61 results about "Gradient method" patented technology

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention provides a coal ash composite material goaf closed filling parameter intelligent matching method, and belongs to the technical field of deep learning and mining engineering crossing. According to the method, dynamic optimization and accurate matching of the filling parameters are realized through combination of data driving and an intelligent algorithm. The method comprises four core links: multi-source data perception and fusion, a material performance prediction model, key parameter identification and boundary constraint, and intelligent matching and optimization decision. According to the method, the modeling capability of the model for the complex coupling relationship is improved through the attention mechanism and the feature cross network; the performance evolution trend of the material under different proportions and process conditions is accurately predicted, key regulation and control parameters are automatically identified, and efficient search and optimal matching of a parameter space are achieved through a strategy gradient method. According to the method, multi-source data can be fused, key parameters can be dynamically identified, and the intelligent matching and optimizing capability is achieved.

The invention provides a structured feedback risk constraint LQR solving method based on optimal control optimization, and belongs to the technical field of control optimization, and the method comprises the steps: obtaining an initial feedback gain matrix meeting a microgrid communication topology sparsity constraint; a zero-order strategy gradient method is adopted, through an outer layer iteration optimization step and an inner layer sampling estimation sub-step, a gradient direction is estimated by utilizing observation change of a target function value; according to the distributed frequency control method, strategy updating is carried out on the basis of an optimal control algorithm, a sparse feedback gain matrix obtained after N rounds of iteration is output, the sparse feedback gain matrix is directly deployed to a local controller of the micro-grid, and distributed frequency control meeting the risk constraint is achieved. Compared with a classical stochastic gradient descent method, the OCP algorithm obtains a satisfactory optimality gap, the convergence speed is higher, and the stability is also improved through dual redescription of maximum and minimum problems.

The invention belongs to the technical field of computers, and particularly relates to a personalized recommendation method and system based on a dynamic heterogeneous graph and reinforcement learning. The method comprises the following steps: firstly, constructing a global heterogeneous information graph of multiple types of nodes offline, and learning static embedding of the nodes by using a graph neural network; secondly, dynamically constructing a session history into a session graph in a real-time interaction process of the user, and aggregating by adopting a graph convolutional network to generate a dynamic state vector of the user; inputting the dynamic state vector into an actor and commentator reinforcement learning framework; and finally, using a dominant function calculated by the commentator network as a stable learning signal, and performing end-to-end joint training on the whole model to optimize long-term cumulative return. According to the method, by introducing the session graph volume accumulation device, the accuracy of dynamic state representation is remarkably improved; and an actor commentator framework is adopted, so that the problem of high variance of a traditional strategy gradient method is effectively solved, and the training stability and efficiency are improved.

The invention discloses a flux linkage interpolation model adaptive gradient scheme-based switched reluctance motor sensorless control method, which comprises the following steps of S1, designing a flux linkage interpolation model FLIM by representing flux linkage characteristics of rotor misalignment and alignment positions of a switched reluctance motor SRM; s2, designing an adaptive gradient optimization algorithm, and estimating the optimal position of the rotor by updating an interpolation function in real time; and S3, aiming at the phase fault problem, providing a fault-tolerant strategy, namely fault phase flux linkage reconstruction FPFR, and reconstructing the flux linkage at the fault phase. According to the method, FLIM magnetic flux link modeling, adaptive gradient optimization and FPFR fault-tolerant control are combined, and the FLIM model not only significantly reduces the modeling complexity, but also lays a foundation for high-precision observation. According to the adaptive gradient method, real-time dynamic optimization is realized through a magnetic flux link error, and the inherent problems of calculation complexity and response lag of a traditional observer are effectively solved. The FPFR strategy reconstructs the fault phase magnetic flux characteristics, and a feasible solution is provided for high-reliability application.

The invention discloses a power transmission line three-dimensional meteorological field reconstruction method and system considering terrain influence, and the method comprises the steps: constructing a physical constraint projection function according to a terrain normal vector and an actual measurement value of each station in a power transmission line; constructing a computational physical operator system oriented to sparse sites; constructing a variational optimization objective function of each station according to the physical constraint projection function and the computational physical operator system; constructing a POD basis matrix according to historical data, and performing iterative solution by adopting a preprocessing conjugate gradient method according to the POD basis matrix to obtain a modal coefficient vector enabling the variational optimization objective function to be optimal, namely an optimal modal coefficient vector; and reconstructing a three-dimensional field according to the optimal modal coefficient vector, and synchronously calculating a space gradient. Reconstruction accuracy, physical consistency and reliability can be improved, and the requirements for a high-precision three-dimensional meteorological field in multiple aspects such as severe convection weather monitoring and early warning of a power transmission line and weather forecast verification can be met.

This invention discloses an intelligent air combat decision-making method combining imitation learning and reinforcement learning, belonging to the field of air combat. The method includes: processing battlefield situation information through an intelligent air combat decision-making model to obtain decision results for guiding the aircraft. The pre-trained intelligent air combat decision-making model is obtained through the following steps: for coarse-grained sparse expert policy data, a behavior cloning algorithm is used to train a neural network architecture for imitation learning and reinforcement learning to obtain a policy network Q1; the policy network Q1 is used as the initial network in a generative adversarial imitation learning algorithm to perform imitation learning on fine-grained dense expert policies to obtain a policy network Q2; the policy network Q2 is used as the initial network in a reinforcement learning algorithm framework for decision network training, and the policy gradient method is used to train the network until convergence to obtain the intelligent air combat decision-making model. This invention is based on the ability to effectively improve sample utilization and reduce cumulative error.

The invention provides a generation method about human driving data trajectory generalization, comprising the following steps: step 1, acquiring a driving data set, driving trajectory data comprising a state vector; step 2, constructing a space-time decoupling attention mechanism ST-DAM, and decoupling the state vector based on ST-DAM to form a potential feature; step 3, constructing a deep enhanced generation network DRGN which comprises a generator G and a discriminator; the discriminator calculates a global utility score of the prediction trajectory sequence based on the utility function; 4, performing optimization training on the DRGN based on the potential features by adopting a strategy gradient method in reinforcement learning RL; and step 5, inputting the new state vector into the trained deep enhanced generative network DRGN through a space-time decoupling attention mechanism ST-TAM, and obtaining a prediction trajectory. According to the method, the high fidelity of the generated trajectory is ensured, and meanwhile, the method has high strategy explosiveness and generalization ability under global dynamics constraint.

This invention aims to optimize the input sequence of actuators with significant time delays in marine vehicles (such as variable buoyancy hydraulic pumps and valves, and slow-response actuators like rudders). Under complex sea conditions and model uncertainties, this method achieves rapid and stable attainment of the target operational state with minimal action cost, while simultaneously satisfying safety and lifespan constraints. The method disclosed in this invention uses a parameter identification model as the prediction kernel, incorporating terminal task deviation, the number of execution actions and non-zero duration, and the safety volume boundary into a multi-objective cost function. It employs an improved LM algorithm with adaptive damping for sequence-level optimization and rolling updates, thereby overcoming the shortcomings of conventional closed-loop compensation and pure gradient methods in terms of slow convergence, oscillation, and difficulty in balancing safety and wear on time-delayed objects. This significantly improves convergence speed, control accuracy, and reliability, while reducing energy consumption and mechanical wear.

The invention discloses a power system oscillation traceability analysis method and system based on multi-method probability fusion, which can effectively solve the problem of oscillation source positioning error caused by sparse measurement data and compressed sensing. Firstly, the oscillation source probability of each node is calculated through an oscillation energy method, a frequency spectrum characteristic method, a correlation analysis method, a voltage fluctuation method and a phase angle gradient method; secondly, calculating the adaptive weight of each method based on an information entropy theory, and completing multi-method probability fusion; the fusion probability is optimized in combination with a power grid topological structure, and an oscillation source candidate sequence is determined; and finally, a comprehensive probability distribution diagram and a detailed analysis report are output through a visual module, and reliable technical support is provided for safe and stable operation of a power system.

The application relates to the technical field of digital site selection planning, and particularly provides a reservoir resettlement area site selection method and system. The method comprises the following steps: constructing a reservoir resettlement area planning model under multi-target optimization, generating an initial site selection planning scheme; performing topological representation on the initial site selection planning scheme to obtain a current resident gathering point-resettlement area distribution matrix, and constructing a solution space; estimating an initial resettlement area quantity; realizing global search by using a simulated annealing algorithm, avoiding local optimization by probabilistically accepting a poor solution, combining the current resident gathering point-resettlement area distribution matrix and an adaptive multi-step neighborhood generation algorithm meeting the resettlement area quantity constraint, iteratively optimizing and adaptively adjusting a search strategy under the feasibility constraint, and converging to obtain an optimal resettlement area site selection scheme. The application organically combines a policy gradient method in reinforcement learning and a simulated annealing framework, and solves the problem of insufficient intelligence of reservoir resettlement area site selection.

The invention relates to the technical field of power system optimization scheduling, in particular to a multi-microgrid system operation optimization method and system considering distributed energy transaction, and the method comprises the steps: constructing a multi-microgrid system composed of M interconnected microgrids; on the premise that all micro-grids in the multi-micro-grid system interact through energy exchange, a target function with the total running cost minimization of the multi-micro-grid system is built; s2, establishing an internal energy balance constraint and a power constraint of the micro-grid, and establishing an operation optimization model of the multi-micro-grid system in combination with the objective function in the step S2; the method comprises the following steps of: decomposing an operation optimization model of a multi-microgrid system into a plurality of local sub-problems for reducing complexity, finding out a dual problem of the local sub-problems, and then solving the dual problem by adopting an iterative sub-gradient method to find out the minimum operation cost of the multi-microgrid system and an energy value exchanged among microgrids; and controlling the operation of each micro-grid according to the energy value exchanged between the micro-grids. According to the invention, the stability and economical efficiency of the multi-microgrid system are improved.

The invention discloses a power grid power flow analysis method and device based on a quotient gradient method and a neural network, and the method comprises the steps: converting a nonlinear algebraic equation set of flow calculation into an ordinary differential equation based on the quotient gradient method, and constructing a physical information neural network model according to the ordinary differential equation. A system state reference trajectory is generated through an ordinary differential equation solver to serve as a training data set, a composite loss function containing a predicted value and reference trajectory error and a predicted value time derivative and theoretical derivative error is defined, and a training model is iteratively trained. And inputting to-be-calculated working condition parameters and steady-state set time into the trained model, and outputting a steady-state power flow solution through forward propagation. According to the method, the convergence, the physical interpretability and the generalization ability under the ill-conditioned or extreme working condition are improved, and the real-time online analysis requirement is met.

The invention discloses a dynamic equipment layout method based on a quasi-object strategy and a multi-target ant colony optimization algorithm. The method comprises the steps of layout initialization, layout legalization operation, a movement strategy based on reference equipment, configuration optimization operation and Pareto optimal configuration selection based on a maximum and minimum target distance method. N configurations Xl (l = 1, 2,..., n) are randomly generated, each ant l represents one configuration Xl, and initial configurations of the n ants are obtained; performing legalization operation on the configuration X1 by adopting a gradient method based on dynamic step length, executing a movement strategy based on reference equipment to obtain a group of compact and legal configurations, and recording the configurations as a configuration library BL; selecting all non-dominated configurations in the set BL, and storing the non-dominated configurations in an external document CS; and for the current ant l, generating a random number o, and judging the size relationship between the random number o and the parameter p to select an optimization strategy, when o is greater than p, selecting to execute a local search strategy based on improved pseudo-random proportion, otherwise, using a global optimization strategy based on ecological niche.

The invention relates to the technical field of hard capsule polishing, and discloses an automatic polishing system based on hard capsules and a device thereof. A data acquisition module of the system acquires surface characteristic data such as hard capsule surface roughness, stain distribution, capsule size and the like; the data analysis module receives the data and then obtains a polishing demand analysis result through random forest regression analysis; the optimization module adopts a genetic algorithm to optimize parameters and rules of the polishing controller according to the result; the control module carries out fuzzy logic calculation on the polishing amount through particle swarm optimization according to the optimized parameters and rules and converts the polishing amount into polishing control signals; after the execution module receives the signal, the polishing amount constraint condition is adjusted through a near-end gradient method, and integer programming is conducted on the polishing process in combination with a branch and bound method so as to automatically adjust the polishing amount; and the detection module detects surface characteristic data after polishing through Bayesian filtering and feeds back the processed data to the control, data analysis and optimization module so as to adjust a polishing strategy.

The invention provides a PI controller parameter optimization method, a PI controller and a grid-connected inverter, and the method comprises the steps: firstly, determining an allowable range of a current loop bandwidth, taking the value range of controller parameters as a constraint, and taking the minimum overshoot, the highest response speed and the minimum reactive current change as targets; and parameters of the current loop dynamic performance model are optimized according to a simple gradient method. According to the method, high accuracy can be achieved through less test data through the current loop dynamic performance model, the relation between controller parameters and output variables can be accurately reflected, a reliable basis is provided for parameter optimization, the parameter optimization process is more efficient due to application of the simple gradient algorithm, and the method is suitable for large-scale popularization and application. The optimal solution can be found in a short time, the optimized controller can achieve the good decoupling performance of a current loop, and especially when the active current changes greatly, the optimized controller can remarkably reduce the change of the reactive current, so that the dynamic performance of the system is improved.

The invention relates to the technical field of label noise of table data, a distance measurement-based method and a neural network-based method are currently common label noise screening methods, and the methods are difficult to distinguish wrongly labeled samples and difficult samples with fuzzy categories near a decision boundary; according to the table data noise recognition and correction method based on Stein scoring, the difference of the attribution degree between sample and data feature distribution and label export distribution is analyzed, and the logarithmic probability density gradient of a sample is estimated through a diffusion model; moving the sample along a gradient represented by a scoring function by using a gradient method until the sample is converged to a centroid, forming a moving track moving from an original position of a sample space to a high-density centroid of data of a domain, and calculating the data feature distribution by comparing the differences of the directions and lengths of the moving track of the sample in the data feature distribution and the distribution exported by the label. Samples of potential tag errors in the tabular data are identified and tags thereof are corrected.

This application relates to the field of wireless communication technology, providing an antenna robustness design method and apparatus based on hybrid deep learning. This invention simulates manufacturing process errors by combining sampling methods within the range of process errors, obtaining design variables and... S Sensitivity analysis was performed on the Gaussian distribution curves of the statistical mapping relationship between parameter responses to identify variables more sensitive to manufacturing errors, thereby reducing the dimensionality of variables and decreasing the complexity of subsequent antenna robustness optimization. A hybrid deep learning model was used to construct an antenna response substitution model, replacing traditional electromagnetic simulation, significantly shortening the optimization cycle while ensuring the accuracy of response prediction and avoiding distortion of optimization results due to model errors. By constructing a robustness objective function and employing a genetic algorithm to optimize antenna parameter robustness, a highly robust optimal solution was found, avoiding premature convergence of the gradient method in multi-peaked environments caused by random errors.

A generator set control method and apparatus, and a device. Comprising: constructing a state transition model for sub-problems of a single unit, and adding as a state in the model a penalty price corresponding to a Lagrange multiplier for each time period Using a reinforcement learning algorithm to train a startup / shutdown strategy and a power increase / decrease strategy for each unit; using a surrogate sub-gradient method to relax constraints coupled to different units in a UC problem, using the surrogate sub-gradient method to perform iteration and Lagrange multiplier updating, solving sub-problems in the iteration process using a trained reinforcement learning agent to perform sequential decision-making, and iterating repeatedly until convergence, so as to obtain an optimal solution to a dual problem; and performing a feasibility operation on a resulting unit commitment state, and controlling generator set nodes.

The application provides a global sparse texture filtering method based on edge structure preservation, including introducing a texture inhibition function in a penalty term, and constraining the gradient of an output image, the texture inhibition function inhibits texture, noise and unnecessary detail information in the image by setting two threshold values, then using the inhibited gradient as the input of the denominator of the penalty term, so that the penalty term can sufficiently distinguish texture and structure; sparse regular L1 norm is used to constrain the penalty term, non-convex optimization is converted into a convex optimization problem by introducing a sub-gradient, and an alternating direction multiplier method is used for iterative solution, so that better edge preservation is achieved; sparse L p Norm is used to constrain the penalty term and a preconditioned conjugate gradient method is used to accelerate and improve the calculation efficiency, so that more robust and sparse image smoothing effect is achieved. The application can improve the robustness of the algorithm in distinguishing texture and structure, retain better semantic information, and achieve better edge structure preservation and smoothing performance.

The invention discloses a multi-agent pursuit game control method and system in a complex environment, and the method comprises the steps: enabling a non-smooth pursuit index to be converted into a smooth and guided form through a'minimizable maximum and minimum approximation + ReLu soft obstacle avoidance 'fusion frame, and carrying out the millisecond-level solving through a gradient method; the collision constraint is converted into soft penalty through a ReLu function, and safety-agility quantitative compromise is achieved; finally, a distributed closed-loop strategy and a system architecture are given, and the pursuit game problem under the conditions of limited motion performance, high real-time performance requirement and existence of obstacles is solved. The problems of pursuit, obstacle avoidance, collision avoidance and the like are unified under a micro-optimized framework, the system design is simplified, and subsequent expansion and improvement are facilitated.

The application relates to a time-optimal three-axis reorientation method and device for an inertia-symmetrical rigid spacecraft, which determines an angular velocity analytical expression according to inertia symmetry of the spacecraft and a time-optimal control switch structure set in advance; through introduction of a new inertia axis coordinate system, the symmetry characteristic of a reorientation track is explicitly derived; a Radau pseudo-spectrum discrete method is used to discretize a quaternion dynamic equation, so that the time-optimal reorientation problem is converted into a nonlinear programming problem; through given two parameter values, the discretized quaternion dynamic equation is converted into a linear equation set and is solved, a discrete quaternion value sequence obtained through decomposition is used, a gradient method or a Newton method is used to optimize the two parameters, and a time-optimal reorientation track is determined according to an optimization result. The method does not need to perform numerical integration or solve a large-scale nonlinear programming problem, and the calculation efficiency is significantly improved, so that fast and accurate track generation of an inertial rigid spacecraft is realized.

The invention belongs to the technical field of aero-engine modeling, and particularly relates to an engine model solving method adopting Newton-Raphson and feedforward neural network methods. According to the method, a mixed algorithm of a feed-forward neural network agent model and a Newton-Raphson method is combined, and a rough value of an iteration independent variable is directly predicted by adopting a feed-forward neural network, so that meaningless and local extremal regions on an iteration path can be avoided to a great extent, and high-quality iteration variables are quickly provided for the Newton-Raphson method; the convergence capability of the Newton method is greatly improved, the iteration step number is reduced, and the calculation speed of the Newton method is improved. And a normalized training set processing mode is adopted, so that the training set scale and the network scale are greatly reduced, the network estimation precision is improved, and the application complexity of the hybrid solving method is reduced. According to the hybrid solving method, the simulation solving convergence of the violent dynamic process of the dynamic real-time model of the engine is remarkably improved, and compared with a gradient method, the calculation speed is increased.

The present application relates to the technical field of flood control optimal scheduling, in particular to a reservoir group flood control optimal scheduling method and system based on quadratic programming, the method comprising the following steps: according to the water conservancy facilities and hydrological elements in the basin, the system sorts out the spatial distribution of key nodes and their hydraulic connection, constructs a flood control scheduling topological network, and obtains a reservoir group flood control optimal scheduling topological graph; a reservoir group flood control optimal scheduling quadratic programming model is established according to the reservoir group flood control optimal scheduling topological graph; based on the MINOS library of GAMS, the least gradient method and quasi-Newton method are used to optimize and solve the reservoir group flood control optimal scheduling quadratic programming model. The present application couples the propagation of flood between reservoir groups in the mathematical equation set, avoids complex simulation sequential coding, is easy to model, has good universality, and the model is easy to seek an optimal solution, can balance the reasonable scheduling of the flood control capacity, peak shaving effect and reservoir storage capacity of the reservoir, and improve the accuracy and efficiency of flood regulation.

The invention discloses a project recommendation method and system based on knowledge graph multi-hop reasoning, and relates to the technical field of knowledge graphs. Comprising the following steps: obtaining a query triple, inputting the query triple into a knowledge graph multi-hop reasoning framework, obtaining dynamic entity embedding enabling the same source entity to generate differential semantic representation under different query relationships through a dynamic representation module, and obtaining a multi-hop reasoning path through a double-agent collaborative decision module; the reasoning path is evaluated through a quaternary dynamic reward module, and a dynamic total reward is obtained; according to the dynamic total reward, a strategy gradient method is adopted to update parameters of the double-agent collaborative decision-making module, an optimized multi-hop reasoning path is obtained, and an item recommendation result meeting the user preference relation is generated according to the optimized multi-hop reasoning path. According to the method, the adaptability of the knowledge graph multi-hop reasoning framework to different reasoning scenes can be enhanced, and the accuracy of project recommendation results is improved.

The application discloses an automatic model reinforcement method based on neural network interpretability. The method comprises the following steps: developing PytorchFI again, injecting faults based on IEEE 754 standard, and building a fault test environment, developing a batch fault injection tool and a redundancy reinforcement tool for model weights; constructing a model based on reinforcement learning, designing a standard Markov decision process for interaction between an agent and a neural network model, including state space, action space and reward function and the like; using SAC algorithm as a neural network architecture search algorithm to explore the redundancy ratio of in-layer convolution kernels; calculating the importance of the convolution kernels and sorting them based on the neuron integral gradient method; combining the SAC algorithm and the neural network interpretability to design an automatic model reinforcement framework, and redundantly reinforcing important convolution kernels. The application realizes model reinforcement based on neural network interpretability, can accurately identify the importance and redundancy demand of the convolution kernels, improves the fault tolerance and robustness of the model, has a higher fault detection rate and a lower space-time overhead, and is suitable for various convolutional neural network architectures.

The technical scheme of the invention discloses an efficient hybrid simulation system construction method for unmanned aerial vehicle control design and evaluation, and the method can achieve efficient and real turbulence simulation in a boundary-free space, and supports the dynamic convergence and dispersion of multiple unmanned aerial vehicles. Through self-adaptive block and Laplacian initialization, false waves caused by dynamic increase and deletion of grid blocks are remarkably suppressed, and the numerical stability is improved; aLM / ASM is adopted in a near field, so that boundary layer grid refinement is avoided, and a tight support core is coupled to consider both precision and stability; the stability of the convection open boundary under the inflow / outflow coexistence working condition is enhanced, which is superior to the traditional Neumann / original convection condition; simulation has parameters and is efficient, the parameters can be directly calibrated through a gradient method, and the result credibility is improved; compared with a traditional CFD method, the efficiency is extremely high, and design iteration of a real-time or interaction-level controller can be met under the condition that complex turbulent motion can be captured.

This application relates to a method and system for solving conjugate gradient finite element models based on sparse convolution preprocessing. The method includes establishing a structural finite element model, generating a structural stiffness matrix A and a load vector b, and constructing a linear equation system Ax=b, where A is a sparse symmetric positive definite matrix. A preprocessing sub-generator is constructed by training different structures using a sparse convolutional neural network. The stiffness matrix A is input into the preprocessing sub-generator to obtain a preprocessing factor. A symmetric positive definite preprocessor is constructed based on the preprocessing factor. The linear equation system Ax=b is solved using the preprocessed conjugate gradient method to obtain the displacement response vector x. This application optimizes the condition number of the preprocessed matrix to improve convergence speed and reduce solution time. It adapts the sparse convolutional U-net structure to large-scale sparse matrices, improving training efficiency and forming a unified and scalable preprocessing framework for structural engineering, providing a general and efficient preprocessing strategy for large-scale finite element model analysis.

According to the satellite tracking method provided by the invention, a high-frequency dynamic gradient increasing algorithm is combined with program tracking, so that factors such as ephemeris errors and atmospheric disturbance are compensated in real time. The correction direction is directly calculated by the gradient method, and compared with serial search of step tracking, the convergence speed is higher, the gradient method is particularly suitable for the rapid motion characteristic of an LEO satellite, and the average received signal strength can be remarkably improved; according to the method, a predictive switching strategy based on multi-target QoS evaluation is introduced, the next service satellite can be selected from the global optimum angle, and the short vision of a traditional strategy is avoided. By comprehensively balancing the link quality, the service duration and the switching time consumption, the scientificity of the switching decision is ensured, and the switching frequency and the total signal interruption time can be effectively reduced, so that the data throughput and the operation efficiency of the whole ground station are improved.

The application discloses a kind of cascade reservoir flood control storage capacity gradual release optimization scheduling method and the system for realizing this method, this method includes: first, the objective function and constraint condition of cascade reservoir flood control storage capacity gradual release optimization scheduling are constructed, and the initial state of the water level process of cascade reservoir flood control storage capacity gradual release is given;Then, based on the step-by-step optimization method as the framework, the multi-stage cascade reservoir flood control storage capacity gradual release optimization scheduling problem is decomposed into multiple two-stage sub-problems, and the gradient method is embedded in each sub-problem calculation process, and the initial state is updated by gradient depth search;Finally, the global optimal solution is approached by iteration optimization one by one, and the cascade reservoir flood control storage capacity gradual release optimization scheduling process considering optimal benefit is output.The application reduces the calculation complexity, improves the local depth search capability, avoids the dimension disaster problem, and is suitable for large-scale cascade reservoir group optimization scheduling.