1361 results about "Optimization problem" patented technology

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 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 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 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 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 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 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.

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.

The invention discloses a micro-grid optimization scheduling method based on an improved Loueer fox algorithm, relates to the technical field of micro-grid operation, and realizes collaborative optimization scheduling of a distributed power supply, an energy storage device, a load and the like in a micro-grid system by simulating various perception and behavior strategies of the Loueer fox. Aiming at the demand of distributed power supply system micro-grid optimization scheduling, the micro-grid system operation economy is taken as a main target, a function corresponding to a micro-power supply scheduling scheme is constructed, improvement is carried out based on an RFO algorithm, and a quantum fractal annealing disturbance strategy, a hyperbolic tangent self-adaptive olfactory disturbance strategy and a Riemannian metric accumulation disturbance strategy are introduced, so that the optimal scheduling of the micro-power supply system is realized. Through the fusion of the triple innovation strategies, the defects of poor population diversity, easy premature convergence, insufficient balance capability, low precision and the like when the original RFO algorithm and the traditional intelligent algorithm are used for solving the complex optimization problem are solved, and the efficient solution of the micro-grid optimization scheduling scheme is realized.

The invention discloses a covert communication optimization method based on deep reinforcement learning under limited character input, and belongs to the technical field of wireless communication. According to the method, an optimization problem which takes the minimum average bit error rate as a target function and takes hidden requirements, power and the like as constraints is constructed on a hidden communication model of a transmitting end, a receiving end, an intelligent reflecting surface and a monitor. Firstly, the bit error rate of a receiving end is given, the detection performance of a monitor is analyzed, and the KL divergence upper bound and the corresponding hidden constraint are deduced. The objective function and the constraint set of the constructed problem are non-convex, the optimization problem is trained and solved by constructing the optimization problem into a deep reinforcement learning model, the modulation order which is difficult to optimize by a traditional algorithm is optimized by using the advantages of deep reinforcement learning, a deep deterministic strategy gradient algorithm is selected to train the model, and then the error rate is minimized. And the covert communication performance is improved.

The invention relates to the technical field of bridge construction equipment, and discloses a bridge fabrication machine walking attitude real-time control system based on multi-sensor fusion, and the system comprises a heterogeneous sensing and data collection module which obtains the kinematics and structural strain data of a bridge fabrication machine in real time; the offline modeling and calibration module is used for pre-establishing an initial dynamic model and a key mapping relation; the online state estimation and model self-adaption module is used for performing state estimation, model correction and disturbance identification and outputting comprehensive state information; the multi-constraint predictive control decision module is used for solving an optimization problem containing structural security constraints and generating an optimal control instruction; and the execution and feedback module analyzes the optimal control instruction to drive an execution mechanism and adjust the attitude to form a real-time closed loop. According to the invention, a scheme of fusing multi-source data and carrying out online adaptive model correction is adopted, and the fundamental defect that the control precision is obviously reduced along with time lapse and environment change due to the fact that the model is not matched with an actual system state is overcome.

The invention provides a generative intelligent optimization method and device, and relates to the technical field of generative artificial intelligence optimization computation.The method comprises the steps that a mathematical model containing decision variables, an objective function and constraint conditions is established for a target optimization problem, and scene parameters are pre-calculated through a traditional optimization algorithm to obtain a theoretical optimal solution; constructing a training data set containing scene parameters and a theoretical optimal solution; training the data set through a conditional generation type artificial intelligence model, minimizing the difference between a generation decision and a theoretical optimal solution, and establishing a mapping relation from scene parameters to an optimal decision, so that the mapping relation implicitly learns a constraint condition satisfaction mode; current scene parameters are collected in real time and input into the trained model, a near-optimal decision scheme is generated through reverse denoising or hidden variable decoding, and the near-optimal decision scheme is applied to real-time scenes such as industrial control. According to the method, a high-quality solution close to theoretical optimum is realized, the online solution time consumption is remarkably reduced, and the real-time requirement is met.

The invention discloses an edge computing network task unloading and resource allocation method based on optimal service quality, which comprises the following steps of: constructing a system architecture comprising a cloud server, a plurality of edge servers and mobile equipment, and establishing a multi-dimensional system model covering task characteristics, service cache, communication transmission, computing resources, service cost and service quality; constructing an optimization problem aiming at maximizing the long-term service quality of all mobile equipment, and forming a mixed integer nonlinear programming model under the constraints of mobile equipment cost constraint, storage capacity limitation, bandwidth, computing resources and the like; a double-time-slot hierarchical decision-making mechanism is designed, and multi-dimensional joint optimization of service caching, task unloading and resource allocation is realized through a collaborative mechanism that short-term resource allocation is constrained through a long-term caching decision and short-term performance feedback optimizes long-term caching. According to the method, the overall quality of service (QoS) can be effectively improved and the task processing delay and energy consumption can be reduced under the condition that the cost of the mobile equipment and the system resource limitation are met.

The invention discloses a satellite edge computing task scheduling method and system based on multi-agent deep reinforcement learning, and the method comprises the steps: S1, constructing a system model which comprises a user equipment model and a satellite edge computing server model; s2, constructing a target function, wherein the target is to minimize the total delay and total energy consumption of task processing of the user equipment and the satellite edge computing server; s3, modeling an original optimization problem into a decentralized partial observation Markov decision process, proposing an efficient cross-plot Transform multi-agent near-end strategy optimization algorithm, and training a task unloading and resource allocation model until the algorithm converges; and S4, the user equipment makes an appropriate task unloading and resource allocation decision based on the trained task unloading and resource allocation model according to the current satellite edge computing environment state and task requirements. According to the method, satellite movement and calculation and communication resource constraints of equipment in a satellite edge calculation environment are fully considered, joint optimization of task unloading and resource allocation strategies is realized through a multi-agent deep reinforcement learning algorithm, the total delay and total energy consumption of the system are effectively reduced, and meanwhile, the task completion rate is improved.

The present application relates to the field of power system optimization. Disclosed are a multi-objective particle swarm optimization method and system based on multi-strategy improvement. The method comprises: using a multi-strategy improved multi-objective particle swarm optimization algorithm to solve a multi-objective optimization model of a power supply of a generator state monitoring apparatus; and combining three improved strategies, i.e., adaptive adjustment of an inertia weight, coexistence of a decomposition algorithm and Pareto dominance, and introduction of a mutation factor. The present application overcomes the defects of conventional multi-objective particle swarm algorithms, and achieves a better distribution of a Pareto front, thereby obtaining the best Pareto optimal solution set. The present application solves the problems of conventional multi-objective particle swarm algorithms in solving a multi-objective optimization problem, such as premature convergence to a local non-dominated solution, and sub-optimal distribution of a Pareto front caused by an improper external archive update strategy, thereby improving the operational efficiency and reliability of a power supply system of an apparatus.

The invention belongs to the field of temperature control, and particularly relates to a reaction kettle temperature control method and system for synthesizing isothiocyanate. The method comprises the following steps: acquiring temperature, material concentration and coolant flow in a kettle in real time, calculating a reaction rate estimated value through a nonlinear mixed model, and calculating an active factor by combining catalyst input time and an index inactivation function; performing unequal weight combination on the three parameters to generate a scheduling variable, and calculating a current value and a second-order difference value of the scheduling variable; selecting a reference state space model according to the current value of the scheduling variable, and correcting the matrix by using a second-order difference value to obtain an LPV model; constructing a quadratic programming optimization problem, wherein an objective function comprises a temperature tracking error penalty term and a control input increment penalty term for adjusting the weight; when the temperature deviation exceeds the threshold value continuously, a control input value penalty term is added; and solving an optimization problem and outputting a first element of the control sequence to a temperature control execution mechanism. According to the invention, the temperature control precision and stability of the isothiocyanate synthesis reaction kettle are improved.

The invention discloses a mixed regular variational mode decomposition method, device and equipment suitable for an unsteady flow field of an aircraft and a storage medium, and relates to the technical field of flow analysis, and the method comprises the steps: determining a bandwidth estimation value and a frequency separation weight coefficient based on the unsteady flow field, an expected mode number, a spatial mode and a time evolution coefficient, constructing a first regular term and a second regular term, weighting to obtain a mixed regular term, introducing a flow field reconstruction term to establish a flow field modal decomposition variational optimization problem, converting the problem into a frequency domain optimization problem through Fourier transform, and simplifying the problem into a sub-convex optimization problem by adopting an alternating direction multiplier method; an analytic solution is obtained through a variational method and iteratively updated to obtain a flow field space mode, a time evolution coefficient frequency domain representation, a center frequency and a Lagrange multiplier, the time evolution coefficient is obtained through inverse Fourier transform, a mode decomposition result is obtained, and therefore high-precision mode separation of the unsteady flow field is achieved. And the calculation efficiency and the iteration convergence speed of the variational optimization problem are improved.

Methods for optimizing path planning for agricultural vehicles are provided. In one embodiment, a field is defined, passes completely covering the field are created, the ends of each pass are transformed into nodes, distance and time matrices for the nodes are computed, and an optimization problem incorporating the time and distance matrices, agricultural constraints, and time window requirements is formulated. Prior to solving the optimization problem, verification that a feasible solution to the optimization problem exists is performed, and constraints are adjusted if a feasible solution does not exist. The optimization problem may be solved using a quantum annealing process to achieve an optimized route that performs well in an agricultural setting.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention discloses a GIS in-place robot coordination control method with a posture correction function, and belongs to the technical field of robot coordination control. The method comprises the following steps: establishing a 2D-3D coordinate mapping corresponding relation; fusing the corresponding relation with an operation state predicted by an IMU-based inertial measurement and system dynamics model; performing error calculation on the obtained 6D pose information and target pose information; constructing a nonlinear kinetic equation of the robot, and converting a control target into an optimization problem of various constraints; and converting the obtained control instruction into a torque signal for actually driving the robot to move. According to the method, the influence of mismatching on pose estimation is effectively inhibited by establishing the 2D-3D coordinate mapping relation; through a joint torque PID control algorithm, the torque required by each joint is calculated in real time, and high-precision tracking of an expected trajectory is realized; an MPC optimization control framework with multiple constraints is constructed, and precise, smooth and safe coordination control over the GIS in-place process is achieved.

The invention discloses a hybrid robot adaptive motion control method and system, and relates to the technical field of robot motion control, and the method comprises the steps: obtaining the initial state of a robot system; establishing a nominal dynamics prediction model, and defining a safety set and a control barrier function set; statistical characteristics of model prediction residuals are obtained through online learning; correcting the dynamic model and generating a feed-forward compensation amount, and adaptively adjusting a safety set and a barrier function according to uncertainty; constructing a rolling optimization problem integrating the closed chain constraint and the barrier function hard constraint; solving the optimization problem to obtain an expected control quantity, and adjusting a safety parameter to obtain a rollback control quantity when solving fails; and synthesizing the feed-forward compensation quantity and the control quantity to generate a driving instruction. According to the method, the problem of model mismatching is solved through online learning and residual compensation, safety self-adaption is achieved through uncertainty, performance and safety are considered through a unified optimization framework, and the adaptability, precision and robustness of the system under dynamic disturbance are remarkably improved.

The invention discloses a large-scale complex correlation earth observation task planning method based on reinforcement learning, and relates to the technical field of task resource planning. The method comprises the following steps: acquiring historical data of earth observation task planning, and constructing a sample data set; constructing a graph neural network and a reinforcement learning network; using the sample data set to train the graph neural network and the reinforcement learning network at the same time; acquiring a demand information list, a satellite information list, a data transmission task information list and an observation information list, and processing the information lists into demand feature vectors, satellite feature vectors, data transmission task feature vectors and observation feature vectors; and inputting the four feature vectors into a trained graph neural network to obtain an environment feature vector, inputting the environment feature vector and the demand feature vector into a trained reinforcement learning network, and outputting an earth observation task planning result. The method can solve the scheduling optimization problem under the condition of multiple tasks and limited resources, improves the task planning efficiency, and can be widely applied to the fields of satellite task scheduling, intelligent manufacturing, automatic driving and the like.

The invention belongs to the technical field of unmanned surface vessel control, and particularly relates to an unmanned surface vessel trajectory tracking method based on parallel model predictive control, which comprises the following steps: S1, establishing an unmanned surface vessel mathematical model including dynamic uncertainty and external unknown disturbance; s2, constructing a mathematical model of the virtual artificial system, and designing a finite time auxiliary parallel controller based on the virtual artificial system; s3, constructing a Lyapunov tightening constraint condition with virtual and real information; s4, designing a predictive control optimization problem based on a flat luggage apunov model, and proving the stability of the dynamic model of the unmanned surface vessel; s5, the stability of the finite time auxiliary parallel controller is proved; s6, analyzing the feasibility of the prediction control optimization problem based on the flat luggage Yapunov model; and S7, simulation verification research is carried out, trajectory tracking is carried out on the unmanned ship under the dynamic uncertainty and external unknown disturbance conditions, and the effectiveness of the trajectory tracking method is verified.

The invention discloses a cooperative control method and system for a charging pile and a photovoltaic energy storage system, and the method comprises the following steps: obtaining data of different sources, inputting the data into a fusion neural network model, carrying out the prediction of time series data through a deep learning model, carrying out the intelligent decision, and optimizing the electric energy dispatching; constructing an energy scheduling model of quantum calculation, processing a plurality of variables based on quantum calculation, calculating in a multi-target environment by using a quantum annealing algorithm, and solving a multi-target globally optimal solution; edge computing equipment is deployed, data processing and computing capabilities are transferred from a central server to local nodes of the charging pile, the energy storage system and the photovoltaic system, local data processing and real-time optimization are carried out, the multi-objective optimization problem is solved through quantum annealing and a quantum approximate optimization algorithm, the computing efficiency of the scheduling system can be greatly improved, and the scheduling efficiency is improved. And the calculation bottleneck faced by the traditional method is avoided. Data from different sources are integrated, intelligent decision making is carried out through deep learning, and the self-adaptive capacity of the system is enhanced.

The invention relates to an excavator trajectory planning method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of engineering machinery trajectory planning. The method comprises the following steps: establishing a forward kinematics model through a D-H homogeneous coordinate transformation method; based on the forward kinematics model, establishing an inverse kinematics model through a geometric method; setting six path points in the track according to the mining track; based on an inverse kinematics model, converting the space coordinates of the six path points into space angles of joints; interpolation is carried out on the joint path points through a five-time B spline curve, and a track is generated; determining an optimization objective function according to actual application requirements, and setting constraint conditions according to a specific excavator type; and finally, the optimization problem is solved through an IADEMOPSO algorithm, an optimal Pareto solution is selected by using an average optimal evaluation formula, and an optimal track is generated. The track planning of the excavator can be realized.

The invention discloses a proxy model auxiliary evolution method based on two-stage adaptive switching and Voronoi niche. The method comprises the steps of generating an initial sample by adopting optimal Latin hypercube sampling, obtaining a real fitness value through simulation, constructing an initial training data set, and setting an initial iteration counter k and an evaluation budget threshold value; training a proxy model based on the current data set, and executing evolutionary algorithm optimization by using the proxy model to obtain a current optimal individual; judging two stages according to an iteration counter k and a distance threshold value; the development execution stage comprises the following steps: evaluating the real fitness and updating a data set; the exploration execution stage comprises the steps of multi-modal approximate detection, Voronoi niche division, multi-modal collaborative search, real fitness evaluation and data set updating; judging a termination condition and outputting a result; according to the method, three core technologies of an adaptive stage switching mechanism, multi-modal approximate detection and Voronoi niche division and multi-extremum collaborative filling are creatively integrated, so that the defects of insufficient global exploration, low local development precision and poor convergence efficiency of a traditional method in a high-dimensional multi-modal expensive optimization problem are effectively overcome; and the solving efficiency and reliability of a complex and expensive optimization problem are remarkably improved.

The invention discloses a factor graph optimization method for aircraft cluster positioning and navigation, which comprises the following steps: for each aircraft in an aircraft cluster, establishing a corresponding local factor graph based on a kinematic model and observation data of the aircraft; constructing a local optimization objective function of each aircraft based on the local factor graph; the method comprises the following steps: solving a local optimization objective function of each aircraft based on a distributed optimization algorithm through interaction and cooperation information among aircrafts in an aircraft cluster, and enabling position estimation of each aircraft to be converged to a global consistent solution; according to the method, the factor graph is constructed for the aircrafts in the cluster, the optimization problem is disassembled and distributed to each aircraft by introducing the idea of distributed calculation, the position of the cluster is solved through mutual communication cooperation among the aircrafts, the positioning precision is optimized, the problems of low calculation efficiency, insufficient calculation resources and the like existing in traditional centralized optimization are solved, and the method is suitable for large-scale popularization and application. And the robustness of the system under the conditions of communication limitation and satellite observation data deviation is enhanced.

The invention discloses a secure collaborative unloading method in an ultra-dense millimeter wave cache network, and the method specifically comprises the steps: constructing a network architecture, and configuring an optimization problem under multiple constraints in the network architecture; randomly generating an initial solution set according to the optimization problem, defining the initial solution set as an original population, and searching and iterating the original population by adopting an improved sparrow algorithm to obtain a target population; further searching by adopting an improved sparrow algorithm according to position information codes of all individuals in the target population, and outputting historical optimal position information; and performing safe collaborative unloading resource configuration according to the historical optimal position information of the target population. According to the method, the cache configuration is unloaded by the base station, and the task unloading cache configuration is jointly optimized and calculated under the conditions that the security vulnerability cost, the time delay limit, the user calculation capacity and the transmitting power are met, so that the task unloading energy consumption is reduced to the minimum.

The invention discloses a wind power generation system model prediction control method and device based on event triggering. The method comprises the following steps: establishing a wind power generation system model; based on the wind power generation system model, whether an event triggering mechanism is activated or not is judged according to the deviation between the current state and the target state of the system and the maximum sampling interval time; designing an LMPC algorithm based on event triggering: if an event triggering mechanism is activated, solving an optimization problem in a finite time domain to obtain an optimal control sequence of the system, and fusing Lyapunov function constraint to ensure the closed-loop stability of the system; wherein the optimization problem takes a minimum weighted sum of a system output tracking error and a control input variable quantity as a target; and deploying the LMPC algorithm based on event triggering into a control device of the wind power generation system so as to realize optimal control of the wind power generation system. The stability and robustness of the wind power generation control system can be effectively improved.