315 results about "Weighted least squares method" patented technology

The invention relates to the field of power system monitoring and fault prediction, in particular to a lightning arrester fault type prediction method and system based on an extremely cold environment working condition. Probabilistic prediction of future key indexes of the lightning arrester is realized, and physical state data is generated through real-time simulation of a digital twin platform; through weighted least square method fusion correction and fuzzy logic, an integrated decision model constructed by a dynamic Bayesian network, system output fault mode classification and early warning decision, the effects of early warning of fault risks in advance in an extremely cold environment and improvement of the safety of a power system are achieved.

The invention discloses a water surface target tracking method and system based on multi-physical parameter fusion, and the method comprises the steps: obtaining a physical layer observation parameter of a target through a water surface monitoring radar, and constructing a multi-dimensional nonlinear observation vector; establishing a multi-mode motion model set including constant speed, variable speed and turning, and realizing dynamic switching among motion modes through a Markov chain; adjusting a process noise covariance and an observation noise covariance based on a residual covariance estimation result in the sliding window by adopting an adaptive extended Kalman filtering algorithm; distributing weights according to the measurement variance of each physical parameter, optimizing the Kalman gain through a weighted least square method, and completing the updating and estimation of a target state; dynamic switching of motion modes is achieved through a Markov chain, and when state estimation residual errors of continuous preset times exceed a preset threshold value, model mismatch is judged, and a Markov chain model switching mechanism is triggered; according to the method, the sea condition adaptability, the calculation efficiency and the engineering expandability can be improved.

The invention discloses a physical and data hybrid driven mechanical arm dynamic model identification method which is characterized by comprising the following steps: S1, acquiring parameters of each axis of a robot, establishing a dynamic model of a mechanical arm, and linearizing the dynamic model according to a minimum inertia principle to obtain a dynamic minimum parameter set and a corresponding regression matrix; s2, an excitation track of the mechanical arm is designed based on the finite Fourier series; s3, the mechanical arm runs the designed excitation track, angles, angular velocities, angular accelerations and torque data of all joints of the mechanical arm in the movement process are collected and filtered, and an observation torque vector and an observation matrix are calculated; s4, performing preliminary estimation on the minimum parameter set by using an iterative reweighted least square method; and S5, aiming at the dynamic characteristics of LuGre friction force in the movement process of the mechanical arm, establishing a mechanical arm dynamic model under physical and data hybrid driving.

The invention discloses a field personnel positioning method cooperating with multi-view images and spatial vectors of an unmanned aerial vehicle, and the method comprises the steps: detecting a personnel target in real time through a pre-trained lightweight model, obtaining a surrounding radius in combination with a self-adaptive surrounding radius algorithm, and achieving the collection of a multi-view image sequence; carrying out quality quantitative analysis on the multi-view image sequence by adopting a multi-source fixed weight quality evaluation algorithm; a DBOF coordinate system is established, and coordinate conversion is completed; extracting high-frequency matching points based on a cross-image bidirectional matching mechanism, and performing composite distortion correction on the high-frequency matching points; carrying out weighted least square method solution on the multi-view observation equation set to generate a three-dimensional space coordinate of the target point in a DBOF coordinate system; and introducing a robust iterative optimization strategy to eliminate abnormal values, and outputting geographic coordinates. On the basis of low-cost monocular vision, the real-time performance and robustness of personnel positioning in a complex field environment are improved through a dynamic surround sampling and geometric resolving mechanism, and spatial data support is provided for field rescue.

The invention discloses a mining equipment positioning system and method based on radar, ultra wide band and inertial navigation, and the system is characterized in that a multi-source sensor unit, a data synchronization and collection module and a preprocessing module are connected in sequence, and a state fusion module is connected with the preprocessing module, a UWB ranging error compensation module, a radar motion estimation module and a pose output module; the upper computer is connected with the pose output module; the method comprises the steps of collecting multi-source data; timestamp alignment and format conversion processing are carried out, and feature and speed constraint information extraction is carried out on the point cloud data; fitting correction is carried out on the ranging data by adopting a weighted least square method, and position information is obtained through an error weighted average algorithm; feature matching is carried out, and in combination with a KISS-ICP point cloud registration method, a speed estimation result is obtained; carrying out data fusion, and obtaining a pose estimation result by adopting an extended Kalman filtering algorithm; and outputting equipment position and attitude information. According to the invention, high-precision real-time estimation of the attitude and position information of the mining equipment can be realized.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single beacon virtual long baseline positioning method and system capable of estimating an unknown sound velocity. The method comprises the following steps: constructing a virtual long baseline array by combining stored dead reckoning data; constructing a virtual long baseline positioning problem by combining the relationship between the underwater acoustic signal propagation time and the position and sound velocity of the AUV; auxiliary variables are introduced, a pseudo-linear positioning equation is obtained based on algebraic conversion, and an AUV position initial solution is obtained based on a weighted least square method; and constructing a second-step linear estimation problem by exploring the relationship between the auxiliary variable and the AUV position, and solving by using weighted least squares to obtain a more accurate AUV position estimation solution. Compared with a single-beacon positioning method which does not consider sound velocity unknown, the method provided by the invention can obtain more ideal position estimation performance, and the practical application capability of an underwater single-beacon positioning system is enhanced.

The invention provides a multipath fusion phased array radar antenna unit coordinate determination method and system, and the method comprises the steps: obtaining an antenna array surface power distribution diagram, recognizing the boundary and geometric center of an array, and planning a spiral and cross-shaped composite sampling path; on the planned sampling path, collecting phase data at a plurality of inclination angles and frequencies; based on the signal-to-noise ratio and the angle correction factor, carrying out adaptive weighted fusion calculation on the adjacent point phase difference under multiple conditions; establishing an overdetermined equation set by using the fused phase difference, and iteratively solving the accurate coordinates of the antenna unit through two rounds of weighted least squares; and establishing a drift model to perform dynamic compensation, and performing real-time calibration by using a unit with determined coordinates as a reference point. According to the invention, through a multi-angle and multi-frequency redundancy measurement strategy and in combination with a signal-to-noise ratio-based adaptive weighting algorithm, environmental interference can be effectively identified and suppressed. Even in a complex electromagnetic environment, the system can still keep stable measurement precision.

The invention discloses a power system inertia estimation method based on multi-mode decomposition and reconstruction, and belongs to the technical field of power system inertia identification control, and the method comprises the steps: obtaining a system frequency deviation signal and three-phase voltage and current data in real time; inputting the frequency deviation signal into a signal processing algorithm, extracting a multi-mode oscillation signal, and reconstructing the multi-mode oscillation signal into an equivalent single oscillation mode signal; the frequency change rate of the reconstructed signal is calculated, and the frequency change rate is obtained and analyzed; after a fault occurs, analyzing the moment corresponding to the maximum value of the frequency change rate, and combining power deviation to trigger inertia constant calculation; according to the frequency change rate and the power deviation, calculating a system inertia constant through a dynamic equation at a triggering moment, and converting the system inertia constant into a per unit value; and reconstructing a frequency signal of a non-measurement node by adopting a weighted least square method to finish inertial estimation. Through a synchronous phasor data dynamic decomposition and reconstruction technology, high-precision online sensing of inertial parameters under complex working conditions is realized.

The invention relates to a multi-channel data drift compensation method based on digital information, and the method comprises the steps: obtaining multi-channel data, calculating a mean value, a standard deviation and a correlation coefficient for each channel, and judging whether drift exists or not based on a preset threshold value; if drift exists, the drift distance is calculated based on mean value estimation and standard deviation estimation, and a self-adaptive compensation model is established; determining an optimal compensation parameter through a self-adaptive compensation model, and compensating a drift channel in combination with the dynamic change condition of the channel; correcting the drift channel by adopting the compensation parameter; counting the missing data proportion and distribution condition, and selecting an interpolation strategy according to the distribution density and the local fluctuation characteristics; carrying out interpolation repair on the data vacancy position by adopting a local weighted least square method; smoothing the transition region after interpolation repair, and selecting an optimal smoothing algorithm through minimizing a mean square error and a peak signal-to-noise ratio; and outputting the multi-channel data after drift compensation and interpolation repair.

The invention discloses a multi-modal data fusion acquisition system and method, and the method comprises the steps: carrying out the feature extraction of the text information, frequency domain image and infrared image data of a vibration signal through a Bi-GRU network, and fusing the data of a plurality of modals into a comprehensive feature matrix; in the process, key features can be extracted from each mode, and the model can effectively capture the non-Euclidean relationship between different modes by constructing an adjacent matrix and performing graph convolution operation for a subsequent relationship. According to the method, a traditional matrix-based operation mode is broken, the dependency relationship between modes can be better processed, and the quality of feature fusion is improved. And feature fusion is further optimized by using least square weighted least square (WLS) fusion and a multi-modal factorization technology, so that the robustness of the system to noise and abnormal data is improved.

The invention provides an ultrasonic sensitivity detection method based on a large workpiece, relates to the technical field of detection, and aims to solve the problems of inconsistent sensitivity in a full thickness range and insufficient deep defect detection precision of traditional ultrasonic detection. According to the method, multiple groups of depth-adaptive equivalent reflectors are arranged on a test block, and a gain value is dynamically adjusted in combination with a fuzzy neural network controller, so that sensitivity adaptive compensation in a full thickness range is realized, and a detection error is ensured to be stabilized within + / -1%; according to the dynamic reflector interval design based on the material attenuation coefficient and the acoustic parameter, the reflector distribution is optimized, and the manual calibration complexity is remarkably reduced. And fitting a TCG curve through a segmented weighted least square method and carrying out simulation verification to generate a high-precision DAC curve, so that high-confidence output of quantitative defect evaluation is realized. The method improves the sensitivity, accuracy and engineering applicability of ultrasonic detection of large workpieces, and is especially suitable for defect detection of workpieces with complex curvatures.

The invention provides a dynamic modeling method and device based on residual compensation and electronic equipment, and aims to realize parameter estimation of a minimum inertial parameter set by adopting an iterative reweighted least square method based on obtained joint information of a mechanical arm so as to determine a dynamic preliminary identification model. And according to the dynamic preliminary identification model, the mechanical arm tracks a preset trajectory to obtain a residual moment data set and joint information. And on the basis of the residual moment data set and the joint information, constructing a dynamic residual model by adopting a nonlinear dynamic sparse identification method. And combining the dynamic preliminary identification model and the dynamic residual error model to obtain a final dynamic model. According to the scheme, parameter identification is achieved through the iterative reweighted least square method, the influence caused by numerical magnitude differences and outliers of the torque of all the joints of the mechanical arm is reduced, the precision of the dynamic identification model is improved, compensation is conducted in combination with the dynamic residual error model constructed based on the nonlinear dynamic sparse identification method, and the dynamic identification precision is improved. And the final dynamical model precision is improved.

The invention discloses a multi-source fusion positioning method, positioning system and equipment for resisting non-line-of-sight interference. The method comprises the following steps: S1, acquiring RSS (Received Signal Strength), TDOA (Time Difference Of Arrival) and AOA (Attention Of Arrival) of N base stations; s2, selecting a base station with the strongest signal as an initial reference station according to the RSS mean value, and generating all base station combinations containing the reference station; s3, constructing a joint positioning equation for each base station combination, and carrying out initial position calculation on each combination by adopting a weighted least square method to obtain an intermediate position estimated value and a corresponding residual error; s4, identifying the NLOS base station by using sliding window-based multi-feature residual dynamic threshold judgment and effective combination-based NLOS weight penalty verification respectively; and S5, calculating the base station combination according to the residual error and the geometric precision factor to obtain high-precision position estimation. According to the method, unreliable NLOS base station signals can be intelligently identified in a complex electromagnetic environment, the weights of the unreliable NLOS base station signals are reduced, and finally more accurate target position estimation is fused.

The invention provides a high-precision real-time multi-channel cooperative control method and system. The method comprises the following steps: establishing a conversion relation among a geodetic coordinate system, a geocentric coordinate system and a station center coordinate system; each device solves the observation data of the target into the global ECEF coordinate of the target through the conversion relation according to the geographic coordinate of the device; fusion of observation data of multiple cameras (integrated in a turntable cabin) is realized by adopting an iterative weighted least square method, and the optimal estimation of the initial position of a target is obtained; and taking the fused position as an observed quantity, inputting the observed quantity into a Kalman filter constructed based on a uniform-speed motion model, dynamically adjusting an observation noise covariance and a process noise covariance, and predicting and tracking a target motion state, thereby realizing multi-camera cooperative control.

The invention relates to the technical field of pavement detection, in particular to a 3D scanning and variable efficient pavement finish milling method and system. The method comprises the following steps: acquiring milling state data, and preprocessing the acquired state data, including acquiring position data and attitude data of a milling machine and structural data of a road surface; constructing a three-dimensional model according to the preprocessed structural data, calculating a milled design surface based on the three-dimensional model, and generating a control instruction according to parameters of the design surface, including comparing and analyzing the parameters of the design surface with position data and attitude data of the milling machine through a preset control algorithm; when a milling design surface is calculated, a weighted least square method and a genetic algorithm are adopted, an ideal pavement plane equation and optimization design parameters are automatically determined according to measuring points and setting requirements, complex mathematical calculation and scheme design do not need to be manually carried out, and the operation difficulty is reduced.

The invention discloses a gravity satellite groundwater storage variable vertical signal separation method based on a physical model, and the method comprises the steps: S1, building a physical response model of ground surface deformation and gravity change and groundwater storage variables, and enabling the physical response model to comprise a ground surface deformation response model and a gravity change response model; the method specifically comprises the following steps: step S2, constructing a gravity satellite and InSAR data joint inversion framework, and establishing an observation equation set; s3, regularization constraint conditions are introduced, and a weighted least square method is adopted for solving; and S4, carrying out groundwater reserve vertical separation and verification based on a mass conservation principle. The method overcomes the defect that an existing method depends on dense well observation data, fine monitoring of groundwater storage variables is achieved through physical model constraints, and the method can be widely applied to regional groundwater resource monitoring and management.

The invention discloses a satellite time-varying gravity field inversion method based on adaptive de-mixing, and belongs to the technical field of satellite gravity measurement. Constructing a normal equation; accumulating the normal equations and solving parameter initial values through a weighted least square method; calculating a post-test residual error of the observation value and recovering a pre-elimination parameter; applying zero mean constraint to the daily time-varying gravity field parameters; establishing an autoregressive model to describe time correlation of the day solution sequence, applying autoregressive model constraint, updating a normal equation and jointly solving a time-varying gravity field model; outputting a daily time-varying gravity field model and a monthly time-varying gravity field model according to a parameter estimation result; evaluating the time-varying gravity field model through spectral domain analysis; according to the satellite time-varying gravity field inversion method based on self-adaptive de-mixing, self-adaptive de-mixing errors can be achieved, the stability and precision of time-varying gravity field inversion are improved, and the influence of background model mixing errors in satellite time-varying gravity field inversion is effectively weakened.

The invention provides a processing method and system for scoliosis detection based on point cloud features. According to the system, a depth camera is used for monitoring the dynamic state of a subject through a point cloud data acquisition module, and back three-dimensional point cloud information is obtained; the scoliosis area acquisition module acquires a clustered two-dimensional diagram by using a density-based spatial clustering algorithm and a point cloud reprojection algorithm, and acquires an area of interest by using an image morphological processing algorithm and a polynomial curve fitting method; the point cloud registration module obtains symmetrical point clouds by using an iterative reweighted least square method and a principal component analysis algorithm, performs coarse registration by using a Rodrigues rotation matrix and then executes a point cloud registration algorithm; and the transverse asymmetry analysis module obtains a symmetry plane by using the registered point cloud, and obtains a transverse depth difference point cloud by using a surface reconstruction algorithm. The invention provides a transverse asymmetry analysis method based on a point cloud registration algorithm and a surface reconstruction algorithm, scoliosis screening is performed through the depth camera, and popularization of scoliosis screening is facilitated.

The invention provides a method and a system for constructing and optimizing an environment map based on an ultra wide band (UWB). The method and the system are suitable for an extreme environment. The method comprises the following steps: firstly, initializing a two-dimensional grid map by a system, and collecting signal data through UWB equipment; when the LOS path is determined, the corresponding grid is defined to be in a free state, and otherwise, the corresponding grid is marked to be in an occupied state. For NLOS features, obstacle locations are mapped onto a map. Then, the map is refined through a mean filter, a morphological filter and a region growing algorithm, and the accuracy of obstacle representation is improved; and finally, the optimized map information is utilized, and the weight of a positioning algorithm is adjusted through a weighted least square (WLS) method, so that the positioning precision is improved. The method has the advantages that environment obstacle information can be effectively constructed, the environment obstacle information is suitable for navigation after being refined, and a map can be normally constructed in dark light and smog environments. The system only depends on a UWB sensor, and accurate positioning is achieved in an obstacle environment.

The invention belongs to the technical field of image processing, and particularly relates to a PCB plug hole flatness detection method and system based on 3D point cloud data, and the method comprises the steps: obtaining the original 3D point cloud data of a PCB, and intercepting a plug hole center and a local point cloud block according to a design file; a copper ring point cloud set is extracted, and the fitting weight of each sampling point is calculated based on the distance from each sampling point in the set to the center of the plug hole, the unit normal vector of the sampling point and the original height of the sampling point; performing weighted least square fitting on all the copper ring point clouds by using the fitting weight to obtain a virtual reference curved surface; extracting an effective point cloud set, calculating the correction height of the effective point cloud, and calculating a flatness index in combination with the mean value of the correction height and the module length of the local gradient; the flatness index of the physical space is obtained, and whether the flatness of the plug hole is qualified or not is judged. According to the method, the problem of misjudgment caused by board warping is effectively solved, and the detection precision and the anti-noise capability are improved.

The invention discloses an underwater target positioning method based on data-model dual drive. The method specifically comprises the following steps: data acquisition: acquiring initial data by using a receiver; performing sight distance reasoning: constructing and training a sight distance information reasoning neural network based on data driving, preprocessing the collected initial data, inputting the preprocessed data into the neural network, and outputting arrival time estimation of a sight distance path; joint estimation: adopting a model driving method, calculating the arrival time difference of sound signals received by a receiver by using the obtained sight distance arrival time, designing underwater target position and signal propagation speed joint estimation based on the arrival time difference, and calculating initial estimation values of the target position and the propagation speed by using a weighted least square method; and robust iteration: based on a maximum likelihood estimation function, performing robust iteration optimization on the obtained preliminary estimation value to obtain a stable and convergent final joint estimation result. The target positioning method is convenient to popularize and apply in different underwater environments, and has relatively high adaptability and expansibility.

The invention provides a product storage life evaluation method based on composite sampling inspection success and failure data. An evaluation object is a complex mechanical and electrical product. Comprising the following steps: carrying out point estimation and confidence limit estimation of failure probability on special test data, and carrying out randomization processing on user use data containing fuzzy samples by adopting uniform distribution or truncated normal distribution; carrying out compatibility test on the special test data and the user use data; fusing the special test data meeting the compatibility test and the user use data by adopting a Bayesian method, and obtaining special stage failure probability estimation after updating of different storage periods; and further screening out degradation failure sample data based on the updated special failure probability estimation, and performing storage life evaluation on the degradation failure data by adopting a weighted least square method. The method combines the characteristics of two kinds of sampling data, and can solve the evaluation problems of small evaluation data volume, fuzzy samples and the like.

A measurement spectrum obtained by irradiating a particle labeled with a plurality of fluorescent dyes with excitation light is separated for each fluorescent dye with high accuracy. Provided is a particle analysis system (1) including a plurality of photodetectors (23) configured to acquire light generated by irradiating a particle labeled with a plurality of fluorescent dyes with excitation light; and an information processing unit (132) configured to calculate fluorescence intensity of each fluorescent dye by performing separation processing on a measurement spectrum based on measured values from the plurality of photodetectors (23) with a single staining spectrum of each fluorescent dye, in which the separation processing is performed by using a weighted least squares method (WLSM) including a weight determined based on a variation in the measured values.

The invention belongs to the technical field of three-dimensional surface reconstruction, and particularly relates to a robust three-dimensional surface reconstruction method based on multi-scale fusion. Acquiring three-dimensional point cloud data, fitting local implicit curved surfaces of neighborhood points by adopting a weighted least square method, and generating a geometric reconstruction grid model; extracting geometric features in different receptive fields through multi-scale convolution, introducing channel attention, space attention and a gating mechanism to perform weighted fusion on the geometric features in different receptive fields, enhancing a global dependency relationship by adopting a feature mixing operation, and generating a deep learning grid model; performing double-branch fusion on the geometric reconstruction grid model and the deep learning grid model, highlighting a geometric sensitive area, and obtaining a fused enhanced feature; inputting the fused enhanced features into an implicit function decoder, and predicting a signed distance function value of each vertex; and based on the predicted signed distance function field, a contour surface extraction algorithm is adopted to generate a triangular mesh model, so that three-dimensional surface reconstruction is completed.

The present invention discloses a multi-source heterogeneous meteorological data assimilation and fusion method, which includes the following steps: synchronously acquiring meteorological data from multiple data sources through a distributed data interface and performing preprocessing, where the data sources include satellite remote sensing data, ground meteorological station data, and meteorological radar data; after completing the preprocessing of the meteorological data from multiple data sources, based on the weighted least squares method, respectively fusing the data from multiple data sources into meteorological element values of each meteorological element; assimilating the obtained meteorological element values with a numerical weather prediction model. The present invention has the following advantages and effects: The present invention can accurately identify abnormal data and perform targeted repair, and dynamically adjust the weights of data sources through weighting, effectively avoiding the interference of low-quality data on the fusion result, and then improving the final prediction accuracy by improving the numerical weather prediction model assimilation step.

The invention discloses a receiver autonomous integrity monitoring method suitable for a complex environment. The method comprises the following steps: firstly, constructing a weighting model based on a vehicle course angle; then, acquiring a pseudo-range observed quantity of a satellite and preprocessing the pseudo-range observed quantity, and establishing a criterion for evaluating the comprehensive positioning precision of a current consistent measured value by utilizing pseudo-range residues after positioning, so as to form a satellite interior point subset of the epoch; and finally, carrying out approximate estimation on a user equivalent distance measurement error of a preferred constellation in the epoch satellite interior point subset, introducing a position geometric accuracy factor of the satellite constellation, constructing an evaluation function, selecting an optimal sample set, and finally solving the optimal sample set by adopting a weighted least squares (WLS) method to obtain a distance measurement result. And obtaining a final position solution result of the receiver. According to the invention, the positioning precision and fault detection capability of the receiver are improved, so that autonomous integrity monitoring of the receiver is completed.

The invention discloses a ship water gauge scale line fitting algorithm, and relates to the technical field of ship water gauge scale calculation, and the method comprises the steps: obtaining a ship water gauge region image through an image collection device to extract a measurement point coordinate, and building a mapping relation between a pixel position and a physical draft; a weighted least square method is adopted to perform nonlinear curve fitting, a weight coefficient is calculated based on a local neighborhood standard deviation, a polynomial order p is dynamically adjusted according to an adjusted decision coefficient, and the decision coefficient is iteratively optimized to improve the balance between amplitude and model complexity. And the system repeatedly corrects model parameters through a dynamic verification mechanism of the residual sum of squares and a preset threshold value, and finally an accurate water gauge scale fitting curve is generated. The method effectively solves the problems that traditional manual observation is prone to environmental interference and large in measurement error, high robustness is kept in a complex water area environment, and reliable data support can be provided for the fields of ship load evaluation, channel safety management and the like.

The invention discloses a power grid power flow real-time calculation and state estimation method and system based on a PMU, and belongs to the technical field of electric measurement and electric fault positioning. The method comprises the following steps: synchronously measuring electrical variables, including a voltage phasor and a current phasor, of a power grid node through PMU equipment to obtain synchronous phasor data, and verifying time and topology consistency; the power grid observability grade is judged based on PMU configuration node distribution corresponding to the verified electric measurement data; directly generating a real-time state estimation result through linear calculation based on PMU electric measurement data if the whole domain is observable; and if a part is observable, fusing PMU and SCADA data, carrying out nonlinear state estimation through a weighted least square method, distributing a high weight for the PMU electrical measurement data, and dynamically adjusting the SCADA weight to suppress bad data. According to the method, through observability adaptive judgment, the precision and efficiency of state estimation are improved, a data basis is provided for power grid fault positioning, and the method is suitable for practical application scenes of various PMU coverage degrees.

The invention discloses an industrial robot precision calibration method suitable for multiple installation states, a storage medium, equipment and a computer program product, and the method comprises the steps: enabling an industrial robot to be in three installation states: vertical fixation, inverted hanging fixation and side hanging fixation, and building a world coordinate system; the method comprises the following steps: collecting space coordinates of a target ball of the laser tracker under different world coordinate systems, unifying the space coordinates to the same world coordinate system through coordinate conversion, and carrying out identification parameter error correction on the space coordinates of the target ball of the laser tracker subjected to space coordinate conversion and theoretical coordinates based on an iterative reweighted least square method to obtain the identification parameter error of the industrial robot. And the precision calibration of the industrial robot is realized. According to the calibration method, the robot has unified precision performance during vertical fixation, inverted hanging fixation and side hanging fixation, so that the time for re-calibration can be saved, and the working efficiency is improved.

The invention relates to the technical field of satellite navigation and wireless communication, and discloses an indoor positioning method and system based on feeder multiplexing Beidou double-frequency, and the method comprises the steps: receiving outdoor B1I and B2a signals through a high-gain Beidou double-frequency receiving antenna, carrying out the frequency separation and signal conditioning through a double-frequency separation filter, and carrying out the positioning of the B1I and B2a signals; and transmitting the B1I signal to an indoor distributed antenna for broadcasting through the existing communication feeder network, and transmitting the B2a signal to a central directional antenna to establish a time synchronization reference. A mobile terminal receives a dual-frequency signal, capturing and tracking are carried out through digital signal processing, and pseudo-range and carrier phase observation values are extracted. And performing pairing processing on observation data of the same antenna, calculating a dual-frequency pseudo-range difference value, eliminating system errors, and performing position calculation by adopting a weighted least square method and Kalman filtering. The method solves the problem that the traditional GPS signal cannot cover the indoor environment, and has a good application prospect in the scenes of underground commercial complexes, tunnels, parking lots and the like.