83 results about "Nonlinear optimization problem" patented technology

The invention discloses a vehicle kinetic parameter identification method based on constrained nonlinear optimization, which comprises the following steps: establishing a vehicle two-degree-of-freedom transverse kinetic model based on a small-slip-angle tire and a road cross slope, acquiring a vehicle historical driving data measurement value, acquiring a to-be-estimated vehicle kinetic parameter, and identifying the to-be-estimated vehicle kinetic parameter according to the to-be-estimated vehicle kinetic parameter. Constructing a constrained nonlinear optimization problem about the yaw velocity, solving to obtain an estimated value sequence of the kinetic parameters of the vehicle, carrying out statistics on the sequence, and taking a statistical median as a final estimated value; on one hand, a vehicle transverse dynamics model including a road cross slope is adopted; on the other hand, the constraint of vehicle parameters is considered, a nonlinear optimization problem is constructed, and the vehicle front axle distance (the longitudinal distance from a front axle to the vehicle mass center), the vehicle rear axle distance (the longitudinal distance from a rear axle to the vehicle mass center), the front wheel cornering stiffness, the rear wheel cornering stiffness and the vehicle total mass are identified at the same time.

The invention discloses a composite material storage life prediction method based on variable transformation and a genetic algorithm, and relates to the technical field of composite material performance evaluation and life prediction, and the method comprises the steps: obtaining performance degradation data at different temperatures through an accelerated aging test, and building a semi-empirical performance degradation model; specific variable transformation is introduced, a nonlinear optimization problem is converted into a linear regression problem, partial model parameters are rapidly solved by adopting a least square method, and meanwhile, key nonlinear parameters are globally optimized by using a genetic algorithm, so that the defect that a traditional method is easy to fall into local optimum is overcome. And establishing a temperature-service life relationship by combining with an archenius model, and realizing storage life prediction at normal temperature. The method also integrates sensitivity analysis and uncertainty quantification functions, and provides reliability evaluation for a prediction result. The method fully considers the influence of the environment temperature, meets the requirement of efficient prediction, and improves the precision and stability of life prediction.

The invention discloses a hydrogen energy storage capacity configuration and port comprehensive energy system multi-energy coupling collaborative optimization method, and belongs to the field of energy system control and optimal configuration, and the method comprises the steps: constructing a hydrogen-containing port comprehensive energy optimization system model comprising a hydrogen energy storage capacity configuration model and a port operation cost minimization model; and determining the hydrogen energy dissatisfaction cost of the port operator according to the foreground theoretical model and the potential economic loss evaluation of the hydrogen energy accident risk. According to the hydrogen energy storage capacity configuration, the operation cost function and the hydrogen energy dissatisfaction cost of the port operator, establishing a hydrogen energy storage capacity configuration and port integrated energy system multi-energy coupling collaborative optimization model including minimization of the sum of the port operation cost and the port operator dissatisfaction cost; and converting a nonlinear optimization problem into a linear optimization problem. The method effectively balances the operation cost of the port and the subjective worries of the port operator, thereby reducing the operation cost while maintaining the acceptable level of the satisfaction of the operator.

The invention relates to a priority-based high-visibility stream-by-stream data packet sampling method and system. Comprising the following steps: step 1, flow initiation and priority marking; step 2, flow establishment and controller decision making; step 3, rule issuing and strategy execution; and step 4, data forwarding, scheduling and sampling: a priority scheduling mechanism of a data plane and global optimization of a control plane are decoupled and coordinated. In a data plane, a terminal host marks a degradation priority for a data packet according to the number of bytes sent by a stream, and a switch performs strict queue scheduling and probability sampling based on the priority. In a control plane, a controller carries out collaborative modeling on the configuration of routing, sampling probability and degradation threshold as a nonlinear optimization problem with maximization of sampling utility as a target, and an efficient online primitive-dual interior point method is adopted for solving. According to the method, high visibility and load balancing are finally ensured, and meanwhile, the control and calculation overhead is remarkably reduced.

The present invention discloses a method for online identification of the dynamic parameters of an articulated serial robot, comprising the following steps: S1. For the articulated serial industrial robot to be identified, a robot dynamic model that takes friction into account is established; S2. The dynamic model of the articulated serial industrial robot is linearized; S3. An optimal excitation trajectory is designed for the identification experiment, using a five-term finite Fourier series to obtain the excitation trajectory by solving a constrained nonlinear optimization problem; S4. The serial robot runs the excitation trajectory while simultaneously collecting the position information of each joint of the robot and the driving current of each joint at each moment, and online calculating the parameters to be identified at each moment. The present invention can perform online identification of robot dynamic parameters, improves the efficiency of robot dynamic parameter identification, and can be used in industrial scenarios such as robot mobile handling, polishing and grinding, and heavy-load hydraulics.

The application discloses a flexible load intelligent optimization scheduling method for improving the economy of photovoltaic access distribution networks. A multi-type flexible load grading and scheduling optimization model is established. On the one hand, according to the demand side response demand, the flexible load is divided into three categories, namely interruptible load, translatable load and adjustable load, a flexible load operation optimization model is constructed, the reasonable allocation of various flexible loads in different periods is realized, the peak regulation pressure and resource waste problems in the distribution network are effectively alleviated, the purchase of electricity during the power consumption peak can be effectively avoided, and the economy of the distribution network is improved. On the other hand, for the discrete and continuous mixed data distribution environment, a Latin square quantum-inspired genetic algorithm (LSQGA) is used to process the nonlinear optimization problem in the environment, so that the optimal feasible region in the macro data space can be quickly searched, and the feasibility of the optimization model under the extreme scenario is improved.

The invention relates to the technical field of new energy automobile intelligent driving, and discloses a new energy automobile multi-mode sensing trajectory planning method and system, and the method comprises the steps: obtaining multi-mode original sensor data, and carrying out the preprocessing; feature extraction and cross attention fusion are carried out; performing dynamic obstacle prediction, and setting a dynamic safety buffer area; constructing a behavior decision input state vector, and reasoning and adjusting a behavior decision; generating a candidate trajectory curve and performing feasibility check on the candidate trajectory curve; comprehensively evaluating the candidate track set; predicting a nonlinear optimization problem by taking the initial optimal trajectory curve as a reference trajectory, and solving and verifying the nonlinear optimization problem; according to the method, robust fusion and adaptive weight adjustment of multi-modal sensor data are realized by establishing a sensor health monitoring mechanism, a dynamic weight distribution strategy and a cross attention fusion network.

Traditional algorithms for solving constrained optimization problems are complicated to implement, difficult to interpret, and require significant computational resources. Disclosed embodiments convert constrained optimization problems into parametric optimization problems, in which at least a subset of the constraints are converted into parametric quadratic penalty (PQP) terms that each depends on a translational parameter. The parametric optimization problem may be used for optimization in a power system (e.g., for optimal power flow, economic dispatch, etc.). When solving the parametric optimization problem, the translational parameters are updated to ensure convergence. The parametric optimization problem can be solved with reduced computational expense, using only a linear equation solver to solve a sequence of primal variables only, thereby reducing computational complexity and expense. In addition, the disclosed embodiments provide a means to incorporate constraints into machine-learning algorithms. The disclosed algorithmic framework also provides interpretability and insights for analysis.

The invention discloses a vehicle three-way cooperative auxiliary control method fusing PINN tire transverse and longitudinal force prediction. Comprising the following steps: constructing a dynamic model of a vehicle based on a force balance equation and a moment balance equation of the vehicle; constructing a physical information neural network model; training the physical information neural network model by taking the dynamic model as a dynamic constraint and taking the minimum physical residual error loss as a target to obtain a physical prediction model; the physical prediction model is fused, a cost function with the longitudinal resultant force, the additional yawing moment, the slippage loss power and the motor loss power of the vehicle under the action of the auxiliary control quantity as factors is constructed, the cost function is solved with the minimum cost function as the target, and the minimum cost function is obtained. The auxiliary control quantity used for conducting three-way cooperative auxiliary control on the vehicle is obtained; and adopting the auxiliary control quantity pair. According to the method, the problems that tire model parameters are difficult to calibrate and the cost is high during vehicle three-way cooperative auxiliary control, and key performance indexes of a cost function in a nonlinear optimization problem have characterization defects are solved.

The present application relates to the technical field of wisdom agriculture, and particularly relates to a cloud-edge collaborative computing offloading method for wisdom agriculture. The present application constructs an adaptive task quantity prediction function, a response time model, a power consumption model and a load model, converts actual problems in the system into basic mathematical models, and specifically abstracts problems into optimal solution problems of mixed integer nonlinear optimization problems. Finally, the optimal computing offloading result is found by using the defined adaptive function and the particle swarm algorithm. Compared with other cloud-edge collaborative computing offloading methods applied in wisdom agriculture, the present application overcomes the error conflicts caused by traditional static modeling, improves the accuracy of the computing offloading result, and greatly improves the real-time performance and accuracy of terminal task computing.

The invention relates to the technical field of energy efficiency optimization, in particular to a self-adaptive load intelligent energy supply platform of a fuel cell range extending power system. Multi-source load data is collected through the environment perception and multi-source load monitoring module, after preprocessing and feature extraction, the dynamic power optimization decision module constructs a real-time rolling optimization model, the multi-constraint nonlinear optimization problem is solved in real time based on an equivalent hydrogen consumption minimization strategy, the optimal fuel cell output power is calculated, and the fuel cell output power is optimized. Precise energy supply is achieved, and the energy utilization efficiency is improved; accurate power following under complex working conditions is realized through multivariable decoupling control, cooling fan and air inlet valve optimization calculation is performed based on the optimal fuel cell output power, fault detection decision, fault category determination and corresponding optimization operation execution are performed by using an SVM support vector machine, and stable operation of the system is guaranteed.

The invention discloses a virtual filling method for chemical mechanical polishing based on neural network gradient feedback, and belongs to the field of semiconductors and CMOS (complementary metal oxide semiconductor) super-large-scale integrated circuits. The method is divided into three stages of layout data processing, neural network prediction and optimization solution and virtual filling insertion, an input GDS layout is divided into uniform windows, a fillable area is determined according to pattern information in the windows, the window density and the upper and lower limits of the window density are extracted, window data is converted into a nonlinear optimization problem, and the optimization problem is solved. The flatness loss of the layout after CMP is predicted by calling a neural network to serve as an optimization target, meanwhile, the input density of each window is iterated according to gradient information provided by the neural network, and finally, a virtually filled layout file is output. According to the method, the interpretability of a prediction result is enhanced in the calculation process, and the problems of low efficiency and suboptimal solution of a traditional optimization algorithm in the aspect of large-scale optimization are solved.

The invention discloses a three-dimensional positioning method and system for a bimodal target, and relates to the technical field of indoor positioning, and the method comprises the steps: collecting the phase ranging data of a connected state base station and the received signal strength indication data of a broadcast state base station, and forming a bimodal perception data set; selecting a static positioning model or a dynamic positioning model according to the motion state of a to-be-positioned point, and converting a positioning problem into a nonlinear optimization problem based on the bimodal perception data set; and solving the nonlinear optimization problem by adopting a CBS algorithm to obtain the position estimation of the point to be positioned in the three-dimensional space. According to the method, three-dimensional positioning of a static target and smooth track positioning of a dynamic target are realized at the same time, the optimization range is effectively constrained, the system deployment complexity and the calculation load are reduced, the precision and robustness of indoor three-dimensional positioning are effectively improved, and the method is suitable for application scenes with strict requirements for quick response and low-cost deployment.

The application relates to a vehicle longitudinal and lateral motion cooperative control method based on a fast solving algorithm, which comprises the following steps: calculating a desired yaw angular velocity according to a steering wheel turning angle and a current vehicle running speed; constructing a nonlinear optimization problem according to the desired yaw angular velocity and a current actual motion state of the vehicle, wherein an objective function of the nonlinear optimization problem is used to track the desired yaw angular velocity, and meanwhile, lateral speed and tire slip rate of the vehicle are inhibited; solving the nonlinear optimization problem to calculate desired slip rates of four tires; calculating additional torques of the tires according to actual slip rates and desired slip rates of the tires; and sending the additional torques of the tires into an executor of the vehicle for cooperative control. Compared with the prior art, the application can effectively inhibit the tire slip rate, avoid vehicle skidding, and the provided fast solving algorithm can greatly improve solving efficiency and improve real-time performance.

This application belongs to the field of unmanned surface vessel (USV) control technology, specifically relating to a method and apparatus for predictive control of USV trajectory tracking using dynamic reference linearization. The method includes: establishing and discretizing a nonlinear dynamic model of the USV to construct a predictive model; constructing an online nonlinear optimization problem including terminal stability components based on this model; generating a dynamic reference trajectory in each control cycle based on the optimal solution of the previous moment, and linearizing the nonlinear model point-by-point along this trajectory to obtain a high-fidelity linear time-varying predictive model; further transforming the original nonlinear programming problem into an efficiently solvable quadratic programming problem; and outputting control quantities in real time by solving this problem and integrating reference commands. This application ensures the theoretical stability of the closed-loop system, achieves a synergy of high precision, high real-time performance, and high reliability, and effectively improves the trajectory tracking performance of USVs in complex marine missions.

The invention discloses a parameter and tire force real-time estimation method and system based on multi-model fusion, and relates to the technical field of vehicle parameter identification, and the method comprises the steps: building a three-state monorail vehicle dynamics model and a tire model according to vehicle parameters and dynamics parameters; performing formula transformation on the three-state monorail vehicle dynamics model to respectively obtain formulas of longitudinal, transverse and yaw angular accelerations; dividing parameters in the formula into parameters which can be collected by a low-cost sensor and parameters to be estimated, and respectively updating the formulas of longitudinal, transverse and yaw angular accelerations; constructing a nonlinear optimization problem based on the updated formula; according to the nonlinear optimization problem, based on vehicle parameters and kinetic parameters in the historical driving process, parameter estimation is carried out, and based on a parameter estimation result, real-time state estimation is carried out on tire force. Complete kinetic parameters can be estimated in real time through a low-cost sensor, and the real-time state of the tire is estimated based on the complete kinetic parameters.

The application provides a multi-parameter joint calibration method based on graph optimization of inertial and ultra-short baseline, applies the graph optimization technology to the multi-parameter joint calibration of inertial / ultra-short baseline, and the whole process comprises the following steps: establishing a multi-error parameter state model, constructing a multi-error parameter calibration factor, calculating based on a nonlinear optimization problem of an incremental interval balance, and finally obtaining accurate multi-error parameters. Compared with the prior art, the application can simultaneously estimate the responder position error, the rod arm error and the installation angle error, sufficiently utilizes historical observation data, solves the nonlinear problem of the optimization equation through repeated iteration and relinearization, and has the advantages of high calibration precision and fast calibration speed.

The application belongs to the technical field of data product operation and power data asset optimization, and discloses a power data asset value dynamic application method and device based on multi-scene coupling, which comprises the following steps: constructing the interaction relationship between power generation data assets, power consumption data assets, power grid data assets and multiple application scenes, and constructing system operation core constraint conditions; quantifying the value linkage relationship between different application scenes and the adaptability of various data assets to each application scene; taking the maximization of comprehensive value as an objective function, fusing multi-scene benefits, costs and data constraints, and forming a high-dimensional nonlinear optimization problem; designing a solution method based on an adaptive improved whale optimization algorithm to output an optimal value application strategy under the premise of meeting real-time requirements. The application realizes the optimal value application and maximum benefit of power data assets in a multi-scene environment.

The application discloses a parameter and tire force real-time estimation method and system based on multi-model fusion, relates to the vehicle parameter identification technical field, and comprises the following steps: establishing a three-state monorail vehicle dynamics model and a tire model according to vehicle parameters and dynamics parameters; performing formula transformation on the three-state monorail vehicle dynamics model to obtain formulas of longitudinal, lateral and yaw angle acceleration respectively; dividing parameters in the formulas into low-cost sensor collectable parameters and to-be-estimated parameters, and updating the formulas of longitudinal, lateral and yaw angle acceleration respectively; constructing a nonlinear optimization problem based on the updated formulas; performing parameter estimation based on the nonlinear optimization problem, vehicle parameters and dynamics parameters in a historical driving process; and performing real-time state estimation on tire force based on the parameter estimation result. The application can estimate complete dynamics parameters in real time through a low-cost sensor, and estimate the real-time state of the tire based on the complete dynamics parameters.

The present invention discloses a multi-timescale energy management method and system for a dual-rotor hybrid electric vehicle, belonging to the technical field of hybrid electric vehicle energy control. The method of the present invention first constructs a multi-timescale framework to optimize the vehicle's fuel economy on a long timescale and its dynamic performance on a short timescale. The long-timescale energy management strategy solves the nonlinear optimization problem using an improved alternating direction multiplier method, achieving optimal power distribution within the prediction range and improving computational efficiency. The short-timescale energy management strategy estimates the internal combustion engine output torque and observes the dual-rotor motor coupling torque, using an external motor to compensate for the additional power generated by the internal combustion engine speed regulation, thereby ensuring the stability of the output shaft torque.

The present invention discloses a method and device for controlling the motion of a robotic arm based on a noise-resistant neural solver. The method comprises the following steps: obtaining the kinematic parameters of the robotic arm, and obtaining the Jacobian matrix of the end of the robotic arm based on the kinematic parameters; constructing a time-varying nonlinear optimization problem based on the Jacobian matrix in combination with the joint angular velocity constraints of the robotic arm and the end trajectory tracking task; solving the time-varying nonlinear optimization problem based on the Lagrange multiplier method, the KKT condition, and the nonlinear complementary problem function to obtain a time-varying system of equations, and then establishing an error function; constructing a double-integral recursive neural network solver based on the error function; using the double-integral recursive neural network solver to obtain the joint variable values ​​of the robotic arm; and transmitting the target control value to a lower-level controller so that the lower-level controller drives the robotic arm to complete the desired path tracking task. The present invention can accurately control the robotic arm and can be widely used in the field of data processing technology.

The application discloses a kind of hypersonic vehicle multi-source energy management methods, belong to the technical field of calculation, reckoning or counting.For the nonlinear optimization problem of multi-source energy distribution in mode conversion process, the method first establishes the mechanism model of TBCC engine and multi-source power generation system, quantifies the correlation between power generation and fuel consumption;Subsequently, an adaptive sparse Kriging surrogate model is proposed, which improves the generalization ability of large-scale data through low-rank approximation of covariance matrix and dynamic selection strategy of inducing points;Further, an optimization function is designed to minimize the cumulative fuel consumption, and an improved DQMR-DDPG algorithm is integrated to solve the problems of policy oscillation and Q-value overestimation through state hierarchical experience pool, double Q-network and adaptive soft update mechanism.The application realizes real-time optimization of energy management under complex conditions, while meeting the thrust, power and safety constraints, significantly reducing fuel consumption during mode conversion phase.

The invention discloses an air conditioner temperature non-sensitive control method based on online learning, and the method specifically comprises the steps: S1, collecting indoor environment parameters, outdoor meteorological parameters, equipment operation parameters and energy price parameters, and forming a multi-source parameter time sequence; s2, constructing a Gaussian process state space model in which a multi-source reliability gating factor is introduced, and generating a cooling and heating load state prediction sequence; s3, constructing a nonlinear optimization problem based on the cooling and heating load state prediction sequence; s4, setting a switching penalty serialization item in the nonlinear optimization problem; s5, solving a nonlinear optimization problem by adopting a sequential quadratic programming algorithm, and generating a control variable updating sequence; s6, an air conditioner operation control strategy is generated based on the control variable updating sequence; and S7, calculating a prediction deviation and updating a multi-source reliability gating factor after the control period is finished, and completing an online learning process. According to the method, through reliability gating prediction and continuous optimization, air conditioner operation stability and somatosensory stability are both considered.

The invention relates to the technical field of robots, and discloses a robot, a recharging control method and device thereof and a storage medium. The method comprises the steps that the relative pose of the robot relative to a charging pile is acquired; based on the relative pose, solving the constructed nonlinear optimization problem with the operation constraint, and generating a reference trajectory from the position of the robot to the target preparation point; wherein at the target preparation point, the terminal motion state of the robot comprises a non-zero linear speed suitable for the robot to directly enter a charging docking state; performing real-time tracking on the reference trajectory by adopting model prediction control, and outputting a control instruction based on a tracking result to drive the robot to move along the reference trajectory; and after the robot arrives at the target preparation point and moves to be connected with the charging pile, the robot stops moving and is charged. According to the method, seamless charging in the robot recharging process can be achieved, namely seamless connection from advancing to charging is achieved, and the recharging efficiency and stability are improved.

The invention discloses a twinborn control-oriented magnetic drive rotor key parameter online identification method, which comprises the following steps of: constructing a parameterized digital twinborn model of a magnetic drive rotor, and defining time-varying key internal parameters in the model as parameter vectors to be identified; in a set time window, an objective function is defined, and a parameter identification problem is converted into a nonlinear optimization problem which minimizes the objective function; aiming at the problem, system parameter self-adaptive identification is carried out based on an LM optimization method, and a parameter updating vector is obtained; and transmitting the parameter update vector to a system matrix of the parameterized digital twinborn model for state prediction at the next moment, and outputting the parameter update vector to complete online identification of the key parameter. According to the method, the precision and real-time performance of parameter identification and the adaptability of an embedded platform are effectively improved, and a reliable model basis is provided for high-precision control, performance degradation monitoring and predictive maintenance of the magnetic drive rotor.

The invention discloses a power distribution network low-carbon high-quality scheduling method, system, equipment and medium considering distributed robust optimization segmented affine, and belongs to the technical field of power distribution network scheduling, and the method comprises the steps: taking the minimization of the carbon emission of the product of the main network carbon potential and the net load power as a target function; a Wasserstein distance is adopted to measure the deviation between historical data and real distribution, and multiple types of operation constraints are considered at the same time; the above distribution robust optimization model is converted and solved by adopting a piecewise affine theory, an original nonlinear optimization problem is converted into a linear optimization problem based on a simplex dominant uncertainty set, and the model solving efficiency and robustness are improved. According to the method, the uncertainty of new energy output and carbon potential is effectively dealt with through Wasserstein distance constraint, and the robustness is high; by means of segmented affine transformation, nonlinear robust optimization is simplified into linear optimization, and the solving speed is remarkably increased.

The invention discloses a beam forming optimization method based on sum rate maximization. According to the method, a multivariable coupling nonlinear optimization problem is constructed, and an iterative optimization algorithm based on an alternating optimization framework is provided to jointly optimize a digital beam matrix, a holographic beam matrix and an RIS phase shift diagonal matrix. According to the method, the IOA based on the Dinkelbach algorithm is utilized to solve the holographic beam forming problem; aiming at a joint optimization problem of a digital beam matrix and an RIS phase shift diagonal matrix, an original problem is converted into a positive semidefinite programming problem with a concave difference form by introducing a positive semidefinite matrix, a penalty term is introduced to process non-convex rank-one constraint, and an IOA solving method based on positive semidefinite programming and continuous convex approximation is provided. The method provided by the invention is superior to a traditional MISO SWIPT system and an RIS-assisted SWIPT system. In addition, through holographic beam forming, higher system sum rate and resource utilization rate can be realized on the premise of satisfying energy collection constraints.

The application discloses a multi-parameter collaborative optimization method for deterministic polishing of a robot in a confined space, and the method is characterized in that: through a multi-step collaborative optimization framework, a material removal amount prediction model, a polishing track, a robot posture and dynamic error compensation are integrated into a unified optimization process, the nonlinear coupling influence of robot posture adjustment and feed speed fluctuation on the removal amount distribution is reduced, the collaborative problems of removal amount out-of-tolerance, coverage uniformity and interference avoidance caused by multi-parameter coupling in a narrow flow channel are solved, and finally, the high-dimensional nonlinear optimization problem is decomposed through an alternating direction multiplier method, the convergence efficiency is significantly improved, the processing surface can meet the preset tolerance requirement, and the deterministic polishing with high precision in the confined space is realized.

The invention discloses an aquifer hydraulic diffusion coefficient solving method based on a semi-analytical collocation method, belongs to the technical field of groundwater seepage analysis, and aims to solve the problems that an existing finite element method is tedious and time-consuming in grid division when processing is complex, and a finite difference method depends on regular grids and is difficult to adapt to irregular boundaries and the like. The method comprises the following steps: constructing a mathematical model of a two-dimensional seepage problem under a polar coordinate system, solving a groundwater seepage control equation through a variable separation method under a space-time coordinate system to obtain a complete primary function, establishing a space-time collocation constraint system, forming a linear algebraic equation set about an unknown coefficient, and solving the linear algebraic equation set. Establishing an objective function taking a hydraulic diffusion coefficient as an unknown parameter, and combining boundary conditions to form a mathematical expression of a nonlinear optimization problem; according to the method, point collocation calculation is directly carried out in a time-space domain through a Trefftz time-space point collocation method, and the calculation process is remarkably simplified. The method is suitable for complex geometric boundary problems, and the application range of numerical simulation is expanded.

The invention discloses a displacement estimation method and system for transcranial ultrasonic shear wave viscoelastic fluid imaging, and belongs to the technical field of ultrasonic medical image processing, and the method comprises the steps: dividing ultrasonic radio frequency data after beam forming into a plurality of independently processed data blocks; the ultrasonic radio frequency data comprises two frames of ultrasonic radio frequency signals collected before and during shear wave propagation; constructing a cost function for each data block, wherein the cost function comprises a data similarity item and a mixed-order displacement continuity constraint item; performing linearization processing on the cost function, and converting a nonlinear optimization problem into a linear problem; establishing and solving a linear equation set to obtain local displacement of each data block; and splicing the local displacement of each data block to form a global displacement field. According to the method, high-precision estimation of the transcranial shear wave displacement and reliable evaluation of the viscoelastic fluid property of the brain tissue can be realized, relatively efficient and accurate displacement estimation is kept in a transcranial high-noise environment, and the performance of transcranial ultrasonic shear wave elastography is improved.