28 results about "Linear problem" patented technology

According to the plateau area load prediction method and system based on the signal decomposition and the neural network, an original signal is decomposed into a plurality of intrinsic mode components with physical significance by adopting completely adaptive noise set empirical mode decomposition, mode aliasing is suppressed, and multi-scale features are extracted to effectively suppress load demand oscillation; the problem of non-stationarity of the load of the service area is solved; then, K-means clustering grouping is carried out on the components, similar modes are combined to reduce calculation redundancy, and the problems of heterogeneity and local feature redundancy in complex time series data are effectively solved; performing nonlinear dimension reduction processing on the grouped reconstructed signals by adopting kernel principal component analysis, eliminating noise interference and extracting key features, thereby effectively solving the multiple nonlinear problem; and finally, inputting the dimensionality-reduced features into a long-short-term memory network modeling time sequence dependency relationship, and realizing high-precision prediction through adaptive weighted integration of component prediction results.

The invention provides a structure reliability analysis method based on an adaptive variable fidelity model, and the method comprises the steps: generating an initial sample point set which is uniformly distributed and has representativeness through an improved random sampling method KMODMC, enabling the initial sample point set to comprise a low-fidelity sample set and a high-fidelity sample set, training a BP neural network through employing the low-fidelity sample set, and carrying out the training of the BP neural network through employing the high-fidelity sample set; a low-fidelity BP neural network model is obtained; meanwhile, based on an error training Kriging model of a high-fidelity sample set and a low-fidelity model predicted value, an error correction Kriging model is constructed, the low-fidelity BP neural network model and the error correction Kriging model are combined to form a multi-fidelity mixed agent model, and adaptive iterative optimization is performed through a double-model alternate point adding sampling strategy. And finally obtaining a high-precision multi-fidelity hybrid agent model for structural reliability evaluation. According to the method, the problems of high cost of high-fidelity simulation calculation and insufficient precision of a low-fidelity model are solved, and the adaptive capacity and prediction precision of the model in a complex nonlinear problem are effectively improved.

The invention discloses a semi-analytic solution-based energy flow calculation method and system for an electricity-gas integrated energy system, and the method comprises the steps: firstly building an ordinary differential-algebraic model of an electricity-gas network based on a numerical boundary extrapolation and semi-discrete method; based on a semi-analytical theory of differential transformation, establishing a linearized electricity-gas network model; carrying out block calculation on a matrix and self-adaptive adjustment on a time window; and finally solving the model to obtain an energy flow calculation result. According to the method, the original nonlinear partial differential equation of the gas network is converted into the linear algebraic equation, the complex nonlinear problem in traditional gas network modeling is simplified, errors caused by local linearization and time difference are avoided, the calculation scale is reduced, and the calculation robustness is improved.

The invention relates to a low-carbon economic dispatching method and system for an integrated energy system, and the method comprises the steps: accurately describing the variable efficiency characteristics of energy conversion equipment through constructing a dynamic energy concentrator model based on a BP neural network, and processing a nonlinear problem through employing a linearization method; a typical source load prediction scene is generated in combination with a scene generation and reduction technology, and an energy flow-carbon flow double-layer optimization model is established. Wherein the energy flow layer takes the minimum operation cost as a target and considers equipment operation, multi-energy flow balance and transaction constraints; a stepped carbon transaction mechanism and demand response guided by carbon potential are introduced into a carbon flow layer, and the purpose is to minimize the cost of carbon transaction and demand response. And through iterative solution of the double-layer model, collaborative optimization of a system energy purchase and sale plan, equipment output and load change is realized until an optimal scheduling scheme is converged. According to the method, collaborative optimization of the energy flow and the carbon flow is realized, and double improvement of the scheduling of the comprehensive energy system in economy and low carbon is promoted.

This invention discloses a real-time attitude estimation system for non-cooperative industrial targets based on strong tracking filtering, relating to the fields of industrial machine vision and automation control technology. This system decouples the complete six-degree-of-freedom attitude estimation problem into a linear translational sub-state space and a nonlinear rotational sub-state space. This decomposes the nonlinear calculations that would normally be performed in six dimensions into a three-dimensional linear problem and a three-dimensional nonlinear problem. The translational sub-state only performs a simplified Kalman gain iteration with closed-form solutions, and matrix operations are simplified to low-order linear recursion. Although the rotational sub-state uses strong tracking filtering, it only needs to perform nonlinear updates in the reduced low-dimensional space. The Jacobian matrix sparsification pre-computation and incremental update unit pre-stores the constant part of the observation equation's derivative calculation offline and only incrementally updates the sparsely changing part online. This allows the system to be fully deployed on ARM architecture edge devices and stably meet millisecond-level processing requirements.

The application discloses a kind of space target ultra-short arc initial orbit intelligent determination method, belong to aerospace technology field.The application is based on artificial intelligence technology to realize space target ultra-short arc initial orbit determination, there is no strict requirement to complex nonlinear problem, and can develop more complex perturbation condition and initial orbit determination under the operation law of multiple body, coverage is wider;The application generates multiple groups of target orbits randomly and calculates the observation and state quantity of each visible arc segment, constructs the sample data set with ultra-large capacity, which is helpful for neural network to learn the nonlinear relationship between observation and state quantity using rich samples;The application builds and trains long short-term memory network model, determines the optimal network model weight parameter by designing and adjusting hyperparameter, can obtain the fast prediction solution of unknown space target ultra-short arc initial orbit according to observation;The application can meet more space target ultra-short arc initial orbit determination tasks by considering different inputs and outputs.

The invention discloses an electrocatalyst activity attenuation prediction method based on microdynamics, and relates to the field of electrochemical catalyst.According to the electrocatalyst activity attenuation prediction method based on microdynamics, an electrocatalytic reaction and an inactivation reaction are dynamically coupled in a microdynamics model for the first time, the limitation of traditional thermodynamic stability analysis is broken through, and the activity attenuation process under the working condition can be truly reflected. By introducing a residual active site coverage variable and adopting quasi-steady-state approximation, the modeling and solving problems of a dynamic process of continuous loss of active sites are successfully solved, and a complex rigid differential equation set is simplified into an analyzable linear problem. The method not only can predict a stability attenuation curve, but also can quantitatively diagnose key steps influencing the service life through half-life period control degree analysis, realizes crossing from'phenomenon prediction 'to'mechanism interpretation' and'reverse design ', and provides a powerful theoretical tool for rationally designing a high-stability electrocatalyst.

The invention provides an original real-time electricity price prediction method based on a quantum complex neural network and Hilbert-Huang Transform (HHT), and the real-time electricity price prediction method based on the quantum complex neural network and the Hilbert-Huang Transform (HHT). Aiming at the non-stationarity of electricity price data on the historical level, firstly, the time internal correlation of each feature channel is extracted through an HHT time sequence analysis method, and complex multi-dimensional time sequence prediction is simplified into a simple regression task, so that a tedious time sequence modeling process is avoided; for the non-linear problem of electricity price data, a quantum neural network is used for capturing the coupling relation between different factors, and the calculation speed and the model efficiency are further improved by means of the parallelism of quantum calculation. Based on the two advantages, the method can realize accurate real-time electricity price prediction.

The application provides a topology optimization structure design method based on a space control point, and a space point control technology for structure topology optimization. The SPCT constructs a field function by using the space arrangement of the control points, effectively reduces the number of design variables of the topology optimization, improves the optimization calculation efficiency, and avoids the appearance of the chessboard effect and the medium density elements in the traditional topology optimization method. The control point placement method is intuitive and convenient for controlling the initial configuration or sample data, and further improves the optimization efficiency. In addition, the application derives the sensitivity information of the SPCT topology optimization model, establishes a gradient-based optimization framework, and can use a non-gradient algorithm to solve the scheme without the aid of gradient information to solve complex nonlinear problems. The technology has obvious advantages in reducing design variables and improving calculation efficiency, and has broad application prospects in the field of structure optimization design.

A method and apparatus for simulating the operation of a cascade reversible hydropower station based on piecewise McCormick relaxation is disclosed. The method includes: constructing a simulation model of the reversible hydropower station operation based on the operating conditions of the station and aiming to maximize the total output of the station; linearizing the nonlinear part of the simulation model using piecewise McCormick relaxation and piecewise linearization to simplify it into a mixed-integer linear programming model; and iteratively relaxing the simulation model based on upstream runoff data and operating parameters to obtain the simulation results. This invention uses piecewise McCormick relaxation to transform the nonlinear problem into a linear problem, while employing an iterative relaxation algorithm to improve the solution accuracy. It does not require narrowing the feasible boundary of McCormick relaxation, has fewer iterations, and a faster convergence speed, thus improving the speed and stability of the solution while ensuring simulation accuracy.

The application provides a structure reliability analysis method based on an adaptive variable fidelity model, and the method comprises the following steps: generating an initial sample point set with uniform distribution and representativeness through an improved random sampling method KMODMC, the initial sample point set comprising a low-fidelity sample set and a high-fidelity sample set; subsequently, training a BP neural network by using the low-fidelity sample set to obtain a low-fidelity BP neural network model; at the same time, training a Kriging model based on the error of the high-fidelity sample set and the predicted value of the low-fidelity model to construct an error correction Kriging model; combining the low-fidelity BP neural network model and the error correction Kriging model to form a multi-fidelity hybrid proxy model; and performing adaptive iterative optimization through a double-model alternating point sampling strategy to finally obtain a high-precision multi-fidelity hybrid proxy model for structure reliability evaluation. The application solves the problems of high cost of high-fidelity simulation calculation and insufficient precision of a low-fidelity model, and effectively improves the adaptability and prediction precision of the model in complex nonlinear problems.

The invention discloses a structure nonlinear dynamic response incremental modal overlay analysis method and system, and belongs to the field of aerospace structure dynamics calculation. The method comprises the steps of constructing a finite element model and obtaining an initial matrix; dispersing the time-varying temperature field into a plurality of time increment intervals, updating material parameters in each interval, and calculating an instantaneous matrix; carrying out eigenvalue analysis and converting the kinetic equation into a modal coordinate system; solving a modal response increment by adopting a numerical integration method and converting the modal response increment to a physical coordinate system for superposition; and circularly executing until a full-time-history response is output. According to the method, the problem of rigidity nonlinearity caused by the temperature is solved through incremental linearization and modal order reduction, high calculation precision and high efficiency are achieved, and the method is suitable for dynamic design and analysis of aerospace structures in the high-temperature variable-temperature environment.

The invention belongs to the technical field of electric vehicle load optimization, and particularly discloses an electric vehicle load multi-target double-layer optimization method considering peak load shifting, comprising the following steps: establishing an electric vehicle load multi-target double-layer optimization model; converting a double-layer optimization problem of the multi-target double-layer optimization model into a single-layer multi-target linear problem through a KKT condition and a maximum M method; further converting a single-layer multi-target linear problem into a single-layer single-target problem; solving a single-layer single-target problem to obtain a Parteo leading edge; and determining an ideal solution at a Parteo leading edge, and taking the ideal solution as a final result of an original double-layer multi-objective optimization problem to realize electric vehicle load multi-objective double-layer optimization. According to the method, the problem that the upper-layer model in the double-layer model is single-target optimization due to the fact that comprehensive indexes such as power distribution network side peak-valley difference and load fluctuation are not considered in an existing double-layer optimization operation model is solved, and the peak-valley difference and the load fluctuation can be reduced while the income of an electric vehicle user is guaranteed.

This invention discloses a method, apparatus, and medium for solving variable quantum linear problems in subspaces. The method includes: determining the system of linear equations to be solved, constructing a variable quantum circuit and a Krylov subspace, and using the generalized minimum residual method and the variable quantum circuit to calculate an approximate solution of the system of linear equations to be solved in the subspace. This addresses the shortcomings of existing technologies and can reduce the time complexity and computational load of solving linear problems.

The application discloses a kind of based on user balance flow distribution with maximum toughness as target damaged road network recovery order optimization method, to solve the problem of low efficiency, decision lacks scientific basis after disaster traffic network recovery.The method comprises the following implementation steps: first, construct damaged road network toughness evaluation framework, in combination with traffic network topological structure and flow demand, define toughness index as the reciprocal of recovery time and total travel time product;Second, establish damaged road network toughness optimization scene parameter, abstract road network into connected graph and define recovery scheme set;Then, construct the mixed integer linear programming model with maximum toughness as target, convert nonlinear problem into linear problem by cutting line approximation, and set flow balance, travel demand and other constraint conditions;Finally, the model is globally optimized and solved using high-performance solver, to generate the best recovery scheme.The method of the application can scientifically quantify road network toughness and optimize recovery order, avoid the blindness of experience decision, quickly improve the traffic efficiency of post-disaster traffic system, provide efficient and scientific decision support for post-disaster traffic recovery, and have important practical application value.

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.

The invention discloses a battery DCR prediction method and related equipment, and the method comprises the steps: dividing the early-stage decrease stage and the later-stage increase stage of battery aging DCR through inflection points, constructing a growth rate model with a temperature index relation and a time power function coupled, solving parameters through dual linear fitting, and combining with a comprehensive working condition temperature to establish a prediction model. And substituting the duration and the initial DCR to obtain a target DCR value. According to the method, the model is enabled to accurately adapt to the DCR change rules of different aging stages through stage division, an actual mechanism is fitted, and prediction rationality is greatly improved. The coupling model can quantify the cooperative influence of temperature and time, effectively improves the prediction precision of the whole temperature interval, and is especially suitable for extreme temperature conditions. Nonlinear parameter solving is converted into a linear problem through double linear fitting, and the parameter matching degree and the solving precision are improved while calculation is simplified. The prediction model based on the comprehensive working condition can adapt to actual temperature fluctuation to output an accurate result, and a reliable basis is provided for battery aging evaluation.

This invention discloses an improved canonical perturbation high-order expansion prediction method and system for the critical flutter wind speed of long, flexible structures. The method includes: Step 1: Acquiring aerodynamic and structural parameters based on the Scanlan self-excited force model; Step 2: Constructing the bending-torsional coupling control matrix equation and introducing small parameters for perturbation expansion; Step 3: Using the improved canonical perturbation method, distinguishing between equal and unequal frequency cases, eliminating long-term terms by constructing eigenvalues, and solving for eigenvalues ​​and modal vectors step by step; Step 4: Deriving explicit analytical solutions for modal frequencies, damping ratios, amplitude ratios, and phase differences; Step 5: Determining the modal frequencies and directly solving for the damping ratio through a single iteration to determine the critical flutter wind speed. This invention decomposes complex nonlinear problems into linear superpositions of components, obtaining explicit closed-form solutions for the parameters. The calculation can be completed in a single iteration, significantly simplifying the analysis process and clearly revealing the influence mechanism of various structural and aerodynamic parameters on flutter instability.

The application discloses a fast complementary filter method for attitude estimation by using a low-cost MARG sensor, and comprises the following steps: 1) estimating an attitude quaternion by using data output by an accelerometer; 2) using a gravity filter for compensation of a gyroscope; and 3) fusing the gravity and the magnetometer. The application discloses the fast complementary filter method for attitude estimation by using the low-cost MARG sensor, and converts the attitude estimation problem based on the accelerometer into a linear problem in view of a nonlinear problem in the attitude quaternion calculation process. In order to improve the accuracy of MARG attitude estimation, a new complementary filter structure is designed, and the new complementary filter structure is characterized in that the gravity is estimated by fusing the measurements of the accelerometer and the gyroscope. Meanwhile, the magnetic distortion detection is introduced into the algorithm, and the output of the magnetometer is fused with the estimated gravity based on the Markley algorithm.

The invention provides a multi-element energy storage collaborative fluctuation stabilizing method, which comprises the following steps of: 1, performing empirical mode decomposition on an original new energy grid-connected power signal to obtain a plurality of IMF components; 2, reconstructing the IMF component according to the grid-connected power and the amplitude-frequency response capability of each energy storage device, and dividing a low-frequency component, an intermediate-frequency component and a high-frequency component corresponding to different time scales respectively; step 3, establishing a charge-discharge efficiency linearization model of each energy storage device, performing linearization modeling based on an MLD theory, and processing a nonlinear problem through a piecewise linearization method; and 4, by taking the minimum grid-connected fluctuation and the highest energy utilization rate as targets, constructing a multi-element energy storage collaborative fluctuation stabilizing stochastic optimization model, and optimizing the output strategy of each energy storage device. Through EMD decomposition and reconstruction, separation of power fluctuation at different time scales is realized, so that different types of energy storage devices give full play to the frequency response advantage, and the cooperation efficiency and the energy utilization rate are improved.

The application discloses a kind of based on BEO-XGBoost algorithm's side slope stability evaluation method, and the invention includes the following steps: S1, the factor that influences side slope stability is analyzed, determines the parameter for being used for analyzing side slope stability, constructs side slope dataset;S2, the data distribution situation is analyzed, and data set is handled;S3, establishes XGBoost model, and data set is divided into training set and test set;S4, constructs BEO model and optimizes XGBoost;S5, BEO-XGBoost model is trained and tested.The BEO algorithm used in the application is a powerful heuristic algorithm developed based on the biological behavior of black eagles, which simulates the hunting, migration and breeding behavior of black eagles.Through simulating these behaviors of black eagles, BEO can gradually find the optimal solution from a complex search space, which makes BEO suitable for optimizing complex nonlinear problems such as machine learning models.XGBoost is an ensemble algorithm based on decision trees, which uses gradient descent to optimize the residual error of each tree, thereby continuously improving the accuracy of the model.Using the BEO-XGBoost model to predict the stability of the slope provides a new and highly adaptable hyperparameter optimization strategy that combines the global and local search mechanisms of BEO and significantly improves the accuracy of slope stability prediction through dynamic adaptive search.

The invention discloses a cross-domain target threat degree dynamic assessment method based on an analytic hierarchy process and an entropy weight method, and relates to the technical field of target threat degree dynamic assessment. According to the method, a hierarchical structure is constructed through the AHP-entropy weight method, a complex nonlinear problem is decomposed into multistage linear subtasks, the calculation complexity is remarkably reduced, and the real-time performance is improved; traditional static prior knowledge dependence is abandoned, weight coefficient self-correction is achieved based on the comparison relation between real-time data streams and elements, and it is ensured that the weight dynamically adapts to scene changes; subjective weighting and objective entropy weight are fused, single-dimensional deviation is eliminated through a multi-level calibration mechanism, a comprehensive evaluation system cooperatively driven by data and knowledge is constructed, and the result robustness is improved.

The invention discloses an electric power system energy and standby scheduling method and device, electronic equipment and a medium, which are used for solving the technical problem of how to consume high-permeability new energy and ensure the safety of an electric power system through electric power system energy and standby scheduling. New energy uncertainty is considered, truncation constraints are introduced, and an energy and standby scheduling model of the power system is constructed; the optimization problem of the energy and standby scheduling model is composed of a first-stage problem and a second-stage problem; performing approximate segmentation considering the total fluctuation of the new energy on the second-stage problem to obtain a plurality of piecewise linear problems; based on a plurality of piecewise linear problems, an energy and standby scheduling model is converted into a deterministic semi-definite programming model in combination with dual variables; and performing optimization solution on the semi-definite programming model through main-sub problem alternative optimization to obtain an energy and standby scheduling result of the power system.

This invention proposes a composite planning method for a three-dimensional reentry trajectory of a spacecraft, comprising: analyzing the three-degree-of-freedom model of the reentering spacecraft to obtain models of the longitudinal and transverse planes; optimizing the trajectory of the longitudinal plane; evenly dividing the entire flight time domain and calculating the change in horizontal distance within each period; planning the trajectory of the transverse plane; smoothing the junctions of the transverse trajectories between adjacent time periods; inversely calculating the temporal variation of the trajectory deflection angle using the longitude and latitude relationships of the transverse trajectories; and planning the three-dimensional reentry trajectory of the spacecraft based on all state variables and control variables. This method can effectively improve the reliability of spacecraft reentry and address the poor convergence and low terminal accuracy of traditional gradient algorithms for solving complex nonlinear problems. It also avoids the reliance on initial values ​​and high computational complexity of dynamic programming methods.

The application discloses a navigation control method and related device of a tracked vehicle, and relates to the field of automatic driving control. A discretization error tracking model is used to build a linear relationship between a discretized form of a pose tracking error vector and a tracked speed error vector, so that the control variable and the state variable are in a linear relationship. Solving the linear problem can obtain the left tracked speed error and the right tracked speed error. The expected value of the left tracked speed is calculated based on the left tracked speed error and the reference value of the left tracked speed, and the expected value of the right tracked speed is calculated based on the right tracked speed error and the reference value of the right tracked speed. The reference value of the bilateral tracked speed is calculated according to the left tracked speed error, the right tracked speed error and the steering curvature of the target reference point on the preset reference path, so that the expected value of the bilateral tracked speed can make the actual driving path of the tracked vehicle approach the reference path. In summary, the scheme improves the adaptability and control accuracy of the navigation control method for the tracked vehicle.

This invention discloses an underwater multi-target tracking method based on kernel mean embedding confidence propagation. It employs a kernel mean embedding method, approximating the probability density function of the target state with a small number of kernel particles. This overcomes the problem of incomplete observability of the system with lower computational complexity, transforming the strongly nonlinear problem of passive tracking in a low-dimensional measurement space into a linear problem in a high-dimensional feature space. This solves the problems of strong nonlinearity in underwater multi-target measurement functions and incomplete observability of target states, thus improving the tracking accuracy of underwater passive tracking. Furthermore, this invention overcomes the problem of excessively high computational complexity caused by the traditional BP algorithm using random particles or Gaussian mixture density to approximate the probability density distribution through a kernel mean confidence propagation method under the Gaussian assumption. It handles the matching problem between measurement results and targets with lower computational complexity, significantly improving the scalability of the system.

The application discloses a kind of linear system solving method, device, medium and equipment based on mixed subspace, method includes: first determining linear system to be solved and pre-processing linear system to be solved, secondly, mixed subspace for solving linear system and quantum circuit for solving linear system are respectively constructed, the target solution of pre-processed linear system to be solved is calculated using mixed subspace and quantum circuit, using the comprehensive solving performance of mixed subspace, to solve the deficiencies in the prior art, it can reduce the time complexity and computation of linear problem solving, speed up the solving speed of quantum linear algorithm.

The application proposes a new escape routing method based on linear programming, which is used for the wiring design of printed circuit board, and comprises the following steps: step S1, equivalent the nonlinear problem of escape routing to linear programming problem, and establish network flow model; step S2, describe each constraint of the wiring rule as the constraint condition of the linear programming, so as to realize the conversion of the nonlinear problem to linear problem solution; step S3, determine the main obstacle point of the congestion part, re-describe the path selection problem of the point as ILP problem with linear constraint, and then locally optimize and solve; step S4, repeatedly iterate the LP solution of linear programming to judge congestion and ILP local optimization, so as to realize the legalization of overall wiring, that is, keep the path of the optimized point, continue to alternately use linear programming solution and local heuristic optimization for the remaining points, until the wiring area no longer appears congestion; the application greatly improves the wiring efficiency and reduces the cost consumption under the condition of meeting the signal integrity and stability constraint.