behavior efficiency evaluation indexes are established for nuclear power master control room operators, correlation analysis is adopted to explore the relationship between psychological quality and behavior efficiency, and a behavior efficiency prediction model based on psychological quality is established based on a multiple regression equation. The technical problem of how to improve the working efficiency of a nuclear power plant and further prevent human errors by improving the adaptability of psychological conditions and post requirements of nuclear industry personnel is solved.

An induction energy transmission system includes a set-down plate, a supply unit including a supply induction element arranged below the set-down plate and designed to inductively provide energy, a set-down unit including a receiving unit with a receiving induction element designed to receive the inductively provided energy, and a control unit designed to control the supply unit by using a parameter set so as to control the supply unit and to receive a parameter of the parameter set from the set-down unit. The control unit receives in addition an information parameter set from the set-down unit to determine a coefficient of a multivariable regression equation and based on the coefficient to determine a correction factor for a parameter of the parameter set or determine a new parameter set.

This technology improves the accuracy of predicting material thinning by considering multiple different flow conditions. [Solution] The thinning prediction system comprises a fluid simulator 3 that calculates fluid analysis values ​​by fluid simulation using the amount of thinning in the test piece 10 as a result of a water flow corrosion test and the shape of the test piece 10 for a test piece 10 used in a water flow corrosion test; a multiple regression equation generation unit 4 that generates a thinning prediction equation with the amount of thinning as the objective variable and the fluid analysis values ​​as the explanatory variables; and a thinning amount calculation unit 5 that calculates the amount of thinning in the target pipe based on the shape data of the target pipe and the thinning prediction equation, wherein the thinning prediction equation is a combination of multiple provisional prediction equations generated under different flow conditions in the test piece 10.

The invention provides a rain and snow separation calculation method and device, equipment and a storage medium. Relates to the technical field of meteorological and hydrological data processing. The method comprises the following steps: acquiring daily meteorological data of a target area, wherein the daily meteorological data comprises the daily maximum temperature Tmax, the daily minimum temperature Tmin, the daily average temperature Tmean, the daily temperature difference Tdr, the relative humidity RH and the daily precipitation P; inputting daily meteorological data of the target area into the rain and snow separation model, and calculating to obtain snow-melting runoff DSnow; wherein the rain and snow separation model is a multiple regression equation based on heat condition parameters and water vapor condition parameters, the heat condition parameters comprise at least three of Tmax, Tmin, Tmean and gas Tdr, and the water vapor condition parameters comprise RH and P. Rain and snow separation discrimination is carried out based on the relation of climate condition factors. When the region and the climate change, the judgment basis is only related to the climate condition, so that obvious misjudgment does not occur along with the change of the region.

The invention discloses a sea area plankton density prediction method based on environmental factors and acoustics, and belongs to the technical field of marine organism prediction.The sea area plankton density prediction method comprises the steps that multi-source data are obtained from a target sea area, and the multi-source data comprise acoustic echo data and environmental factor data; extracting a noise level from the acoustic echo data; judging whether the noise level exceeds a preset threshold value or not, and if yes, performing denoising processing on the acoustic echo data to obtain a pure acoustic signal feature set; normalizing the features in the pure sound wave signal feature set and the environmental factor data; and obtaining an estimation result of the plankton distribution density through a multiple regression equation according to the normalized pure acoustic signal characteristics and the environmental factor data. The sea area plankton density prediction method based on environmental factors and acoustics solves the problem of inaccurate prediction caused by complex marine environment in the aspect of plankton density prediction at present.

The application provides a CTMU-based adaptive variable current source ultrasonic flow measurement method, and belongs to the technical field of downhole pipeline flow measurement. The method comprises the following steps: adopting a least square method to establish a flow rate influencing factor matrix, a measured flow rate matrix, a residual variable matrix and an unmeasured flow rate matrix; based on the flow rate influencing factor matrix, the measured flow rate matrix, the residual variable matrix and the unmeasured flow rate matrix, a multiple regression equation is established; based on the multiple regression equation, the unmeasured flow rate of fluid in a downhole pipeline is predicted; or, according to the measured flow rate and the flow rate influencing factor, a sum of squared deviations of the measured flow rate is calculated; and based on the sum of squared deviations of the measured flow rate, the unmeasured flow rate is predicted. The application can improve the accuracy of ultrasonic flow measurement and has strong ease of use and practicality.

To provide a method for predicting a replacement time of a furnace wall refractory brick, which optimizes the number of explanatory variables of a multiple regression equation and improves prediction accuracy while reducing a calculation time.SOLUTION: When the replacement time of the refractory bricks 12 of the furnace wall of the argon-oxygen vacuum smelting furnace 1 is predicted by the multiple regression equation, the number of combinations of physical quantities selected as the explanatory variable x of the multiple regression equation from among a plurality of types of physical quantities related to the replacement time is sequentially increased from two, and calculation is performed by the multiple regression equation each time, and a combination of the physical quantities when the error between the predicted value and the measured value of the objective variable y of the calculated multiple regression equation falls within an allowable range is determined as the combination of the explanatory variable x for prediction of the replacement time SELECTED DRAWING: Figure 1

This invention discloses a method for monitoring formation compressive strength while drilling by integrating logging and cuttings logging data, comprising: (I) establishing a drill bit compressive strength prediction model while drilling based on engineering logging information of the target area to obtain the compressive strength while drilling based on logging information; (II) calculating the logging compressive strength based on logging information, and comparing the logging compressive strength with the compressive strength while drilling based on logging information to determine the inversion correction coefficients for compressive strength in different lithologies; (III) using Pearson correlation analysis to determine the characteristic minerals and elements of the lithology, establishing a mineral and element multiple regression equation for the inversion correction coefficients of rock compressive strength while drilling, and using the inversion compressive strength and the multiple regression equation for the compressive strength correction coefficients while drilling to monitor the compressive strength value of the drilled formation in real time. This invention can correct the interpretation results of rock compressive strength while drilling in real time through cuttings logging, mineral diffraction logging, and element logging information, thereby improving the accuracy of compressive strength prediction while drilling.

This invention discloses a method for constructing evaluation indicators for power batteries of pure electric vehicles based on big data analysis, including the following steps: A1. Marking the vehicles; A2. During the operation and use of the P1th vehicle; A3. The P2th...P... n Repeat steps A2 and A5 for each new car, and remove the first DB. ND The parameters are based on the data before the 1% mark and the data after the 99th mark following a normal distribution. A6. Use the labeled samples as the target values. A7. Use neural network models MM1...MM. n The calculated values ​​at each sampling point are used as the dependent variable. A8. Regression analysis is used. A9. One or more regression equations with the lowest overall error rate are extracted from the multiple regression equations and used as a continuous parameter prediction model. Compared with traditional neural network model calculation methods, this invention significantly reduces the computational load. Compared with single correlation regression prediction methods, it has better accuracy and adaptability, and has broad practical application value.

The application provides a drilling fluid reservoir protection performance optimization method and system based on data correlation analysis, which comprises the following steps: performing attribution analysis on the correlation between initial drilling fluid parameters and target permeability recovery values; constructing a multiple regression model between all target drilling fluid controllable parameters and target permeability recovery values; calculating an optimal multiple regression equation based on the multiple regression model; generating parameter graph nodes and edges for each target drilling fluid controllable parameter to obtain a parameter graph structure of the target drilling fluid controllable parameters; combining the parameter graph structure and the optimal multiple regression equation to construct a parameter optimization graph network; inputting the initial drilling fluid parameters into the trained parameter optimization graph network to obtain optimal drilling fluid parameters output by the parameter optimization graph network; and adjusting the initial drilling fluid formula to an optimal drilling fluid formula according to the optimal drilling fluid parameters. The application has the effect of efficiently and accurately optimizing the reservoir protection performance of drilling fluid.