88 results about "Linear approximation" patented technology

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The invention discloses a reservoir group medium-and-long-term power generation optimization scheduling method based on gallery iteration static optimization, and the method comprises the steps: firstly calibrating convex hull parameters based on a planar convex hull linear approximation method, constructing a scheduling model, and solving an initial solution (including the storage capacity and the output flow of each time period); dividing the whole output feasible region into small-scale regions in a high-density manner, and respectively calibrating convex hull parameters of each region; comparing the initial solution with the small-scale region boundary period by period, determining the region to which the initial solution belongs, and updating the convex hull parameter of the corresponding period; constructing a time period corridor by taking the initial solution as a reference, and constructing a new model in combination with the updated parameters to solve a current iterative solution; and if the target function value change is smaller than the threshold value, converging and outputting the optimal solution, otherwise, updating the scale parameter and repeating iteration. According to the method, the optimal solution is approximated through static calibration-parameter selection, linear errors are corrected, efficient solution can be achieved, operation of the cascade hydropower stations can be guided, and coordinated utilization of water resources is optimized.

The application discloses a wireless resource allocation method for a LEO satellite-ground fusion uplink communication system, which comprises user base station selection, power and carrier allocation and power and carrier allocation methods for base station to satellite link, first establishes a system energy efficiency maximization model for subcarrier and power allocation; then quotes a slack variable to convert the model into a target function lower bound model; uses a symbolic function to couple power allocation variables and carrier allocation variables; and further decomposes the target function lower bound model into a base station access and carrier allocation optimization model and a power allocation optimization model; linear approximation method and successive convex approximation method are respectively used for solving, so that the wireless resource allocation of the uplink communication system is realized. By using the method, 0-1 integer optimization variables are removed, the calculation complexity is low, and higher uplink system energy efficiency is obtained.

The present application relates to the technical field of power system, in order to solve the problem that the current reactive power flow calculation result cannot be applied to the actual operation system, provide a kind of power system optimal reactive power flow calculation method and system.Power system optimal reactive power flow calculation method includes with the active power network loss in power system as minimum to construct objective function, then combine linearized power flow equation, the reactive power output limit value corresponding to synchronous generator in power system and the linearization approximation constraint converted by the constraint of the internal potential and output current of inverter, construct optimal reactive power flow model and solve to obtain the new operation mode of power system;After obtaining the new operation mode of power system, recalculate the power flow distribution of power system, obtain the active power network loss of power system under new operation mode, in the process of successive linear approximation, the active power network loss is continuously reduced.It can obtain the optimal reactive power flow calculation result, guarantee the stability of power system.

The application discloses a corona discharge load state detection method based on an oscillation circuit voltage ratio relationship and relates to the technical field of corona discharge detection. An ideal Royer circuit is constructed; a peak value protection circuit is used for collecting a peak voltage Vm; a direct current input voltage of a sine wave self-excitation oscillation boost circuit is Vin; a ratio relationship of Vm and Vin in the ideal Royer circuit under no-load is calculated to obtain a first relationship formula; a ratio relationship of Vm and Vin in the ideal Royer circuit under load is calculated to obtain a second relationship formula; first-order linear approximation fitting is performed on the first relationship formula and the second relationship formula to obtain a target relationship formula; and the corona discharge load state is determined according to the target relationship formula. Through establishing a quantitative relationship between the ratio of the peak voltage of the primary side of the Royer circuit which is easy to measure and the input voltage and the secondary direct current high voltage and the load current reflecting the needle tip state, safe, quantitative and adaptive contamination detection is realized.

The application discloses a kind of based on ST-GAN's multi-source data dynamic risk early warning method and system, the method includes the following steps: S1. constructing the spatiotemporal generation confrontation network suitable for spatiotemporal data characteristics, utilize the network to generate extreme precipitation data, realize spatiotemporal unbalanced data reduction, obtain the equalization data of final output;S2. the equalization data of final output with high-dimensional space variable is carried out multi-source data fusion, and nonlinear spatiotemporal information conversion equation, and by local linearization obtains linear approximation model, to predict future time series;S3. under the present situation that extreme rainfall data amount is relatively insufficient, neural network based on dual learning theory accurately learns the parameter of nonlinear spatiotemporal conversion, estimates extreme weather event.The application is through spatiotemporal generation confrontation network (ST-GAN) and multi-source data fusion engine, significantly improves the precision and efficiency of natural disaster warning.

This invention claims protection for a CNN-LSTM algorithm neural network accelerator based on FPGA. The CNN hardware implementation includes a data input line buffer module, a convolution calculation module, a ReLU activation function module, an intermediate result buffer module, and a pooling calculation module. The LSTM hardware implementation includes an LSTM control module, a gate function calculation module, and a sigmoid activation function linear approximation module. The FC hardware implementation includes an FC control module, a fully connected layer calculation module, a ReLU activation function module, and a data output buffer. The purpose of this invention is to design a high-performance, low-power, and highly flexible CNN-LSTM neural network accelerator tailored to specific application scenarios. The innovation lies in the fact that, compared to traditional neural network accelerators, this invention uses a parallel pipelined design method to implement a CNN-LSTM algorithm neural network accelerator, which significantly improves the low power consumption and data throughput of the neural network accelerator. Furthermore, the parallel processing capabilities of the FPGA enable the algorithm to run at a faster speed.

The invention relates to a computer-implemented method for creating a model for detecting an anomaly or an operating state or a change in an operating state in a technical system (1), comprising the following steps: - Providing (S1) time series of operational variables, each indicating a temporal progression of an operational variable, as training data; - Performing (S2) a piecewise linear approximation on the time series of the operating variables to obtain segments of concatenated linear functions for each of the operating variables, obtaining change points (T) as time points between the segments of all operating variables, and summarizing the change points (T) of all operating variables to define state segments between two temporally successive state segments that define time intervals of the time series of the operating variables; - Determining (S3) characteristics of the time-dependent behavior of the operating variables for each of the state segments in order to form a state vector for each (S4); - Training (S6) a classification model based on the state vectors; - Implement (S7) the classification model in a control unit of the technical system (1).

The invention discloses a multi-stage pipe network leakage positioning method and system based on a hydraulic model sensitivity matrix and sparse inversion, and relates to the field of monitoring and leakage detection of an urban water supply pipe network, and the method comprises the steps: S1, linearizing a pipe network continuity equation and an energy equation at a normal operation point of a baseline working condition, obtaining a sensitivity matrix of the leakage rate and the demand disturbance to the pressure residual error, and establishing a linear approximation relation; s2, constructing an optimization problem with L1 regularization based on the relationship; eliminating a demand disturbance item, converting into a non-negative LASSO problem, and completing suspicious node positioning; s3, convex combination approximation of sensitivity of nodes at the two ends is introduced for the suspicious pipe section, an optimization target is constructed, and the internal leakage continuous position and the leakage amount of the pipe section are estimated; and S4, a joint optimization problem is constructed based on the pressure residual data of the multiple time periods, consistent selection of leakage positions of the multiple time periods is kept, and final leakage positioning is realized. According to the method, pipe network leakage positioning is accurately and efficiently completed through multi-stage progressive positioning.

To efficiently evaluate an error of a quantum gate.SOLUTION: Acquiring measurement data indicating an execution result of a quantum computer for a quantum circuit in which a quantum gate array including a first quantum gate is repeated N times (N is an integer of 1 or more); determining, from a first matrix indicating an ideal value of the quantum gate array, a period k (k is an integer of 1 or more) at which the first matrix raised to the power of k becomes a unit matrix; Processing for generating a linear approximation function for linearly approximating the influence of an error on measurement data by approximating the repetition of a quantum gate array and processing for estimating the error by using the linear approximation function and the measurement data are executed.SELECTED DRAWING: Figure 1

The invention discloses an elevator energy consumption control system based on multi-objective optimization, and the system comprises a parameterization module which generates a self-adaptive non-uniform B-spline control point set and a corresponding non-uniform node vector; the elevator discrete nonlinear dynamics module is used for constructing an elevator discrete nonlinear dynamics model; the index function construction module is used for constructing an elevator finite time domain nonlinear optimal control performance index function; the iteration module constructs a linear approximation system; the Newton-GMRES module is improved, and the optimal correction amount of the adaptive non-uniform B-spline control point set is obtained; the updating module is used for generating an updated self-adaptive non-uniform B-spline track; the motor drives the signal generation module to drive the elevator to run along the updated self-adaptive non-uniform B spline track; and the circulation module is used for repeatedly executing the steps from the index function construction module to the motor driving signal generation module. According to the method, the speed and precision of track generation and adaptive adjustment are remarkably improved.

The application provides a secant approximation method for nonlinear constraints of a redundant drive system, and belongs to the technical field of dynamic control distribution of the redundant drive system. The method comprises the following steps: firstly, according to the control input model of the redundant drive system with any pair of constraint components being nonlinear constraints, a closed region formed by the intersection of a rectangle and an ellipse in a geometric plane is obtained; then, after the closed region is divided into the union of the rectangle and the ellipse triangle, the approximation result of the closed region is obtained by performing the approximation of the rectangle and the triangle combination on the ellipse triangle, so that the linear approximation of the pair of nonlinear constraint components is realized. The application jointly uses the triangle and the rectangle to perform the approximation on the region surrounded by the nonlinear constraints, converts the nonlinear constraints into multiple linear constraints, converts the nonlinear constraint set into a linear constraint set, effectively solves the problem that the control reachable set cannot be determined due to the nonlinear constraint relationship between the actuators in the redundant drive system, and is helpful to realize the real-time control of the redundant drive system.

A battery diagnosis apparatus includes a voltage sensor to generate a voltage signal indicating a battery voltage of a battery, a current sensor to generate a current signal indicating a battery current of the battery and a control circuit. The control circuit determines a capacity curve indicating a relationship between the battery voltage and a charge capacity in a set voltage range based on the voltage signal and the current signal collected at each unit time for a constant current charging period. The control circuit determines a differential curve indicating a relationship between the battery voltage and a differential capacity in the set voltage range based on the capacity curve. The control circuit determines an approximate straight line of the differential curve using a linear approximation algorithm, and determines whether lithium deposition is present in the battery based on the approximate straight line.

The invention discloses a pluggable power supply module regulation and control and network port power supply optimization method based on deep learning, and the method comprises the following steps: S1, collecting power parameters of a power supply module and a port, building a bipartite graph topology, and forming historical data; s2, bus voltage ripples are injected and decoded, a sideband signaling channel is established, and a feature sequence is generated through alignment; s3, performing time sequence prediction on the feature sequence to form a prediction result; s4, Koopman dimension raising is executed, a linear approximation model is generated, and a control strategy is calculated; s5, executing a control strategy to carry out output adjustment and port allocation, and issuing a signaling to implement current limiting; and S6, voltage, current and temperature are collected, deviation comparison is carried out, a condition maintenance strategy is met, and otherwise, protection is triggered and write-back adjustment is carried out. According to the invention, through deep learning prediction, Koopman dimension raising control and port energy mutual assistance, intelligent regulation and control of the pluggable power supply module and network port power supply optimization are realized.

The application provides a trusted federated learning system and method based on a blockchain and zero-knowledge proof, comprising a disturbance module.In the disturbance module, a non-interactive zero-knowledge protocol is applied, so that the noise added to the local model can be verified. The trainer and the verifier independently and respectively use linear approximation to construct the same circuit containing specific Gaussian noise, and the trainer uses the circuit and a public generator and model parameters to construct a proof. The verifier uses their circuit to verify the received proof; a verification module. The verification module constructs a sharded blockchain consensus process. Each verifier first verifies the accuracy of the local model gradient update using a public data set. The verification result is used as a voting summary to form a consensus in the network. For block data containing local model selection information, a Byzantine fault tolerance protocol is introduced to ensure data credibility. The application can be proved to achieve differential privacy and can analyze the upper bound of the influence of noise on the model performance.

The present invention addresses the problem of calculating the root of a power function. An arithmetic device according to the present invention calculates a root Y of a power function (Y = AX, wherein A is a constant, and X is a prescribed numerical range). The arithmetic device comprises: an input unit into which input data indicating an exponent X is input; a first arithmetic circuit that executes a first arithmetic process on the basis of a linear approximation equation of a graph of a power function in a first numerical region of a numerical range of the exponent X; a second arithmetic circuit that executes a second arithmetic process on the basis of a linear approximation equation of a graph of a power function in a second numerical region of the numerical range, the second numerical region differing from the first numerical region; and a selector that outputs output data indicating the root Y calculated by the first arithmetic circuit when if exponent X indicated by the input data inputted into the input unit belongs to the first numerical region, and outputs output data indicating the root Y calculated by the second arithmetic circuit if the exponent X indicated by the input data inputted into the input unit belongs to the second numerical region.

A control model generation device includes: an observation value acquisition unit to acquire a plurality of observation values that are outputs of a control target having non-linear characteristics; a state space model estimation unit to estimate a state space model that expresses a linear approximate curve related to the plurality of observation values acquired; and an upper bound model estimation unit to calculate an estimation error, and estimate an upper bound model that expresses an upper bound of the estimation error, the estimation error being an error between each of the observation values acquired and the linear approximate curve expressed by the state space model estimated. Furthermore, the control model generation device includes a control model generation unit to generate a control model that expresses an equation of motion of the control target using the state space model estimated and the upper bound model estimated.

The invention discloses a convolutional neural network adaptive structured pruning method for end-side equipment image classification, a storage medium and equipment, and belongs to the technical field of image processing. The method mainly comprises the following steps: based on a sparse linear approximation theory, constructing an index-oriented sparse index; iteratively executing multiple rounds of unstructured pruning and recovery training on the pre-training model; and after iteration is finished, redundant parameters and invalid convolution kernels in the current image classification model are scanned, the invalid convolution kernels are physically deleted from the memory, a computational graph of the image classification model is reconstructed, and structured pruning and network reconstruction are completed. According to the method, the importance of each convolutional layer in image feature extraction is automatically evaluated by constructing an index-oriented sparse index, the number of reserved weight parameters of each layer is dynamically calculated, unstructured rarefaction is gradually realized by adopting an iterative'pruning-recovery training 'mechanism, the image classification precision can be kept under a high compression ratio, and the image classification efficiency is improved. Full-adaptive pruning is realized, and the method is suitable for resource-limited scenes.

This disclosure describes systems, methods, and apparatus for an adaptive engine with a bifurcated nonlinear model. The adaptive controller uses a nonlinear model having a control portion and an estimation portion, wherein the estimation portion uses a time-varying linear system to approximate nonlinear behavior of the system. Further, the time-varying linear system receives a structure of the underlying matrices for every frame of control samples allowing the time-varying linear system to model large nonlinearities and to pre-process this linear approximation for each frame. At the same time, the time-varying linear system also uses estimated model parameter tensors in the underlying matrices that are updated or adapted every control cycle, in real-time, throughout a frame, such that the linear approximation is also able to approximate small nonlinearities in the system. This bifurcation of a linearized model provides a faster and more robust adaptive controller.

A state-observed and updated model predictive control strategy is proposed for image-based visual servoing (IBVS) of micro air vehicles (MAVs). The proposed strategy can accurately adjust the pose of MAVs without global positioning system (GPS). Specifically, image features are defined on a virtual image plane to decouple the translational motion of MAVs. Then, a linear velocity observer is developed to provide high-quality linear velocity information for MAVs in real time. The image dynamics on the virtual image plane are linearized by first-order Taylor expansion, and a controller is constructed based on model predictive control to efficiently solve the optimal control input. Furthermore, the state input of the controller is updated at each control period to eliminate the cumulative error of the rolling optimization on the linear approximation dynamics, ensuring the accuracy of IBVS. Experimental results demonstrate the performance of the proposed observer and control strategy.

The invention provides a concrete chloride ion diffusion coefficient analysis method and system in a natural exposure environment, and relates to the technical field of material durability evaluation and testing. The method comprises the following steps: acquiring chloride ion content data and initial chloride ion content at different depths; constructing a nonlinear mathematical model containing an error function based on a chloride ion diffusion theory; carrying out linear approximation on the model by utilizing a series expansion of an error function, and generating a linear expression taking the to-be-optimized parameter as a coefficient; calculating residual sum of squares under different parameter values by changing to-be-optimized parameter values and solving corresponding linear equations, and determining an optimal parameter value by using a minimum residual sum of squares; and carrying out back calculation to obtain the chloride ion diffusion coefficient and the surface chloride ion content. According to the method, a traditional nonlinear fitting problem is converted into single-parameter global optimization under a linear framework, the calculation complexity is reduced while the precision is ensured, and engineering practical application is facilitated.

The application discloses a kind of AC-DC distribution network distributed convex optimization method and device, method includes: the optimization operation model of AC-DC distribution network containing power electronic transformer is established;Second-order cone relaxation technique and successive linear approximation method are used, and the optimization operation model is converted into successive convex approximation programming model;By target cascade analysis method, the successive convex approximation programming model is decomposed into sub-problems, and distributed convex programming model is constructed, and cyclic iteration is solved, and optimal solution is obtained;Operation control center above the optimization solution is used as control instruction, and the operation control of power electronic transformer and each distributed power supply in AC-DC distribution network in distribution network is carried out, active network loss reduction is realized, and the voltage deviation of entire distribution network is inhibited.The device includes: processor and memory.The application improves the operation performance of distribution network by coordinating the active and reactive power flow of distributed power supply and PET, reduces the active loss, and reduces the voltage deviation of the entire network.

The invention discloses a two-parameter approximate representation and rapid inversion method of a north pole double-track sound velocity profile, and relates to marine environment parameter inversion. Comprising a two-parameter approximate representation method of a dual-track sound velocity profile, a surface refraction type normal wave cross-type dispersion curve extraction method, a horizontal invariant dual-track sound velocity profile rapid inversion method and a horizontal variation dual-track sound velocity profile rapid inversion method. According to the overall change characteristics of the north pole dual-track sound velocity profile, linear approximation is carried out on the sound velocity profile, and the dual-track sound velocity profile is represented through two parameters influenced by width and strength. Because a sound signal frequency dispersion structure crossing phenomenon is caused by the north deep sea surface double-channel sound velocity profile, and the stronger the double-channel intensity is, the more serious the crossing phenomenon is, the north deep sea double-channel sound velocity profile can be inverted by comparing simulated and actually measured sound field cross-type frequency dispersion curves and combining a double-parameter representation method of the double-channel sound velocity profile. The method can be realized by using a single hydrophone.

The invention provides an active power distribution network linear load flow calculation method and system based on active and reactive coupling characteristic analysis, and relates to the technical field of active power distribution network reconstruction. According to the method, firstly, an active power distribution network model containing multiple types of flexible resources is established, and a quantitative analysis method of active and reactive combined regulation capability is provided based on operation constraint and regulation characteristics of the flexible resources, so that the influence of multi-source cooperation on voltage, current and power flow distribution is comprehensively evaluated. According to the operation characteristics of the static load characteristics of the node voltage of the power distribution network, an improved linearization power flow calculation model is constructed, and the coupling relation between the active power and the reactive power in the power distribution network is rapidly solved by performing linear approximation and coupling decoupling processing on a power flow equation. According to the method, the precision of traditional nonlinear load flow calculation is reserved, the calculation complexity is remarkably reduced, and the controllability and the real-time performance of the active power distribution network in multiple scenes are improved.

The present application relates to a new power system technology, in particular to a user load curve similarity measurement method based on piecewise linear approximation; by piecewise linear approximation to the user daily load curve electricity consumption data, the piecewise breakpoints of each two users are sorted in order, and the daily load curve is re-segmented; the slope, electricity consumption average and end time of each segment after re-segmentation are re-characterized; the difference between the slope and the total electricity consumption in different segments is calculated to evaluate the difference between the shape and the value of the daily load curve of different users; the weighted average of the shape difference and the value difference is calculated to calculate the total difference of the daily load curve; the total difference matrix of all users is constructed to calculate the daily load curve similarity between any two users; the present application overcomes the sensitivity of the existing user load curve similarity measurement method to extreme value and noise.

The application is suitable for the technical field of steam trap pump, in particular to a double-floating-ball steam trap pump and a method for determining the volume and buoyancy parameters of the floating ball, the method accurately captures the nonlinear mapping of the buoyancy requirement and working condition from the test data through multiple rounds of iterative feature extraction, reduces the reference buoyancy prediction error; the accurate cubic function formula of the spherical floating ball immersion volume is used to eliminate the huge geometric error caused by linear approximation; the total buoyancy requirement is simultaneously transmitted to the individual floating ball margin term, the total drainage constraint and the Lagrange optimization target to form a complete quantitative design link, and the optimal allocation of the double floating ball volume is realized through the analytical solution; a differentiated attenuation model is established for floating ball A and floating ball B respectively to compensate for the effects of corrosion and erosion in long-term operation. The above technical means can solve the problems of large volume and buoyancy parameter error and inadaptation to the double-floating-ball steam trap pump obtained by the existing method.

The invention relates to the technical field of power systems, and provides an optimal reactive power flow calculation method and system for a power system in order to solve the problem that a current reactive power flow calculation result cannot be applied to an actual operation system. The optimal reactive power flow calculation method for the power system comprises the following steps: constructing a target function by taking the active power network loss in the power system as the minimum; and combining the linearized power flow equation, a reactive power output limit value corresponding to a synchronous generator in the power system and linearized approximate constraints converted from constraints of internal potential and output current of an inverter to construct an optimal reactive power flow model, and solving the optimal reactive power flow model to obtain a new operation mode of the power system according to the optimal reactive power flow model. After the new operation mode of the power system is obtained, the power flow distribution of the power system is recalculated, the active power network loss of the power system in the new operation mode is obtained, and the active power network loss is continuously reduced in the successive linear approximation process. The optimal reactive power flow calculation result can be obtained, and the stability of a power system is guaranteed.

The invention relates to a method (100) for adjusting a plurality of measurements of a hybrid positioning system, comprising the following steps, which are carried out by a data processing device: - Obtain (101) the majority of measurements with respect to at least two target objects, wherein the majority of measurements includes at least one distance measurement and / or at least one position measurement, - Linking (102) the plurality of measurements with a state estimation using a functional model for the hybrid positioning system, wherein the state estimation is related to the plurality of measurements with respect to a position and / or a distance measurement in relation to the at least two target objects, - Linearizing (103) the functional model using a Jacobi matrix J, where the Jacobi matrix J specifies the linear approximation of the functional model around a current state estimate, - Collecting (104) stochastic information regarding a distribution of measurement errors in a sparse matrix Q, - iterative compensation (105) of measurement uncertainty and / or measurement errors based on a weighted least-squares procedure using the linearized functional model and the collected stochastic information, - Adjusting (106) the majority of measurements based on compensation (105). Furthermore, the invention relates to a computer program and a device for this purpose.

A seismic inversion method under joint constraints of a physical model and prior information, comprising: extracting seismic wavelets on the basis of seismic data, and determining a wavelet amplitude scaling factor; collecting statistics about priori information of impedance parameters; using seismic structure interpretation data and well logging data to establish an initial impedance parameter model; obtaining a linear approximate equation on the basis of an interface weak elasticity difference hypothesis, and using the linear approximate equation to forward simulate a seismic gather and calculate an inversion residual error; using a generalized linear inversion idea to rewrite a target function into a function related to the impedance parameters, using an iterative reweighted least square algorithm to obtain the impedance parameters, and updating the impedance parameters; and repeating the steps above until the inversion residual error reaches the requirement or reaches the maximum number of iterations, and outputting a final processing result. The inversion method can achieve stable and high-precision prediction of impedance parameters.