58 results about "Nonlinear least squares optimization" patented technology

The invention discloses an underwater vision-inertia-acoustic odometer positioning method fused with a DVL, and belongs to the technical field of robot navigation positioning. According to the method, firstly, an underwater image acquired by a camera and carrier motion data acquired by an IMU and a DVL are used as input information to carry out time alignment and space coordinate system conversion; performing feature point extraction on the image to construct a visual re-projection residual error, and dynamically adjusting a weight ratio of visual information based on the number of feature points and the re-projection residual error; calculating IMU data increment between adjacent frames based on pre-integration to construct an inertial residual error; calculating the displacement change between image frames according to the speed information measured by the DVL to construct a DVL residual error; establishing a nonlinear least square optimization function based on Bayesian maximum posteriori estimation; vision, inertia and acoustic speed measurement data are fused at the rear end through a sliding window mechanism, a multi-constraint joint optimization model is constructed, and the robustness and accuracy of a vision-inertia-acoustic odometer system in the underwater environment are improved.

The invention discloses a multi-sensor fusion-based position and posture estimation method for a working device of a mining robot, which realizes accurate position and posture estimation by installing a body posture sensor and an environment sensing sensor and combining a kinematic model, visual inertia state estimation and a nonlinear least square optimization algorithm. The method comprises the following steps: firstly, carrying out preliminary prediction on a kinematic model through feedback data of an encoder and an inclinometer, and constructing a local visual inertia estimator by utilizing a camera and an MEMS to correct a prediction result; and then, a combined constraint modeling and optimization method is adopted to improve the precision of the pose of each working mechanism. Besides, geometric features of the bucket are extracted through a semantic segmentation network and a template matching technology, six-degree-of-freedom pose estimation of the bucket is completed, finally data fusion of a body pose sensor and an environment perception sensor is performed through a factor graph optimization algorithm, and comprehensive pose estimation of the whole working device is achieved. The working pose of the mining robot can be monitored in real time, the operation precision is improved, human errors are reduced, the working efficiency and safety can be optimized through data analysis, and more effective support is provided for path planning of a working device of the mining robot.

The invention discloses a method and system for calibrating an inertial sensor by a Doppler log, and relates to the technical field of underwater integrated navigation, and the method comprises the following steps: obtaining an IMU pre-integration factor and a DVL speed residual factor according to IMU observation data and DVL observation data; the method comprises the following steps: constructing a factor graph structure model by taking prior information of initial states of a gyroscope and an accelerometer as prior factors, taking the prior factors, an IMU pre-integration factor and a DVL speed residual factor as factor nodes, taking state variables of the gyroscope and the accelerometer as variable nodes and taking a relationship between the factor nodes and the variable nodes as edges; and correcting the state variables by using the factor graph structure model to obtain theoretical outputs of the gyroscope and the accelerometer after inertial navigation error compensation. Observed DVL data are used for carrying out online calibration on IMU errors, an underwater integrated navigation problem is modeled as nonlinear least square optimization, global optimal estimation of all-time state quantities is achieved, and the divergence speed of inertial navigation position errors is restrained.

The invention belongs to the technical field of ground penetrating radars, and particularly relates to a layered medium parameter estimation method based on hyperbolic fitting. The layered medium parameter estimation method based on hyperbola fitting specifically comprises the following steps: step 1, establishing a multilayer medium hyperbola constraint equation, and obtaining a ground penetrating radar theoretical measurement time constraint equation based on the constraint equation; 2, preprocessing the radar echo signals, extracting feature points, and calculating refraction points generated at the layered interface based on the extracted feature points; and 3, substituting the refraction point calculated in the step 2 into the theoretical measurement time constraint equation of the ground penetrating radar obtained in the step 1, establishing an electromagnetic wave transmission time error model, and realizing parameter estimation of the layered medium by using a nonlinear least square optimized parameter estimation method.

The invention provides an unmanned aerial vehicle formation collaborative navigation method based on an open information fusion architecture, belongs to the field of unmanned aerial vehicle navigation, and aims to solve the navigation information fusion problem of asynchronous updating of multi-sensor data and available dynamic change of measurement information in a traditional formation collaborative navigation method. In the prior art, when asynchronous data fusion and measurement information availability change are coped, the defects of precision loss, poor flexibility and expansibility and the like exist. According to the method, a state variable and a measurement information set are constructed under an open architecture, a probability model of collaborative navigation state estimation is established based on Bayesian reasoning, and on this basis, a factor graph model is constructed by combining an incidence relation between a measurement model in posterior probability and prior information. By designing an independent measurement factor for each sensor, when sensor data is updated, flexible response to asynchronous data fusion and dynamic change of measurement information is realized by optimizing changed measurement factor nodes in real time and dynamically adding and deleting the nodes. And gradually minimizing an error cost function of each factor node through a nonlinear least square optimization method to realize efficient fusion of multi-sensor data and real-time updating of a navigation state. According to the method, the efficiency, flexibility and expansibility of asynchronous information fusion of the collaborative navigation system are remarkably improved, and reliable technical support is provided for unmanned aerial vehicle formation collaborative navigation.

The invention discloses an underground mine positioning and mapping trajectory optimization method based on underground control point constraint, and belongs to the field of positioning and mapping, and the method comprises the steps: respectively obtaining GNSS data, laser data, IMU data and image data; according to the GNSS data, the laser data, the IMU data and the image data, completing initialization of a laser radar-vision-inertial odometer system and GNSS initialization; extracting point cloud features of the distributed underground control points in real time based on an RANSAC algorithm, and generating a global coordinate constraint factor of nonlinear least square optimization; and performing relative attitude calculation according to the global coordinate constraint factor to complete underground mine positioning and mapping trajectory optimization. According to the method, the problems of accumulated error overrun and point cloud dislocation layering easily caused by pose drift in a special environment of an underground mine are solved.

The invention relates to a chained two-dimensional positioning system based on an ultra-wideband signal and an implementation method thereof, and belongs to the technical field of wireless positioning. The UWB signals are transmitted in a chained manner, and high-precision two-dimensional positioning of a plurality of link nodes can be realized in a tunnel environment by only one terminal node. The system utilizes known terminal node positions, tunnel geometrical shapes and distance measurement data between adjacent nodes, combines a nonlinear least square optimization algorithm, constraint optimization and graph optimization technologies and an error correction and filtering method, overcomes the limitation of a traditional positioning technology in a complex environment, and improves the positioning accuracy. The method has the advantages of high precision, low cost and flexible deployment, and is suitable for multiple application fields such as tunnel monitoring, intelligent transportation and industrial automation.

The invention relates to a method for decoupling organic matter primary-secondary cracking hydrocarbon generation conversion rate based on a gold tube hydrocarbon generation thermal simulation experiment. The method comprises the following steps: step 1, acquiring kerogen gold tube hydrocarbon generation thermal simulation experiment data; step 2, parametric modeling of the kerogen hydrocarbon generation process; step 3, constructing a least square target model under a multi-constraint condition; step 4, performing nonlinear least square optimization solution under the constraint condition; and 5, calculating the hydrocarbon generation conversion rate. According to the method, conversion rate calculation is carried out on the cumulant of each hydrocarbon generation process obtained through decoupling, the conversion rate evolution rule of the kerogen oil generation, gas generation, crude oil cracking gas generation, non-reactive carbon formation and other processes along with maturity change in the crude oil cracking process can be obtained, and a quantitative basis is provided for refined hydrocarbon generation evaluation.

The invention relates to the technical field of image correction of computed tomography equipment, in particular to a cone beam computed tomography equipment correction method, and aims to solve the problems that geometric parameters projected by existing CBCT equipment are inaccurate, and a three-dimensional image subjected to three-dimensional reconstruction has obvious artifacts. In order to achieve the purpose, the method comprises the following steps: acquiring position coordinates of a marking substance in a phantom coordinate system and projection coordinates of the marking substance on a flat panel detector coordinate system when a conical ray originates from a plurality of positions; based on the position coordinates and the projection coordinates, calculating a projection matrix when the conical ray originates from a plurality of positions; calculating an average internal reference matrix of the plurality of internal reference matrixes based on the plurality of projection matrixes; determining a calculation projection coordinate corresponding to the position based on the average internal reference matrix and the position coordinate; and a nonlinear least square optimization algorithm is adopted to minimize the distance error between the projection coordinates and the calculated projection coordinates, and a calibration projection matrix is obtained. Random errors are suppressed by calculating an arithmetic average of the plurality of internal reference matrices.

The invention discloses a battery SOC prediction method and system based on dynamic electrochemical impedance spectroscopy measurement in the technical field of lithium battery state prediction, and the method comprises the steps: superposing DIBS square wave signals as excitation current input on the basis of the main charging and discharging current of a battery, and collecting the voltage and current data of the battery in real time; according to the impedance spectrum, fitting a fractional order equivalent circuit model by adopting a nonlinear least square optimization method to obtain initial parameters of the fractional order equivalent circuit model; the initial parameters and voltage and current data collected in real time are input into a self-adaptive dual augmented extended Kalman filter, the main filter and the secondary filter operate cooperatively, state prediction and parameter prediction are executed alternately, and real-time synchronous prediction of SOC and fractional order equivalent circuit model parameters is achieved. The method is suitable for the SOC estimation requirement of the battery under the dynamic working condition, the working state of the battery does not need to be interrupted, and the method has good linearity, robustness and adaptivity.

The invention provides an indoor local magnetic field modeling method and device based on a magnetic source equivalence principle, and belongs to the technical field of indoor localization, and the method comprises the steps: collecting the data of a three-axis magnetometer in a space range through an array type magnetic sensor, and obtaining the original measurement information of an indoor magnetic field; based on a magnetic source equivalence principle, an indoor magnetic field is modeled into an equivalent magnetic dipole model consisting of a plurality of equivalent magnetic dipoles, and a magnetic field generated by the model is consistent with a real magnetic field in an observation area by setting magnetic moment and position parameters of the magnetic dipoles; according to the measured value of the array type magnetic sensor, an observation model is established in combination with an equivalent magnetic dipole model and used for describing the relation between the measured value and model parameters; through a nonlinear least square optimization method, parameters of the equivalent magnetic dipole model are solved according to the observation model, the equivalent magnetic dipole model of the indoor magnetic field is obtained, and the parameters comprise the magnetic moment and the position of the magnetic dipole. The precision and the reliability of the model are remarkably improved.

The invention provides a radar channel calibration method and device and a storage medium, and aims to form a second array steering vector matrix by establishing a signal model which has no channel amplitude-phase error and simultaneously has an azimuth error, a pitch error and a roll error. And constructing a nonlinear least square optimization problem according to the relationship among the first array steering vector matrix, the amplitude-phase calibration coefficient of the multiple channels of the radar and the second array steering vector matrix, and minimizing and solving related parameters based on constraint conditions. Therefore, influences caused by radar azimuth errors, pitching errors and rolling errors in the radar darkroom calibration process can be completely eliminated. Decoupling is realized by the estimated multi-channel amplitude-phase calibration coefficient, the azimuth error, the pitching error and the roll error, namely, the calibration coefficient can accurately reflect the channel amplitude-phase error.

The invention relates to the technical field of non-contact temperature measurement, in particular to a transient temperature field measurement method, system and device and a medium, and the transient temperature field measurement method comprises the steps: synchronously collecting an original spectral signal and an original grayscale image of a transient temperature field; discretization processing is carried out on the corrected spectrum, a theoretical radiation spectrum corresponding to each temperature element is obtained, an image gray value-radiation brightness mapping relation is established, and temperature element weight initial distribution is obtained; optimizing by using an optimal solution algorithm to obtain an optimal temperature primitive weight; and inverting the temperature field by using the optimal temperature primitive weight and the histogram of the original grayscale image to obtain a final fusion image of the transient temperature field. According to the method, the dynamic reconstruction of the two-dimensional temperature field of the explosion fireball is realized by constructing the temperature primitive model and combining nonlinear least square optimization and image mapping of the image detector, and the problems of large single-point inversion error and low spatial resolution of the transient high-temperature field are solved.

The invention discloses a Beidou inertial navigation positioning method and system based on pseudo-range prediction correction factor graph optimization, and the method comprises the steps: obtaining observation feature data and inertial navigation data, and constructing a current navigation state vector of a vehicle; according to the signal state of the Beidou satellite navigation system, training a reinforcement learning model through observation characteristic data and inertial navigation data, and determining a pseudo-range value; according to a factor graph optimization method, introducing a pseudo-range value as an observation factor, and converting a navigation state vector of the vehicle at the current moment into a multi-factor constrained optimal problem; and carrying out global optimal state sequence solution on the multi-factor constrained optimal problem through a Levenberg-Marquardt algorithm by adopting nonlinear least square optimization, so as to obtain a positioning estimation result of the vehicle. According to the invention, dynamic complementation and intelligent cooperation of Beidou and inertial navigation information can be realized, and the precision of positioning estimation is improved. The Beidou inertial navigation positioning method and system based on pseudo-range prediction correction factor graph optimization can be widely applied to the technical field of satellite positioning.

The invention provides an electric power system abnormity identification method and system based on a mechanism model and artificial intelligence, and belongs to the technical field of electric power system safety. Comprising the following steps: constructing a steady-state model according to a power grid branch power balance and node energy conservation relationship, calculating a power balance deviation of a time sequence by using the steady-state model, marking each data point according to whether the power balance deviation exists or not, and forming a prior data set; extracting data points which are judged to have no balance deviation to form a primary detection data set, and constructing a Bayesian-Kalman fusion detection model to perform primary detection on the primary detection data set; extracting data points judged as non-abnormal data to form a secondary detection data set, constructing an FCM-GEP detection model, and performing secondary detection on the secondary detection data set; and fusing the two detection models into an abnormal data detection function through an unconstrained nonlinear least square optimization method, wherein the abnormal data detection function is used for continuously acquiring a prior data set in real time to carry out supplementary detection on non-abnormal partial data.

The invention provides a regional navigation positioning method, system and device based on sequential information, and a medium, which can effectively improve the positioning precision and reduce the calculation complexity when processing the sequential information, and have better precision and robustness. Under a satellite navigation denial condition, aiming at distance measurement information at different moments obtained by land-based radio area navigation, a factor graph model is established to represent an information transmission process between different moment states of a positioning target. Compared with a traditional Kalman filtering framework, the factor graph algorithm is better in positioning precision, higher in flexibility, lower in calculation complexity and better in robustness when sequential information is processed. According to the method, state estimation is converted into a nonlinear least square optimization problem, a Gaussian Newton method is adopted for iterative solution, and compared with extended Kalman filtering, the matrix operand is reduced by more than 40%.

The application discloses a node processing review and precision improvement method based on three-dimensional laser scanning, relates to the technical field of digital manufacturing and steel structure engineering precision control, and comprises the following steps: through multi-scale structure feature identification, basic attributes and topological relationship modeling are combined, effective guidance of design information to the whole detection process is realized, through enhanced feature tree driven scanning path planning combined with multi-view point cloud fine splicing strategy, the collaborative mechanism of scanning implementation and design features is embodied, high-quality data acquisition of key functional features is realized, through multi-scale feature corresponding set construction combined with an adaptive nonlinear least square optimization model, the idea of multi-level constraint collaborative participation registration is embodied, high-stability rigid body transformation solution is realized, through deviation field construction combined with function area confidence and effective error index calculation, the ability of detection result transformation to engineering decision is embodied, and the scientificity of correction and rework decision is improved.

The invention discloses an underwater robot trajectory tracking method and a related device, and relates to the technical field of underwater robot autonomous navigation, and the method comprises the steps: synchronously collecting the original data of an underwater robot camera, an IMU and a DVL; performing time synchronization on IMU and DVL data by taking a camera timestamp as a reference, and converting DVL speed data into an IMU coordinate system; calculating a relative translation vector and a time interval based on the relative pose transformation matrix of the adjacent key frames, and determining an average speed estimation value of the underwater robot between the adjacent key frames; comparing the average speed estimation value with the actual measurement speed of the DVL, and determining a speed error function of the DVL; and constructing a weighted error cost function containing visual re-projection, IMU pre-integration and DVL speed error terms, solving optimal estimation of key frame poses and map point states through nonlinear least square optimization, and outputting an optimized trajectory of the underwater robot. According to the invention, the precision of trajectory tracking of the underwater robot is effectively improved.

The invention discloses a control moment gyroscope implicit parameter tracking method based on two-stage identification, and belongs to the technical field of spacecraft execution mechanism health monitoring and fault diagnosis. According to the method, through four core steps of electromechanical coupling model establishment, first-stage sliding window batch NLS coarse identification, Wiener process degradation modeling and second-stage adaptive EKF fine correction, a complete process from electrical telemetry data to implicit parameter accurate identification is realized. The method comprises the following steps: constructing a mapping relation between an electrical signal and an implicit parameter based on an SKF friction torque model and a direct current brushless motor voltage equation, and obtaining a preliminary implicit parameter value through nonlinear least square optimization by using electrical measurement data in a sliding time window; a nonlinear drift Wiener process is adopted to describe the evolution rule of hidden parameters along with time, and a first-stage result serves as an observation value and is subjected to smooth correction through extended Kalman filtering; according to the method, through a two-stage framework design of combining a sliding window batch NLS with a Wiener process driven adaptive EKF, the interpretability of a physical model and the sequence modeling capability of a random process are fully combined, working condition interference and parameter degradation information are effectively decoupled, noise interference of an identification result is remarkably reduced, and the degradation trend of equipment is accurately tracked. According to the method, the problems of serious electromechanical coupling interference, insufficient parameter identification precision, poor degradation trend tracking capability and the like in the existing CMG health monitoring technology are solved, reliable technical support is provided for health management and life prediction of the single-frame control moment gyroscope, and the reliability and safety of a spacecraft attitude control system are remarkably improved.

The present invention discloses a method and system for calibrating an inertial sensor using a Doppler odometer, relating to the technical field of underwater integrated navigation. The method comprises: obtaining an IMU pre-integration factor and a DVL velocity residual factor based on IMU observation data and DVL observation data; constructing a factor graph structure model using prior information of the initial states of a gyroscope and an accelerometer as prior factors, the prior factors, the IMU pre-integration factor, and the DVL velocity residual factor as factor nodes, the state variables of the gyroscope and accelerometer as variable nodes, and the relationships between the factor nodes and the variable nodes as edges; and obtaining theoretical outputs of the gyroscope and accelerometer after inertial navigation error compensation by correcting the state variables using the factor graph structure model. The IMU error is calibrated online using observed DVL data, and the underwater integrated navigation problem is modeled as a nonlinear least squares optimization method to achieve a global optimal estimation of the state quantity over the entire time period and suppress the divergence rate of the inertial navigation position error.

The application provides an online adaptive model prediction method for an engine based on dimension reduction Kalman filtering, which comprises the following steps: S1, obtaining a state space equation of the engine; S2, performing dimension reduction on a health parameter by using a dimension reduction matrix V to obtain a state space equation after dimension reduction of the parameter; S3, performing offline optimization on the dimension reduction matrix V by using a nonlinear least square optimization algorithm to obtain an optimal dimension reduction matrix V; and S4, estimating and predicting the health parameter of the engine and an output vector of the engine by using a dimension reduction Kalman filtering algorithm to obtain a real-time updated and optimized health parameter, and substituting the real-time updated and optimized health parameter and the optimal dimension reduction matrix V into the state space equation of the engine to obtain a state space equation with minimum dimension reduction error and adaptive adjustment of the health parameter, namely, an adaptive estimation model of the engine. The application can give the best health parameter of the variable cycle engine according to the current working state of the engine by using the dimension reduction Kalman filtering algorithm.

The invention provides a fuzzy neural network fitting method and device, equipment and a storage medium, and the method comprises the steps: initializing a network structure and parameters of an input layer, a fuzzy layer, a hidden layer and an output layer; a forward propagation process is executed, the training data of the input layer is fuzzified through the fuzzy layer and then transmitted to the hidden layer, a forward propagation result is calculated through the output layer, and a loss function is calculated; a back propagation process is executed based on the loss function, a gradient of parameter updating is obtained through layer-by-layer transmission, parameters are updated by using an LM nonlinear least square optimization algorithm, and a fuzzy neural network after fitting is completed is obtained after iteration is finished; and evaluating the performance of the fuzzy neural network. According to the method, the expression ability of uncertainty and fuzziness of the input data is enhanced by utilizing fuzzification processing; the LM algorithm is introduced to optimize the weight and bias, and the training speed can be remarkably increased while the convergence stability is kept.

A scene-dependence-free external parameter calibration method for a rotary laser radar and inertial navigation comprises the following steps: constructing a kinematic model between the laser radar and a motor by adopting a modeling method based on DH parameters; the method comprises the following steps: extracting first plane feature information in an environment, constructing a kinematic model in combination with an optimization strategy to carry out nonlinear least square optimization, and obtaining a pose transformation matrix of a laser radar under a motor coordinate system as a first external parameter; obtaining an initial pose sequence through a laser radar odometer; an objective function is constructed based on inertial navigation pre-integration information and plane constraints, and a pose transformation matrix of the motor and inertial navigation is obtained and serves as a second external parameter; and obtaining a pose change relation between the laser radar and the inertial navigation through a chain type combination relation of coordinate transformation, the first external parameter and the second external parameter. The method does not need to depend on other external scene data for external parameter expression, can achieve the calibration of the rotary laser radar and inertial navigation external parameters at any position, and is more practical.

The present application relates to a kind of multi-camera joint calibration method without public view, medium and equipment, by collecting the several two-dimensional images of several ball calibration targets and target tracking instrument conversion matrix;Several three-dimensional point clouds of calibration ball and ball center coordinates are collected;The ball center coordinates of robot end calibration ball and corresponding flange pose under tracking instrument coordinate system are collected;Two-dimensional ball center coordinates in each two-dimensional image are extracted, and three-dimensional ball center coordinates are converted to obtain the external parameter of each 2D camera;According to the coordinates of the ball center of each calibration ball, the external parameter of each 3D camera is solved;Space transformation closed loop equation is constructed;Linear solution of end tool parameter and robot base external parameter is solved, and linear solution is used as initial value to construct the nonlinear least square optimization problem with the goal of minimizing space transformation closed loop equation residual, and the optimal solution of end tool parameter and robot base external parameter is solved, and calibration is completed.Thereby the problem of high-precision collaborative work of multi-source sensor in complex intelligent manufacturing scene is improved.

The invention relates to the technical field of aircraft and robot navigation, and discloses a GNSS (Global Navigation Satellite System) and laser odometer coupled positioning method based on a factor graph sliding window, which comprises the following steps: acquiring original observation data of a GNSS and converting the original observation data into a northeast ENU coordinate system; obtaining original observation data of the laser radar and an inertial measurement unit IMU, and calculating through a laser inertial odometer LIO algorithm to obtain a relative pose transformation sequence at adjacent moments; constructing a factor graph; setting a sliding window; triggering one-time nonlinear least square optimization at a preset time interval; and updating a rotation translation transformation matrix from the laser odometer coordinate system to the ENU coordinate system in real time according to an optimization result. According to the invention, the efficiency of fusion calculation and the fusion error of the laser speedometer are greatly reduced by setting a factor graph sliding window and optimization time, the overall calculation amount is small, and the real-time performance is high; meanwhile, a conversion matrix from the laser speedometer to the ENU system is estimated in real time, and long-time accurate recursion after the GNSS fails is ensured.

The present application relates to a kind of unmanned aerial vehicle low-altitude visual navigation method based on motion blur auxiliary, comprising: acquisition monocular camera image and IMU data, quantification the image blur degree of the monocular camera image, to extract the blur parameter of dominant motion, for calculating global measurement blur data;The IMU data is pre-integrated, obtains the relative motion amount between adjacent frames, and based on system prior state estimation, the image plane motion field of current time is predicted, for simulating global theoretical blur data;The global measurement blur data and the global theoretical blur data are compared, and after being combined with adaptive weight, as motion blur consistency error, construct nonlinear least squares optimization objective function;According to the nonlinear least squares optimization objective function, Gauss-Newton method iteration is solved, generates the state vector of minimum total error, for calculating health score, to implement three-level mode switching.

The application discloses a watershed space-time rainfall generation method and system based on rainfall weighted flow distance, comprising the following steps: calculating the distance from each rainfall station to the drainage outlet in a watershed, and performing normalization processing; constructing a rainfall weighted distance constraint in any time period; converting the constraint relationship into a nonlinear least square optimization problem; solving the optimization problem by using a sequential quadratic programming algorithm; repeating the optimization process for all time periods to obtain a watershed rainfall proportion matrix, wherein the watershed rainfall proportion matrix is used for representing the proportional distribution relationship of a rainfall process at different rainfall stations and in different time periods. The application can flexibly generate various rainfall distribution scenarios under the condition of meeting specific rainfall space-time index constraint conditions, so as to quantitatively analyze the influence of rainfall center movement on urban flood process.

The invention provides a dynamic identification method and system for a GFM inverter dominant power distribution system and a related device. The method comprises the following steps: step 1, establishing a phasor domain mathematical model of a GFM inverter in a phasor domain; step 2, constructing a nonlinear least square optimization model based on the phasor domain mathematical model, obtaining a parameter estimation value corresponding to the GFM inverter, substituting the parameter estimation value into the phasor domain mathematical model, and solving to obtain an optimized output phasor; step 3, optimizing a system dynamic prediction model pre-constructed based on the NARX neural network by using the optimized output phasor obtained in the step 2 to obtain an optimized system dynamic prediction model; and step 4, based on the pre-constructed equivalent model of the GFM inverter in the phasor domain, utilizing the optimized system dynamic prediction model obtained in the step 3 to dynamically predict the dominant power distribution system of the GFM inverter, and through organic combination of physical optimization and data driving, high-precision and high-generalization system dynamic representation is realized.

The invention discloses a constraint vector fitting system and method fusing a shared pole structure and a local pole structure, designs a novel VF structure, introduces mixed pole configuration combining a shared pole and a local pole, and can maintain high-quality fitting of a Y parameter even in the presence of a loading phase. Therefore, the sensitivity of fitting precision to phase distortion is reduced; through post-processing identification of the model and iterative elimination of phase contributions corresponding to local poles and linear terms, compatibility with a traditional VF is ensured; in consideration of the characteristic that the VF mainly solves the residual error, the constraint on the number of transmission zeros is reconstructed into a group of linear constraints on the residual error, so that the problem can still be solved in the VF framework; in addition, a nonlinear residual constraint proposed in a physical realizability requirement is formalized into a nonlinear least square optimization problem to be introduced into a fitting process; the VF problem with physical constraints is solved by adopting an augmented Lagrange method ALM, and unified balance of fitting precision and physical consistency is realized.

The invention discloses an advanced fitting analysis method and system for an SFG spectrum. The method comprises the following steps: after preprocessing original SFG spectral data, automatically generating an initial amplitude and a central position of a Gaussian peak in combination with data local maximum intensity within a specified search tolerance range according to a predefined peak group and a nominal center of a target peak; fitting the spectral data by using a multi-Gaussian function model and a nonlinear least square optimization algorithm to obtain fitting parameters of each Gaussian peak; refining and combining Gaussian peak parameters after successful fitting; automatically identifying and calculating the intensity of a specific key characteristic peak; and generating a detailed SFG spectrum fitting graph and a fitting parameter report. According to the method, accurate, stable and automatic deconvolution and quantitative analysis of the SFG spectrum are realized, and objective and repeatable data with deep physical and chemical significance is provided for the fields of material science, surface chemistry and the like.