24 results about "Interior point method" patented technology

The invention discloses a disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method. When a minimum electricity generating cost incremental quantity model only containing one balance node tidal current model is solved, the total demand unit change of a system is fixed on a balance node, and an obtained lambda, namely electricity generating cost minimum incremental quantity cannot reflect actual operation of the system in most cases during total demand unit change of a system. The disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method adopts solves the provided model by means of an original antithesis interior point method, and the lambda of the system is controllable according to selected disperse relaxing factors.

The invention discloses an industrial robot motion pose error calibration method based on constraint solution. The industrial robot motion pose error calibration method comprises the steps that S1, joint angles, tail end coordinates and temperature data of multiple measurement points are collected; s2, correcting the kinematic model by using the thermal elongation and constructing a parameter identification model; s3, constructing a mixed integer nonlinear constraint pool according to mechanical limitation; s4, introducing a semi-definite programming slack variable to convert the non-convex constraint into a linear matrix inequality; s5, combining a primal-dual interior point method and an alternating direction multiplier method to iteratively solve the optimal kinematics parameter correction; s6, introducing a square root information filtering improvement mechanism to correct a state vector square root factor, and calculating an updated nominal kinematics parameter; and S7, based on the condition number of the error Jacobian matrix, judging observability and repeatedly calibrating. According to the method, the problem of precision reduction caused by thermal errors is solved, and the identification precision, the numerical stability and the self-adaptive calibration capability of the kinematics parameters of the robot are improved.

The application discloses a radar complementary sparse frequency waveform sequence set design method based on a complex circle flow surface algorithm, and specifically comprises the following steps: according to a monitored spectrum environment, delimiting a usable spectrum range and an unusable spectrum range; converting a weighted integral sidelobe level of a waveform sequence set into a quadratic function form with a waveform sequence set vector as a variable, constructing an objective function to describe the weighted integral sidelobe level of the waveform set; calculating a power spectrum of the waveform set, controlling a weighting coefficient according to an expected spectrum, performing weighted summation on the power spectrum, and converting a weighted summation expression of the power spectrum into a quadratic function form with the waveform sequence set vector as the variable; and performing weighted summation on a WISL objective function and an EFS objective function to construct a joint objective function. The CCM optimization algorithm is applied to the complementary sparse frequency waveform sequence set design problem for the first time, effectively solving the constant modulus constraint problem of the waveform sequence, and compared with the existing cyclic iteration algorithm and the interior point method.

The invention discloses a valve core soft landing optimal trajectory planning and control method, and relates to the field of high-speed switch valve control, and the method comprises the following steps: (1) building a non-linear dynamic model of a high-speed switch valve under multi-field coupling; (2) giving a soft landing state constraint equation and a soft landing objective function of the high-speed switch valve to construct a soft landing nonlinear programming problem of the valve element of the high-speed switch valve; (3) solving an optimal soft landing curve of the valve element of the high-speed switch valve by using a primal-dual interior point method; (4) constructing a sliding mode observer to estimate the displacement of the valve element, feeding the displacement back to a sliding mode controller, and inversely solving a driving voltage curve; soft landing of the valve element is achieved. According to the method, the optimal soft landing track of the valve element of the high-speed switch valve can be solved, soft landing control is conducted on the valve element, noise and vibration are reduced, abrasion is reduced, and the service life is prolonged. The method is suitable for the fields of high-speed switch valve control strategies, soft landing trajectory planning and control and the like, and a new thought is brought to the high-speed switch valve driving control strategies.

This invention relates to a method and system for optimizing coating trajectory to ensure uniformity of spacecraft coating thickness. The method includes: acquiring a model of the surface to be coated, generating a regular grid initial coating curve and setting constraint parameters; mapping the curve to a complex surface using a pseudo-projection algorithm, and generating a fully covered, non-intersecting initial trajectory after trajectory correction; based on an experimentally calibrated coating deposition model, constructing a multi-objective optimization function that minimizes coating thickness deviation while considering trajectory smoothness and operation time, using trajectory waypoint coordinates, coating gun attitude quaternions, and coating segment duration as optimization variables, and applying constraints such as coating distance, velocity, and attitude; using an interior-point solver for iterative optimization, limiting variable iteration deviations to ensure model accuracy; and outputting an directly executable optimized trajectory. This invention can automatically adapt to the coating requirements of complex surfaces, significantly improve coating thickness uniformity, control deviations within the allowable range of aerospace processes, and effectively improve operational accuracy and efficiency.

This invention provides a convex optimization design method and system for eVTOL based on power kit selection, belonging to the field of vertical takeoff and landing aircraft design technology. The convex optimization design method includes: obtaining the parameters of the power kit and continuous flight dynamic parameters; constructing aerodynamic models, load characteristic models, battery discharge models, and efficiency models for the eVTOL cruise phase based on the parameters to determine constraints; constructing a convex optimization objective function that minimizes energy consumption per unit range based on the models; solving the convex optimization objective function using the interior-point method to obtain optimal weights and continuous parameters; and obtaining the optimal power kit and mass parameters of each system based on the constraints and optimal weights. This invention achieves simultaneous optimization of discrete power kit selection and continuous parameters under complex constraints, significantly improving optimization accuracy while meeting the strict safety and performance constraints of the entire eVTOL flight profile.

This invention discloses a probabilistic-nonprobabilistic-probabilistic box hybrid reliability model and solution method. It employs truncated probability variables to describe uncertain parameters with sufficient samples in the structure; nonprobabilistic interval variables to describe uncertain parameters with insufficient sample numbers in the structure; and probabilistic box variables to describe uncertain parameters with both cognitive and random uncertainties. Based on interval analysis of interval variables, a decoupling method for probabilistic box variables is proposed, and then the double-nested problem is optimized and solved. A hybrid reliability index is introduced to represent the hybrid reliability analysis results, and a hybrid optimization algorithm combining an outer genetic algorithm and an inner interior point method is used to solve the problem, avoiding solution errors caused by getting trapped in local optima. Finally, the failure probability interval and the corresponding hybrid reliability index interval are obtained.

The invention discloses an active power distribution network optimal power flow calculation method and system considering short circuit constraint, and relates to the technical field of power system operation and optimal scheduling. The method comprises the following steps: constructing a system model containing data of a bus, a generator and the like, and defining an optimization variable vector; setting a power generation cost minimization objective function; constructing constraint conditions including power balance equality constraint and variable boundary, branch power flow and short-circuit capacity inequality constraint, wherein the short-circuit capacity constraint is based on the IEC 60909 standard and replaces the short-circuit current constraint; deducing a Jacobian matrix and a Hessian matrix of the short circuit capacity constraint; and constructing a Lagrange function, solving the Lagrange function by adopting an interior point method, and outputting an optimal operation result meeting all constraints. The method achieves the global constraint of the operation point of the active power distribution network, avoids the out-of-limit short-circuit capability of equipment during a fault, gives consideration to the safety and economy, and is efficient and stable in solving.

The invention discloses a scheduling strategy obtaining method and device for an electric power system containing a hydroelectric generating set, and belongs to the technical field of electric power system control, the method comprises the steps that a combination problem containing the hydroelectric generating set is modeled into a multi-stage stochastic programming model, and the model can reasonably describe the implementation condition of a random variable in actual scheduling. Further, an original model is decomposed into a main problem and a sub-problem, the main problem solves unit start-stop integer variables, and the sub-problem completes solution of continuous variables under a deep random dual dynamic programming framework. A full-input convex neural network is introduced to carry out efficient approximation on a value function, an optimization problem of each time period in a sub-problem is solved through a primal-dual interior point method to obtain an optimal solution, a dual solution and gradient information, training of the full-input convex neural network is achieved, efficient solving is finally achieved, and then efficient scheduling is carried out on a power system.

This invention provides a method for evaluating optimal DC power flow in power systems based on quasi-interior point embedding and mixed precision. The method includes acquiring grid data and establishing a DC optimal power flow calculation model; constructing a quasi-interior point embedding system; obtaining the optimal solution by solving the quasi-interior point embedding system using fast, flexible, fully pure embedding, and mixed precision methods; and evaluating the optimal DC power flow scheduling scheme based on the optimal solution. This invention constructs a quasi-interior point embedding system based on the idea that the control iteration points of the interior point method move along the central path towards the constraint boundary and the optimal solution. This allows for flexible selection of initial values ​​and leverages the characteristic that the central path of the interior point method always lies within the feasible region, thus ensuring that the quasi-interior point embedding system ultimately obtains the optimal feasible solution. By employing piecewise low-order approximation, computational efficiency is effectively improved. The use of mixed precision techniques to solve linear equations reduces computation time and resource consumption while ensuring the accuracy and stability of the solution, providing a more efficient and accurate optimization scheduling scheme.

The invention discloses an intelligent physical property prediction method and device, equipment and a medium, and relates to the field of physical property prediction. The method comprises the steps that the eccentricity of a target refrigerant is calculated; inputting the group number and eccentricity of the target refrigerant into the physical property prediction model to obtain a thermodynamic physical property prediction value; the method for determining the physical property prediction model comprises the steps that an augmented group contribution prediction model is constructed, and model parameters in the model comprise a group contribution value, an eccentricity contribution value and a bias term; constructing an objective function and constraint conditions according to model parameters in the augmented group contribution prediction model; regularization terms of physical property parameters are introduced into the objective function; a physical property nonlinear mapping function is introduced into the constraint condition; training data are input into constraint conditions, an interior point method is adopted to solve a target function, and the augmented group contribution prediction model after the model parameters are determined serves as a physical property prediction model. According to the invention, the prediction precision, interpretability and isomer distinguishing capability of physical property prediction can be improved.

The invention provides a power system scheduling method based on time sequence diffusion and analytic expression functional synapse. The method can solve the problems that under extreme weather, load data are scarce, the solving precision of a non-convex nonlinear scheduling problem is low, pulse neural network training is unstable, output is discrete, and low energy consumption and environmental protection of a scheduling scheme are difficult to consider at the same time. According to the method, firstly, an extreme weather load sample is generated through a time sequence diffusion model, scarce data is supplemented, a scheduling model with energy consumption and carbon emission as targets is constructed, a prediction-correction primitive-dual interior point method is adopted to solve preliminary unit output, output is optimized through an enhanced pulse neural network of analytic expression functional synapse, and the power consumption and carbon emission are calculated. And system constraints are ensured to be met through correction measures. The method provided by the invention can improve the scheduling reliability under the extreme working condition, improve the non-convex problem solving precision, solve the pulse neural network defects, consider low energy consumption and environmental protection, and is suitable for the scheduling of the power system containing clean energy.

The application relates to the technical field of intelligent construction and engineering management, and discloses a building construction site resource optimization scheduling system based on deep learning, which comprises a data acquisition interface unit, a dynamic graph tensor construction unit, a double-head dual perception prediction unit, a hot start solving unit and a scheduling instruction execution unit. The system converts construction site data into a heterogeneous graph tensor containing physical interference potential field characteristics, uses a double-head deep network to predict a quadratic programming target parameter and a Lagrange multiplier vector of a KKT condition in parallel, and uses the multiplier vector as an initial dual variable of an original dual interior point method solver to perform hot start solving. By introducing physical potential field characteristic embedding and Lagrange multiplier hot start mechanism, the application can introduce spatial constraint information and task logic relationship of the construction site into the resource scheduling solving process together, generate a scheduling solution meeting preset constraint conditions under a constraint model, reduce the iteration number of the original dual interior point method solver, and improve the solving efficiency under a dynamic scheduling scenario.

The application discloses a multi-source uncertain information structure reliability evaluation model and an accurate solving method. A truncated random variable is used to describe sample sufficient uncertain parameters in the structure; a hyper-ellipsoid convex set non-probability variable is used to describe some sample insufficient uncertain parameters in the structure; a fuzzy random variable is used to describe uncertain parameters with both fuzziness and randomness; and an evidence variable is used to describe evidence information given by experts or experiments. A similar non-probability mixed reliability index is introduced to measure the absolute safety of the structure, and a hybrid optimization algorithm of outer genetic algorithm and inner point method is used for solving, so as to avoid solving errors caused by falling into local optimum. When the structure is not absolutely safe, a maximum possible failure point corresponding to the similar non-probability mixed reliability index is taken as an initial point, a first order second moment (AORM) method is used to solve a similar probability mixed reliability index, and a second order second moment (SORM) method is used to correct the similar probability mixed reliability index to obtain an accurate result.

The invention discloses a cognitive sonar waveform design method using ambiguity function constraint, and belongs to the technical field of sonar target detection. According to the method, on the basis of a traditional water injection algorithm, a Q function representing reverberation output intensity is introduced to serve as a constraint condition on the target of maximizing the signal to interference and noise ratio (SINR); by deriving frequency domain expressions of a broadband ambiguity function, a narrowband ambiguity function and a Q function, constructing a nonlinear non-convex optimization model including SINR maximization, an equivalent bandwidth constraint (constant alpha), a Q function constraint (constant beta) and a transmitted waveform energy constraint (Ex); a fmincon solver (interior point method) in MATLAB is combined with a MultiStart global search mechanism to solve the model, and an optimal solution is selected through multiple times of random initialization to avoid initial value sensitivity. According to the waveform generated by the method, the Q function value at the Doppler frequency shift of the target can be reduced while the relatively high SINR is maintained, reverberation is effectively inhibited, and the shallow sea slow target detection performance is improved.

This invention discloses a method and apparatus for optimizing the design of a space-based natural fly-around trajectory for a medium-to-high orbit target, belonging to the field of spacecraft orbital dynamics and optimization design technology. It includes: establishing a dynamic model of a medium-to-high orbit satellite considering complex perturbations; generating initial values ​​for the fly-around trajectory using a dual-mode approach combining direct position offset and CW equation analysis; constructing a multi-objective weighted optimization objective function, jointly constraining the root mean square of the relative distance difference, the extreme difference of the relative distance, and the mean absolute value of the difference in the relative distance interval time to evaluate trajectory morphological stability; setting three-dimensional coordinate constraints in the target satellite orbital coordinate system to avoid invalid solutions, and using the interior point method to solve the constrained nonlinear optimization problem; and achieving trajectory correction through dynamic model extrapolation, quantitative evaluation of performance indicators, and iterative optimization. This invention overcomes the defect of large extrapolation errors of the CW equation under complex perturbation environments by combining an accurate dynamic model with optimization methods, resulting in a natural fly-around trajectory with high accuracy and long holding time.

The invention discloses an elliptic curve point multiplication algorithm window size determination method based on hardware parameters, and relates to the technical field of embedded computing, and the method comprises the steps: obtaining the hardware parameters of a target embedded device, inputting the hardware parameters into a performance quantification model, outputting a plurality of cost parameters by the performance quantification model, constructing a weighted target function, and determining the window size of the target embedded device according to the weighted target function. Solving the new weighting objective function by adopting an interior point method to obtain a plurality of candidate K values in a continuous domain; and substituting the candidate K value into a weighted target function, selecting a target solution which enables the weighted target function to be optimal from the candidate solutions meeting hardware constraint conditions, taking the target solution as a final integer window size K value, and applying the final integer window size K value to all operations for operating an elliptic curve point multiplication algorithm in the target embedded equipment. The technical problem that the utilization rate of equipment performance is low due to the fact that a fixed numerical value selection mode is adopted for the window size of an elliptic curve point multiplication algorithm in the prior art is solved.

The invention discloses a machine learning driven airfoil design method, equipment, a medium and a program product, and the method comprises the steps: (1) building a mathematical model of the floating amount and the lift-drag ratio of an airfoil node by taking weight and rigidity as constraints; (2) establishing an airfoil profile population based on space filling Latin hypercube sampling; (3) calling DE mutation operation and a maximum and minimum distance screening method by adopting uniformly distributed random numbers to generate a first generation of sub-populations; (4) optimizing by adopting an interior point method to generate a second generation sub-population; and (5) selecting airfoil profile filial generations according to an error-driven adaptive screening strategy assisted by the agent model, evaluating the airfoil profile filial generations, selecting an optimal individual from the airfoil profile filial generations according to a feasibility rule, judging whether an optimization result of the optimal individual reaches the standard or whether the current judgment frequency reaches the maximum judgment frequency, if so, outputting an optimal variable parameter, and if not, returning to the step (3). According to the method, an evolutionary mechanism adaptive to airfoil constraint calculation difference is designed to reduce calculation cost, so that the design period of airfoil lift-drag ratio optimization is shortened.

The application provides an aircraft online trajectory optimization method based on a parallel real-time interior point method of nested partitioning, which firstly converts the aircraft online trajectory optimization problem into a standard second-order cone programming problem; then solves the SOCP problem by using a primal-dual interior point method based on homogeneous self-dual embedding and scaling, rearranges the coefficient matrix of the KKT sparse linear system by using a nested partitioning algorithm, and obtains a balanced elimination tree; based on the elimination tree, the LDL matrix decomposition and triangular system solving tasks are divided into sub-tree / trunk sub-tasks, and each sub-tree calculation and trunk task calculation are distributed to each CPU calculation core, so that the calculation tasks of each CPU calculation core are very balanced, thereby improving the calculation efficiency; the application has high parallelism and good parallel performance, can realize a calculation speedup ratio of 3.2-3.5 times on a DSP platform, is suitable for medium / large-scale problems, has low memory occupation, and is suitable for rocket-borne / airborne embedded environments.

The present application relates to the technical field of integrated energy system optimization scheduling, and discloses a method for unit commitment of an integrated electricity-heat system based on an interior point Benders decomposition, which comprises the following steps: firstly, a mixed integer linear programming model is constructed, and decision variables such as power unit start-stop / scheduling, heat system output and heat supply network temperature are determined, so as to minimize the total operation cost of the system, integrate multiple constraint conditions, and use the method of characteristics to analyze the partial differential equation of pipe temperature conduction, thereby improving the accuracy of dynamic temperature description; and secondly, the model is solved by combining the improved Benders decomposition algorithm of the interior point method, so as to realize parallel solving of the main problem and the sub-problem. Through simulation verification of an IEEE standard system, the method can realize collaborative optimization scheduling of the electricity-heat system, adapt to wind power uncertainty, ensure the stability of the system, reduce the operation cost and improve the energy utilization efficiency.

The invention relates to the technical field of optimal scheduling of an integrated energy system, and discloses an electric heating integrated energy system unit combination method based on interior point Benders decomposition. The method comprises the following steps: firstly, constructing a mixed integer linear programming model, determining decision variables such as power unit start-stop / scheduling, thermodynamic system output and heat supply network temperature, aiming at minimizing the total operation cost of the system, integrating multiple constraint conditions, analyzing a pipeline temperature conduction partial differential equation by adopting a characteristic line method, and improving the temperature dynamic description accuracy; and the model is solved through an improved Benders decomposition algorithm combined with an interior point method, and parallel solving of the main problem and the sub-problems is achieved. Through IEEE standard system simulation verification, the method can realize electric-thermal system collaborative optimization scheduling, adapts to wind power uncertainty, guarantees system stability, reduces operation cost, and improves energy utilization efficiency.

The invention discloses a six-axis manipulator control method based on global interior point iteration multi-starting-point solution, which comprises the following steps of: acquiring an initial D-H parameter of a manipulator, and performing coordinate transformation to obtain a theoretical attitude matrix of the manipulator; an actual tail end attitude matrix of the manipulator is obtained, and the pose error between the theoretical attitude matrix of the manipulator and the actual tail end attitude matrix of the manipulator is calculated; a global interior point iteration multi-starting-point method is adopted, new D-H parameters are obtained through optimization according to the initial D-H parameters and a manipulator actual tail end posture matrix, and the global interior point iteration multi-starting-point method is that step-by-step optimization is conducted on variables to be optimized through an interior point method and a multi-starting-point strategy; and the pose of the manipulator is controlled based on the obtained D-H parameters. According to the method, the powerful capability of the interior point method on local optimization and the advantages of multi-starting-point global search on global coverage are fused, the optimal solution meeting the constraint condition can be efficiently and accurately found, the method is applied to control over the six-axis mechanical arm, and a new effective way is provided for improving the absolute positioning precision of the six-axis mechanical arm.