134 results about "Differential game" patented technology

The invention relates to the technical field of garbage disposal, and discloses a household garbage AI intelligent unmanned transport vehicle and a garbage disposal transport method.The transport vehicle comprises a garbage transport vehicle body, a data acquisition module, a path optimization module, a dynamic path adjustment module, a multi-vehicle collaborative scheduling module and an execution module; the method comprises the steps that a garbage transportation task is initialized, and garbage stations and the state of a transportation vehicle are collected; calculating an optimal path, and performing real-time adjustment based on dynamic data; by adopting multi-vehicle cooperative scheduling, path conflicts are avoided, and the transportation efficiency is optimized; garbage collection, transportation and delivery are completed, and transportation data are recorded. According to the method, efficient automation of garbage transportation is achieved through intelligent data collection, path optimization, dynamic adjustment and multi-vehicle cooperative scheduling, the optimal path is calculated through the variational method and the Euler-Lagrange equation, the driving route is dynamically adjusted in combination with the Hamilton-Jacobi-Bellman equation, multi-vehicle scheduling is optimized based on the differential game and the Nash equilibrium, and the efficiency of garbage transportation is improved. And high-efficiency transportation and no path conflict are ensured.

The invention relates to the technical field of adaptive network shunting, and discloses an adaptive network shunting method and system based on deep reinforcement learning, and the method comprises the steps: obtaining the flow data of a heterogeneous network, building a protocol fingerprint database, and converting the data of different protocols into a unified network state feature vector; constructing a multi-agent reinforcement learning framework, including defining a state space, defining an action space and defining a reward function, and optimizing a strategy of each agent based on a differential game; adjusting the weight of each agent reward function by using a meta-learning model, training a multi-agent reinforcement learning framework by using deep reinforcement learning, and learning an optimal shunting strategy under different heterogeneous network conditions; and deploying the trained strategy to a heterogeneous network environment, and dynamically selecting an optimal network path according to the current network state. And the heterogeneous data processing capability is obviously improved.

The invention provides an unmanned aerial vehicle bionic group trajectory emergence method and system of a space-time gradient field, and relates to the technical field of unmanned aerial vehicle group collaborative navigation and adaptive control, and the method comprises the steps: generating a four-dimensional space-time gradient field tensor matrix through a dynamic field coupling convolutional network; in combination with density-speed response data of fish school escape behaviors and unmanned aerial vehicle dynamics constraints, a bionic group interaction rule set is constructed through nonlinear differential game modeling; adopting a space-time finite element discretization method and combining with GPU acceleration parallel solving to determine an individual trajectory basis function of the unmanned aerial vehicle; performing optimization processing through a Lyapunov function to generate a group recombination instruction; and generating a space-time gradient field enabled unmanned aerial vehicle bionic group trajectory emergence scheme by integrating the updated space-time gradient field, the group recombination instruction and the historical trajectory data. According to the invention, high-precision, low-delay and high-robustness autonomous navigation capability is provided for the unmanned aerial vehicle group, and the task success rate is improved.

The invention discloses a robot path planning method based on deep reinforcement learning, and relates to the fields of intelligent agriculture, path planning, robots and the like. The method comprises the following steps: firstly, sensing a farm environment, defining a state space and an action space of a robot, and setting a multi-modal weighted combined reward mechanism and an experience playback buffer area; a learnable weight coefficient is introduced into a Critic network loss function in a traditional DDPG algorithm, an entropy regularization item is added into a target function of an Actor network, then a differential game is selected through an adaptive attenuation # imgabs0 # greedy strategy to generate a control strategy or a DDPG algorithm to generate an action, finally the action or the control strategy is executed, network parameters and target network parameters are updated, and a target network is obtained. And dynamically updating the experience playback buffer area. Compared with other path planning methods, the method improves the adaptability of path planning to a dynamic environment, and also has good efficiency and safety in a complex agricultural environment.

The invention discloses a strategy fusion and differential game-based decentration cooperative guidance method, which comprises the following steps of: aiming at the problem of cluster system collapse caused by missed missile leading, combining the differential game with a consistency formation theory to enable missiles to approach a target in a predetermined formation so as to realize decentration; aiming at the problem that an HJI equation coupled by a nonlinear system is difficult to solve, constructing a neural network by utilizing a self-adaptive dynamic programming technology to carry out online solving on an optimal control strategy of pursuit and escape parties; for the dilemma that multiple escape strategies are difficult to select due to the fact that pursuit control strategies always appear in pairs, an interest value function is introduced, and a strategy fusion mechanism is provided; for the problem of a local minimum value possibly occurring in an artificial potential field method, a dynamic adjustment factor is introduced, and collision avoidance among intelligent agents of the whole cluster system is achieved.

The invention discloses a decentralized differential game cooperative guidance method for a missile cluster under a switching communication topological structure, and the method comprises the steps: introducing a virtual leading missile-slave missile architecture, enabling each missile to construct a virtual leading missile from an initial position, enabling nodes in a simplified virtual leading missile group system to interact through the state information of a topological structure, coordinating a virtual state, and achieving the cooperative guidance of the missile cluster under the switching communication topological structure. And generating a virtual consensus state track so as to realize decentration control. Communication topological structure switching is considered, an optimal control scheme for tracking an expected trajectory is solved in combination with a differential game theory and self-adaptive dynamic programming, and a network weight updating mechanism is designed in the process of solving an optimal control strategy for a nonlinear system line. According to the scheme, the average residence time limiting condition of switching signals is put forward, hypotheses and conditions needing to be met by part of parameters and variables are given, under the scheme, the designed multi-Lyapunov function can achieve exponential convergence, and the stability performance of a missile cluster using the guidance scheme under the topology switching condition is guaranteed.

The invention relates to the technical field of control, and provides a process disturbance-oriented bidirectional anti-interference control system and method, and the system comprises the steps: receiving an external disturbance signal and controlled object state monitoring data in parallel through a process sensing module, generating a disturbance feature vector through frequency band decomposition, and processing the state monitoring data through a nonlinear manifold learning algorithm, deep recognition of response features is realized; the disturbance coupling module takes the feedforward control pre-estimated quantity and the feedback control compensation quantity as optimization variables and takes the disturbance suppression ratio and the response recovery time as revenue functions to construct a differential game model, and the revenue functions are optimized through a gradient iterative algorithm; the bidirectional fusion module realizes dynamic weighting of two types of control quantities based on the coupling weight matrix; and the intelligent control module obtains a control deviation integral and response recovery time in real time, and dynamically adjusts parameters of the revenue function and parameters of weighted fusion through the reward function.

The invention discloses a many-to-many attack and defense game method and device in an obstacle environment, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: building a many-to-many attack and defense problem model of an unmanned aerial vehicle cluster, building a one-to-one attack and defense sub-game problem, and carrying out the one-to-one attack and defense on the basis of the one-to-one attack and defense sub-game problem. Expanding the defense winning area under the differential game to obtain an expanded defense winning area under the differential game, and expanding the defense winning area under the reinforcement learning to obtain an expanded defense winning area under the reinforcement learning; and based on a many-to-many attack and defense problem model, the extended defense winning area under the differential game and the defense winning area under the extended reinforcement learning, carrying out many-to-many defense decision making to obtain a many-to-many defense decision planning result. The defense unmanned aerial vehicle can intercept attack unmanned aerial vehicles as many as possible, and the target area is protected to the maximum extent.

The invention provides a reinforcement learning method and system for unmanned aerial vehicle intelligent decision, and a storage medium, and relates to the technical field of unmanned aerial vehicles. According to the invention, on one hand, the decision-making scheme of the unmanned aerial vehicle can be updated through the detection result of the unmanned aerial vehicle, thereby scheduling the task execution scale of the unmanned aerial vehicle formation in real time, and improving the defense effect. And on the other hand, a boundary grid reward obtained based on the differential game is designed, it is ensured that a reward mechanism can accurately reflect the target achievement situation of both game parties, and through continuous iterative optimization of reinforcement learning, the unmanned aerial vehicle can gradually learn an effective unmanned aerial vehicle formation defense strategy in a complex environment.

The invention relates to the field of intelligent control of marine navigation, and discloses a formation dynamic interception path planning method and device based on a differential game theory, and the method comprises the steps: constructing a differential game model comprising a formation and intercepted ships; designing revenue functions for the two parties; obtaining a Nash equilibrium strategy by solving a Hamiltonian-Jacobian-Bellman equation of the game, and solving an individual optimal control instruction of each formation member; and fusing state information among formation members through a consistency protocol, adjusting individual instructions to meet anti-collision and formation cooperative constraints, and generating a final control instruction. According to the method, historical adversarial data is learned by using the neural network, and the weight of the revenue function is adaptively and dynamically adjusted to cope with different tactical scenes; and the whole decision-making process is set under a model prediction control framework for rolling optimization, so that the real-time performance, the collaboration and the intelligent level of interception path planning are greatly improved, and the interception efficiency is effectively improved.

The invention relates to the technical field of network security, and discloses a network security management method and system based on a big data model, and the method comprises the steps: 1, converting multi-source heterogeneous data into a graph node containing a device entity, an I P entity and a user entity, and an interaction edge containing a timestamp and a relation type; step 2, constructing an attack chain dynamic evolution equation based on graph nodes and interaction edges, wherein the equation comprises an attacker action parameter and a defender action parameter; and step 3, inputting a time fusion Transform model to obtain probability distribution of a future attack action sequence by using a state variable corresponding to the attack chain dynamic evolution equation. According to the method, the technical scheme of constructing an attack chain dynamic evolution equation based on a differential game model, predicting an attack action sequence through time fusion Transformer and dynamically updating a defense strategy through meta reinforcement learning is adopted, and the technical effects of accurately modeling an attack evolution path, predicting an attack behavior in advance and generating a self-adaptive defense strategy in real time are achieved.

The invention provides an Internet of Vehicles virus propagation defense strategy optimization method based on differential game, and belongs to the field of computer information security. The method comprises the following steps of: 1, establishing a node-level dynamic model according to a network topology structure of the Internet of Vehicles; 2, establishing a differential equation model for describing virus transmission according to states (a low-susceptibility state, a high-susceptibility state, an infection state and a recovery state) of nodes; 3, based on the game theory, analyzing the income and loss of attackers and defenders, and constructing a differential game model; and 4, solving an optimal strategy (Nash equilibrium) of the attack and defense parties by utilizing a Hamiltonian function and combining a Pontryagin optimal value principle. And 5, through experiments, comparing the proposed method with a uniform strategy and an uncertain attack strategy, and verifying the effectiveness and superiority of the model in an actual Internet of Vehicles environment.

The invention discloses a power distribution network reactive power differential game distribution method and system, and relates to the technical field of power system optimization scheduling, and the method comprises the following steps: constructing a power distribution network differential game voltage deviation optimization model, and calculating to obtain a sensitivity matrix; based on a reactive power distribution mechanism module, priority and nearby adjustment is realized; constructing a full-period multi-target model, and solving to obtain equipment output and voltage change information in a full period; and feeding back to the hour-level voltage deviation optimization model to obtain an optimal reactive power distribution scheme and a control strategy. According to the method, the generalization capability of increasing a population through chaotic mapping, the local optimization capability of an adaptive weight factor enhancement algorithm and the global search capability of a reverse learning and Cauchy variation improvement algorithm are considered, so that the algorithm jumps out of local optimization, and the optimization result is updated through non-dominated sorting screening so as to obtain a Pareto optimal solution.

The invention discloses a modular mechanical arm actuator additive fault optimal fault-tolerant control method and device of a zero sum differential game, and relates to the field of robot control algorithms, and the method comprises the steps: representing a nonlinear damping characteristic through a joint friction torque, describing the dynamic interaction of multiple joints through a cross-linking coupling item, and determining the optimal fault-tolerant control of the additive fault of the modular mechanical arm actuator; a dynamical model containing faults is constructed. And uncertain items in the model are updated online by adopting a radial basis function neural network identifier, so that the model precision is improved. A performance index function is constructed based on position errors, actuator faults and controller input are regarded as two opposite parties of a zero and differential game, the performance index function is approximated through a single evaluation neural network, a Hamiltonian-Jacobi-Axaxi equation is approximately solved, and an optimal fault-tolerant control strategy is obtained. According to the method, the game theory is combined with the neural network, the dynamic unknown fault problem of the modular mechanical arm is effectively solved while the system energy consumption integration is reduced, and real-time optimal control over the modular mechanical arm is achieved.

The invention discloses a multi-missile self-learning differential game cooperative guidance method based on disturbance feed-forward compensation, and the method comprises the steps: providing a finite-time convergence sliding-mode nonlinear expansion state observer for solving the problems of unknown partial parameters and external disturbance in a multi-missile cooperative guidance system; a sliding mode control method is combined with an extended state observer, a novel fixed-time convergence sliding mode surface and a sliding mode reaching law are constructed, and real-time estimation and efficient compensation of unknown disturbance in a system are achieved. Strictly proving the fixed convergence time of the observer by using a Lyapunov function, and analyzing a disturbance estimation error; compared with a traditional ESO, the method has the advantages that the speed and precision of state tracking are remarkably improved, and the anti-interference capability of the multi-missile cluster in a complex interference environment is effectively improved.

The invention relates to an average field game optimization method and system in an edge computing network based on content popularity. The method comprises the following steps: (1) setting a system model; comprising the steps that an edge computing network is set, and the edge computing network comprises a cloud data center, a plurality of edge nodes with storage units and a plurality of users; (2) modeling a video service-oriented cache decision problem in the edge computing network as a random differential game problem, namely an SDG problem, according to a residual storage space state of each edge node and a state change of a task popularity degree based on content popularity; and (3) solving the SDG problem by using a mean field game method, obtaining Nash equilibrium oriented to a video service cache decision problem in the edge computing network, and obtaining an optimal cache strategy of each edge node. According to the method, the satisfaction of user requirements is concerned, and the caching income of the edge node is considered.

The invention discloses an APT (Advanced Persistent Threat) attack defense method based on edge intelligence, which aims to improve the dynamic perception capability, strategy response capability and resource adaptability of an edge network to APT attacks, and comprises the following steps of: firstly, constructing a topological adjacency matrix of an edge node communication network and defining five states and state conversion processes of a life cycle of edge equipment; the method comprises the following steps: firstly, designing an attack defense model based on optimal control and differential game, then introducing a system evolution model based on hidden confrontation, and accurately simulating dynamic interaction of attack and defense in an actual edge network, secondly, designing an attack defense model based on optimal control and differential game, and finally, adopting a Nash strategy reinforcement learning mechanism based on a multi-agent deep Q network, optimizing an edge game strategy, and finally, obtaining an attack defense result. And the attack detection performance is improved. According to the method disclosed by the invention, the modeling precision and defense effectiveness of the system in a complex APT attack scene are remarkably improved, and the method is suitable for key infrastructure network environments with relatively high requirements on safety, timeliness and expandability, such as industrial Internet and Internet of Things.

The invention discloses a rectangular area unmanned aerial vehicle hunting method based on differential game and reinforcement learning. The our unmanned aerial vehicle cluster obtains environment state information; spatial constraint loss is calculated through a rectangular flight area limitation loss function module, time sequence consistency loss is calculated through a batch time sequence behavior constraint module, and the spatial constraint loss and the time sequence consistency loss are combined to generate a state behavior joint hidden variable fusing space and time sequence constraints. And fusing the hidden variable with the current environment state, inputting the fused hidden variable and the current environment state into a differential game value evaluation module, obtaining a future expected value based on Nash equilibrium by solving a differential game between our part and the black flight unmanned aerial vehicle, and updating decision network parameters based on the future expected value. The cluster performs the optimized hunting action and stores empirical data for continuous training. According to the method, the decision stability, the cooperation efficiency and the task success rate of unmanned aerial vehicle cluster hunting in the rectangular limited flight area are effectively improved.

The invention belongs to the technical field of aerospace, and particularly discloses a spacecraft cluster game method and system based on generative adversarial reinforcement learning, and the method comprises the steps: building a relative motion dynamics model of pursuit and escape parties, and building a game relation model of the pursuit and escape parties based on a two-person zero sum game model; solving a Nash equilibrium solution of the pursuit game by using a differential game method based on the game relationship model of pursuit and escape parties, and generating an expert database; designing and generating an adversarial reinforcement learning algorithm based on the expert database; and solving the game relationship between the pursuing and escaping parties by using a generative adversarial reinforcement learning algorithm to obtain an optimal game strategy. According to the method, the optimal control method and the artificial intelligence method are combined to solve the approaching strategy of the spacecraft cluster virtual main satellite to the non-cooperative target, the problems that convergence is difficult, reward setting is difficult and the effect is poor when the artificial intelligence method is used for solving the orbit game problem are solved, and finally efficient and accurate approaching to the target is achieved.

The invention provides a differential game method for solving the man-machine sharing control problem of an automatic driving vehicle, and the method comprises the steps: building a man-machine non-cooperative differential game model based on a driver target trajectory and a trajectory planned by an automatic driving system and considering the control constraint of a matrix inequality form, the non-cooperative differential game model adopts Nash equilibrium to represent a man-machine game problem; a neural network and other function approximation methods are introduced based on the non-cooperative differential game model, and a differential game system of unknown parameters is established; an integral parallel learning method is adopted, and track data is used as unknown parameters in a numerical integral identification system; and based on the identified differential game system, learning a Nash equilibrium solution in a man-machine game problem through an adaptive iterative learning algorithm. According to the scheme, on the premise of not depending on accurate parameters of a system model, the situation that control is restrained is considered, and the control strategy of the driving system is improved by solving the man-machine non-cooperative Nash equilibrium solution.

The invention discloses a non-signalized intersection vehicle collision avoidance strategy method based on a differential game, and each vehicle can select to make a decision independently and can act in a cooperative mode. The problem is described as a dynamic differential game, that is, two vehicles make decisions according to the expected behavior of the opposite vehicle. The method considers a non-cooperative game (the vehicle only optimizes the own cost) and a cooperative game (the vehicle coordinates a decision to optimize the joint cost). Different from a traditional discrete game theory in which the acceleration of a vehicle is divided into a plurality of segments, the method provided by the invention proposes a differential game to generate an acceleration signal of continuous time, and the signal can be freely changed during the crossing period of the whole intersection. The effectiveness of the differential game method is verified through analogue simulation, and compared with a traditional discrete game theory method, the method can enable the vehicle to pass through the non-signal intersection more quickly, and meanwhile, the requirements for safety and comfort are met.

The invention provides a driving collision avoidance early warning method based on differential game and a processing device, and belongs to the technical field of vehicle collision early warning, and the method comprises the steps: collecting vehicle trajectory parameters, processing the collected vehicle trajectory parameters, obtaining vehicle prediction trajectory parameters, and carrying out collision risk detection on a vehicle. Obtaining the probability of occurrence of the vehicle collision risk; obtaining the safety early warning distance of the conflicting vehicle, judging the relationship between the safety early warning distance of the conflicting vehicle and the distance between the conflicting vehicle and the stop line of the intersection, and determining the collision risk of the conflicting vehicle according to the probability of occurrence of the vehicle collision risk and the relationship between the safety early warning distance of the conflicting vehicle and the distance between the conflicting vehicle and the stop line of the intersection. Judging whether an early warning game process is triggered or not; if the early warning game process is triggered, the differential game method is adopted for collision avoidance early warning conflicting vehicles, appropriate early warning decision making is carried out, collision avoidance early warning is carried out based on the differential game, and uncontrolled intersection vehicle collision early warning in a complex scene is more accurate and reliable.

The invention discloses a three-dimensional target defense differential game method based on motion camouflage. The method comprises the following steps: establishing a three-dimensional coordinate system and three-dimensional dynamic models of a target, a pursuer and a defender; decomposing a target-pursuer-defender three-party game into two sub-games; aiming at each sub game, constructing a Hamiltonian containing a relative kinetic equation, solving a co-state equation by applying a Pontryagin maximum / minimum principle, deducing a guidance strategy of each party, and updating the strategy in real time through a discretization time sequence; based on initial conditions and the guidance strategy, the saddle point solution of each time period is solved through a simulation environment, and a dynamic guidance instruction is generated to complete the capture task of an attacker. According to the method, aiming at a target-pursuer-defender (TAD) three-party game, an equilibrium strategy in a three-dimensional space is designed by decomposing a game problem, and in combination with motion camouflage constraints of biological mimicry, efficient interception of a defender to a pursuer and dynamic protection of a target are realized.

The invention discloses a one-to-one target defense game decision-making method under perception limitation, electronic equipment and a medium. The method comprises the following steps: establishing a target defense game scene, and constructing a target defense game model; solving a maximum pursuit angle through an Apollonius circle; solving a critical pursuit angle through an infinitesimal method; and according to the maximum pursuit angle and the critical pursuit angle, solving an intelligent agent optimal strategy. On the basis of a differential game, a target defense game scene and a game model are constructed, an infinitesimal method is combined with an Apollonius circle, and classification discussion is performed on the pursuit angle of the game so as to solve the optimal decision of the intelligent agent.

The present application relates to the technical field of smart water affairs and urban public safety, and particularly relates to a kind of artificial intelligence driven urban pipe network flood resilience evaluation method and system, comprising: constructing Riemann metric tensor by using metric matrix containing potential barrier function, and establishing the Riemann manifold embedding space of pipe network physical state;Then, based on fluid energy, a Hamilton function is constructed, and a dynamics prediction model with physical conservation is obtained by using symplectic neural network and symplectic discrete integral format training;Subsequently, a second-order Hamilton-Jacobi-Aleksandrov partial differential equation describing stochastic differential game is constructed, and a physical perception neural network is used to solve and extract the resilience safety boundary;Finally, real-time data is mapped to the manifold space, the Riemann gradient is calculated, and a quadratic programming problem is solved to generate control instructions. The problem of lack of physical constraints and safety bottom line in pipe network control under extreme random working conditions is solved, and real-time closed-loop control with physical consistency is realized.

The invention discloses a synchronous orbit electric propulsion satellite short-range game control method based on differential game, which comprises the following steps: under an LVLH coordinate system, establishing a near-circular orbit relative motion dynamic model of spacecrafts of both pursuit and escape parties, and further determining a state space form in a spacecraft pursuit and escape game process; based on a state space form in a spacecraft pursuit game process, introducing a Hamiltonian function, and constructing a short-distance pursuit game model with optimal time; introducing a co-state equation, and describing a short-range pursuit game process as a two-point boundary value problem; and converting the problem into a parameter optimization problem, and carrying out optimization solution on the problem by utilizing an improved difference method so as to realize short-range game control.

This invention provides a spacecraft escort control method based on zero-sum differential games, belonging to the field of aerospace technology. The control method includes the following steps: obtaining spacecraft escort problem parameters for both the pursuing and defending satellites; establishing a spacecraft escort model based on zero-sum differential games and inputting initial parameters into the model; transforming the spacecraft escort model into a two-point boundary value problem model; using a genetic algorithm to solve the two-point boundary value problem model to obtain the state trajectory within the game time, thus completing the spacecraft escort control. This spacecraft escort control method can effectively solve the spacecraft escort problem under continuous thrust, and is an effective extension of existing continuous thrust escort problem models and methods. The model employs a differential game method described by a zero-sum cost function, thus simplifying the solution of the difficult-to-solve weighted performance index escort problem and improving the solution efficiency of the spacecraft escort game problem.

The invention provides a large-scale regional pursuit game method with collision avoidance guarantee, which comprises the following steps: according to an H-J reachability principle, designing an avoidance set and a target set of a 1-to-1 strategy and a 2-to-2 strategy respectively, substituting the avoidance set and the target set into an H-J-I equation, solving a value of a game value function in a discrete state space, and storing the value; building a collision avoidance alliance according to the boundary grid information of the 2-to-2 strategy and the distance between the defender members, and determining a 2-to-2 matching result; performing bilateral graph maximum matching calculation according to the 2-to-2 matching result and the boundary grid information of the 1-to-1 strategy to obtain a 1-to-1 matching result; and adopting the calculated H-J-I equation value functions of the 2-to-2 strategy and the 1-to-1 strategy to respectively solve the optimal control quantity of the defender members implementing the 2-to-2 strategy and the 1-to-1 strategy. According to the method, the problem that the defense party self-subject differential game strategy in the pursuit game problem lacks the collision avoidance constraint can be solved, and effective interception of a large-scale attacker cluster is realized.

The present application relates to the technical field of robot tracking control, and discloses a kind of man-machine shared tracking control method based on differential game, comprising: establishing man-machine differential game model;Estimate human control strategy and objective function parameter;Design robot shared control strategy;To the shared control strategy determined by robot, realize man-machine shared tracking control;The present application uses differential dynamic game to model the dynamic negotiation relationship between human participants and robot, so that the robot adjusts its own willingness under the game framework by estimating the willingness of human to eliminate tracking error, and designs robot tracking control strategy on this basis;Further, the present application considers the safety of man-machine cooperation, designs robot safety control strategy based on control barrier function, so that it can realize safe shared control together with robot tracking control strategy.

The invention relates to an Internet of Vehicles advanced malicious software propagation control method based on a game theory, and belongs to the field of network propagation and control. The method comprises the following steps: 1) regarding a vehicle-end device, a road-end device and a cloud-end device in the Internet of Vehicles as network nodes, and connecting the vehicle-end device, the road-end device and the cloud-end device by using edges if communication relationships exist among the nodes; 2) establishing a dynamic model of malicious software spreading in the Internet of Vehicles by applying a differential game theory, and describing the spreading process of the malicious software; 3) combining the propagation strategy of the attacker and the detection and blocking measures of the defender to analyze the earnings of the two game parties; 4) designing an acceleration algorithm, and rapidly solving an optimal strategy (Nash equilibrium) of both parties in the game model; and 5) verifying the effectiveness and practicability of the game model through a comparison experiment of a random strategy, a uniform strategy and an uncertainty attack strategy.