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

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 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 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 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 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 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 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 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.

The invention provides an image reconstruction method and device for very long baseline interference measurement data, and relates to the technical field of radio astronomy observation, and the method comprises the steps: processing the very long baseline interference measurement data of a target observation object in batches, reading the visibility data of each observation baseline, and processing the abnormal data, obtaining target visibility data; performing image deconvolution on a dirty image corresponding to the target visibility data to obtain a clean image and a clean model of the target observation object with dirty lobe interference removed; based on a target antenna gain factor corresponding to the observation baseline solved by the cleaning model, correcting observation data of the target observation object, and iteratively optimizing the cleaning model to determine a target cleaning model and correction parameters; and on the basis of the preset parameters and the target cleaning model of the at least one target observation object determined by the nonlinear least square method, the target cleaning model and the correction parameters, automatic image reconstruction is carried out, and the insufficient capacities of VLBI data batch processing, visual output and the like are relieved.

The application discloses a kind of intelligent wide-spectrum film thickness real-time monitoring system and method, it is related to online monitoring technical field, the system includes: approximate model acquisition module, synchronous design parameter and match and obtain approximate film model set;Model update module, according to design parameter and material intrinsic parameter update model parameter, generate alternative model set;Spectrum acquisition module, through wide-spectrum light source collection reflected light and convert into reflected interference spectrum;Model adjustment and output module, based on nonlinear least square method iteration optimization alternative model, output the target film model of minimum residual error;Monitoring result extraction module, extract film thickness from target model as film thickness monitoring result.Further achieve the technical effect of improving thickness measurement adaptability, improve thickness measurement precision.

This invention discloses a method for determining the structural error during kinematic calibration simulation of a parallel mechanism, applicable to the calibration of industrial parallel robots. The method involves establishing structural constraint equations for the parallel mechanism to determine independent variables in the structural parameters; combining the structural constraint equations and independent variable values ​​to calculate the values ​​of non-independent variables in the structural parameters; calculating the motion of each prismatic joint under ideal structural parameter conditions with a given target pose, establishing the correlation between pose parameters and motion; given an initial structural error value, calculating the structural error value conforming to geometric constraints using constraint equations and nonlinear least squares method, and calculating the norm of the residuals. If the residuals meet the requirements, the structural error for kinematic calibration simulation of the parallel mechanism can be generated; if the residual value does not meet the requirements, the generated structural error is used as input, and a new structural error value is generated again using constraint equations and nonlinear least squares method, and the residual calculation is repeated until the residual value meets the requirements, thus generating a structural error that meets the requirements and accurately verifying the structural error identification accuracy of the error model.

The application discloses a kind of offshore wind power typical fault transient waveform fast simulation method and system, it is related to offshore wind turbine grid connection and power grid fault simulation technical field, including: obtaining the original typical fault voltage transient waveform at offshore wind turbine grid connection point;Wavelet analysis method is used to the fault voltage transient waveform is filtered and handled, and approximate signal and detail signal are obtained by decomposition, fundamental wave and main oscillation waveform in detail signal are extracted, RLC second-order fault simulation circuit is established, and reconstruction waveform is formed by waveform superposition, reconstruction waveform analytical expression is obtained, the fault transient characteristic parameter required to be solved is determined;The initial value of characteristic parameter is initially fitted, and the final value of characteristic parameter is iteratively solved using nonlinear least square method;Equivalent line parameter value is solved by fitting using fault characteristic expression;It is substituted into fault simulation circuit, and recurrence waveform is generated by simulation simulation.The application can quickly and accurately reproduce offshore wind power typical fault transient waveform.

The invention provides a method for determining the water saturation of a fracture-cavity medium rock core. The method comprises the following steps: acquiring output data of the rock core at different inclination angles and different oil-water injection ratios; calculating the output data to obtain initial core water saturation; drawing a scatter diagram of the water saturation along with the water content by taking the water content of the outlet end of the rock core as a horizontal coordinate and the initial water saturation of the rock core as a vertical coordinate; fitting data in the scatter diagram by using a nonlinear least square method based on an oil-water diversion model to obtain a relationship between different inclination angles and the fracture-cavity rock core moisture content; and calculating based on the relationship of the water contents of the fracture-cavity rock cores with different inclination angles to obtain the water saturation of the standard fracture-cavity rock core under different inclination angles. The method disclosed by the invention quantitatively reveals the rule of influence of the fracture-cavity inclination angle on the water saturation in the rock core, and overcomes the limitation that a traditional sandstone rock core testing method is not suitable for fracture-cavity rock core testing.

The invention relates to the technical field of automotive electronics, and particularly discloses an angle self-calibration method and system for a vehicle-mounted millimeter-wave radar, and the method comprises the steps: collecting the measurement data of a static object in a view field range through the radar when a vehicle carrying the vehicle-mounted millimeter-wave radar runs in a straight line, the measurement data includes a measured azimuth angle and a relative velocity of each stationary object. According to the method, the key function is constructed by collecting the measurement data of the static object, the angle error coefficient and the vehicle speed error coefficient are jointly estimated in combination with the nonlinear least square method, the parameter estimation precision is improved through data optimization means such as azimuth interval classification and filtering smoothing, and finally accurate compensation of the radar measurement azimuth is achieved. The problem of error drift in the dynamic driving process of the millimeter wave radar can be effectively solved, the identification precision of the radar on static vehicles and roadside obstacles is improved, and the functional reliability of vehicle-mounted safety systems such as ACC and vehicle distance early warning is guaranteed.

The invention discloses a power grid company dual-carbon marketization business market scale prediction method and device, and the method comprises the following steps: constructing a basic hypothesis of power grid company dual-carbon marketization business diffusion, the hypothesis comprising a customer group division hypothesis, a dual-channel propagation hypothesis, and a price acceptance mechanism hypothesis; based on the hypothesis, an improved Bass diffusion model is established, and the model comprises a final customer number change speed equation and a final customer number accumulation equation; a nonlinear least square method is adopted, historical data and expert scoring and questionnaire survey data are combined, and parameter estimation and goodness-of-fit verification of the improved Bass diffusion model are completed; on the basis of a parameter estimation result, market scale prediction is carried out on developed carbon transaction business and undeveloped carbon capture business of the power grid company; according to the invention, a scientific quantitative basis is provided for market planning, channel optimization and price making of the dual-carbon business of a power grid enterprise, and data support is also provided for making dual-carbon industrial policies of government departments.

The invention provides a pile sinking vibration intelligent monitoring method and device. The method comprises the steps that S1, acceleration signals in the pile sinking process are synchronously collected through a plurality of three-axis inertial sensors deployed in a to-be-monitored area; s2, the data processing terminal converts the acceleration signal into a speed time domain signal and performs interference filtering to obtain effective PGV data; s3, performing real-time curve fitting on the effective PGV data of different monitoring points through a nonlinear least square method, dynamically correcting a vibration attenuation coefficient, and automatically calculating a dynamic safety distance according to a preset safety threshold value; s4, a composite vibration isolation barrier is constructed between the construction area and the protected building according to the dynamic safety distance and the real-time vibration monitoring result; and S5, monitoring the vibration reduction rate after the construction of the composite vibration isolation barrier in real time, and dynamically adjusting the layout parameters of the stress release holes according to the deviation between the vibration reduction rate and the target limit value to realize accurate vibration over-filtering.

The invention relates to a lifting appliance container identification and positioning method and system based on multi-view vision, and the method comprises the following steps: calibrating a plurality of cameras installed on a container lifting appliance, obtaining a multi-view image of a lifted container, and carrying out the identification and positioning of the corners of the container through a real-time detection network, extracting a region of interest; oRB feature points are extracted, multi-view feature point matching is carried out, and a linear equation set of spatial points is established by using a multi-view triangulation method; the linear equation set is solved through a weighted nonlinear least square method, the optimal approximate world coordinate of the target container angle is obtained, and the weight in the weighted nonlinear least square method is obtained through dynamic calculation according to a multi-factor fusion strategy. Compared with the prior art, the method has the advantages that high-precision positioning of the container in a complex port environment can be realized, and the like.

The invention relates to a vehicle-mounted IMU installation deflection angle online estimation method and device. The method comprises the steps that multiple sets of static pose data of a vehicle where a to-be-calibrated vehicle-mounted IMU is located are acquired; pre-processing the plurality of groups of static pose data, wherein the pre-processing comprises screening, mean value calculation and normalization processing; constructing an overdetermined equation set through coordinate transformation and relative rotation among different static poses of the vehicle on the basis of the multiple groups of static pose data after preprocessing; and solving the overdetermined equation set through a nonlinear least square method to obtain the installation deflection angle of the to-be-calibrated vehicle-mounted IMU. According to the method, through multi-attitude constraint and least square estimation, noise interference is effectively suppressed, the precision and robustness are far better than those of a traditional static method, the cost is extremely low, and operation is easy and convenient.

The present application relates to the field of radar technology, especially to a weather radar range resolution calibration method and system. The method comprises the following steps: collecting oversampling original baseband I / Q data of a point target in multiple pulse periods; determining pulse signal-to-noise ratio, and screening effective pulses according to a threshold value; performing time alignment of the effective pulses in a sub-sampling stage based on cross-correlation analysis, performing non-coherent average of power modulus of all the aligned pulses, and generating an average pulse power envelope; converting the average pulse power envelope to a logarithmic domain, and selecting discrete data points located in a main lobe region; using a nonlinear least squares method to iteratively fit a generalized super-Gaussian mathematical model according to the discrete data points, and obtaining a continuous analytical function describing the pulse envelope; determining a pulse time width at which the power drops to a peak value-6dB according to the continuous analytical function; and determining a real range resolution according to the pulse time width. The measurement robustness is improved, and the calibration accuracy is sub-meter.

This invention provides a method and device for monitoring soil salinity in facilities, belonging to the field of soil salinity monitoring technology. This invention pre-defines a well-defined salinity decoupling function, mechanistically separating the combined effects of temperature, soil moisture content, and clay content on pore water conductivity to obtain standardized pore water conductivity, which is the essential physical quantity reflecting salinity levels. This solves the problems of ambiguous physical meaning of pore water conductivity values, environmental noise drowning out the true signal, and leading to misjudgments of salinity. This invention utilizes selected stable window data, with the optimization objective of minimizing the variance of the calculated pore water conductivity sequence. It employs a nonlinear least squares method to iteratively update the parameters in the salinity decoupling function online, significantly improving the model's local applicability and accuracy at that point. This solves the problems of inaccurate cross-point measurements and long-term performance degradation caused by unchanging model parameters.