40 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 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 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 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.

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 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 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.

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 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.

The invention provides an aircraft on-line trajectory optimization method based on a nested subdivision parallel real-time interior point method. The aircraft on-line trajectory optimization method comprises the steps that firstly, an aircraft on-line trajectory optimization problem is converted into a standard second-order cone programming problem; then solving the SOCP problem by adopting a primal-dual interior point method based on homogeneous self-dual embedding and scaling, and rearranging a coefficient matrix of the KKT sparse linear system through a nested subdivision algorithm to obtain a balanced elimination tree; an LDL matrix decomposition and triangular system solving task is divided into sub-tree / trunk sub-tasks based on an elimination tree, sub-tree calculation and trunk task calculation are allocated to each CPU calculation core, the calculation tasks of each CPU calculation core are very balanced, the calculation efficiency is improved, and the method is high in parallelism degree, good in parallelism performance and suitable for large-scale popularization and application. According to the method, the calculation speed-up ratio of 3.2-3.5 times can be achieved on a DSP platform, the method is suitable for medium-large scale problems, the occupied memory is low, and the method is suitable for rocket-borne / airborne embedded environments.

This invention provides a multi-hop routing and load distribution method, system, and storage medium for space-based computing constellations. The method includes: Step 1: Establishing a model for matrix-vector multiplication computation tasks performed by the space-based computing constellation, defining source nodes that encode and split tasks, relay nodes that transmit tasks hop-by-hop, and relay nodes and destination nodes performing distributed computation on tasks during end-to-end routing; Step 2: Establishing an optimization problem to minimize task execution latency, proposing a computing power awareness and load distribution algorithm based on block coordinate descent to decompose the original problem into routing and load distribution problems; Step 3: Designing a computing power awareness routing algorithm based on dynamic programming to select the optimal relay node, and designing a multi-hop encoding load distribution algorithm based on interior point method to achieve communication and computation load balancing. The beneficial effects of this invention are: avoiding the transmission of large amounts of raw data to ground centers, distributing task processing during end-to-end routing, and reducing task execution latency.

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 application provides a deterministic service flow scheduling method and device, equipment and a storage medium. The method responds to access application information sent by a service device end, identifies the service device end according to the access application information, and judges whether the service device end is a registered device. If the service device end is a registered device, linear programming solving processing is performed using an interior point method according to the recorded access application information and the access application information, to obtain scheduling configuration information. The scheduling configuration information is subjected to consistency inspection to judge whether there is a resource overflow condition. If there is no resource overflow condition, the scheduling configuration information and the access application information are sent to a target service device end and a target network forwarding device end corresponding to a predicted allocation resource combination, to realize service flow scheduling.

The present invention discloses a FDA-MIMO sonar fast gridless joint distance-angle estimation method and product based on a two-dimensional generalized fast interior point method, comprising: s1: performing frequency mixing and matched filtering on a received signal to obtain an extended virtual received signal model; s2: summarizing the estimation problem as an NsCP problem and initializing parameters related to the interior point method; s3: using a restricted memory BFGS algorithm and an augmented KKT condition to update the primal variable and the dual variable, performing a 2D-FFT fast feasible domain cone verification on the obtained variables, repeating this step until the result is within the feasible domain, and then going to s4; s4: performing a convergence check on the obtained variables, if converged, obtaining the optimal solution to the NsCP problem, and going to s5, otherwise going to step 3; s5: based on the optimal solution, retrieving sparse atoms through Vandermonde decomposition of a multi-level Toeplitz matrix, and extracting the distance and angle estimation of the target using linear correspondence.

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 invention discloses an anti-detection and anti-interception method for a distributed multi-station measurement and control system, and the method comprises the steps: S1, collecting basic measurement and control demand information between all ground stations and an aircraft through a central ground station node, and obtaining or estimating the channel state information or position information of potential eavesdropping nodes and interception nodes; s2, establishing two hypothesis models of'position ascertained 'and'position uncertain', and evaluating the anti-detection performance and the anti-interception performance of the measurement and control behavior under the current beam design; s3, constructing an optimization problem based on the evaluation model established in S2, approximately processing a non-convex expression by adopting SDP and SCA methods, and solving the reconstructed optimization problem by adopting an interior point method; s4, the center ground station issues each ground station transmitting beam coefficient obtained through optimization to the corresponding node; and S5, the ground stations cooperatively execute joint measurement and control information transmission according to the received beam weights, and space superposition is formed under the guarantee of a time-frequency synchronization and phase control mechanism.