2082 results about "Optimization problem" patented technology

The invention discloses a multi-aircraft cooperative formation flight path planning method based on a leader and follower model, and relates to the technical field of environment perception and unmanned aerial vehicle cluster cooperation. The method comprises the following steps of: firstly, designing a multi-agent double delay depth deterministic strategy gradient (LFMATD3) based on a leader-follower model, and converting an optimization problem model into a Markov decision process model by introducing an artificial potential field model and a reward function; and secondly, constructing an independent agent for each unmanned aerial vehicle, optimizing a behavior strategy of the unmanned aerial vehicle by combining a reward function based on an algorithm framework of deep reinforcement learning, and performing flight path planning and realizing dynamic formation control. The method provided by the invention can effectively improve the formation stability and collaboration of the unmanned aerial vehicles in a complex environment.

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention discloses a Gaussian representation SLAM method based on dense matching priori and factor graph constraint, which comprises the steps of inputting a current image and a key frame image, outputting a point graph corresponding to the image through a pre-trained model, returning a matching condition of two frame image points and respective point cloud information, and obtaining a point-level matching result based on a point-level matching result. The method comprises the following steps: constructing a joint optimization problem of a current frame and a key frame by taking a luminosity consistency error and a geometric projection error as targets, performing joint estimation on a camera pose and a point cloud of the current frame, realizing high-precision pose solution, generating point diagram data after Gaussian scene representation and rasterized rendering processing, and transmitting the point diagram data to a rear end for global optimization. And the rear end receives the pose and point cloud data, executes loopback detection to identify repeated key frames, and performs Gaussian rendering through an optimized key frame image to complete global dense three-dimensional reconstruction. The method effectively solves the problem of track drift and scene inconsistency caused by lack of pose priori and global geometric constraints in an existing system.

The invention provides a self-adaptive constraint model active mismatch prediction control method based on voltage and current ripple resistance of a power converter in a hybrid energy storage system, and the method comprises the steps: building an island DC micro-grid system model, collecting the voltage and current information of a system, and designing a model prediction control frame of a power loop and a cost function of the model prediction control frame; designing a model predictive control framework of the current loop and a cost function of the model predictive control framework; designing an active model mismatch mechanism to enhance the response capability to dynamic change; determining an optimization problem with a normal value, and designing an adaptive constraint optimization algorithm to dynamically adjust constraint parameters and optimize control performance; and designing a steady-state and dynamic-mode trigger for adapting to the running state of the system. According to the invention, the trajectory tracking precision of the system under dynamic and steady-state working conditions can be significantly improved, current ripples caused by control delay can be effectively eliminated, damage to electrical equipment due to overlarge ripples can be avoided, and the service life of the equipment can be prolonged.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention discloses a stability analysis method for a hybrid grid-connected system of a grid-following converter, and the method comprises the steps: constructing precise impedance models of a grid-following type (GFL) converter and a grid-constructing type (GFM) converter respectively based on a phase-locked loop (PLL) mechanism and a virtual synchronous generator (VSG) control strategy, and the details are shown in an abstract figure 1; deducing the total equivalent impedance of the system at a point of common coupling (PCC) according to a circuit equivalence principle; performing stability analysis and sensitivity evaluation on different converter combination proportions, topological structures and key control parameters of the hybrid grid-connected system based on an impedance ratio criterion; and verifying the accuracy of the analysis result through frequency domain simulation. The method can be widely applied to a new energy power system, solves the problem of system stability evaluation and optimization under the high-proportion renewable energy access background, and has the characteristics of high adaptability, accurate model and comprehensive analysis.

The invention discloses a dynamic optimization system and method for a compound essential oil ratio, and aims to solve the problems that the traditional technology depends on artificial experience, the optimization efficiency is low and the quality tracing is insufficient. According to the method, raw material molecular fingerprint data are collected in real time through a high-precision spectrum sensor array and a micro-fluidic chip, a production environment field domain is constructed in combination with a computational fluid mechanics model, a formula optimization problem is encoded into an Ising model by using a quantum annealing algorithm, and global search is realized through a quantum tunneling effect. The system integrates digital twin bodies, covers molecular dynamics simulation, phase equilibrium prediction and olfactory receptor activation probability simulation, and accurately maps a physical system state; a multi-modal data fusion network and a reinforcement learning framework are adopted to dynamically adjust formula parameters, and an NSGA-III algorithm is combined to optimize efficacy collaboration degree, cost and stability indexes. And the execution control module realizes nanoscale flow regulation through the piezoelectric micro-injection valve array, and realizes non-tampering evidence storage of formula parameters and quality data based on a block chain technology. Dynamic closed-loop optimization of the compound essential oil ratio is achieved, the production efficiency, the product consistency and the supply chain transparency are remarkably improved, and the method is suitable for the field of intelligent manufacturing of high-end essential oil.

The invention belongs to the technical field of communication, and discloses a positioning method and system based on differential time delay and an alternating direction multiplier method, and the method comprises the steps: obtaining the position information and differential time delay of a receiver; performing joint estimation on the position of the target object and the position of the transmitter based on an alternating direction multiplier method; wherein during joint estimation, a first variable and a second variable are introduced, an original optimization problem is decomposed into two sub-problems related to the first variable, the second variable and a Lagrangian multiplier coefficient during iterative calculation in the joint estimation process, and in each iterative process, analytical solutions of the two sub-problems are solved respectively; and updating the first variable, the second variable and the Lagrange multiplier coefficient. According to the method, the accuracy of target object position estimation can be improved while the calculation complexity can be remarkably reduced.

The invention relates to a multi-driving-style high-risk automatic driving cut-in scene test method and system, and the method comprises the steps: collecting original driving track data, carrying out the clustering of driving styles, and generating a risk cut-in scene track cluster with consistent driving styles through employing a Cutin-TimeGAN network model in combination with physical feasibility constraints; constructing a cost function, calculating a cost function value for each risk cut-in scene trajectory cluster, and selecting a target cut-in reference trajectory as a target reference state vector; constructing a game framework, and combining a target reference state vector to construct a utility function according to the state vectors of the VUT and the test confrontation vehicle in the framework; constructing a risk confrontation utility function based on the predicted collision time; a game optimization problem is constructed and solved, an optimal interaction track of the test confrontation vehicle is obtained, and a closed-loop high-risk automatic driving scene switching test is carried out based on the optimal interaction track; the system is used for implementing the method. Compared with the prior art, the method has the advantages that testing of authenticity and high-risk confrontation of multiple driving styles is considered.

The invention relates to the technical field of deep learning model training optimization, in particular to a fine-grained resource scheduling method and system in DNN model parallel training, and the method comprises the steps: converting a DNN model into calculation graph representation, and constructing a calculation graph in combination with the topological information of calculation equipment; modeling calculation stage division in a DNN model training process and resource mapping from each calculation stage to calculation equipment as a stage division and resource mapping combined optimization problem; and iteratively solving the combinatorial optimization problem by using a heuristic algorithm, and optimizing the computing equipment resource mapping of each computing stage by dynamically adjusting the weight of constraint conditions and minimizing the overhead and load difference of the computing equipment. According to the method, the calculation cost, the communication overhead, the overall load balancing and the solving time are comprehensively considered in the target function, efficient calculation resource allocation and task scheduling optimization are achieved, the training and reasoning efficiency of a large-scale deep learning model is improved, and the method has a good application prospect in the field of deep neural network distributed parallel training.

The present invention provides a method for optimization of active and passive beamforming and signal reception configuration of a dual IRSs-assisted ISAC system. By jointly optimizing active beamforming at a base station, the reception of a sensing signal by the base station, and passive beamforming at an IRS, the achievable rate of communication users is maximized while ensuring that the signal-to-noise ratio of the sensing signal meets the minimum requirement. In the present invention, to solve the complex nonconvex problem generated, first, fractional programming is used to decouple an optimization problem, then, a successive convex approximation algorithm and an alternating direction method of multipliers are used to transform an intractable nonconvex problem into multiple tractable subproblems, and finally, an alternative optimization method is used to efficiently solve for a high-quality sub-optimal solution. Simulation results show that the provided solution has good convergence and effectiveness, and the solution can effectively improve the performance of IRS-assisted ISAC systems.

The invention discloses a self-adaptive dynamic control method and system for the machining process of a numerical control machine tool, and relates to the field of intelligent control, and the method comprises the steps: collecting machining data in real time through physical and virtual sensors, and constructing a standardized data set after layering preprocessing; a CNN-LSTM hybrid model is utilized to extract spatial-temporal characteristics to realize working condition classification, and an NSGA-II algorithm is combined to solve a multi-objective optimization problem to generate an optimal control parameter solution set; parameters are dynamically adjusted through fuzzy PID, and a GRU model is adopted to predict machining errors for feed-forward compensation, so that closed-loop control of perception-decision-execution-feedback is formed. The system continuously monitors the actual machining deviation, parameters are optimized again when the actual machining deviation exceeds a threshold value, and cooperative improvement of machining precision and efficiency is achieved. The method has the advantages that NSGA-II multi-target optimization, fuzzy PID correction and GRU error prediction compensation are recognized through CNN-LSTM working conditions, closed-loop feedback iteration is combined, the machining precision and efficiency are improved in a balanced mode, the service life of a tool is prolonged, and the method is suitable for complex working conditions.

The invention discloses a distribution line load prediction and optimal scheduling method and system, and relates to the technical field of intelligent scheduling of power systems, and the method comprises the steps: generating a time-aligned multi-source fusion input data set; constructing a mixed time sequence load prediction model, and introducing a weighted quantile loss function in a model training process; constructing a joint probability distribution model of the renewable energy output and demand response participation rate, and sampling joint probability distribution; constructing a rolling time domain power distribution network optimization scheduling model; a two-layer mixed strategy is adopted to deal with uncertainty, an optimization problem is decomposed into a plurality of sub-problems, and an alternating direction multiplier method with adaptive penalty parameters is used for distributed solution. According to the method, a multi-objective optimization scheduling model is established in a rolling time domain, and dynamic closed-loop optimization is realized; and by introducing a two-layer hybrid solving strategy and an ADMM distributed algorithm with an adaptive penalty parameter, the calculation efficiency and expandability are remarkably improved while the global consistency is ensured.

The embodiment of the invention discloses a power distribution network operation optimization method and device, equipment and a storage medium, and the method comprises the steps: constructing a multi-period second-order cone optimal power flow model of a power distribution network, and solving to obtain a multi-period operation optimal solution of the power distribution network; converting the optimization problem of the second-order cone optimal power flow model into a Markov decision process to obtain a Markov decision model; determining a target state corresponding to the optimal solution of multi-period operation of the power distribution network, and constructing a pre-training data set based on the optimal solution and the corresponding target state; and pre-training the decision-making agent by using the pre-training data set, training the pre-trained decision-making agent by using an algorithm in reinforcement learning based on the Markov decision-making model, and outputting an optimal power distribution network multi-period operation strategy. The distributed photovoltaic bearing capacity can be effectively improved, the overall optimization efficiency is improved, and the problem of poor dynamic scene adaptation is effectively solved.

The invention provides an active power distribution network multi-time scale rolling optimization method driven by multi-agent graph reinforcement learning. The method comprises the following steps: dividing slow time scale equipment and fast time scale equipment; the slow time scale is optimized and modeled into a multi-agent Markov game, and a global state space and a combined action space corresponding to switch actions, reactive compensation equipment gears and voltage regulation equipment gears are defined; switching action candidate vectors are generated based on a basic loop of the power distribution network, and action space dimensions are compressed through a scheme of excluding conflicting actions of the same common branch; fitting an action value function of each agent by adopting a graph neural network, generating a dimensionality-reduced discrete action strategy according to the global state space, and carrying out collaborative optimization through a distributed decision and a global reward mechanism; and on the basis of the slow time scale optimization result, the fast time scale optimization problem is converted into a mixed integer second-order cone programming model, and an instruction is solved in a rolling manner.

The invention relates to a satellite cabin load layout optimization method based on a quantum algorithm, and the method comprises the following steps: listing a mathematical expression form of an optimization target and a limiting condition according to a satellite cabin load layout requirement, and generating a constraint optimization problem model; wherein the optimization target and the limiting condition at least comprise a satellite quality characteristic requirement, a space geometric compatibility requirement, a thermal control requirement, an electromagnetic compatibility requirement, an installation and maintenance requirement and an installation position requirement of special instrument equipment; converting the layout constraint optimization problem mathematical model into an optimization objective function in a secondary unconstrained binary optimization form; solving the optimization objective function by using a quantum optimization algorithm to obtain an optimization vector; and calculating the load position and attitude of the satellite cabin by optimizing the vector, and carrying out inversion to obtain a final satellite cabin load layout scheme. According to the method, the non-deterministic polynomial problem of satellite cabin load layout optimization can be solved in an accelerated manner by utilizing the characteristics of superposition, entanglement, interference and the like of quantum bits, and a new technical approach is provided for improving the design efficiency of a satellite cabin load layout optimization scheme.

The invention relates to the technical field of computing resource allocation, in particular to a joint optimization method for a task processing sequence and resource allocation, and provides an end-side collaborative MEC computing architecture, and a joint optimization model for a task scheduling sequence and computing resource allocation is constructed on the basis of the MEC computing architecture and a partial unloading mechanism; meanwhile, by considering limited computing resources of the terminal equipment, time delay constraints of tasks and maximum task number constraints of parallel processing allowed by the terminal equipment, an optimization problem with the purpose of minimizing system overhead is formulated. And then the original optimization problem is decoupled into a task unloading decision sub-problem and a joint task processing sequence and computing resource allocation sub-problem, an alternating iterative optimization method is adopted for solving, the sub-problems are optimized one by one in combination with a convex optimization technology, and the optimal solution of the original problem is solved step by step through a continuous alternating iterative optimization process. Simulation results show that system time delay and energy consumption expenditure are reduced, and the completion rate of tasks and the utilization rate of computing resources are improved.

According to the new energy automobile flexible assembly line multi-target dynamic intelligent scheduling optimization method coping with the uncertain disturbance influence, the flexibility of the assembly manufacturing unit and the flexibility of the assembly manufacturing process sequence are comprehensively considered, and a dynamic intelligent scheduling optimization scheme is generated for multiple optimization targets. The method comprises the following steps: firstly, constructing a multi-target dynamic intelligent scheduling optimization problem mathematical model of an automobile flexible final assembly line; furthermore, the optimization problem is converted into a Markov decision process, and states, actions, multi-target reward components and reward component aggregation thereof are defined. And then, carrying out a multi-target deep reinforcement learning training process oriented to a new energy automobile flexible final assembly line, and constructing and forming a scheduling agent. And finally, realizing self-adaptive decision making of the automobile flexible assembly line under an uncertain disturbance condition based on the scheduling intelligent agent. According to the method, the robustness and the execution efficiency of the new energy automobile flexible assembly line in a dynamic environment can be remarkably improved, and the method has good practical value and popularization prospect.

The invention discloses a beneficiation whole process optimization control system and method based on a large model, and relates to the technical field of beneficiation process optimizing.The beneficiation whole process optimization control method comprises the steps that a global prediction model is built based on unified feature embedding, and the global prediction model is distilled into an edge lightweight model with a conditional branch selection mechanism; carrying out hot start on the edge lightweight model in combination with historical experience obtained by knowledge graph retrieval, constructing a dual-objective optimization problem, and carrying out search by utilizing improved difference based on loss constraint to generate a candidate control strategy set; performing calibration and confidence screening on the candidate control strategy set under a multi-scene condition to generate a safety control instruction; in the execution process of the safety control instruction, data drift is monitored. According to the method, the prediction precision is ensured, and meanwhile, the real-time performance and the availability in a low-computing-power environment are considered.

The invention belongs to the technical field of virtual power plants, and particularly relates to a multi-physics field coupled virtual power plant energy storage health state monitoring and predicting method, which comprises the following steps: acquiring electrochemical parameters and thermodynamic parameters of an energy storage facility in real time, preprocessing the acquired data, and storing the preprocessed data into a database; calculating the current SOH of the energy storage facility, constructing a physical information neural network (PINN), and predicting the SOH of the energy storage facility based on the current SOH and historical data in the database; displaying a curve graph of data acquired in real time, the current SOH, an SOH prediction chart and alarm information in a visual mode; and with maximization of SOH and minimization of energy loss and thermal risk as targets, a Pareto optimization problem is constructed, and an optimal charging and discharging strategy is solved. According to the method, multi-parameter perception, physical mechanism and deep learning are fused, and high-precision real-time monitoring and prediction of the energy storage health state of the virtual power plant can be realized.

The invention provides a water-based adhesive coating control method and system based on artificial intelligence optimization, and the method comprises the steps: obtaining a historical data set composed of process parameters of a coating process and coating quality parameters, carrying out the clustering, and obtaining a global induction point set according to a clustering result; determining a stage induction point subset according to the target parameter value of the current coating stage and the boundary of the target parameter, and updating the sparse Gaussian process regression model by using the global induction point set and the stage induction point subset; calculating a quality fluctuation index, determining the length of a prediction time domain based on the quality fluctuation index, constructing an optimization problem in the prediction time domain by adopting a multi-level opportunity constraint mode, and when the deviation value between the actual value of any key process parameter and the prediction trajectory based on the sparse Gaussian process regression model exceeds a deviation threshold value, determining that the prediction trajectory does not exceed the deviation threshold value. And solving the optimization problem to obtain an optimal control action sequence in the prediction time domain, and determining a final control action from the optimal control action sequence and sending the final control action to an execution mechanism.

The invention discloses an inference task collaborative scheduling system and method oriented to multiple terminals of a swan gap. The inference task collaborative scheduling system comprises a communication module Broker, a system monitoring module SystemProfilter, a scheduling module Scheduler and an inference task execution module Worker. According to the method, a structured resource state vector is constructed based on an NDK native interface so as to comprehensively represent the real-time availability of equipment; a lightweight MQTT protocol is adopted to support low-overhead and high-robustness cross-terminal state synchronization and task distribution; a comprehensive load scoring mechanism fusing task priorities and dynamic weights is designed, a multi-objective optimization problem based on a non-dominated sorting genetic algorithm (NSGA-II) is introduced, task delay is minimized in a combined mode, terminal loads are balanced, and energy consumption is controlled; meanwhile, the execution time delay is efficiently estimated in combination with a proxy model based on linear regression, and the scheduling overhead caused by real reasoning and calling is avoided; the whole architecture is deeply adaptive to a swan-mong system, and is compatible with an Android platform through modular packaging and unified communication interface design, and efficient, self-adaptive and cross-platform collaborative scheduling oriented to a swan-mong multi-terminal reasoning task is realized for the first time.

The invention discloses a building energy system multi-type demand response operation strategy optimization method and system, and relates to the technical field of building energy management and demand response optimization control, and the method comprises the steps: collecting building user side multi-source information, building a building cooling load prediction structure, and bringing the structure into an optimization scheduling model input system. Constructing a building energy system optimization scheduling equipment model; collecting power grid side information, and dynamically loading an optimal scheduling model for a time-of-use electricity price scene and a peak clipping scene based on judgment of power grid demand response type information of the next day; analyzing an optimization problem of a unified objective function in a double-type response scene, introducing a power reservation coefficient based on a time-of-use electricity price scene, and controlling flexible resource retention; a mixed integer linear programming algorithm is adopted to solve the multi-scene optimization scheduling model, and a corresponding strategy, scheme and power configuration plan are generated; according to the method, flexible dynamic regulation and control and unified scheduling in a multi-response scene are realized, and the strategy adaptability and collaboration are improved.

The invention relates to a mechanical arm optimization control method and device based on a coupled physical neural network, and belongs to the field of mechanical arm optimization control. The method comprises the following steps that S1, a mechanical arm optimization problem is extracted; s2, constructing accurate solution data; s3, establishing a constraint condition network and training; s4, establishing a target function network; s5, taking the objective function as a loss function, and establishing a dual-network coupling architecture; s6, training a dual-network coupling architecture; s7, outputting a predicted optimal solution result; s8, performing deviation correction on the predicted optimal solution by using a deviation correction method to obtain an optimal solution; and S9, mechanical arm parameters are adjusted according to the optimal solution to achieve control. According to the method, a multi-task objective function can be quickly coped with, so that a large number of repeated solving data sets are avoided, and the target required by a product is responded more timely and accurately; meanwhile, the innovative training method not only ensures the global search capability, but also effectively balances the calculation cost and the solving precision, and provides efficient technical support for solving a complex optimization problem.

Methods and systems determine a solution for an optimization problem using a quantum computer and a classical computer. The method comprises: receiving or determining, by the classical computer, a description of the problem; receiving or determining, by the classical computer, one or more quantum circuits defining gate operations to be executed by the quantum computer; determining, by the classical computer, an optimized first parametric quantum circuit comprising execution, by the quantum computer, of the gate operations; determining, using the quantum computer, an optimized input value in the input space; and determining, by the classical computer, the solution to the optimization problem based on the optimized input value and / or an output value corresponding to that optimized input value.

The invention relates to the technical field of radar waveform optimization, in particular to a synthetic broadband waveform optimization method based on reinforcement learning. According to the method, a deep reinforcement learning framework is adopted, and end-to-end optimization is carried out on the center frequency and amplitude parameters of multiple sub-pulses. The method comprises the following steps: constructing a synthetic broadband waveform signal model, and parameterizing sub-waveforms to determine a search space of an optimization problem; converting a waveform optimization problem into a reinforcement learning form, and defining a state space, an action space, a reward function and a state transition mechanism; based on a deterministic soft action value iteration strategy algorithm, point spread function features are extracted in combination with a Transform encoder, and sub-pulse parameters are dynamically adjusted. Through the method, the main lobe resolution can be effectively suppressed and improved, the target detection performance of a radar system is improved, and the method has good global optimization capability and environmental adaptability and is suitable for cognitive radar and adaptive waveform design scenes.

The invention belongs to the field of intelligent traffic systems, and particularly relates to a congestion scene emergency decision-making method based on dynamic traffic environment modeling, which comprises the following steps: by introducing a congestion potential field concept, fusing multiple factors such as traffic density, speed, cart proportion and the like into a quantifiable risk index, and distinguishing a current congestion index from a future congestion index; the layout problem of the monitoring points is converted into a mathematical optimization problem with the aim of improving the decision effect from experience design; in the decision-making process, not only is passing efficiency considered, but also dimensions such as safety, user experience and resource utilization rate are included; according to the closed-loop intelligent management system integrating traffic flow long-time-sequence evolution prediction, congestion risk assessment based on the physical potential field theory, multi-dimensional comprehensive decision and monitoring point layout collaborative optimization, the opening and closing opportunity of an emergency lane is scientifically and prospectively determined, and the layout of sensing equipment is synchronously optimized; the traffic jam is relieved to the maximum extent, the road passing efficiency and safety are improved, and meanwhile energy consumption is reduced.

The invention discloses a quantum Isin model construction method for a security constraint unit commitment optimization problem, and relates to the field of quantum computation.The quantum Isin model construction method comprises the steps that a security constraint unit commitment optimization model is constructed, and parameters of a mixed integer programming problem are obtained; using a Benders decomposition method to decompose a mixed integer programming problem into a main problem and a sub-problem; substituting the optimal binary solution to solve the sub-problem to obtain a new cut plane and expand a cut plane set; constructing a compact high-dimensional quadratic function fitting cutting plane set; solving a positive semidefinite programming problem to obtain a high-dimensional quadratic function parameter; converting a quadratic unconstrained binary optimization model constructed based on a high-dimensional quadratic function into an Isin model; solving the Isin model to obtain a quantum bit state, and solving an optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solution. According to the invention, the problem of huge consumption of quantum bit resources in the prior art is solved, especially the problem of difficulty in processing NP with complex constraints and more variables is solved.

The invention provides a reduced Gaussian kernel adaptive filtering method and system based on nearest center estimation, and relates to the field of data processing, and the method comprises the steps: obtaining an initial parameter and a training set, and constructing an adaptive filtering system based on the initial parameter; performing cluster division on an input signal, obtaining a feature vector of the input signal by calculating a reduced Gaussian kernel, and selecting a corresponding sub-filter according to a cluster for prediction to obtain prediction output; constructing a target optimization problem according to a maximum correlation entropy criterion based on an error between prediction output and expected output, and converting the target optimization problem into a convex optimization problem; solving a convex optimization problem by hybridizing three conjugate gradients, and updating the weight of a sub-filter; and repeating the steps to converge the error of the adaptive filtering system, and completing the adaptive filtering process. According to the method, the problem of dimension disasters existing in a traditional feature mapping method is solved by reducing the Gaussian kernel, the calculation complexity is remarkably reduced, and meanwhile unnecessary calculation overhead is greatly reduced through nearest center estimation.

A method of controlling a vehicle with a plurality of motion actuators comprises: determining a longitudinal inclination and speed of the vehicle; selecting an uphill drive mode if the longitudinal inclination is greater than an inclination threshold and the absolute speed is less than a speed threshold, and otherwise selecting a regular drive mode; obtaining motion requests; in accordance with the selected drive mode, providing a solution to an optimization problem related to optimal control of the motion actuators in accordance with the obtained motion requests; and controlling the motion actuators in accordance with the solution to the optimization problem. The optimization problem is dependent on a control effectiveness matrix which is defined differently in the uphill drive mode and the regular drive mode.