51results about How to "Reduced solution time" patented technology

The invention discloses a virtual power plant planning-operation two-stage distribution robust optimization method based on scene driving, and the method comprises the steps: S1, building a capacity configuration model and a master-slave game model based on a master-slave game framework of a power supply, a load and energy storage; s2, considering the correlation and uncertainty of fan and photovoltaic output to construct an uncertainty set of wind and light output probability distribution, and generating a plurality of typical scenes through a sampling and clustering algorithm; s3, performing comprehensive norm constraint on the confidence interval of the uncertain set under the driving of a typical scene, and establishing a distribution robust optimization model of the virtual power plant in two stages of planning and operation, wherein the distribution robust optimization model considers uncertainty; wherein the distributed robust optimization model comprises a capacity configuration model and a master-slave game model; and S4, solving the distribution robust optimization model containing the master-slave game model based on the modified Kriging model and a column and constraint generation algorithm. By utilizing the method, the influence of uncertainty on the planning rationality and the operation economy of the virtual power plant can be reduced.

The invention discloses a multi-element aggregation regulation and control method suitable for user-side flexible resources, and belongs to the field of power system operation and control. The multi-aggregation regulation and control method is designed for aggregating the large-scale user side flexibility resource regulation capability to participate in centralized management and control and coordinated scheduling, meanwhile, the system flexibility requirement is considered, and in order to meet the flexible supply and demand balance and weaken the conservative property of inner approximation feasible region calculation under the condition of avoiding an infeasible solution, the method has a good application prospect. According to the method, the flexible resource aggregation feasible region can be accurately described, the optimization effect is not affected while the solving time is remarkably shortened, and the method can be used for solving the flexible resource aggregation feasible region under the constraints of three aspects of the disposable computing power, the storage space and the computing time. And centralized control and coordinated scheduling of large-scale flexible resources are realized.

The invention discloses a four-rotor unmanned aerial vehicle obstacle avoidance method based on a digital twin system. The method comprises the following steps: step 1, establishing an unmanned aerial vehicle digital twin system; 2, designing a feedforward-feedback control strategy based on model predictive control in a physical space; 3, designing and deploying an off-line trajectory planning framework in the virtual space; according to the invention, on the basis of establishing the unmanned aerial vehicle digital twin system, through the design of the trajectory planning framework and the feedforward-feedback control strategy, the unmanned aerial vehicle trajectory planning quality and the motion control precision are improved.

The invention discloses a virtual power plant scheduling optimization method and system based on schedulable space prediction. The method comprises the following steps: constructing a target virtual power plant schedulable space prediction model based on Gaussian process regression; respectively constructing a virtual power plant bidding model and a coal-fired power plant bidding model based on net income maximization; constructing a day-ahead electricity market clearing model based on market energy consumption cost minimization; constructing a double-layer game bidding model based on the virtual power plant bidding model, the coal-fired power plant bidding model and the day-ahead electricity market clearing model; iteratively solving the double-layer game bidding model at least based on the schedulable space of the virtual power plant until a convergence condition is met, and outputting an equilibrium solution; and adjusting the wind and light output strategies of the virtual power plant in different power demand periods based on the equilibrium solution. Theoretical support is provided for making a bidding strategy for the virtual power plant to participate in the day-ahead electricity market transaction, supply and demand balance of an electric power system can be maintained, and the stability of the system is improved.

The invention provides a DNN model-based division and reasoning acceleration method and system for vehicle-mounted edge intelligence. The method comprises the following steps: constructing a DAG graph based on a vehicle-mounted DNN model; constructing an auxiliary network flow graph of the DAG; adding a head node representing local calculation of a vehicle and a tail node representing calculation of an edge server or an auxiliary vehicle, and setting the weight of an edge as time delay overhead for segmenting the edge; obtaining different layering modes of an auxiliary network flow diagram of the DAG and corresponding maximum tolerance point sets meeting local time delay, memory and energy consumption limitation of the vehicle; correspondingly modifying the weight of the edge in the auxiliary network flow graph of the DAG based on each maximum tolerance point set, and calculating the maximum flow of each modified network flow auxiliary graph; and selecting a division mode corresponding to the maximum flow as a DNN model division mode. According to the method, the vehicle and road cloud cooperative reasoning is realized through a DNN model division mode, so that the DNN task cooperative reasoning completion time delay is reduced, and the vehicle safety and the user experience are improved.

According to the method for evaluating the buckling load deviation probability characteristics of the rocket reinforced shell structure, in view of the fact that a traditional deterministic method cannot effectively cope with uncertainty influences such as external environments and material attributes, the uncertainty of variables is modeled by utilizing a Copula mixed model, and the buckling load deviation probability characteristics of the rocket reinforced shell structure are evaluated by combining an improved probability weight point collocation method through a division and treatment strategy. Performing independent uncertainty propagation analysis on each parameter cluster; according to the method, high-dimensional parameter space response probability distribution is efficiently obtained, and the buckling load deviation probability is evaluated by integrating cluster results; the method provides a basis for the evaluation of the buckling load deviation probability distribution characteristics of the reinforced shell structure considering the multi-parameter random deviation, and assists the reliability analysis and optimization design.

The invention relates to the technical field of oil exploration, in particular to a U-shaped factorization Fourier neural operator for oil exploration. The system comprises a conversion module, and a lifting operator lifts an input function to a potential space through a linear full-connection network; the U-Net neural network module is used for acquiring high-frequency information; the operator layer module enters a Fourier neural operator layer to learn low-frequency information; and a projection operator is projected back to a physical space through a linear full-connection network. According to the technical scheme, the U-shaped factorization Fourier neural operator can effectively reduce the influence of lost high-frequency information on the solving precision, the difficulty of learning the partial differential equation by the U-shaped factorization Fourier neural operator is reduced, the partial differential equation solver is a core component of petroleum industry software, and the cost is reduced. The assembly is integrated into petroleum industry software and is a key technology for increasing storage and production, improving the recovery efficiency and optimizing the production process.

The invention discloses a full-connection Isin machine and an updating method thereof. The full-connection Isin machine comprises a spinning unit array, a global control module, an input driving module and an output driving module, the spinning unit array comprises a plurality of spinning units, any two spinning units interact with each other, a memristor array formed by memristor units is arranged in each spinning unit, and the output driving module is connected with the input driving module. The memristor array receives spin values from other spin units and stores weights equivalent to interaction; the driving module selectively activates and drives the memristor arrays in the plurality of spin units to perform spin updating operation; the output driving module enables all the spinning units to output spinning values; the global control module carries out global control on the input driving module and the output driving module so as to realize parallel control on all the spinning units, and each spinning unit receives spinning values from all the other spinning units and inputs the updated spinning values into all the other spinning units when each spinning update is carried out.

The application discloses a kind of relative radiation calibration methods and systems of remote sensing image based on quantile, it is related to remote sensing image processing technical field, the method includes: collecting satellite historical image data and separating according to spectral band;The value of each column of single-band image detector is sequentially arranged in ascending order, and quantile value is uniformly extracted to generate the quantile feature sequence of each column;The arithmetic mean value of all columns under the same quantile level is calculated, and the global reference quantile distribution is constructed;Based on least square method, the linear mapping relationship between each column quantile sequence and reference distribution is constructed, and the radiation correction gain and bias coefficient of each detector column are obtained;The original image is corrected column by column using the coefficient;The present application effectively eliminates the vertical strip noise caused by the non-uniformity of sensor response, overcomes the limitations of traditional calibration under the meteorological conditions and platform maneuvering, significantly improves the timeliness and image radiation quality of high-frequency and high-precision calibration.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The application relates to a coaxial heat exchange well heat exchange efficiency prediction method in oil exploitation, which comprises the following steps: a coaxial heat exchange well coupling heat transfer physical model is established; according to the engineering structure and the geological conditions of a target block, a standardized heat transfer model directly used for mathematical solution is established; according to the temperature control equation in the oil pipe and the annular space, the wellbore temperature field partial differential equation is calculated by using Laplace transformation, the fluid temperature at a position of the wellbore is calculated, and the wellhead outlet temperature is obtained; the analytical solution of the formation temperature changing with the radial distance and the time in the target block is obtained, the range of the formation temperature drop in the radial direction after the coaxial heat exchange well runs for a predetermined time is evaluated; and the coaxial heat exchange well heat exchange efficiency is calculated and predicted according to the obtained wellhead outlet temperature and the known well inlet temperature. The application can solve the problems of long solution time in numerical simulation and inaccurate calculation results in the analytical solution based on the steady-state assumption, improve the oil production efficiency and reduce the energy consumption.

The invention belongs to the field of power equipment multi-physical field rapid calculation, and discloses a magnetic field rapid analysis and calculation method under a transformer short-circuit fault and a related device. The method comprises the following steps: calculating vector magnetic potentials of a plurality of previous time steps through a full-order calculation model of a transformer short-circuit magnetic field in a high-dimensional space; constructing a snapshot matrix of a solution vector and a snapshot matrix of a nonlinear term; obtaining a group of orthogonal bases based on an intrinsic orthogonal decomposition method and in combination with the snapshot matrix of the solution vector; constructing a reduced-order subspace by using an orthogonal basis; projecting the full-order calculation model of the short-circuit magnetic field of the transformer into the reduced-order subspace; based on a discrete experience interpolation method and in combination with the snapshot matrix of the non-linear term, obtaining an interpolation matrix of the non-linear term; updating a nonlinear term in the transformer short-circuit magnetic field reduced-order calculation model by using the interpolation matrix, and then calculating to obtain a vector magnetic potential in a reduced-order subspace; and reconstructing the vector magnetic potential back to the high-dimensional space, and calculating the magnetic field under the short-circuit fault of the transformer according to the reconstructed vector magnetic potential.

The invention provides a variable-time-period fire-storage monthly scheduling method considering daily maximum discharge energy demand. The method comprises the following steps: acquiring a net load power prediction mean value of a plurality of continuous time periods; carrying out clustering processing on the net load power prediction mean value; combining the adjacent time periods, belonging to the same category, of the net load power prediction mean value into the same unit start-stop decision period; according to the pre-obtained maximum discharge power and minimum chargeable power of the energy storage system, constructing energy storage charge and discharge constraints of the energy storage system; and based on the energy storage charging and discharging constraint, determining a start-stop scheme in the unit start-stop decision period according to a scheduling model. According to the method, clustering analysis is carried out on the net load prediction mean value, so that the number of unit start-stop decision time periods is reduced, a monthly scheduling model is reduced, energy storage charging and discharging constraints under the most extreme condition are constructed, and therefore the purposes of optimizing the scheduling model scale and meeting the energy storage charging requirement under the extreme net load scene are achieved.

The application discloses a simulation method and device of a multi-physical field coupling resonator, a storage medium and a terminal, wherein the method comprises the following steps: acquiring a resonator model to be solved; identifying a plurality of physical fields in the resonator model to be solved; performing cubic grid division on the resonator model to be solved, and dividing the resonator model to be solved after the grid division into a plurality of grid areas; and acquiring system matrices of all the grid areas by using a finite element method; wherein a degree of freedom vector in the system matrices comprises physical field components of the plurality of physical fields in the resonator model to be solved; the system matrices of all the grid areas are sequentially spliced based on a coupling mode of the same physical field components in the matrices, so as to acquire the system matrices of the resonator model to be solved; and the system matrices of the resonator model to be solved are solved, so as to acquire a target solving physical quantity. The application realizes the simulation combination of the plurality of physical fields in the resonator, reduces the solving time, and also reduces the memory required for calculating the matrices.

The present invention provides an electromagnetic finite element analysis method and device based on solving matrix equations with mixed precision, which comprises the following steps: determining a double-precision finite element matrix equation of a target electromagnetic system and reordering its initial coefficient matrix to obtain a first coefficient matrix; performing LU symbol decomposition on the first coefficient matrix to obtain the positions of each non-zero element in the lower and upper triangular matrices; converting the first coefficient matrix from double precision to single precision to obtain a second coefficient matrix; determining the values ​​of each non-zero element in the lower and upper triangular matrices according to the second coefficient matrix and constructing the lower and upper triangular matrices respectively; converting the terms related to the excitation of the target electromagnetic system in the double-precision finite element matrix equation to single precision, and obtaining the target finite element matrix equation based on the lower and upper triangular matrices; and determining the solution that meets the double-precision solution accuracy requirement corresponding to the double-precision finite element matrix equation according to the lower triangular matrix, the upper triangular matrix and the target finite element matrix equation, so as to improve the analysis accuracy and save computing resources.

The application provides a security-constrained economic dispatch method based on a dual Halpern iteration, and belongs to the technical field of power system optimization dispatch and artificial intelligence computing infrastructure. The method comprises the following steps: constructing an original model of a security-constrained economic dispatch problem SCED; modeling the renewable energy output uncertainty by using a Gaussian mixture model, converting the chance constraint in the original model into a deterministic linear constraint, and thus converting the original model into a convex quadratic programming problem; constructing a limited Wolfe dual model for the convex quadratic programming problem, solving the dual model by using a Halpern iteration, and obtaining a dispatch optimization result. The application can solve the problems of low calculation efficiency, large memory overhead, low GPU utilization rate and difficulty in stable solution in a super-large-scale scenario in large-scale SCED, and can quickly generate an executable dispatch scheme, so that the calculation efficiency and engineering applicability of online dispatch and rolling optimization are improved.

The present invention discloses a high-dimensional sequential graph optimized null space dimension reduction covariance estimation method and system, the null space dimension reduction covariance estimation method comprises: obtaining measurement data of a sliding window, the measurement data comprises data in the sliding window collected by a number of sensors; establishing a factor graph optimization estimation of the measurement data, the factor graph optimization estimation comprises parameters to be estimated; performing dimension reduction processing on the parameters to be estimated; and solving the covariance of the factor graph optimization estimation after dimension reduction. The present invention linearizes the high-dimensional space and maps the covariance solution problem to a low-dimensional space by obtaining the null space of the high-dimensional parameters, thereby greatly reducing the covariance solution time while ensuring the optimality of the covariance estimation.

This invention discloses a high-precision RTK positioning method based on an improved adaptive genetic algorithm-based satellite selection strategy, belonging to the field of satellite navigation and positioning technology. A subset of satellites with a specified number of satellites is selected to minimize the loss of the Position Accuracy Factor (PDOP) and satellite geometric volume. The satellite selection problem is transformed into a combinatorial optimization problem. A fitness function is designed with the reciprocal of PDOP and geometric volume as its core, and adaptive crossover and mutation probabilities are introduced. The genetic operation parameters are dynamically adjusted according to the population evolutionary state to balance global search and local convergence capabilities. Iterative evolution is performed under the condition of satisfying subset size constraints. When the convergence condition is met, the subset of satellites with locally optimal PDOP values ​​is output for subsequent RTK ambiguity resolution and positioning. This invention can quickly converge to a near-globally optimal satellite geometry configuration in polynomial time while also considering satellite quality, significantly reducing computational complexity and improving the real-time performance and reliability of RTK positioning.

This invention discloses a normalizing control method and production method for high magnetic induction oriented silicon steel, which involves normalizing and annealing hot-rolled coils at different temperature stages: the first normalizing temperature is 1135~1140℃, and the normalizing time is 55~70s; the second normalizing temperature is 1130~1135℃, the dew point is controlled to be positive, and the normalizing time is 20~30s; the third normalizing temperature is 930~940℃, the dew point is controlled to be positive, and the normalizing time is 55~75s; the fourth cooling temperature is 860~870℃, and the cooling time is 8~15s. This invention, through an improved normalizing process, achieves more complete nitride solidification at normalizing temperatures above 1130°C under a fixed furnace atmosphere dew point, while simultaneously reducing nitride solidification time: the first normalizing stage is reduced to below 70 seconds, with a minimum of 55 seconds; the second normalizing stage is reduced to below 30 seconds. During the cooling process at approximately 930-940°C, finer AlN precipitation is more uniform, and the AlN precipitation time after 930-940°C is reduced to below 75 seconds, with a minimum of 55 seconds. This invention significantly shortens the normalizing time, improves normalizing production efficiency, and increases production capacity by controlling the uniformity of the grain size on the surface of the normalized hot-rolled plate.

The invention discloses a high-strength and high-toughness cast aluminum alloy as well as a preparation method and application thereof, and the cast aluminum alloy comprises the following components in percentage by mass: 4%-11% of Si, 0.2%-0.8% of Mg, 0.3%-1.8% of Cu, 0.10%-0.14% of Ti, 0-1.0% of Zn, 0.10%-0.30% of Fe, 0.015%-0.030% of Sr and the balance of Al and inevitable impurities. Zn + Cu is less than or equal to 2%, and Cu / Mg is greater than or equal to 2; the preparation method comprises the following steps: carrying out heat treatment on an aluminum alloy ingot; the heat treatment comprises three stages of solid solutions, and the temperatures of the three stages are respectively 490-500 DEG C, 530-540 DEG C and 545-555 DEG C. The aluminum alloy is high in tensile strength and yield strength and high in ductility, the solid solution time is remarkably shortened during preparation, and the production cost is remarkably reduced.

The present invention provides a method for selecting an estimable parameter subset for reaction process mechanism modeling. First, according to the mechanism analysis, a model of the reaction process is established, the nominal value of the parameter is obtained in the prior knowledge, and the simplified Hessian matrix of the parameter is calculated at the nominal value of the parameter. Then, the original parameter is converted based on the simplified Hessian matrix of the parameter, a Monte Carlo simulation is designed, and the simplified Hessian matrix is ​​used to calculate the mean square error of the model output prediction, and the parameter subset that minimizes the mean square error of the model output prediction is selected as the optimal parameter subset. The present invention avoids the problem of solving the sensitivity matrix, thereby saving a lot of calculation time.

This invention relates to the field of vehicle assisted driving technology and provides a vehicle adaptive cruise control method based on projection constraint strategy optimization, comprising the following specific steps: Step 1, Strategy trajectory sampling: Step 1.1, Simulation environment setup; Step 1.2, Defining state space and behavior space; Step 1.3, Reward function and safety supervisory function design; Step 1.4, Trajectory sampling process; Step 2, Strategy evaluation and optimization. This method achieves collision-free adaptive cruise control with low computational requirements and exhibits high comfort and traffic efficiency. It is trained using reinforcement learning and incorporates a safety supervisory trust domain to prevent the strategy from increasing the safety supervisory value while pursuing reward maximization. For the first time, a zero collision rate is achieved in an adaptive cruise control strategy trained by reinforcement learning, and the strategy can achieve a high degree of consistency with the expected speed when there is no interference from target vehicles.

The present invention discloses a method for a surface unmanned boat to attack a protected target area based on differential game. Based on the motion model of the offensive surface unmanned boat, the attack point of the offensive surface unmanned boat is obtained. According to the position coordinates of the attack point of the offensive surface unmanned boat and the position coordinates of the offensive surface unmanned boat, the attack angle of the offensive surface unmanned boat, the first-order differential of the inner product between the unit vector of the attack angle of the offensive surface unmanned boat and the unit vector of the current heading angle and the state condition of the offensive surface unmanned boat are obtained; and then the heading control input of the offensive surface unmanned boat is obtained to construct the offensive strategy of the offensive surface unmanned boat. In the process of solving, it is not necessary to calculate a complex differential equation group, which greatly reduces the difficulty and time of solving the strategy, and can adapt to the highly dynamic sea battlefield form.

The invention relates to the technical field of power systems, and particularly discloses a time sequence operation simulation method considering the comprehensive benefit of a new energy delivery base, and the method comprises the following steps: S1, constructing a new energy delivery base time sequence operation simulation model; s2, calculating comprehensive benefits of the new energy delivery base; and S3, solving the new energy delivery base time sequence operation simulation model to obtain an optimal new energy delivery base operation scheduling scheme. The fast solving method for the time sequence operation simulation model has the advantages that a non-dominated dung beetle optimization algorithm and a decomposition solving method are utilized, the solving scale and the solving time are reduced, and the purpose of fast solving is achieved.

The invention belongs to the technical field of power systems, and particularly discloses a representative scene-based power system robust unit combination acceleration optimization method, which comprises the following steps of: constructing a robust uncertainty set according to historical predicted power generation data and historical actual power generation data; constructing a unit commitment optimization model; calculating the system net load and the system net load rate of the power system in each time period; respectively constructing a first auxiliary mixed integer linear programming model and a second auxiliary mixed integer linear programming model, and determining a minimum net load representative scene and a maximum net load representative scene; determining a necessary unit set; determining a must-stop unit set; and constructing a robust unit commitment simplified model by taking the set of units needing to be started and the set of units needing to be stopped as optimization schemes so as to realize accelerated optimization of the robust unit commitment problem of the power system. According to the method, the calculation efficiency of the robust unit combination problem can be improved. The problem of low calculation efficiency of the robust unit combination problem in the prior art is solved.

The application provides an unmanned vehicle trajectory planning method based on energy optimization, comprising: updating complex irregular obstacle information of surroundings in the unmanned vehicle based on point cloud information; calculating posterior probability of a grid according to point cloud information fed back each time, and constructing an occupancy grid map in real time; searching for path points for avoiding obstacles by using an A* algorithm based on vehicle kinematics according to terrain and environment information constructed by a sensor; obtaining a series of relatively dense path points by using a path backtracking method, and performing curve fitting by using a uniform B-spline curve without control points; constructing an Euclidean distance map to further construct an unconstrained optimization problem about minimum energy consumption, driving speed, driving acceleration and obstacle avoidance distance, and solving the optimization problem by using an open source solver to obtain an optimal trajectory with optimal energy consumption, satisfying dynamic feasibility and being able to avoid obstacles; and performing time redistribution on the optimal trajectory, so that the speed and acceleration values corresponding to the trajectory points do not exceed a threshold value.

The invention discloses a transmission and distribution cooperation-based mountainous area power grid source-grid load-storage progressive coordination planning method, system and device, and a medium, and the method comprises the following steps: generating an original operation scene through a sampling method according to historical load data and historical output data, and reducing the original operation scene into a typical operation scene; solving the source network load storage coordinated optimization model by adopting a decomposition algorithm, and obtaining an optimal investment decision of each planning stage through iteration; and performing load flow calculation on the typical operation scene based on the power grid frame parameters and the optimal investment decision, and calculating a new energy consumption rate. According to the invention, by constructing a full-voltage class coordinated optimization framework and a progressive rolling planning mechanism, the technical problems of bidirectional power flow, poor planning collaboration, insufficient uncertainty processing capability and the like caused by the access of high-proportion new energy to the mountain power grid are effectively solved.

The present application provides a kind of based on M-HODLR solving electromagnetic scattering method of metal medium composite structure, to solve the problem of large memory consumption and low accuracy of electromagnetic scattering result when solving electromagnetic scattering of metal medium composite structure.The implementation steps of the present application are: the surface of metal medium composite structure is completely meshed;Define the base function on each mesh element after meshing and sort the base function;Fill the total impedance matrix;Total impedance matrix is blocked using M-HODLR method;Each sub-matrix block after blocking is compressed using enhanced ACA method;Solve the matrix equation of the generated metal medium composite structure and calculate the electromagnetic scattering result.The present application has the advantages of significantly reducing the calculation memory, reducing the solving time and the high accuracy of the electromagnetic scattering result of the metal medium composite structure calculated.

The present invention discloses an aircraft trajectory optimization method combining Birkhoff interpolation and LGL pseudo-spectral method, and uses the pseudo-spectral method in the numerical solution method to solve the aircraft trajectory optimization problem model. In view of the problem that the differential matrix condition number is high due to the discretization interpolation problem in the process of solving the optimization problem by the pseudo-spectral method, the condition number of the differential matrix in the pseudo-spectral method is greatly reduced by introducing Birkhoff interpolation, thereby reducing the solution time of the aircraft trajectory optimization problem model. The present invention introduces a genetic algorithm in the bionic algorithm to pre-solve the nonlinear programming problem after the discretization transformation of the pseudo-spectral method, and then uses the result of the genetic algorithm solution as the initial value of the sequential quadratic programming algorithm solution, thereby avoiding the divergence of the sequential quadratic programming algorithm due to the sensitivity of the initial value, improving the solution efficiency of the aircraft trajectory optimization problem model by the pseudo-spectral method discretization, and finally improving the trajectory optimization efficiency of the aircraft and the accuracy of problem solving.

The invention provides a flexible job shop machine and AGV combined scheduling method, system and device, and the method comprises the steps: carrying out the training of a Q-FOX through a certain number of iterations, obtaining an optimal hyper-parameter, and fixing the value of the hyper-parameter; solving the joint scheduling problem of the flexible job shop machine and the AGV through the value of the hyper-parameter; and constructing a decision process model capable of dynamically adjusting parameters, and searching an optimal solution with an optimal adaptive value from the whole population based on a population fitness function so as to automatically adjust the joint scheduling parameters of the flexible job shop machine and the AGV. According to the method disclosed by the invention, by utilizing the hyper-parameter automatic tuning function of the Q-FOX algorithm, the convergence speed of the algorithm can be improved, the solving time is shortened, and the robustness of the reinforcement learning algorithm is improved.