21 results about "Quasi-Newton method" patented technology

The application belongs to the technical field of concrete strength detection, and relates to a micro core sample method strength conversion method and system for UHPC. The method comprises the following steps: preparing a plurality of different micro core sample samples and pouring a plurality of standard test piece samples matched with the micro core sample samples; performing a compression test on all the micro core sample samples and the standard test piece samples to calculate a measured conversion coefficient; constructing a mathematical model of a predicted conversion coefficient, determining a parameter to be calibrated and a value range of the parameter, constructing an objective function, performing parameter inversion by using a differential evolution algorithm and a limited memory quasi-Newton method, determining a calibrated value of the parameter, and updating the mathematical model of the predicted conversion coefficient; taking a micro core sample from a concrete entity structure on site, performing a compression test on the micro core sample, calculating the predicted conversion coefficient, and then calculating a standard test piece compression strength. The application can accurately establish a conversion relationship between the micro core sample compression strength and the standard test piece compression strength, and improve the strength detection precision of the UHPC entity structure.

The invention belongs to the technical field of preparation of carbon-based adsorbents, and relates to an adsorption performance prediction method and optimization method of a carbon-based CO2 adsorbent. According to the adsorption performance prediction method, the continuous distribution characteristics of the pore structure of the carbon-based material are represented by an N2 adsorption isotherm under 77K, and the construction of a global mapping framework of preparation conditions-the continuous distribution characteristics of the pore structure-CO2 adsorption separation performance is realized by combining a convolutional neural network and a multi-layer perceptron deep learning algorithm. And meanwhile, multiple rounds of preparation parameter optimization are carried out by adopting a limited memory quasi-Newton method algorithm, and dynamic closed-loop optimization of model performance and parameter regulation and control is realized in combination with an active learning strategy. Through multiple rounds of active learning iteration processes, the prepared biochar sample has a narrow micropore-mesopore coordinated hierarchical pore structure, and the CO2 and N2 separation capacity is remarkably improved. The invention provides a new method support for directional design and efficient application of the carbon-based CO2 adsorbent.

The application discloses a kind of based on storage optimization's hybrid beam forming method, system and storage medium, its method includes steps: according to the number of radio frequency link, antenna quantity and channel state calculation transmitting end hybrid precoding matrix constraint condition;According to constraint condition, the objective function of transmitting end hybrid precoding matrix optimization is established;According to the relationship of time delay and / or spectral efficiency and / or memory occupancy and quasi-newton method storage capacity calculation optimal storage capacity;According to the optimal storage capacity corresponding finite memory quasi-newton method, the optimal solution of transmitting end hybrid precoding matrix optimization objective function is calculated;According to the optimal solution of transmitting end hybrid precoding matrix optimization objective function, transmitting end hybrid precoding matrix is obtained and is used for hybrid beam forming.The application solves the problem that the system performance of existing hybrid beam forming method is poor and the calculation time delay is high.

The invention relates to the technical field of petroleum and natural gas engineering drilling, and discloses a track profile dynamic optimization method and system for drilling, and the system comprises a track model library, an initial value generation unit, an optimization solving unit, a constraint evaluation unit and a result output unit. The orbit model library constructs three-section and five-section orbit section models based on a constant tool face angle, and the optimization solving unit fuses a quasi-Newton method and an artificial bee colony algorithm to realize orbit parameter optimization under multiple variables and multiple constraints. According to the method, an adaptive track profile type is selected through real-time drilling data, and optimized parameters are output after initial value solving, algorithm optimization and constraint evaluation. According to the method, the feedback of the drilling process can be dynamically responded, the target window drilling rate is remarkably improved, the track complexity and the dragging pressure risk of a long horizontal section are reduced, the drilling requirements of directional wells and horizontal wells under complex stratums are met, and technical support is provided for efficient and safe development of drilling operation.

The invention provides a while-drilling electromagnetic wave resistivity logging inversion method based on a quasi-Newton method, and relates to the technical field of oil-gas exploration and development, and the method comprises the steps: collecting the actual measurement response data of an isotropic or anisotropic target stratum, constructing a three-layer TI medium stratum model, and determining the to-be-inverted parameters; performing global optimization by adopting a genetic algorithm, and outputting an optimal result as an initial value; constructing a quasi-Newton inversion model containing a regularization term, dynamically updating a Jacobian matrix by using a Broyden updating rule, introducing a diagonal adaptive damping matrix to replace a fixed damping factor, and adaptively adjusting the damping strength according to a parameter relative increment ratio; and inputting the initial value into the improved model for iterative operation until a termination condition is met. The method gets rid of manual experience dependence, avoids local extremum convergence, improves the inversion precision, reduces the number of iterations, improves the calculation efficiency, accurately inverts key parameters, meets the requirements of real-time geosteering engineering, and improves the oil-gas exploration and development efficiency.

The invention discloses an abnormal power fluctuation detection method and system fused with time sequence modeling. The method comprises the following steps: acquiring daily electric quantity year-on-year data of each industry and an industry power consumption data set; the method comprises the following steps: constructing a time sequence model based on year-on-year data of daily electric quantity of each industry, calculating posterior distribution of a predicted value of the time sequence model through an L-BFGS quasi-Newton method, and calculating a data basic confidence interval of a next time point; inputting each characteristic value of the time sequence model one day before the date to be predicted, the mean absolute error of the predicted value of the short-term historical window time sequence model, the residual standard deviation and the industry power consumption data set into a full-connection network to obtain a heteroscedasticity confidence interval; and calculating a final threshold interval based on the two confidence intervals to carry out industry electricity consumption abnormity judgment. According to the method, the problem that the phenomena of false alarm and missing report occur frequently due to the fact that the power utilization characteristic difference between different industries is not considered and the change of the electric quantity rule along with time is difficult to adapt in the prior art is solved.

This invention discloses a DC motor speed control identification method and system based on an improved quasi-Newton method, belonging to the technical field of DC motor speed control system modeling. First, a time-varying nonlinear DC motor speed control system is constructed, and an identification model of this system is obtained. Then, an identification method flow based on the quasi-Newton method under step-size line search is constructed. The optimal step size is obtained through step-size line search for parameter estimation, and a correction strategy is designed to ensure the positive definiteness of the approximation of the Hessian matrix inverse. This invention avoids the oscillation or slow convergence problems caused by a fixed step size by dynamically adjusting the step-size factor, and simultaneously introduces a correction strategy to forcibly guarantee the positive definiteness of the approximation of the Hessian matrix inverse, thereby obtaining more accurate motor model parameters.

The application discloses an electric-gas comprehensive energy system cascade fault prediction method, and relates to the field of comprehensive energy system simulation control. The method comprises the following steps: firstly, an electric-gas comprehensive energy system cascade fault model is established, which comprises power system primary frequency modulation dynamics, natural gas fault propagation dynamics, coupling element dynamics and a fault model. Then, based on Newton interpolation and backward difference principles, an electric-gas cascade fault analysis model based on a numerical differential discrete formula is derived and established. Secondly, aiming at the high-dimension, nonlinearity and rigidity characteristics of the model, a variable order and variable step length calculation strategy is proposed to optimize the solving process, and a matrix updating and decomposition strategy based on the quasi-Newton method is adopted to reduce the operation cost. Finally, a key event positioning technology based on a backward difference vector is used to accurately lock the cascade generator tripping fault time. The application further discloses an electric-gas comprehensive energy system cascade fault prediction system, and the application can efficiently and accurately predict the cascade fault evolution process.

The invention provides a six-degree-of-freedom parallel platform based on iterative initial value estimation and a forward motion solution method thereof. The six-degree-of-freedom parallel platform takes 6-UPU configuration as a research object; comprising a lower platform, an upper platform, six Hooke hinges fixed on the lower platform, six Hooke hinges fixed on the upper platform, and six electric push rods connected with the Hooke hinges of the lower platform and the Hooke hinges of the upper platform. The six-degree-of-freedom parallel platform motion positive solution method based on iteration initial value estimation comprises forward kinematics modeling of a six-degree-of-freedom parallel platform, an iteration initial value estimation method based on space geometry, an iteration initial value estimation method based on particle swarm optimization, an iteration initial value comparison method and a quasi-Newton method based on iteration initial value estimation.

The application discloses a material process parameter self-adaptive optimization method and system based on dynamic Bayesian optimization, and belongs to the field of artificial intelligence assisted material research and development. In view of the problems of high cost and easy falling into local optimization under complex phase change in traditional material research and development, the application constructs an initial set by acquiring material historical process and performance data; a nonlinear proxy model is constructed by using Gaussian process regression and a composite kernel function fusing smooth and rough priori; a function acquisition strategy is dynamically switched according to a prediction variance to balance global exploration and local utilization; within the extracted physical equipment hard constraint boundary, a multi-start quasi-Newton method is used to perform global optimization, and the next round of recommended experimental parameters are output and closed-loop iteration is supported. The application realizes accurate optimization of multi-dimensional parameters with few experimental times, and is mainly used for complex chemical and material process parameter optimization such as polymerization and alloy smelting.

Systems and methods reconstruction for a medical imaging system using a quasi-newton method. An unrolled iterative reconstruction process is used to reconstruct an image from the scan data. The unrolled iterative reconstruction process includes a plurality of cascades that include at least a data-consistency step and a regularization step. The data-consistency step is modified based at least in part on information of already calculated gradients of one or more previous cascades of the plurality of cascades using a quasi-newton computation.

The application discloses a kind of mechanical arm device iterative learning control system and method based on quasi-newton method, target trajectory determination module in the system is used to determine the expected target trajectory of the mechanical arm device, nonlinear equation determination module is used to determine the nonlinear equation related to mechanical arm nonlinear system, pose acquisition module is used to obtain the position of the mechanical arm device under current iteration, control module is used to update the control input of the mechanical arm device when next iteration according to the expected target trajectory of the mechanical arm system, control input under current iteration, position output under current iteration and gain matrix, the mechanical arm device is controlled.The application can make that mechanical arm is in finite time period at any time to the trackable target trajectory Realize high-precision tracking.

The application discloses a solid internal temperature field and thickness efficient joint reconstruction method based on a quasi-Newton method, belongs to the field of ultrasonic nondestructive detection, and comprises the following steps: constructing a multi-parameter inversion problem with boundary heat flow and thickness as decision variables, defining an objective function containing a regularization term, and converting the inversion problem into a nonlinear optimization problem; for the nonlinear optimization problem, the quasi-Newton method L-BFGS is used to invert the heat flow, and the steepest descent method DG is used to invert the thickness, so that the internal temperature of a high-temperature structure and the size and thickness thereof are reconstructed. The application can realize synchronous high-precision and high-efficiency reconstruction of the internal temperature field and thickness size of a solid structure.

The invention discloses a solid internal temperature field and thickness efficient joint reconstruction method based on a quasi-Newton method, and belongs to the field of ultrasonic non-destructive detection, and the method comprises the steps: constructing a multi-parameter inversion problem with boundary heat flow and thickness as decision variables, defining a target function containing a regularization item, and converting the inversion problem into a nonlinear optimization problem; and aiming at the nonlinear optimization problem, combining a quasi-Newton method L-BFGS inversion heat flow with a steepest descent method DG inversion thickness, and reconstructing the internal temperature and the size thickness of the high-temperature structure. According to the invention, synchronous high-precision and high-efficiency reconstruction of the internal temperature field and the thickness size of the solid structure can be realized.

The invention belongs to the technical field of environmental monitoring and hydrological simulation, and particularly relates to a hydrological parameter and particle tracking fused pollution diffusion traceability system, which is characterized in that real-time hydrological observation data and high-resolution hydrodynamic model output are subjected to space-time alignment by constructing a dynamic coupling mechanism; a three-dimensional Lagrange particle tracking model is driven; a quasi-Newton method and finite element method collaborative optimization objective function inversion framework is adopted, and the space coordinates, the release time and the discharge flux of a pollution source are synchronously determined in a multi-source and strong disturbance scene; and non-tampering evidence storage is carried out on the key traceability evidence chain based on the alliance chain architecture. The structural defects of hydrological parameter staticization, inversion algorithm simplification, data credibility deficiency, response mechanism lagging, cross-domain cooperation difficulty and the like in a traditional pollution diffusion traceability system are overcome, and full-chain automation and precision support from pollution identification, path tracking, source inversion to responsibility determination are achieved.

The invention belongs to the technical field of generation power optimization of a wind power plant, and provides a wind power plant power optimization method and system based on BFGS and knowledge generalization mechanism data driving. According to the method, an optimization framework of a quasi-Newton method is adopted, and according to wind power plant power generation data information measured in real time, the gradient estimated by synchronous perturbation stochastic approximation and Hessian matrix inversion estimated by BFGS are utilized to complete updating of all fan control actions; a knowledge generalization mechanism is designed, and real-time generalization and full utilization are carried out on learned action knowledge; the method does not depend on a wind power plant generation power model, and solves the problems that an existing optimization scheme is slow in convergence speed and difficult to effectively improve the limitation that the modeling wind power plant generation power is difficult to analyze.

The present application relates to optical remote sensing satellite micro-vibration isolator dynamics parameter system level optimization method, belongs to spacecraft vibration control technical field;Establish the finite element model of the whole satellite;Modal analysis is carried out to the isolation system, and the function relation of the natural frequency of the isolation system and the axial stiffness k w , lateral stiffness k v Is fitted;The constraint condition of natural frequency f 横向 And f 纵向 Is set;The function relation of the angular displacement of high-resolution camera and the axial stiffness k w , lateral stiffness k v Of the isolator is fitted;The function relation of the angular displacement of the main components and the axial stiffness k w , lateral stiffness k v Of the isolator is fitted;The objective function F is constructed;With the constraint condition of lateral natural frequency f 横向 And longitudinal natural frequency f 纵向 As constraint, the objective function F is solved by nonlinear optimization using quasi-newton method, and the lateral natural frequency f 横向 , longitudinal natural frequency f 纵向 Of the optimized isolation system;The present application controls micro-vibration from the system level, and gives the specific parameter requirement of isolator design and development.

This invention provides a robot trajectory planning method based on second-derivative regularization, comprising: parameterizing the joint space path with path parameters, establishing a joint velocity and acceleration constraint model, and obtaining the original path velocity square distribution and feasible region based on the TOPPRA algorithm; designing a second-derivative regularized objective function in the path velocity square space, including a data fidelity term and a curvature penalty term; constructing a bounded constraint optimization model based on the upper and lower bounds of the feasible region and the values ​​of the first and last ends, and analytically deriving the gradient of the objective function; solving the optimization model using a bounded constraint quasi-Newton method, restoring the obtained optimal path velocity square to a smooth path velocity, recovering the kinematic time series of each joint, and completing the trajectory smoothing planning. This invention can effectively suppress sharp abrupt changes in joint acceleration, achieving robot trajectory planning that balances time efficiency and motion stability.

The application discloses a power flow calculation method and system of AC / DC power system based on quasi-Newton method, establishes a power flow calculation model of conventional DC converter station and flexible DC converter station based on input and output characteristics of AC side and DC side; extends the rectangular coordinate equation of only considering node voltage in traditional power flow calculation to the augmented rectangular coordinate system considering node voltage and current, and establishes a unified power flow calculation model of AC / DC power flow calculation; establishes the direction and step length of the first iteration through Newton method, and obtains the initial value of quasi-Newton iteration; continuously updates the quasi-Newton iteration matrix in the iteration process, so that the calculation of Jacobian matrix and inversion is not required repeatedly; when the power flow solution iteration meets the requirements, the solution of power flow calculation is output. Through the calculation method, the unified iteration of AC / DC power flow can be realized by using the unified power flow model of AC / DC, meanwhile, the quasi-Newton method is adopted to save the time required by each iteration, and has the characteristics of super-linear convergence.

The application provides a method for electromagnetic wave resistivity logging while drilling inversion based on quasi-Newton method, and relates to the technical field of oil and gas exploration and development, which comprises the following steps: collecting measured response data of isotropic or anisotropic target formation, constructing a three-layer TI medium formation model, and determining the parameters to be inverted; performing global optimization by using genetic algorithm, and outputting the optimal result as an initial value; constructing a quasi-Newton inversion model containing a regularization term, dynamically updating the Jacobian matrix by using Broyden updating rule, introducing a diagonal adaptive damping matrix to replace the fixed damping factor, and adaptively adjusting the damping strength according to the relative increment of the parameters; inputting the initial value into the improved model for iterative operation until the termination condition is met. The application is free from dependence on artificial experience, avoids local extremum convergence, improves inversion accuracy, reduces the number of iterations, improves calculation efficiency, accurately inverts key parameters, meets the requirements of real-time geosteering engineering, and improves the efficiency of oil and gas exploration and development.

The invention discloses a malicious software security detection method based on a graph neural network, and the method comprises the following steps: S1, collecting static behavior data and dynamic behavior data of to-be-detected software, and constructing a software graph structure; s2, carrying out standardization and feature coding on the software graph structure, and extracting node, edge and global structure features; s3, initializing the graph neural network model, setting structural hyper-parameters and initializing weight parameters; s4, executing forward propagation to obtain embedded features and preliminary classification results of the software samples; s5, performing quasi-Newton method joint optimization on weight parameters and structural hyper-parameters of the graph neural network model; and S6, processing new sample data, inputting the optimized model, and outputting a malicious software detection result. According to the malicious software detection method fusing the graph neural network and the quasi-Newton method, efficient recognition and accurate classification of unknown and variant samples are achieved.