106 results about "Trust region" patented technology

The invention discloses a water body cleaning equipment path optimization method based on deep reinforcement learning, and the method comprises the following steps: S1, collecting water body environment data through a sensor, and carrying out the preprocessing; s2, establishing a state space and an action space for path optimization, and constructing a reward mechanism; s3, carrying out feature extraction on the preprocessed data by adopting a Transform network; s4, training the strategy network and the value network by using an improved trust region strategy optimization algorithm, optimizing an update step length of the strategy network based on KL divergence constraint, and optimizing a path planning strategy based on a strategy gradient method; s5, performing path planning by using the trained strategy network and value network; and S6, calculating the energy consumption of the water body cleaning equipment, and adjusting the operation mode or operation path of the water body cleaning equipment. According to the method, the path planning of the water body cleaning equipment is optimized by combining Transform and an improved trust region strategy optimization algorithm, and the method has the advantages of being high in environmental adaptability, low in energy consumption, high in cleaning efficiency and high in operation stability.

The invention discloses a process deviation-oriented analog circuit parameter optimization method, which comprises the following steps of: S1, carrying out multi-objective optimization on a given analog circuit under a nominal process condition, identifying and eliminating a design region with poor performance according to an optimization result, and carrying out clustering analysis to obtain an optimal design region; dividing the remaining high-quality areas into a plurality of initial trust areas; s2, modeling the finite Monte Carlo simulation data in the initial trust area by using a heterovariance Gaussian process model, capturing an average level and a change trend of circuit performance, and predicting behaviors of the circuit under process change; and S3, the required yield is kept while the performance is optimized, probability assurance is provided for the performance value by adopting the risk value, and a design point optimization set and a corresponding Pareto optimal solution set are finally obtained through multiple iterations in the optimization stage of considering the process change. According to the method, the high-quality Pareto optimal solution set meeting the yield constraint is found under the process change of unknown distribution, and the size of an analog circuit is optimized.

The invention belongs to the technical field of ocean temperature data identification, and discloses a thermocline identification method and system based on hyperbolic tangent function fitting. The method comprises the steps of data preparation and preprocessing; selecting a section range; fitting a curve; and determining a thermocline upper bound and a thermocline lower bound through a rolling function. According to the method, the hyperbolic tangent function is used for fitting the S-shaped characteristic curve of the temperature-depth profile, and the structure of the ocean thermocline is accurately described. Compared with a traditional Sigmoid function, the hyperbolic tangent function is better in performance when fitting a steep change area, and rapid temperature change of a thermocline central area can be caught more finely. According to the method, fitting parameters (amplitude, steep degree, central position and vertical offset) are optimized by adopting a trust region reflection algorithm, so that a fitting result is ensured to have higher precision and robustness, and the method is suitable for complex ocean profile data.

The invention provides a nonlinear amplitude-phase error correction method and device suitable for broadband FMCW radar imaging. The method comprises the following steps: establishing a frequency modulated continuous wave radar difference frequency signal model containing a nonlinear error; correcting the amplitude error by adopting an envelope normalization correction method; estimating the high-order polynomial phase error by adopting a method of combining a phase unwrapping recursive least square method and an OShea optimization strategy to obtain a high-order polynomial phase parameter; estimating the periodic multi-sine phase error by adopting a method of combining a MUSIC algorithm and a trust region algorithm to obtain a periodic multi-sine phase parameter; and based on estimation results of the high-order polynomial phase parameters and the periodic multi-sine phase parameters, phase error correction is carried out by adopting a time domain resampling method. The device comprises a nonvolatile memory in which the executable code is stored and a processor, and is used for implementing the method. According to the technical scheme of the invention, the problem that the existing method is generally insufficient in noise immunity can be solved.

A design method of a trust region satellite-borne dual-base interference SAR system is provided, relating to the field of radar measurement. Firstly, error sources of the interference SAR system are classified and analyzed, phase errors caused by image decoherence are calculated according to wave positions, and an analytical expression of baseline-related errors is reserved; a maximum phase error caused by imaging processing and an electronic device are estimated; and a baseline error is estimated. Then, the phase errors and the baseline errors are converted into elevation errors through an elevation fuzzy number, thereby obtaining an analytical expression of the elevation errors and the baseline parameters (length and dip angle). With a relative height measurement accuracy index of the system as an optimization object, a feasible solution interval between the baseline length and the baseline dip angle of the system is solved through a trust region algorithm. A minimum value of the baseline length is corrected according to a flying-around safety distance of a dual-satellite formation. According to the method of the present disclosure, the design of the satellite-borne dual-base interference SAR system can be guided, so that the satellite-borne dual-base interference SAR system is ensured to carry out terrain elevation surveying and mapping tasks with high accuracy and high reliability.

The invention discloses a subsynchronous oscillation mode identification method and device for a power system containing a constructed network converter based on a matrix pencil and an LM, and a storage medium, and belongs to the technical field of subsynchronous oscillation mode identification in the power system. The method comprises the following steps: performing mode parameter identification by using a matrix pencil method to obtain an initial value of each mode parameter, and performing iteration by using an LM method to solve an optimal value of each mode parameter. A trust region is introduced into the LM algorithm. And an optimization problem is changed from an unconstrained least square problem to a constrained least square problem, so that the identification precision is improved. Compared with a traditional method, the method provided by the invention has the advantages that the calculation is simpler, the accuracy is higher, and the method has a better development prospect in subsynchronous oscillation early warning.

The invention discloses an earthquake quality factor acquisition method, system and device and a storage medium, and the method comprises the steps: S1, obtaining an earthquake observation record, a speed parameter and an initial quality factor model, and building an earthquake quality factor model; s2, establishing a trust region type least square objective function according to the wave field residual error; s3, updating the trust region type least square objective function by adopting the optimized damping coefficient; s4, updating the seismic quality factor model, and performing iterative calculation of S2-S3 until the trust region type least square objective function tends to be stable or the maximum number of iterations, thereby obtaining an updated seismic quality factor model; and S5, setting a plurality of frequency groups from low frequency to high frequency, carrying out iterative calculation from S2-S4, and calculating to obtain the seismic quality factor by adopting the seismic quality factor model under the maximum frequency group. The calculation amount can be reduced, the modeling efficiency can be improved, and the modeling precision of the seismic quality factor can be maintained.

The invention discloses a cold chain transportation path optimization method based on intelligent scheduling, and the method comprises the steps: collecting the environmental data of a transportation path, and constructing a directed graph structure containing the temperature and length attributes of a path segment; defining a cooling capacity consumption function, calculating an estimated cooling capacity consumption value of the path section, and forming an environment diagram containing the refrigeration cost; constructing a state vector and an action space, and designing a path selection strategy network and a state value network; setting a multi-objective reward function including cold consumption, delivery completion, cold chain failure and supply behaviors, performing multi-round strategy training by adopting an improved PPO algorithm, and optimizing and updating network parameters through a trust region strategy; and finally, calling an optimal strategy output path selection action in actual transportation to realize dynamic avoidance and supply insertion regulation and control of the cold chain interruption risk. According to the invention, intelligent, self-adaptive and energy-efficient optimization of cold chain path selection can be realized.

The invention relates to an RC circuit health state assessment method, belongs to the technical field of circuit health management of equipment, and solves the technical problems of circuit health state assessment inaccuracy and high calculation cost caused by an algebraic ring in the prior art. The method comprises the following steps: constructing a multi-physics field coupling degradation model and a block diagram simulation model of the RC circuit device; the simulation module is used for acquiring a block diagram simulation model; converting the simulation module into a directed graph and obtaining an algebraic ring candidate set; determining a solving execution sequence of the simulation modules; establishing an algebraic ring solver according to a trust region broken line method; and obtaining a complete dynamic response result of the RC circuit device multi-physical field coupling degradation model in the whole simulation time by using an algebraic ring solver, and evaluating the health state of the RC circuit. According to the method, the algebraic ring in the degradation model can be accurately identified and efficiently processed, so that the physical authenticity of a degradation mechanism is ensured, and high-precision tracking of RC circuit performance and reliable evaluation of health state indexes are realized.

The invention discloses a ring main unit intelligent regulation and control method based on deep reinforcement learning, and the method comprises the following steps: building an electrical topological graph model through collecting the real-time operation data of a ring main unit and a power distribution network, extracting the characteristics of nodes and branches, and generating an initial state vector; a comparison strategy embedding layer is arranged in front of the strategy network, and state-action representation is optimized through comparison learning; constructing a reinforcement learning decision structure by adopting a trust region strategy optimization algorithm, and outputting a ring main unit control action sequence; before execution, an out-of-limit action is judged and corrected by a control barrier function convoy layer; and closing and opening control is completed according to the corrected action, rewards are recorded, and incremental learning and parameter fine adjustment are performed by using an experience playback buffer area. According to the invention, by fusing the graph neural network and deep reinforcement learning and introducing a comparison strategy embedding layer and a control barrier function escorting layer, safe, adaptive and efficient intelligent regulation and control of the ring main unit in a complex power distribution network are realized.

The invention discloses a bay water quality monitoring station layout optimization method, and the method comprises the steps: carrying out the grid division of a research region, and obtaining data points; determining an initial range of algorithm search according to the characteristic values of the data points; the optimal step length of data point updating is determined by calculating gradient and curvature information of a target function, the position of the data point is updated based on the comparison result of the actual decline amount and the predicted decline amount of the target function, loop iteration is carried out until the preset convergence condition is reached, and the finally obtained position of the data point is the bay water quality monitoring station site. On the basis of the trust region mapping method, the site selection and layout problem of the bay water quality monitoring stations is rapidly solved through the particle swarm optimization algorithm, the appropriate number of stations can be given according to requirements when the number of the monitoring stations is not restrained, calculation reasonability is high, and the method has high application value.

The invention relates to an overlapped chromatographic peak positioning and splitting method and system based on a numerical stable function. The method comprises the following steps: de-noising chromatographic data and correcting a baseline; calculating a second derivative by using SG filtering, grouping continuous points according to a negative threshold value, and determining an initial position of each peak by using a second-order minimum value; establishing a peak function of bidirectional exponential correction by taking a peak position as an initial value, setting parameters and boundaries such as area, center, width, trailing and the like, constructing a residual error minimization target, and performing robust fitting by adopting a trust region reflection algorithm; and indexes such as suitability, output peak area, center, broadening, separation degree, theoretical plate number and the like are verified by residual errors. According to the method, valley point or prior component information is not needed, the method is insensitive to noise and baseline drift, serious overlapping and trailing / leading edge peaks of multiple components can be effectively processed, convergence is high, the method is insensitive to initial values, parameters can be expanded to different peak shapes, and the method is suitable for qualitative and quantitative analysis of automatic and high-flux chromatographic data.

The present invention discloses an optimization method for the layout of water quality monitoring stations in a bay. The method includes: dividing the research area into grids to obtain data points; determining the initial range of algorithm search according to the characteristic values of the data points; determining the optimal step size for updating the data points by calculating the gradient and curvature information of the objective function, and updating the positions of the data points based on the comparison result of the actual decrease and the predicted decrease of the objective function, and iterating cyclically until the preset convergence condition is reached. The finally obtained positions of the data points are the positions of the water quality monitoring stations in the bay. Based on the trust region mapping method, the particle swarm optimization algorithm is used to quickly solve the problem of the location layout of the water quality monitoring stations in the bay. When the number of monitoring stations is not restricted, it can give the appropriate number of stations according to the requirements, with high calculation rationality and high application value.

The invention relates to the technical field of monocular depth estimation, and discloses a monocular self-supervision depth estimation method based on credible region depth controllable generation. Firstly, a monocular image is collected and input into a depth estimation module for multi-scale feature extraction, an initial depth map of a whole scene is generated, and relative camera attitude transformation between adjacent frames is estimated through an attitude estimation module; identifying a credible area and an uncredible area according to the obtained initial depth map and relative camera attitude transformation; depth estimation of a dynamic region is guided through a pseudo depth and contrast learning method; then, constructing a multi-task joint loss function, and optimizing depth estimation of the untrusted region by utilizing depth information of the trusted region through a comparison loss mechanism; and iteratively optimizing network parameters of the depth estimation module and the attitude estimation module, and outputting a final optimized depth map. According to the method, the depth estimation precision and robustness of the model in a complex dynamic environment are improved through a credible region depth controllable generation strategy.

The invention provides a port integrated energy system dynamic optimization method based on deep reinforcement learning, and the method comprises the steps: 1) constructing a port integrated energy system environment, and defining a system state space, an action space and a reward function; 2) constructing a deep reinforcement learning algorithm based on near-end strategy optimization, wherein the deep reinforcement learning algorithm comprises a strategy network, a value network, a trust region cutting function, a dominant function and a time difference error; 3) constructing a track set generated by interaction of a deep reinforcement learning algorithm and a port integrated energy system environment, wherein the track set is used for updating algorithm parameters; and 4) training the port integrated energy system environment by using the updated deep reinforcement learning algorithm to generate a dynamic scheduling strategy. The dynamic optimization operation of the port comprehensive energy system is realized through the deep reinforcement learning technology, the time sequence decision characteristics of the system are fully considered, the flexibility of the system and the utilization rate of renewable energy sources are improved, and the efficient and flexible operation of the port comprehensive energy system is realized.

The invention discloses an incremental cooperative mechanical arm multi-target trajectory optimization method based on a trust region and corresponding electronic equipment. The incremental cooperative mechanical arm multi-target trajectory optimization method comprises the steps that S1, data are acquired; s2, establishing a model; S3, converting and solving a problem; S4, dividing a track and performing optimization execution; according to the method, the high efficiency and practicability of the cooperative mechanical arm in the complex environment are ensured through the multi-dimensional optimization model, the improved trajectory optimization method based on the trust region is adopted, and improvement is achieved in the aspects of reducing the calculation time, optimizing the trajectory length of the joint space, improving the trajectory smoothness and the like; according to the method, an incremental trajectory optimization framework is used, the environment information can be sensed in real time, and the motion information of the human body is fused into the trajectory optimization process in real time, so that the man-machine safety can be guaranteed, and the trajectory optimization quality and the calculation efficiency are effectively improved; the trajectory optimization method disclosed by the invention is superior to the prior art in processing man-machine cooperation tasks in complex scenes, and the application of the cooperation mechanical arm is perfected.

The invention discloses a hypersonic aircraft trajectory planning method based on an N-K iterative convex optimization algorithm, and relates to the field of trajectory planning. The invention aims to solve the problem that an existing aircraft trajectory planning method is difficult to accurately plan an aircraft trajectory. The method comprises the following steps: obtaining a nondimensionalized hypersonic gliding section dynamic model; obtaining a linearized hypersonic gliding section dynamic model; performing discretization processing on parameters in the linearized hypersonic gliding section dynamic model to obtain a hypersonic gliding section dynamic model after parameter discretization; obtaining a control quantity constraint, a process constraint and a trust region constraint in hypersonic flight vehicle trajectory optimization; and constructing a trajectory planning problem based on iterative convex optimization by using the hypersonic glide dynamic model after parameter discretization, the control quantity constraint, the process constraint and the trust region constraint, and solving the trajectory planning problem based on the iterative convex optimization to obtain a hypersonic flight vehicle trajectory planning result. The method is used for planning the aircraft trajectory.

The invention discloses a power flow calculation method and system for a power system, and aims to relax key variables such as part of generator output and node voltage from equality constraints to inequality constraints in order to solve the problem that part of generator output and node voltage are difficult to give due to large-range change of an operation mode in a novel power system; after the inequality constraint is introduced, the power flow model is changed into a novel power system constraint power flow model from an original nonlinear equation set solving problem; converting an inequality in the constraint power flow model into an equation by adopting a method of constructing an auxiliary function, and then converting the constraint power flow model into a bounded constraint least square problem; aiming at the problem that a traditional optimization algorithm is difficult to converge, a trust region algorithm is adopted for solving, and the adjustment amount of variables such as generator output and node voltage and a final power flow solution are obtained. The method can effectively solve the problem that the power flow has no solution due to the fact that the initial power flow value is difficult to give, and theoretical support is provided for planning and operation of a power system.

The invention discloses a multi-train cooperative tracking control method under relative braking protection constraint. The method comprises the following steps: constructing an optimization model; cooperative tracking control; the step of constructing the optimization model specifically comprises train dynamic model establishment, train operation constraint and optimization control problem; the cooperative tracking control specifically comprises model reconstruction based on an augmented Lagrangian method, solution by adopting a trust region method, and design of a multi-train cooperative tracking control algorithm under relative braking protection constraint. According to the solution algorithm based on the trust region method, efficient solution of the multi-train cooperative tracking control strategy under the relative braking distance is achieved. The cooperative control strategy designed by the invention is solved through a trust region method, so that the control force of the high-speed train can be adjusted at each decision moment. By means of the adjustment, the actual train distance can be rapidly converged to the expected train distance, and the efficiency and safety of train operation are guaranteed.

The invention relates to the technical field of thermal power generating unit operation optimization, and particularly discloses a thermal power generating unit operation optimization method, device and equipment and a storage medium, and the method comprises the steps: determining a target optimization function of a thermal power generating unit and a target constraint condition corresponding to the target optimization function; obtaining a nonlinear mapping function, and converting the target optimization function into representation of the nonlinear mapping function to obtain a target mapping function; determining a current initial operation parameter of the target mapping function in the target constraint condition based on a simulated annealing algorithm; iteratively optimizing the current initial operation parameter by adopting a trust region inner point method to obtain a current target optimization parameter of the target mapping function; updating the current operation parameters of the thermal power generating unit based on the current target optimization parameters to obtain current target operation parameters; and controlling the thermal power generating unit to operate based on the current target operation parameters. According to the scheme, the operation accuracy of the thermal power generating unit is improved by optimizing the operation data of the thermal power generating unit.

The invention discloses an umbrella ladder system modeling optimization method and system based on mechanics and parameter optimization, and relates to the technical field of wind power generation modeling. The efficient umbrella ladder system modeling and optimizing method is provided by combining mechanical analysis and a parameter optimization algorithm, and the performance and reliability of a high-altitude wind energy capturing system are remarkably improved; a CPO optimization algorithm is adopted to carry out dynamic parameter optimization on a floating system and an acting system under the multi-constraint condition, and the output power is maximized on the premise that buoyancy balance, rope speed limitation and parachute area constraint are met. Through dynamic updating of an adaptive learning rate adjustment mechanism and a trust region radius, the algorithm can rapidly converge to an optimal solution, the common problem of local optimum or constraint violation in a traditional optimization method is avoided, the method can adapt to changes of wind conditions of different heights, the system can be effectively prevented from overturning, and long-term stable operation is guaranteed.

The invention discloses an intelligent agricultural instruction recognition method based on deep semantic analysis, and the method comprises the following steps: S1, receiving and preprocessing an agricultural task natural language instruction, and generating text data; s2, constructing a deep semantic analysis model, and generating a structured semantic feature vector; s3, collecting real-time data in a target agricultural environment, and fusing the real-time data with the structured semantic feature vector to construct a state information vector; s4, establishing a strategy function and a value function by adopting a trust region strategy optimization algorithm, and optimizing an agricultural operation strategy; s5, generating a control instruction of the agricultural equipment according to the optimized operation strategy, and driving the equipment to execute operation; and S6, collecting job feedback data, analyzing and updating a state information vector, and adjusting the deep semantic analysis model. The invention realizes an agricultural natural language instruction recognition and execution method fusing deep semantic analysis and strategy optimization, and is used for improving the intelligent processing capability and execution efficiency of agricultural operation.

The invention provides a generator set scheduling method and system in an electricity-carbon market, and the method comprises the steps: obtaining a market response model, a current decision, and a current adjustment range based on the data of the electricity-carbon market and a historical optimal reference point; based on the current decision and the current adjustment range, executing an accelerated solution step to obtain an optimal decision; based on the optimal decision, scheduling the target generator set to obtain a clearing result; the acceleration solving step comprises the following steps: based on a market response model, obtaining trust region sub-problems in a current decision and a current adjustment range, and then carrying out dimensionality reduction solving to obtain a decision adjustment step length; obtaining an update decision amount and an update adjustment range based on the decision adjustment step length; if the updating decision gradient vector is greater than the preset convergence threshold value, executing an accelerated solution step based on the updating decision and the updating adjustment range; otherwise, the accelerated solution is terminated, and the optimal decision is obtained. The scheduling efficiency of the generator set can be improved.

The invention discloses an epidemic disease early warning system and method based on college volunteer participation, and relates to the technical field of epidemic disease early warning. The epidemic disease early warning method based on college volunteer participation comprises the following steps: acquiring related data of a corresponding community based on college volunteers; solving an optimal solution of the speculation model by using a trust region algorithm; finishing feature processing, mapping a feature value into an output value, and obtaining a final prediction result of the corresponding community; and completing the precision evaluation of the corresponding community prediction result. According to the method, the early warning time is greatly shortened, independent early warning of each independent community in a regional range is completed in a point form, the early warning condition of each independent community in the region is combined, a prediction result is fused, an existing relatively mature neural network speculation model is combined, external data such as weather and medical treatment of the region are imported, and the early warning efficiency is improved. The operation of the direct data end is simple, and the subsequent speculation efficiency is improved.

The invention provides a mining area continuous deformation monitoring method and system based on an InSAR technology, and belongs to the technical field of mining area surface deformation monitoring, and the method comprises the steps: connecting a discontinuous InSAR deformation monitoring result based on a mining area single-point settlement rule, and achieving the mining area deformation monitoring in an SAR data coverage period; a point-by-point mining area deformation connection model is constructed based on a function conforming to a mining area single-point deformation evolution law (following an S-type growth mode), namely a Weibull function. Meanwhile, adopting GA-PSO calculation to construct model initial parameters, and adjusting the model initial parameters according to the point position residual error and the model parameter distribution rule; and setting upper and lower limits according to initial parameters of the model, and solving final model parameters by adopting a least square method based on a trust region reflection algorithm, so as to obtain the deformation of the mining area in the monitoring period. The space-time filling of discontinuous InSAR deformation monitoring is realized, and the detection accuracy is improved.

The invention relates to the technical field of meteorological data assimilation, discloses an assimilation method suitable for a nonlinear problem, and aims to solve the problem that an incremental variation assimilation method has no trust region constraint and may not converge in practical application. A scheme for solving convergence and calculation efficiency of non-linear cloud radiance assimilation in theoretical and practical application is discussed. According to the method, the comparison of the total variation assimilation method and the increment variation assimilation method is realized under the same framework, the comparison of the multi-grid method and the multi-external circulation method is realized under the same framework, the problem that direct comparison is difficult to realize in different platforms due to complex parameters in different methods is solved, and the system operation complexity is greatly simplified through unified model configuration. Experiments show that by adopting the linear search total variation assimilation method under the multi-grid framework, the accuracy of an analysis field is stably improved, and the minimization process of a nonlinear total variation objective function is accelerated.

The application is suitable for the technical field of artificial intelligence security, and provides a jailbreaking security evaluation method and system based on dictionary guided sparse logic editing, which comprises the following steps: first, generating a static intensity vector; then, intercepting an original logit vector corresponding to each autoregressive decoding time step of a target large language model, and generating a dynamic gating signal based on the original logit vector; meanwhile, constructing a preliminary sparse intervention vector from the static intensity vector; and then, generating a modified adversarial logit vector based on the preliminary sparse intervention vector, so as to obtain response content for evaluating security. Through the application of extremely sparse local logic editing based on temperature scaling and trust region constraints in the vocabulary space, the high computing power overhead of traditional gradient attacks and the auxiliary model memory overhead of traditional logic intervention methods can be completely eliminated, and efficient, smooth, interpretable and robust large model jailbreaking security evaluation can be realized.

The present application relates to the field of artificial intelligence, and relates to a model trust region-based copyright protection method for smart grid deep learning models. The method comprises: acquiring a smart grid deep model set; for each model in the smart grid deep model set, searching an exclusive dataset for feature data samples having a model prediction value approximate to a model discrimination boundary, so as to obtain a trust region feature point set; performing dimensionality reduction on gradient vectors of the trust region feature point set on the model discrimination boundary according to a linear discriminant analysis method, so as to obtain perturbation vectors of the trust region feature point set; on the basis of predicted label changes of each model before and after the trust region feature point set is combined with the perturbation vectors, generating a model feature identifier set corresponding to the smart grid deep model set; and, on the basis of the model feature identifier set, training a copyright detection model to be trained, so as to obtain a pre-trained copyright detection model.

The present application belongs to the technical field of intelligent transportation system, and relates to a signal-vehicle cooperative optimization method based on MPC and trust region Bayesian optimization. Firstly, traffic flow data collection and road network simulation modeling are performed, and initial path allocation of vehicles is performed, then real-time speed control is performed on intelligent connected vehicles entering the road network, and the best driving speed is calculated by using a Bayesian optimization algorithm based on a trust region according to signal light information, distance from the intersection and driving conditions of surrounding vehicles. Then, the road impedance is updated, and the best driving path of the intelligent connected vehicle is recalculated. Then, the future traffic state is predicted by using model predictive control, and the best green light time of each intersection in different phases is calculated by using trust region Bayesian optimization. The best green light time is imported into the simulation platform, and vehicle path optimization and speed control are performed again, and the cycle feedback is performed. Finally, it is judged whether the vehicle leaves the road network or reaches the simulation end time, and the vehicle control and signal optimization are ended.

The invention relates to the technical field of automatic driving, in particular to a rapid solution optimization method and system for occupying grid trajectory planning, which combines occupying grid downsampling and a spatial index structure of a low-resolution occupying grid, initial trajectory planning based on a fitted curve and local fine path planning based on a trust domain. Through triple optimization and a synergistic effect, the solution speed in actual measurement is increased by 8-10 times, so that real-time planning based on occupied grids becomes possible. The basis function based on dynamics ensures that the generated trajectory itself conforms to the vehicle kinematics, and the quality and the performability of the trajectory are improved. The sparsity perception and the hierarchical optimization strategy make full use of the structural features of the problem, unnecessary calculation is avoided, and accurate putting of calculation resources is achieved.