109 results about "Non-linear least squares" patented technology

The invention relates to a wide-area coverage area high-precision deformation monitoring method based on an InSAR technology, and the method comprises the steps: obtaining the InSAR monitoring data of a plurality of data sources for a wide-area coverage area, carrying out the registration of the InSAR monitoring data, screening interference pairs according to the coherence, carrying out the interference pair combination of a single-view complex image set obtained through registration, and obtaining a single-view complex image set; performing time sequence analysis according to the interference pair combination result to generate a time sequence interference image; performing phase filtering and phase unwrapping on the time sequence interference image; for the time sequence interference image after phase unwrapping, baseline parameters are corrected based on a nonlinear least square method, and atmospheric delay errors are corrected based on a GACOS atmospheric delay model; according to the processed image, constructing a wide-area surface deformation rate map and a preset application scene surface deformation rate map, and analyzing the spatio-temporal evolution law of the wide-area surface deformation rate map and the preset application scene surface deformation rate map; and finally, visually displaying the wide-area surface deformation rate diagram, the preset application scene surface deformation rate diagram and the spatio-temporal evolution law analysis result.

The invention relates to an automatic test method and system for the zero offset of a hemispherical resonator gyroscope. The automatic test system composed of a double-shaft servo turntable control module, a gyroscope control module and a PC (Personal Computer) upper computer is built; a servo turntable control instruction and a gyroscope control instruction are sent through a serial port according to a certain program through an automatic test logic built in a PC upper computer, a gyroscope vibration mode angle is controlled to be automatically switched from 0 degree to 360 degrees (the step length can be set), and turntable angle and gyroscope vibration mode angle data under each vibration mode angle are synchronously collected; and substituting the acquired data into the error model, completing zero offset error parameter identification by adopting a nonlinear least square method, generating an error compensation formula, and writing the error compensation formula into a gyroscope control unit in real time to realize compensation.

The invention discloses an offshore wind power typical fault transient waveform rapid simulation method and system, and relates to the technical field of offshore wind power unit grid connection and power grid fault simulation, and the method comprises the steps: obtaining an original typical fault voltage transient waveform at an offshore wind power unit grid connection point; a wavelet analysis method is adopted to carry out filtering processing on the fault voltage transient waveform, decomposition is carried out to obtain an approximate signal and a detail signal, a fundamental wave and a main oscillation waveform in the detail signal are extracted, an RLC second-order fault simulation circuit is established, waveform superposition is carried out to form a reconstructed waveform, a reconstructed waveform analytic expression is obtained, and fault transient characteristic parameters needing to be solved are determined; preliminarily fitting the initial values of the characteristic parameters, and iteratively solving the final values of the characteristic parameters by adopting a nonlinear least square method; fitting and solving equivalent line parameter values in combination with a fault feature expression; and substituting the waveform into a fault simulation circuit, and carrying out analog simulation to generate a reproduction waveform. According to the method, the typical fault transient waveform of offshore wind power can be rapidly and accurately reproduced.

The invention discloses a lithium battery remaining service life prediction method based on an intelligent hybrid drive model, and the method comprises the steps: carrying out the modeling of a long-term degradation trend of a target system through a double-index degradation model (DEDM), and fitting model parameters through a nonlinear least square method; performing dynamic learning on the residual fluctuation of the actual observation data and the degradation trend through a fuzzy random configuration network (F-SCN); and finally, integrating trend prediction and residual prediction results through Gaussian process regression (GPR) to realize high-precision residual life prediction. According to the method, through collaborative optimization of the physical model and the data-driven model, the problem that trend fitting and local fluctuation are difficult to consider under limited data in a traditional method is solved, and the prediction precision and reliability are remarkably improved.

The invention relates to the technical field of river ice disaster prevention, and discloses a river freeze-up state prediction method under a multi-flow condition, which comprises the following steps: calculating the flat beach flow at the early stage of river freeze-up; calculating the river sealing volume at the early stage of riverway freezing; obtaining the cooling intensity of the river channel in the cooling process; after correlation analysis is carried out on the accumulated negative air temperature in the time period from the flow to the river closure, the flat beach flow, the river closure flow and the cooling intensity, an accumulated negative air temperature prediction model in the time period from the flow to the river closure is established, a nonlinear least square method is adopted for solving, an optimal fitting coefficient is obtained, and an optimal accumulated negative air temperature prediction model is generated; the accumulated negative air temperature of the current year is obtained, the accumulated negative air temperature is predicted, and the riverway freezing-up state of the current year is obtained through comparison; according to the method, the accumulated negative temperature of the frozen river channel can be accurately obtained by establishing the nonlinear relation between the factors such as the river channel condition (flat beach flow), the sealed river flow and the cooling intensity and the accumulated negative temperature, so that the frozen state of the river reach is accurately judged.

The invention relates to a shallow coal seam stope face oxygen concentration dynamic distribution model construction and real-time updating method, belongs to the technical field of coal mine safety monitoring, and aims to solve the technical problems that a traditional monitoring means cannot obtain an oxygen concentration space full view and a static model is difficult to track a dynamic mining environment. According to the technical scheme, at least four sensors are arranged on a coal face to form a gradient monitoring network, and oxygen concentration data of multiple positions are collected in real time; constructing an exponential decay function to characterize the distribution rule of the concentration along the tendency; solving an undetermined coefficient based on a nonlinear least square method with space-time constraint; and dynamically calculating a model updating period t through an environment complexity index ECI, and triggering model iteration. Working face oxygen concentration global dynamic sensing, low-oxygen risk area accurate early warning and ventilation decision intelligence are achieved, and the mine safety prevention and control capacity is remarkably improved.

The invention belongs to the technical field of gas detection and mode recognition, and particularly provides a smell classification electronic nose system based on a multi-sensor array and mode recognition, which is used for solving the problems of poor generalization and low classification precision of a traditional gas sensor on complex smells. The method comprises the following steps that (1) an embedded electronic nose system platform is built, data of five smells of mint sugar, coffee, chrysanthemum tea leaves, orange peel and apples are collected, and the platform comprises (1) a Raspberry Pi 4B main controller used for running of a main program; (2) four BME680 sensor arrays used for collecting multiple non-specific responses of the gas to be detected; (3) the speed-adjustable gas circuit module is used for driving a micro diaphragm gas pump through PWM (Pulse-Width Modulation), and the extraction rate of 0-0.8 L / min can be realized; (2) carrying out a data sampling experiment, and constructing a multi-scene data set; (3) establishment of a pattern recognition model: (1) data processing: performing data enhancement on originally acquired data, fitting a response curve through a nonlinear least square method, extracting characteristics such as slope, curvature and the like, and expanding characteristic dimensions of the original data; (2) carrying out principal component analysis on dimension-reduced data; and (3) model training: establishing a classifier model through various machine learning algorithms to obtain an optimal model. And the accuracy rate can reach 81.7%.

The invention discloses an aerodynamic damping rapid estimation method for a rotating blade, and the method comprises the steps: selecting a test impeller, obtaining an attenuation signal of a blade of the test impeller based on an impact test, and obtaining a structural damping ratio of the blade according to the attenuation signal through a free attenuation method and a nonlinear least square method; a BTT test technology is introduced, and the total damping ratio of the blade is obtained based on the BTT test technology and a nonlinear least square method; based on the structural damping ratio and the total damping ratio of the blade, an aerodynamic damping estimated value of the blade is obtained, and estimation of aerodynamic damping is achieved; according to the method, the BTT data analysis technology and the free vibration attenuation theory are combined, quantitative estimation of the aerodynamic damping ratio of the rotating blade is achieved, and the aerodynamic damping ratios of all the blades under different vibration pitch diameters can be determined; the pneumatic damping contribution of the rotating blade is quantitatively expressed, the vibration response mechanism of the blade in the service state can be deeply understood, and long-acting safe operation of a unit is guaranteed.

The invention provides a receiver autonomous integrity monitoring method, device and equipment and a storage medium, and the method comprises the steps: obtaining satellite observation data and 5G base station observation data, and carrying out the preprocessing; performing weighted nonlinear least square method solution based on the satellite observation data and the 5G base station observation data, and determining an observation value residual error; performing fault detection and fault identification on the satellite system according to the observation value residual error; and performing autonomous integrity monitoring availability judgment based on a fault-free satellite in the satellite system to obtain a judgment result. According to the invention, the positioning precision of the low-altitude aircraft is greatly improved, the integrity monitoring level can be remarkably improved, and the problem of poor monitoring precision in the prior art is solved.

The invention provides an ultra-short baseline calibration method, device and system, and belongs to the technical field of underwater acoustic measurement, and the ultra-short baseline calibration method comprises the following steps: according to an antenna phase center position, a carrier instantaneous attitude, a distance and azimuth information at each observation moment, and installation parameters of an ultra-short baseline acoustic transducer relative to an inertial navigation system, carrying out calibration on the ultra-short baseline; establishing a distance equation between the ultra-short baseline acoustic transducer and the ultra-short baseline acoustic beacon at each observation moment; establishing a residual equation of each distance equation; and solving the residual equation through a nonlinear least square method to obtain installation parameters. According to the method, the installation parameters of the ultra-short baseline acoustic beacon can be efficiently and accurately calibrated, and the problems that beacon laying and recycling operation is complex, cost is high, the operation period is long, efficiency is low, and the beacon is prone to drifting due to the influence of ocean currents are effectively solved.

The invention relates to the technical field of functional group analysis, and discloses an adsorption material surface functional group analysis method and system based on a Gaussian function model.According to the method, proton binding isotherm data of an adsorption material under different pH conditions are obtained through an incremental titration method, and the proton binding isotherm data are input into a Gaussian function model; the method comprises the following steps: carrying out characterization analysis on different types of functional groups, fitting experimental data by using a nonlinear least square method to obtain optimized parameters, verifying the accuracy of the optimized parameters by using a preparatory test means, analyzing the proportions of various functional groups in the adsorption material based on the verified optimized parameters, and generating a surface functional group distribution map. According to the method, distribution of different functional groups is accurately analyzed through the Gaussian function model, the defect that quantitative analysis of a traditional method is not accurate is overcome, high accuracy of a result is ensured, detailed data support is provided for design and optimization of an adsorption material, and the problem that in the prior art, when multiple functional groups coexist, the detection effect is not accurate is solved.

The invention relates to the technical field of radars, in particular to a weather radar distance resolution calibration method and system. The method comprises the following steps: collecting oversampling original baseband I / Q data of a point target in a plurality of continuous pulse periods; determining a pulse signal-to-noise ratio, and screening out effective pulses according to a threshold value; carrying out sub-sampling stage time alignment on the effective pulses based on cross-correlation analysis, and carrying out incoherent averaging on power module values of all the aligned pulses to generate an average pulse power envelope; converting the average pulse power envelope to a logarithm domain, and selecting discrete data points in a main lobe region; according to the discrete data points, a nonlinear least square method is adopted to carry out iterative fitting on the generalized Gaussian mathematical model, and a continuous analytic function describing the pulse envelope is obtained; according to the continuous analytic function, determining a pulse time width when the power is reduced to a peak value of-6dB; and determining the real distance resolution according to the pulse time width. The measurement robustness is improved, and sub-meter calibration precision is achieved.

The invention relates to an S-G filtering ultrasonic signal noise reduction method, in particular to an S-G filtering ultrasonic signal noise reduction method based on nonlinear least square method guidance. The invention aims to solve the problem of effective signal distortion possibly existing in an ultrasonic signal noise reduction process. The method comprises the following steps of: 1, performing nonlinear least square waveform fitting processing; 2, performing multi-scale grading and reference signal guide regularization; and step 3, implementing multi-scale adaptive S-G filtering. The invention belongs to the technical field of ultrasonic signal noise reduction.

The invention provides a UWB multi-base-station circular station distribution structure and a positioning method, which are used for improving the regional three-dimensional positioning precision in an ultra wide band UWB system, and the positioning method comprises the steps: obtaining base station coordinates from a three-dimensional positioning region through a multi-base-station circular station distribution structure designed in a geometric circle; obtaining a timestamp, an arrival phase difference and CIR frame information through DS-TWR multi-base-station efficient communication between multiple base stations and a label; receiving distance and angle data of a plurality of UWB base stations, and introducing Kalman filtering to optimize a dynamic error; constructing an equation by using a nonlinear least square method, completing positioning coordinate calculation, and executing a multi-base-station data fusion positioning algorithm; high-precision three-dimensional positioning can be realized through an innovative circular station distribution structure and a positioning method, and the system has the advantages of simplicity and convenience in deployment, miniaturization, low power consumption, high precision and the like, adapts to variable and complex environments, and has a wide application prospect in regional three-dimensional positioning and connector automatic positioning systems.

Described herein is NLS using a bounded linear initial search space and a fixed grid with pre-calculated variables. Specifically, first and second signals with unknown first and second elevation angles, respectively, are received that have been reflected by an object, with the second signal also having been reflected off the ground. A line of second angles is then established as a function of first angles, a sensor height, and a range to the object. The first angles being bound by a function of the sensor height and the range and a function of the sensor height, the range, and the maximum height. A search algorithm is then used to search for an initial elevation angle pair along the line. The initial elevation angle pair may then be fed into a refinement algorithm (e.g., non-linear least squares) to determine the elevation angles associated with the first and second signals.

The invention discloses a transformer overheating fault prediction method and system based on a nonlinear degradation model, and the method comprises the steps: collecting the concentration data of gas dissolved in oil of a transformer in a set time period before a fault according to a sampling interval, and obtaining historical data after preprocessing; establishing an exponential degeneration orbit model based on a thermal aging equation, fitting the exponential degeneration orbit model with historical data by adopting a nonlinear least square method, and judging whether the equipment is in a stable degeneration stage or not according to a fitting result; when the equipment is in a stable degradation stage, original time is converted into an accumulated degradation amount by using a time scale conversion function, and linear processing of a historical data sequence is completed; solving a pre-established nonlinear degradation model based on the linearized data; and deducing the fault time probability density by using the solving result of the nonlinear degradation model, and predicting the overheating fault of the transformer. According to the invention, abnormal interference caused by random factors can be effectively identified, and the sensitivity and reliability of fault early warning are improved.

The invention discloses a multi-line laser radar SLAM point cloud densification method based on indoor environment characteristics, and the method comprises the steps: employing a first frame of corrected point cloud data as a reference point cloud, and constructing an initialized map: carrying out the feature extraction, data matching and feature fusion of each frame of corrected point cloud data, and then constructing a residual equation; according to the residual equation, iteration is carried out through a nonlinear least square method to optimize the residual of the line and the plane, and the final pose Ri needing to be optimized is output and obtained; performing domain point classification on point cloud data of each frame on each current scanning line according to the optimized pose Ri, dividing the point cloud data into plane points and corner points, obtaining a relation between the plane points of the current frame and surrounding domain points, and judging to execute straight line fitting and point cloud densification processing of the plane points on the current frame; according to the method, the problem that the density of multi-line laser radar data is rapidly reduced at the distance from the radar is solved, and uniform distribution of point clouds at different depths is ensured through an intelligent densification algorithm, so that the quality of a whole indoor environment model is improved.

The invention provides a GNSS / VIO fusion high-precision positioning method based on graph optimization. A system is composed of a data acquisition module, a preprocessing module, a visual inertial odometer module and a graph optimization fusion module. The system integrates a monocular camera, an IMU (inertial measurement unit) and a GNSS-RTK (global navigation satellite system-real-time kinematic) receiver, and establishes a unified multi-factor graph optimization model by constructing a visual re-projection factor, an IMU pre-integration factor and a GNSS-RTK factor. The core innovation point of the method is that local estimation of the visual inertial odometer is corrected in real time in a sliding window by utilizing global absolute position constraint provided by a GNSS (Global Navigation Satellite System), so that the increase of accumulative errors of VIO (Visual Inertial Odometer) is effectively inhibited. The back end of the system adopts a nonlinear least square method to optimize and solve the pose, the speed and the IMU zero offset of the carrier in real time; and when the GNSS signal is shielded or lock losing occurs, high-precision positioning output is still kept depending on the continuity of the VIO. In conclusion, through collaborative modeling and joint optimization of the multi-source sensor, the limitations of insufficient precision, signal interruption and the like of a single sensor in a complex shielding environment are overcome, and high-precision and high-robustness continuous positioning is realized.

The application belongs to the field of curved surface body positioning reference plane adjustment and discloses a curved surface body positioning reference plane adjustment method based on nonlinear least squares, which aims to provide an improved curved surface body positioning reference plane adjustment method based on nonlinear least squares. After the curved surface body is fixed on a workbench, the coordinates of the registration control points on the curved surface body are measured and obtained by a laser tracker. In the Boolean sand model, the two coordinate system origins are translated to the center positions of the reference control points to establish a center auxiliary conversion coordinate system. Seven parameters are solved based on the nonlinear least squares method and the Gauss-Newton iteration method, and the adjustment is performed based on the offset and the scaling ratio. The corresponding displacement and deflection angle values are compensated to the reference plane, and the curved surface body is moved to the optimal working position. The application is applied to the field of curved surface body reference plane positioning.

The present invention discloses a method for evaluating the dielectric loss factor of power equipment under the action of an oscillating wave high voltage, which is applied to the field of high voltage test technology. In addition to evaluating the oscillating wave voltage information in the traditional dielectric loss factor evaluation method, the oscillating wave current is evaluated. Through mathematical deduction, a mathematical expression for the relationship between the dielectric loss factor and the oscillating wave voltage and current waveform parameters is obtained; based on an algorithm combining phase-shifted fast Fourier transform and nonlinear least squares method, the oscillating wave voltage and current sequence is processed to obtain waveform parameter estimates, and then an estimated value of the dielectric loss factor is obtained. The dielectric loss factor evaluation algorithm of the present invention has good anti-noise interference capabilities, and at the same time, the dielectric loss factor estimation accuracy is high.

The application discloses a two-step method for accurately correcting GNSS-IR abnormal phase, and belongs to the technical field of microwave remote sensing. The method is characterized in that: the GNSS observation data is preprocessed, and the modulation term in the signal-to-noise ratio is extracted; then, the nonlinear least square method is used to fit the modulation term, and the phase of each satellite is obtained; finally, the abnormal phase is accurately corrected through the two-step method: in the first step, a signal-to-noise ratio period method is constructed to correct the phase of the whole period and half period jump caused by the limitation of the nonlinear least square method itself; in the second step, the standard fraction method is combined with the Savitzky-Golay filter to accurately detect and repair the phase of less than half period jump caused by the multipath environmental factors. The method can effectively improve the phase quality of each satellite, improve the shortcomings of the traditional phase solving method, and solve the problems in the prior art.

The invention provides a commercial vehicle EBS road adhesion coefficient identification method, system and device and a storage medium, and the method comprises the following steps: building a current sample point based on the obtained current slip rate data and current adhesion coefficient under an ABS triggering condition, historical sample points are constructed based on the obtained multiple historical slip rates and the historical attachment coefficients of the corresponding historical slip rates; preprocessing the historical sample points, and constructing the historical sample points and the current sample points to obtain a two-dimensional data set; performing noise reduction fitting on the two-dimensional data set through an RANSAC (Random Sample Consensus) algorithm, and calculating to obtain a primary fitting model parameter and an interior point data set; secondary fitting is carried out on the primary fitting model parameters and the interior point data set through a nonlinear least square method, and secondary fitting model parameters are obtained through calculation; and a peak road surface coefficient is calculated based on the quadratic fitting model parameters, a current road surface adhesion coefficient is identified based on the peak road surface coefficient, and the road surface adhesion condition is determined. The method can accurately identify the road adhesion coefficient.

The invention provides a method and a system for predicting particle size distribution of rock slag of a full-section tunneling machine. The method comprises the following steps: S1, acquiring a rock slag image on a conveyor belt during construction of the tunneling machine to obtain a binary mask; s2, preprocessing the rock slag image and the binary mask to obtain model input required by different tasks; s3, building a self-supervised learning model, and carrying out pre-training by using an unlabeled data set; s4, performing closed contour traversal and pixel statistics on the segmentation mask output by the semantic segmentation model, and fitting two key distribution parameters of a Rosin-Rammler curve based on a nonlinear least square method; and S5, evaluating a rock slag particle size distribution prediction effect according to a fitting result. According to the method, massive unlabeled image data can be fully utilized, the dependence of manual labeling is reduced, and meanwhile, the prediction effect of the model is improved.

This application belongs to the technical field of hysteresis modeling of ferromagnetic materials, and discloses a method, system, device, and medium for simulating the hysteresis and loss characteristics of magnetic powder cores. The method includes: solving the eddy current field expression inside the metal spherical particles based on Maxwell's equations; multiplying the calculation result of the eddy current field expression by the total number of magnetic powder core particles to obtain the total eddy current loss inside the particles; dividing the surface of the magnetic powder core into equally spaced segments and setting the height of each segment. h Based on the eddy current path conduction coefficient, parameter optimization is performed using the nonlinear least squares method, combined with the skin effect, to determine the expression for the interparticle eddy current field. Based on the total eddy current loss within the particles and the interparticle loss of the metal sphere calculated according to the interparticle eddy current field expression, combined with the Preisach static hysteresis model, a simulation model of the hysteresis and loss characteristics of magnetic powder cores is constructed. This application can simulate the hysteresis and loss characteristics of granular magnetic powder core materials under high-frequency sinusoidal and non-sinusoidal excitation.

The invention discloses a basalt fiber cement silty soil dynamic elastic modulus estimation method for a subgrade in a seasonal frozen region, which comprises the following specific steps: preparing a sample, performing a freeze-thaw cycle test on the sample, then performing a graded loading dynamic triaxial test to obtain dynamic stress and dynamic strain of a soil body under different confining pressures, drawing a hysteretic curve, and estimating the dynamic elastic modulus of basalt fiber cement silty soil on the basis of a Hardin-Drnevich hyperbola model. The maximum dynamic elastic modulus and the final dynamic stress amplitude of the sample are obtained, reference dynamic strain is introduced, and the normalized secant dynamic elastic modulus is obtained; based on a nonlinear least square method, statistical analysis is carried out on test data under the action of different confining pressures and freezing and thawing cycle times, and a pre-estimation model representing correlation between the maximum dynamic elastic modulus and the final dynamic stress amplitude and the confining pressures and the freezing and thawing cycle action times is obtained; through nonlinear regression analysis, obtaining a prediction model of the maximum dynamic elastic modulus and the final dynamic stress amplitude considering the confining pressure and the freeze-thaw cycle effect, and further obtaining a normalized secant dynamic elastic modulus; the method improves the estimation efficiency.

The invention discloses an unmanned aerial vehicle live-action three-dimensional modeling method based on LiDAR assistance, and the method is characterized in that the method comprises the following steps: (1) data collection: employing an IMU + GNSS integrated navigation system to collect the pose data of an unmanned aerial vehicle in real time, carrying out the filtering, intensity value normalization and down-sampling processing of a LiDAR point cloud, and obtaining the pose data of the unmanned aerial vehicle; performing multi-view image feature point extraction and feature point descriptor calculation and matching on the image data; (2) adjusting a conversion matrix through a dynamic weight calculation model; and (3) performing iterative optimization by adopting a nonlinear least square method, converting a registration problem into nonlinear optimization, obtaining an initial conversion matrix from a dynamic weight stage, setting Lie algebra parameterized expression, determining an optimization boundary condition, establishing an iterative optimization process, and gradually and finely adjusting a coordinate conversion relation to enable an overall matching error to be minimum and ensure that a registration error is less than or equal to 0.5 cm. The method has the beneficial effects that the offset of the coordinate system is corrected in real time through the dynamic weight registration algorithm, and the registration error precision is improved by more than three times.

The invention relates to the technical field of cable assembly simulation, in particular to a cable physical property modeling method based on multi-physical parameter fusion, which deeply fuses multi-dimensional physical parameters of a cable, namely mechanical property parameters, material attribute parameters and external force action parameters, and constructs a multi-physical parameter coupled cable physical property model. Accurate description of cable mechanical behaviors is realized, and rapid solving of a cable physical property model is realized based on technologies such as GPU acceleration. Firstly, the model is processed, so that solving difficulty caused by equality constraint and integral calculation is avoided; then, through exploration, determining that a Newton method and a nonlinear least square method are respectively used for solving a cable physical property model with external force and without external force; and finally, the solving efficiency of the cable physical property model is greatly improved by utilizing the parallel computing capability of the GPU.

The invention relates to the technical field of semiconductor processing control, and provides an etching uniformity optimization method based on plasma density adjustment, which comprises the following steps of: 1, acquiring plasma density and etching rate, generating a coordinate system by taking the center of a wafer as an original point and partitioning along the radial direction, mapping the plasma density and the etching rate to the coordinate system, obtaining a continuous space distribution function; 2, constructing a correlation model, and fitting the correlation model by adopting a nonlinear least square method to obtain a relationship between the plasma density and the etching uniformity; and step 3, constructing a multivariable prediction model, monitoring the plasma density in etching, and when the plasma density distribution exceeds a preset threshold value, feeding back and adjusting plasma density control parameters in real time by the multivariable prediction model. According to the method, the relation between the plasma density and the etching uniformity is quantified, then the plasma density space distribution is monitored to predict the etching uniformity, and the plasma distribution deviation is dynamically adjusted to improve the etching uniformity.

The invention provides an electric passenger vehicle ownership prediction method based on a dynamic parameter Bass model. The method comprises the following steps: S1, obtaining the electric passenger vehicle ownership and a multi-source influence factor time sequence number over the years in a target area; s2, performing parameter calibration on an initial innovation coefficient and an imitation coefficient of the Bass model based on a nonlinear least square method; s3, extracting economic, policy and technical principal components from the shadow factors by adopting a principal component analysis method, and establishing a multiple regression equation of p (t), q (t), the principal components and time to realize parameter dynamics; s4, substituting the dynamic p (t) and q (t) into a Bass diffusion differential equation, and solving to obtain an electric passenger vehicle inventory prediction curve in the next ten years and prediction results of all years; and S5, back-testing the prediction curve by using the actual inventory of the historical years, and outputting a final prediction result. According to the method, the medium-and-long-term prediction precision of the electric passenger car in China is remarkably improved, and a quantitative basis can be provided for charging infrastructure layout, carbon emission evaluation and industrial policy making.

The invention relates to a method for measuring the pipe diameter of an underground non-metal pipeline based on a hyperbolic fitting method, and the method comprises the steps: scanning a to-be-measured non-metal pipeline through a two-dimensional ground penetrating radar, and collecting original radar data; preprocessing the original radar data to obtain a preprocessed radar profile image; establishing a geometric model, and converting the geometric model into a hyperbolic equation form with a downward opening; pipeline reflection hyperbola features are extracted from the preprocessed radar profile image, and at least 10 feature points are selected to cover hyperbola vertexes and two wings; substituting the extracted hyperbolic characteristic points into a standard fitting equation, and solving parameters through a nonlinear least square method; the parameters are substituted into a hyperbolic equation, and the radar wave layer velocity v, the outer wall radius of the nonmetal pipeline and the pipe top burial depth are obtained through inversion. According to the method, the data quality is improved by preprocessing radar data on the basis of the hyperbolic fitting method by using the data characteristics of the ground penetrating radar, accurate fitting parameters are obtained by using the hyperbolic fitting means, and finally the pipe diameter is accurately calculated.