23 results about "Linear regression function" patented technology

The invention discloses a construction method and application of a building carbon emission calculation model, and mainly solves the technical problems of large workload and low accuracy of existing building carbon emission calculation. The method comprises the following steps: sequentially acquiring historical carbon emission data of a building at different stages to establish an input data set {Xi}; the input data set {Xi} is preprocessed; correspondingly constructing a building carbon emission measuring and calculating model of the support vector machine based on the linear regression function; the building carbon emission measuring and calculating model is trained; and performing precision evaluation on the building carbon emission measurement and calculation model to obtain a precision evaluation result, establishing a building carbon emission measurement and calculation model of a support vector machine, and performing carbon emission measurement and calculation on the target building based on the model, so that the method has the advantages of high calculation operation speed, high calculation result precision and the like.

The present invention relates to the technical field of bird distance assessment, and specifically to a bird distance assessment method and system based on a bird detection radar. In the present invention, a data acquisition module obtains the center position of a key area, the type of bird, and the bird position, bird speed and bird acceleration at past time points; a past fluctuation analysis module calculates the past distance between the key area and the bird, and assesses the fluctuation of the bird's motion trajectory. If it is abnormal, a distance correction module is introduced for adjustment; a future fluctuation analysis module uses the bird's speed and bird acceleration to predict the bird's future position, calculates the future distance and performs stability analysis; if the future fluctuation analysis is abnormal, the distance correction module is also used for correction, and a correction factor is calculated by a linear regression function combined with maximum speed and type characteristics to ensure consistency between the prediction and actual data; this scheme effectively solves the problem of real-time monitoring of the dynamic behavior of birds and the accuracy of its prediction, and improves the stability and responsiveness of the system.

The invention discloses an urban flood forecasting method based on a rainfall threshold, and relates to the field of numerical simulation and calculation in hydrology, and the method comprises the following steps: 1, collecting a continuous flood event record of a certain ponding point, arranging the record into a format required by a method provided in a patent, and estimating model parameters a, b and c by writing Python codes, or calculating by utilizing a linear regression function of statistical software. According to the urban flood forecasting method based on the rainfall threshold value, the rainfall threshold value relation is introduced into urban inland inundation forecasting by limiting application conditions. The waterlogging forecasting technology only depending on the rainfall condition not only can provide a reliable urban waterlogging forecasting result, but also has very high operation efficiency; the rainfall threshold relation is applied to urban flood forecasting, and the previous law is generally applied to disaster events caused by rainfall with binary characteristics such as landslide and debris flow.

The application relates to the technical field of air conditioners, and discloses a combined air conditioner humidifying method and device based on steam and micro-mist impinging flow state, which comprises the following steps: obtaining a humidifying parameter of a combined air conditioner, and determining a supply air humidity content according to the humidifying parameter; based on the supply air humidity content and a preset linear regression function, an adjustment amount is calculated; based on the adjustment amount and a preset proportional value, a valve opening degree is calculated, and a control signal is generated according to the valve opening degree. The supply air humidity content is determined through the humidifying parameter of the combined air conditioner, and then the micro-mist adjustment amount and the steam adjustment amount are calculated according to the supply air humidity content and the preset linear regression function, so that the signal for controlling the micro-mist and steam valve opening degrees can be generated according to the micro-mist adjustment amount and the steam adjustment amount, the appropriate micro-mist and steam are caused to collide, the high-grade heat energy of high-temperature and high-pressure steam is used for liquid droplet vaporization in micro-mist humidification, the micro-mist humidification efficiency is improved, and the step-by-step utilization of the high-grade heat energy is realized.

The invention discloses a traffic depression assessment method and device and computer equipment. The method comprises the following steps: collecting data of a sample point of a target cell, the data of the sample point at least comprising one of the following: a physical resource block (PRB) utilization rate, uplink and downlink traffic, and an average number of activated users; at least adopting a pre-trained isolated forest model to clean abnormal sample points in the data of the sample points to obtain data of effective sample points; pre-judging the target cell according to the data of the effective sample points to obtain a first judgment result, and the first judgment result is used for representing a pre-judgment result of a traffic suppression state of the target cell; and fitting the traffic data of the target cell by adopting a piecewise linear regression function to obtain a fitting result, and correcting the first judgment result by adopting the fitting result to obtain a second judgment result which is used for representing a final judgment result of the traffic depression state of the target cell.

The invention provides a well drilling leakage position prediction method and device, computer equipment and a storage medium. The method comprises the steps of obtaining well drilling sample data; inputting the drilling sample data as a feature vector into a neural network, estimating the probability of the leakage category to which each drilling sample data belongs by using the drilling sample data based on a Softmax linear regression function, and establishing a Softmax linear regression equation learning model; real-time drilling parameters are collected; and inputting the real-time drilling parameters into the softmax linear regression equation learning model, and outputting a predicted drilling leakage position. According to the method, well drilling sample data is used as a processing basis, the authenticity and accuracy of a result are ensured, it is ensured that a one-dimensional vector can be defined through multi-classification features by taking a Softmax linear regression function as a discrete probability distribution form in the data magnitude and data errors are reduced, and therefore the prediction result of the well drilling leakage position is more accurate.

The application relates to a key segmentation storage method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining an original key, encoding the original key to obtain a target value set; obtaining a target confusion factor and a target random number, and constructing a target linear regression function; sequentially selecting a current substitution value from the target value set, obtaining a current cycle number, analyzing the multiple relationship between the current cycle number and the target confusion factor to obtain a target analysis result; based on the target analysis result, substituting the current substitution value into the target linear regression function to obtain at least two current intermediate values; adding the at least two current intermediate values into a corresponding current intermediate value set to obtain at least two current intermediate value sets; repeating the operation of selecting the current substitution value until a termination condition is met, and obtaining at least two target result value sets; and sequentially splicing each value corresponding to the at least two target result value sets respectively to generate and store at least two final values. The method can improve the security of key storage.

The invention discloses a self-supervised learning speech recognition method and system based on brain activity information, and the method comprises the following steps: inputting speech signal data and brain activity information corresponding to the speech signal data into a self-supervised speech model training unit for model training; and predicting a response relationship between the current voice signal and the brain blood oxygen level conversion signal through a linear regression function, and optimizing model parameters. The neural coding model is established, the brain activity information corresponding to the voice is utilized, the training effect of the self-supervised learning model is improved, and the problem that in the existing power grid field, a voice recognition algorithm based on unsupervised learning is poor in voice recognition effect under the conditions of insufficient training data volume and low signal-to-noise ratio is solved.

This invention relates to the field of image processing technology, and in particular to an automated inspection method for the factory quality of liquid crystal screens based on image processing. The method acquires a grayscale image of the liquid crystal screen, divides the grayscale image into at least two window regions, merges the window regions according to their region type to obtain at least two target regions, marks the defect edge pixels in each target region, and further divides each target region based on the defect edge pixels to obtain multiple final divided regions. It then obtains the edge detection dual thresholds and region exposure coefficients of each final divided region and performs linear regression fitting to obtain the corresponding linear regression function. Using the linear regression function, it obtains adaptive edge detection dual thresholds for each final divided region, which are used to detect scratch defects on the liquid crystal screen. This achieves accurate calculation of adaptive edge detection thresholds for each region, effectively solving the problems of missed detections and false detections caused by traditional global thresholds.

The invention relates to a computer-implemented method (200) for training an artificial intelligence-based model (M) and to a computer-implemented method (600) for supporting an operation on a patient's eye, comprising such a model (M), which is used to calculate and / or provide refractive output data (933) on the basis of input data (931), and in particular to create nomograms. Nomograms from the prior art may either have an accuracy in need of improvement or, if the accuracy is improved, may generate implausible profiles of the nomogram. According to the invention, more accurate nomograms based on the output data (933) are made possible without implausible states. This is made possible by a cost function (915) which comprises a non-linear output value (917), representing a measure of the prediction error, of a univariate linear regression function and at least one non-linear penalty term (919). The invention further relates to a trained model (M) or a weighting matrix (923) of such a model (M), to a computer program product, to a data processing device (800), to a computer-readable medium, and to a laser therapy device.

The present invention discloses a method and device for determining the permeability coefficient of an aquifer under the influence of regional groundwater extraction. The present invention adopts an in-situ standpipe permeameter for measurement, controls the water head of the standpipe and sediment at the same time, determines parameters using a nonlinear regression function solver, and calculates the hydraulic permeability coefficient according to Darcy's law; the method obtains parallel analysis data from the measuring standpipe and the sediment water level gauge, analyzes the ratio of the infiltration rate and the time change rate during the measurement, optimizes the selection of the measurement time interval, and establishes an evaluation model for the permeability coefficient measurement accuracy, further ensuring the accuracy of the measured permeability coefficient. The present invention can reduce the error in the measurement of the riverbed permeability coefficient caused by the fluctuation of the groundwater level in the riverbed or coastal zone, and the parallel analysis data obtained from the measuring standpipe and the sediment water level gauge can significantly improve the measurement accuracy. The in-situ standpipe permeameter can prevent sample disturbance and has great advantages in measurement.

A computer-implemented method (200) for training an artificial intelligence-based model (M) and a computer-implemented method (600) for supporting surgery on a patient's eye are provided, comprising such a model (M) which serves to calculate and / or provide refractive output data (933) based on input data (931), and in particular to generate nomograms. Prior art nomograms may either have insufficient accuracy or, if the accuracy is improved, generate implausible nomogram progressions. According to the invention, more accurate nomograms based on the output data (933) are enabled without implausible states.This is made possible by a cost function (915) comprising a nonlinear output value (917) of a univariate linear regression function representing a measure of the prediction error and at least one nonlinear penalty term (919). Furthermore, a trained model (M) or a weighting matrix (923) of such a model (M), as well as a computer program product, a data processing device (800), a computer-readable medium, and a laser therapy device are described.

This invention provides a method, electronic device, and storage medium for determining false flight trajectory point data, relating to the technical field of false flight trajectory point data determination. The method includes: acquiring a normalized flight data list set D; determining the flight data group corresponding to each type of normalized flight data for each trajectory point, to obtain a flight data group list set EA corresponding to D; and processing EA... j Perform linear fitting to obtain the linear regression function y(i); obtain EA. j The minimum distance between the coordinate point corresponding to each flight data set and y(i) is used to obtain EA. j The corresponding minimum distance list LT j If each minimum distance list set in LT satisfies the preset second judgment condition, then the data corresponding to the latest n flight trajectory points is determined to be false trajectory point data. This invention can prevent false flight trajectory point data from being used by the data monitoring platform, which could lead to data monitoring anomalies on the data monitoring platform.

The invention discloses a broadband oscillation influence factor analysis method for a low-frequency power transmission system, and the method comprises the steps: selecting system operation and control parameters as predictive variables, and selecting the broadband oscillation stability of the system as a response variable to construct a low-frequency power transmission system training set; constructing a multiple linear regression function, and solving a regression coefficient of each prediction variable based on a least square method; and carrying out significance test on the constructed multiple linear regression function, introducing a confidence interval, and carrying out interval estimation on a regression coefficient to carry out hypothesis test. And retaining the predictive variables meeting the requirements, reconstructing a multiple linear regression function, solving a regression coefficient, and obtaining the influence of each predictive variable on the broadband oscillation stability of the system based on regression analysis. According to the method, the influence rule of each parameter on the broadband oscillation stability of the system under the full working condition of the system is described, and the method has a good application prospect.

The invention relates to the technical field of bird distance evaluation, in particular to a bird distance evaluation method and system based on a bird detection radar, and the method comprises the steps: a data obtaining module obtains the central position of a key region, the bird type, and the bird position, the bird speed and the bird acceleration at past time points; the past fluctuation analysis module calculates the past distance between the key area and the bird, evaluates the fluctuation of the movement track of the bird, and introduces the distance correction module for adjustment if the fluctuation is abnormal; the future fluctuation analysis module is used for predicting the future position of the flying bird by using the flying bird speed and the flying bird acceleration, calculating the future distance and carrying out stability analysis; if future fluctuation analysis is abnormal, the distance correction module is also used for correction, correction factors are calculated through a linear regression function in combination with the maximum speed and category characteristics, and the consistency of prediction and actual data is ensured; according to the scheme, the problems of real-time monitoring of the dynamic behavior of the flying bird and prediction accuracy are effectively solved, and the stability and response capability of the system are improved.

The present invention discloses a sliding mode predictive control method for heave motion based on support vector machine time-delay compensation, which belongs to the field of compensation control technology. The specific technical scheme is: using support vector regression to predict the heave motion of the hull, setting a set of hull motion data, constructing a linear regression function, setting a linear insensitive loss function, introducing two slack variables, transforming the optimization problem, and establishing constraints, introducing Lagrangian function, solving parameters, simplifying the regression function, introducing the concept of kernel function, and obtaining the final load displacement of the deep-sea crane heave compensation system. The present invention discloses an extremely short-term prediction method of support vector machine, and combines it with a sliding mode predictive control method based on SSA-Elman neural network, which can realize accurate prediction of the hull heave displacement of the deep-sea crane. After adding the support vector machine prediction model, the controller can effectively solve the control error caused by the time delay problem.

The present disclosure relates to a method, device, medium, equipment and program for determining carbon emission abnormal data, and relates to the technical field of carbon emission monitoring. The method comprises: determining first data and second data, wherein the first data is a value of a specified category parameter in a first period, the specified category is selected from a plurality of categories including carbon dioxide emission, low calorific value of coal, etc., and the second data is a value of a second category parameter in the first period, the second category parameter being correlated with the specified category; substituting the second data into a preset linear regression function to obtain a predicted value of the first data; calculating a difference between the first data and the predicted value to obtain a prediction residual of the first data; and determining that the first data is carbon emission abnormal data in a case where the prediction residual of the first data exceeds a preset range. The method can improve the accuracy of carbon emission abnormal data diagnosis.

The invention relates to the technical field of industrial equipment, in particular to a colorimetric-based support hanger corrosion state evaluation method and device, and the method comprises the steps: collecting an original image of at least one target corrosion point on a target support hanger, and extracting color features corresponding to different corrosion states of the at least one target corrosion point according to the original image; characterization contrast colors corresponding to different corrosion states are calculated according to the color features, and corrosion information of at least one target corrosion point at multiple times is collected based on the characterization contrast colors; and in combination with the time sequence of the corrosion information and the linear regression function, calculating the corrosion rate of the target support hanger so as to evaluate the corrosion state change information of the target support hanger according to the corrosion rate. Visual, standardized and quantitative recognition of the corrosion state of the support hanger can be achieved, the corrosion state change information of the target support hanger is evaluated by calculating the corrosion rate of the target support hanger, and therefore life prediction and safety risk level-to-level management of the target support hanger are achieved.

A battery management apparatus according to an embodiment disclosed in this document may include: an information obtaining unit that obtains an open circuit voltage of each of a plurality of battery cells; and a controller that calculates an average open circuit voltage of the open circuit voltages of the plurality of battery cells, calculates a first linear regression function related to the open circuit voltage of a first battery cell among the plurality of battery cells based on the average open circuit voltage and the open circuit voltage of the first battery cell, a first error between the predicted value calculated based on the first linear regression function and the open circuit voltage of the first battery cell is calculated, and the first battery cell is diagnosed based on the first error.

The application provides a distributed photovoltaic power station fault diagnosis method and device based on AMI data, wherein the method comprises the following steps: constructing a training sample library and a test sample library of the distributed photovoltaic power station; establishing a power generation operation model of the distributed photovoltaic power station according to the training sample library; calculating a linear regression function of the power generation operation model; identifying a fault power station in the test sample library according to the linear regression function; and diagnosing the fault power station by using AMI data. The application can realize rapid identification, accurate positioning and fault cause diagnosis of the distributed photovoltaic power station fault only by using AMI data, so that the efficiency of fault troubleshooting can be improved, and the influence of the power station fault on photovoltaic power generation efficiency can be reduced.

The present invention discloses an audio replacement method and terminal, including the steps of: obtaining a first lossy audio and a first lossless audio; calculating and obtaining a linear regression function between the first lossy audio and the first lossless audio based on a DTW algorithm; calculating and obtaining a loss function value according to the linear regression function; judging whether the versions of the first lossy audio and the first lossless audio are the same according to the loss function value, and if the judgment result is that the versions are the same, replacing the first lossy audio with the first lossless audio according to the linear regression function. Since the present invention does not require manual matching, it can effectively reduce the labor cost of matching lossy audio and lossless audio and improve the speed of matching alignment; at the same time, through the DTW algorithm, the error caused by the time axis discrepancy when calculating the corresponding relationship between the two audio sequences is reduced, thereby improving the accuracy of audio matching, that is, providing a low-cost, fast and high-precision audio replacement method.

The embodiment of the invention provides a digital transformation application recommendation method and system, electronic equipment and a storage medium, and belongs to the field of digital transformation intelligent recommendation of small and medium-sized enterprises. The method comprises the following steps: obtaining enterprise self-diagnosis data, and processing the enterprise self-diagnosis data to obtain target data; the target data is input into a pre-constructed prediction model for prediction, enterprise real application classification is obtained, and the prediction model is constructed based on a linear regression function; and generating a recommendation scheme according to the actual application classification of the enterprise in combination with the actual situation of the enterprise. By constructing an intelligent application recommendation prediction model, specific diagnostic data and business requirements of an enterprise can be analyzed, and a most suitable application solution is automatically matched and recommended for the enterprise, so that decision errors and resource waste caused by information asymmetry and resource limitation are reduced.

The invention relates to the technical field of air conditioners, and discloses a combined air conditioner humidifying method and device based on a steam and micro-fog impinging flow regime, and the method comprises the steps that humidifying parameters of a combined air conditioner are obtained, and the air supply moisture content is determined according to the humidifying parameters; on the basis of the air supply moisture content and a preset linear regression function, the adjustment amount is calculated; and the valve opening degree is calculated based on the adjustment amount and a preset positive ratio, and a control signal is generated according to the valve opening degree. The air supply moisture content is determined according to the humidification parameters of the combined air conditioner, and then the micro-fog adjustment amount and the steam adjustment amount are calculated according to the air supply moisture content and the preset linear regression function, so that the signal for controlling the opening degree of the micro-fog and steam valve can be generated according to the micro-fog adjustment amount and the steam adjustment amount; a proper amount of micro-fog collides with the steam, high-grade heat energy of the high-temperature and high-pressure steam is used for liquid drop vaporization in micro-fog humidification, and gradient utilization of the high-grade heat energy is achieved while the micro-fog humidification efficiency is improved.