61 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 active power distribution network optimal scheduling method and system considering a source-load complementary characteristic, and the method comprises the steps: generating a fan, photovoltaic output power and load curve cluster of different typical scenes through an improved K-means clustering algorithm, and training a long-short-term memory neural network model, and on the basis, fan and photovoltaic output power and load values are predicted, and the source load prediction precision of different typical scenes is improved. A source-load complementary system architecture is constructed according to predicted values of the fan, the photovoltaic output power and the load, an active power distribution network day-ahead optimization scheduling model is established based on the architecture, linear convex optimization transformation is executed, and the model solving complexity is reduced. And solving and generating a day-ahead optimization scheduling decision scheme by adopting a modern interior point method, executing rolling optimization according to an intra-day short-term predicted value, and adjusting and perfecting a day-ahead scheduling plan value.

The invention belongs to the technical field of power quality disturbance detection, and particularly relates to a power quality disturbance detection method based on improved adaptive S transformation, which comprises the following steps of: designing window width control factors of two Gaussian windows, flexibly adjusting time domain window widths and frequency spectrum characteristics of the Gaussian windows, and forming a global adaptive Gaussian window; taking upper and lower limits of time-frequency two-domain resolution as constraint conditions, taking enhanced signal energy concentration ratio as a target function of an optimization scheme, and adopting an interior point method to carry out parameter solving; performing fast Fourier transform on the signal and setting a threshold value, and defining a part higher than a threshold value point as a characteristic frequency point ki; screening calculation at a non-characteristic frequency point, only performing time-frequency transformation at a characteristic frequency point ki, and constructing a fast algorithm for improving adaptive S transformation; and obtaining a final disturbance characteristic information value. According to the method, the global adaptive Gaussian window is constructed as a kernel function for improving adaptive S transformation, the effective window length and the frequency spectrum of the window function can be automatically adjusted along with the change of the detection frequency, window parameters are prevented from being frequently switched for improving the time-frequency resolution, and various power quality disturbances are efficiently and accurately detected.

The invention relates to a laser detection method, device and equipment of a laser scribing machine and a storage medium. The method comprises the following steps: carrying out laser scanning on the surface of a solar cell, collecting a reflected light intensity signal and carrying out adaptive domain transformation processing to obtain a target characteristic matrix; training a material characteristic predictor based on the target characteristic matrix, and predicting material characteristic parameters on the lineation path to obtain a material characteristic prediction value; a state space model of the dual-optical-path laser system is constructed according to the material characteristic predicted value, a Lyapunov control function is established by solving a Lyapunov equation, and constraint conditions for ensuring the stability of the system are obtained; and the constraint condition and the material characteristic prediction value are substituted into a model prediction control optimization problem, a quadratic programming problem is solved through an interior point method, and an optimal control sequence is obtained. According to the invention, accurate prediction of the material characteristics on the scribing path is realized, scribing abnormity caused by sudden change of the material characteristics is reduced, and the anti-interference capability and the control precision of the system are enhanced.

The invention discloses a data processing method. The method comprises the following steps: constructing a linear programming model; a decision variable of the linear programming model represents the number of products transported from a production site to a sales site, the constraint conditions at least comprise a product production capacity constraint for at least one production site and a product demand constraint for at least one sales site, and the target is at least one of the minimum product production cost and the minimum product transportation cost; determining at least two groups of initial solutions corresponding to the linear programming model; solving the linear programming model in parallel based on the at least two groups of initial solutions by using an interior point method so as to iteratively update the at least two groups of initial solutions; for at least one solving process corresponding to each group of initial solutions, the updated solution of the initial solutions is determined based on solving information of at least one group of initial solutions except the initial solutions; the solving information at least comprises value boundary updating information of the decision variable; and taking the updated solution as a target solution of the linear programming model in response to the condition that the updated solution corresponding to the at least one initial solution meets the convergence condition.

The invention relates to an optimization method and system for a hybrid distribution transformer. The method comprises the following steps: acquiring operation data of the hybrid distribution transformer; constructing a magnetic circuit topology matrix, and calculating a reconstruction path to obtain a reconstruction decision matrix; performing feasibility verification and reliability analysis on the reconstruction decision matrix to obtain a feasible reconstruction scheme set and a reliability evaluation matrix; calculating an integration level index of each feasible reconstruction scheme to obtain an integration level evaluation matrix; performing multi-objective optimization calculation to obtain a balance optimization scheme; calculating the dynamic response of the multi-physics field coupling model by using a finite element discretization algorithm to obtain a dynamic response sequence; solving a control strategy optimization problem by using an interior point method to obtain a control strategy; optimizing by utilizing a self-adaptive parameter adjustment algorithm to obtain a parameter correction matrix; obtaining an optimal control strategy based on the parameter correction matrix; and the hybrid distribution transformer completes optimization based on the optimal control strategy.

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 provided is a reactive power optimization method for a power system based on distributed interval optimization, including following steps: S1. constructing a first model, where the first model includes an interval reactive power optimization model of a power system with a high renewable energy penetration rate, and the interval reactive power optimization model includes an objective function, a first constraint, and a constraint of an interval power flow equation; S2. obtaining a first state variable interval based on the constraint of interval power flow equation and a distributed interval power flow algorithm; S3. Converting the first model based on the first state variable interval and a security limit definition method, and obtaining a second model, where the second model includes a deterministic optimization model; and S4. obtaining a voltage and reactive power control strategy based on the second model and a primal-dual interior point method.

The invention provides a form optimization method and system for a free-form surface cable-supported single-layer latticed shell, and relates to the technical field of large-span space structure design, and the method comprises the steps: setting initial conditions for form optimization of the free-form surface cable-supported single-layer latticed shell; based on initial conditions, taking strain energy and curved surface smoothness as optimization targets, taking node height as an optimization variable, and taking a node height feasible region as an optimization constraint, constructing an objective function, taking the optimization constraint as a penalty function and adding the penalty function into the objective function through an interior point method, and converting a constrained optimization problem into an unconstrained optimization problem, so as to achieve the optimization of the curved surface. Establishing an optimization equation under the corresponding weight coefficient in combination with the weight coefficient; and traversing the set weight coefficients, optimizing the form of the free-form surface cable support single-layer reticulated shell for each weight coefficient, solving an optimization equation under the corresponding weight coefficient, exiting the optimization loop until a constraint boundary is reached, and outputting an optimization solution under the current weight coefficient to obtain the optimal form of the free-form surface.

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.

The present invention discloses an IPFC planning and configuration method considering the regulation capabilities of multiple flexible devices. This method fully takes into account the regulation capabilities of multiple flexible devices, takes the minimum IPFC investment and the maximum available transfer capability of the system as the optimization objectives, and constructs a multi-objective optimization model for IPFC planning and configuration. Based on a multi-objective algorithm nested with a multi-objective particle swarm algorithm and the interior point method, the solution of the proposed multi-objective optimization model is realized. The interior point method is used in the inner layer of the algorithm to solve the available transfer capability of the system with IPFC, and the multi-objective particle swarm algorithm is used in the outer layer to calculate the Pareto solution set of IPFC planning and configuration. The proposed model can fully consider the mutual support effect of the existing flexible devices in the system, and the corresponding solution algorithm can accurately and efficiently complete the optimization calculation, having great practical value, and can provide theoretical and technical support for the planning and configuration and demonstration application of IPFC.

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.

The invention discloses a water supply pump station non-convex optimization scheduling method based on a group contract endpoint method, and the method comprises the steps: building an urban water supply pump station optimization scheduling model, enabling the model input to be a decision variable for guaranteeing an annual water distribution mode and a water demand and a water supply pump station optimization scheduling constraint condition, and enabling the model output to be a water supply system pump station energy-saving objective function; according to the optimal scheduling model of the urban water supply pump station, establishing a new objective function convex optimization model considering the pipeline head loss, and establishing a new objective function homotopy equation; and according to the new objective function homotopy equation and the water supply pump station optimal scheduling constraint condition, obtaining an optimal solution of the urban water supply pump station optimal scheduling model on the homotopy path. Compared with the prior art, the constraint form is adjusted through homotopy parameters, the method is suitable for processing complex constraint scenes, and meanwhile the solution stability is improved through the interior point method.

The present application belongs to the technical field of trajectory optimization of morphing aircraft, and specifically discloses a reentry trajectory optimization method of morphing aircraft, comprising: establishing a reentry dynamics model and a multi-constraint condition model of the morphing aircraft; constructing a minimum drag coefficient configuration-Mach number profile and a reentry height flight corridor, and converting the trajectory optimization problem of the morphing aircraft into a trajectory optimization problem with a fixed aerodynamic shape; performing dimensionless, linearization and discretization processing on the reentry dynamics model, and introducing a control quantity transformation, so as to convert the trajectory optimization problem into a standard second-order cone programming problem; using a primal-dual interior point method to discretely solve the second-order cone programming problem, so as to obtain an optimal reentry trajectory satisfying all constraint conditions, and complete the reentry trajectory optimization method of the morphing aircraft. The present application avoids calculating the configuration parameter derivative in the trajectory optimization, and solves the problem that the derivative information of the configuration parameter cannot be obtained from the aerodynamic data.

The invention provides a slope overall process analysis method based on a mathematical optimization particle finite element method, which comprises the following steps of: establishing a control equation of quasi-static and dynamic elastic-plastic problems under a plane strain condition in a slope region, and converting the control equation into a minimum-maximum optimization problem according to a Hellinger-Reissner variation principle, therefore, a complex motion form in the slope area is converted into a simple minimum-maximum optimization problem; finite element discrete analysis is carried out on the minimum-maximum optimization problem, then a standard second-order cone programming problem is constructed, and then an optimal solution of the second-order cone programming problem can be rapidly obtained through an interior point method, namely, a state variable is obtained; and according to the state variables and in combination with a particle finite element method, calculating the motion state before and after slope instability, thereby obtaining the whole-process operation state of the slope. The analysis method provided by the invention not only can judge the safety and stability of the slope in the static state, but also can simulate and analyze the movement process after the slope is damaged.

The application discloses a converter adaptive hybrid synchronization control method based on steady-state margin prediction, belongs to the technical field of new energy grid connection, and comprises the following steps: constructing a grid-connected VSC converter adaptive hybrid synchronization control dq impedance model; constructing a steady-state margin prediction model based on DNN; collecting training data; training the steady-state margin prediction model in step S2 by using the collected training data to obtain a trained steady-state margin prediction model; based on the steady-state margin prediction model in step S3, solving the optimal solution of adaptive coefficients by using an interior point method under the condition of a given steady-state margin; and based on the optimal solution of adaptive coefficients obtained by combining the steady-state margin prediction model in step S3 and the optimization algorithm in step S4, adjusting parameters to realize adaptive small-disturbance stability control of the converter. k a The application discloses a converter adaptive hybrid synchronization control method based on steady-state margin prediction, belongs to the technical field of new energy grid connection, and comprises the following steps: constructing a grid-connected VSC converter adaptive hybrid synchronization control dq impedance model; constructing a steady-state margin prediction model based on DNN; collecting training data; training the steady-state margin prediction model in step S2 by using the collected training data to obtain a trained steady-state margin prediction model; based on the steady-state margin prediction model in step S3, solving the optimal solution of adaptive coefficients by using an interior point method under the condition of a given steady-state margin; and based on the optimal solution of adaptive coefficients obtained by combining the steady-state margin prediction model in step S3 and the optimization algorithm in step S4, adjusting parameters to realize adaptive small-disturbance stability control of the converter. k a The application discloses a converter adaptive hybrid synchronization control method based on steady-state margin prediction, belongs to the technical field of new energy grid connection, and comprises the following steps: constructing a grid-connected VSC converter adaptive hybrid synchronization control dq impedance model; constructing a steady-state margin prediction model based on DNN; collecting training data; training the steady-state margin prediction model in step S2 by using the collected training data to obtain a trained steady-state margin prediction model; based

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 provides a power system DC optimal power flow evaluation method based on quasi-interior point embedding and mixing precision, and the method comprises the steps: obtaining power grid data, and building a DC optimal power flow calculation model; constructing a simulated interior point embedding system; solving the quasi-interior point embedding system based on rapid and flexible full-pure embedding and mixed precision to obtain an optimal solution; and evaluating a direct-current optimal power flow scheduling scheme according to the optimal solution. According to the method, a quasi-interior-point embedding system is constructed based on the idea that an interior-point method controls iteration points to be close to a constraint boundary and an optimal solution along a central path, an initial value can be flexibly selected, and meanwhile, the quasi-interior-point embedding system is constructed by referring to the characteristic that the central path of the interior-point method is always in a feasible region so as to guarantee that the optimal feasible solution is finally obtained; by adopting segmented low-order approximation, the calculation efficiency is effectively improved; the mixed precision technology is adopted to solve the linear equation set, calculation time and resource consumption are reduced on the premise that the precision and stability of the solution are guaranteed, and a more efficient and accurate optimization scheduling scheme is provided.

The present invention discloses a short-packet secure communication method in a wireless power supply Internet of Things, including: establishing an optimization problem model for short-packet secure communication in the wireless power supply Internet of Things; relaxing the integer-constrained packet length variable in the established optimization problem into a continuous variable; decomposing the relaxed problem into two sub-problems, and obtaining the solution of the relaxed problem by iteratively optimizing the two sub-problems. Among them, the first sub-problem optimizes the packet error rate and is solved by the interior point method, and the second sub-problem optimizes the packet length and is solved by the method of successive convex approximation; finally, the continuous packet length variable is constrained to an integer by the greedy algorithm to obtain the finally optimized packet length, and the first sub-problem is solved again by the interior point method to obtain the finally optimized packet error rate. The present invention mainly aims at the communication scenario where the terminals in the Internet of Things are powered by wireless radio frequency energy harvesting. By optimizing the packet error rate and the packet length, the total secure throughput of all Internet of Things terminals is improved.

The invention relates to a biped robot nonlinear model predictive controller FPGA heterogeneous acceleration method, which comprises the following steps: establishing an NMPC model according to a biped robot dynamic model, iteratively solving a quadratic programming form of the NMPC model, and completing a single iteration process: solving by a primitive-dual interior point method and solving a symmetric linear equation set by a minres algorithm; the process of solving the solution of the linear equation set constructed by using the KKT condition based on the quadratic programming form through the primitive-dual interior point method is configured to be executed by a CPU; the process of solving the symmetric linear equation set by the minres algorithm is configured to be executed by the FPGA. According to different calculation characteristics of a solution operator in a biped robot NMPC model, a logic reusable minres algorithm in the solution process is deployed to an FPGA calculation platform, and nonlinear operation is deployed to a CPU for implementation, so that the calculation power characteristic of a heterogeneous chip is fully utilized. According to the method, robot dynamics modeling, an efficient optimization solution algorithm and appropriate computing power resource allocation are considered, and the acceleration effect and verification of the algorithm can be ensured.

The present invention discloses a method for generating a multi-objective non-inferior solution set for inter-basin water transfer based on a convex set feasible domain. The method comprises the following steps: collecting hydrological data from the study area, selecting a primary objective function from pre-constructed objective functions, constructing a plane set covering the entire spatial angle, and searching for boundary points of the target space corresponding to other objective functions; pre-processing the boundary points of the target space, and constructing a convex set boundary range of the target space using a spatial boundary convex hull algorithm; generating an ε-constrained threshold combination within the convex set boundary range of the target space; and for each ε-constrained threshold combination, converting the multi-objective optimization problem into multiple single-objective optimization sub-problems, solving each of these sub-problems using a sparse optimization interior point method, to generate a non-inferior solution set for the inter-basin water transfer scheme. The present invention effectively reduces the possibility of generating infeasible solutions and duplicate solutions in traditional ε-constrained algorithms, and improves the efficiency and quality of generating non-inferior solution sets.

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 a large-scale wind power blade fatigue test load optimization method based on a hybrid optimization algorithm, and relates to the technical field of wind power generation equipment testing. The method comprises the following steps: step 1, determining test parameters and physical characteristic parameters according to the structural characteristics of the wind power blade, and defining optimization variables and an optimization variable range; step 2, establishing a blade region segmentation weighting objective function, setting a differentiation weight and a threshold value for each region, and constructing an objective function of a differentiation punishment mechanism; 3, according to the objective function in the step 2, a Bayesian global optimization algorithm is adopted, a Gaussian process proxy model of the objective function is constructed for optimization, and a global optimal solution of an optimization variable is output; and 4, taking the Bayesian optimization result in the step 3 as a starting point, performing local accurate optimization by applying an interior point method, and outputting a final local optimization result of the optimization variable.

In some aspects, the techniques described herein relate to quantum methods for solving instances of second-order cone programming (SOCP), based on the SOCP instance, for solving a matrix G and a vector h → Define a Newton system for the SOCP instance by constructing a matrix G and a vector h → By normalizing the rows, the condition number of the matrix G is reduced, and the iterative iterations are repeated until the condition is met. → Iteratively determine the matrix G and the vector h → to a quantum linear system solver (QLSS) to generate a quantum state; and performing quantum state tomography on the quantum state using a quantum computing system. → Based on the current value of and the output of the quantum state tomography, u → updating the value of u → determining a solution for the SOCP instance based on the updated values ​​of

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 discloses a cross-basin water transfer multi-target non-inferior solution set generation method based on a convex set feasible region. The method comprises the steps of collecting hydrological data of a research region, selecting a main target function from pre-constructed target functions, constructing a plane set covering full angles of a space, and searching boundary points of target spaces corresponding to other target functions; preprocessing boundary points of the target space, and constructing a convex set boundary range of the target space by using a space boundary convex hull algorithm; generating an epsilon constraint threshold combination in the convex set boundary range of the target space; and for each epsilon constraint threshold combination, converting a multi-objective optimization problem into a plurality of single-objective optimization sub-problems, and respectively solving the single-objective optimization sub-problems by using a sparse optimization interior point method to generate a non-inferior solution set of the cross-basin water transfer scheme. According to the method, the generation possibility of infeasible solutions and repeated solutions in a traditional epsilon constraint algorithm is effectively reduced, and the generation efficiency and quality of the non-inferior solution set are improved.