96 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 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 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 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 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 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 present invention belongs to the technical field of aircraft guidance methods, and specifically relates to a defense guidance method and system based on a three-party differential game of motion camouflage. The method includes: S1, establishing a three-party game coordinate system; the three parties include a pursuit aircraft P, a target aircraft E and a defense aircraft D; S2, according to the three-party game coordinate system, deriving the maneuvering strategy of each participant and the corresponding required heading angle; S3, according to the result derived in step S2 and the initial conditions of the three-party game in step S1, analyzing the captureability of the pursuit aircraft and the defense aircraft. The present invention has the characteristics of being able to reduce the performance requirements for defense aircraft and can be applied to low-performance defense aircraft such as small drones.

The present invention discloses a method and device for solving the differential game capture zone of continuous and pulse guidance laws, comprising the following steps: considering the continuous or pulse maneuvers of both the pursuer and the escaper, establishing a two-dimensional plane pursuit model with zero-control miss distance as the state variable; introducing pursuit scenarios based on the differential game guidance law form of both parties; solving the differential game guidance law in different pursuit scenarios, and writing the continuous guidance law and the pulse guidance law in a unified form; solving the existence condition of the differential game solution based on the fact that the denominator of the guidance law is not zero; and solving the differential game capture zone in different scenarios. The differential game capture zone solution method in the present invention reveals the difference between the capture zones of pulse and continuous guidance laws, and can provide a theoretical basis for the selection of continuous and pulse differential game guidance parameters with different acceleration ratios and time constant ratios. The analysis method is simple and has a wide range of applications.

This invention relates to the field of network security technology and discloses a network security management method and system based on a big data model, comprising: Step 1, converting multi-source heterogeneous data into graph nodes containing device entities, IP entities, and user entities, as well as interaction edges containing timestamps and relationship types; Step 2, constructing a dynamic evolution equation for an attack chain based on the graph nodes and interaction edges, wherein the equation includes attacker action parameters and defender action parameters; Step 3, using the state variables corresponding to the dynamic evolution equation of the attack chain, inputting them into a time fusion Transformer model to obtain the probability distribution of future attack action sequences. This invention employs a technical solution based on a differential game model to construct a dynamic evolution equation for the attack chain, a time fusion Transformer to predict attack action sequences, and meta-reinforcement learning to dynamically update defense strategies, achieving the technical effects of accurately modeling attack evolution paths, predicting attack behavior in advance, and generating adaptive defense strategies in real time.

The application relates to the field of energy management, and discloses an energy consumption management optimization method and system of a comprehensive energy station, which are used for improving the economic optimization ability and the autonomous collaborative operation level of the comprehensive energy station when facing complex uncertainty. The energy consumption management optimization method of the comprehensive energy station comprises the following steps: constructing a device characteristic model representing long memory dynamics of a device based on a fractional calculus theory; establishing a random scene based on an alpha-stable distribution theory, which describes non-Gaussian fluctuations of wind and light loads; constructing an economic cost density field representing energy quality space-time dissipation based on a local entropy production minimization principle; establishing a differential game framework taking the economic cost field as a target and the above model as a constraint; and finally realizing closed-loop optimization through rolling execution and model online correction. The application breaks away from the dependence on a machine learning model, and realizes the economic efficiency optimization and autonomous collaborative operation of the energy station under complex uncertainty from the physical mechanism.

The invention discloses a differential game collaborative guidance method under data packet loss and time delay, which comprises the following steps of: after a data retransmission mechanism is introduced to establish a mapping relation between a packet loss probability and a time delay size, designing a proportional time delay accumulation error based on a neighbor node signal received by a missile node and a reference signal, and ensuring that the probability of packet loss is reduced in the error conversion mode; when the error converges to zero, the states of all the missiles can achieve cooperation. And respectively designing feed-forward and feedback control components by using the smoothed error in combination with instruction filtering inversion control and a differential game method so as to improve the robustness and the optimal performance of the designed guidance law. Meanwhile, for the problem that the optimal performance index function is difficult to solve in the feedback component design process, an evaluation network fitting performance index function is introduced in combination with an adaptive dynamic programming technology. According to the scheme, the error bounded property can be proved through the designed Lyapunov function, and the stability performance of a missile cluster using the guidance scheme under the condition of communication data packet probability loss and communication time delay is guaranteed.

The present invention provides a method, system and storage medium for intelligent decision-making of unmanned aerial vehicles based on reinforcement learning, and relates to the technical field of unmanned aerial vehicles. In the present invention, on the one hand, the decision-making plan of the unmanned aerial vehicle can be updated through the detection results of the unmanned aerial vehicle, so as to real-time dispatch the scale of the unmanned aerial vehicle formation when performing tasks, thereby improving the defense effect. On the other hand, the boundary fence reward obtained based on differential game is designed to ensure that the reward mechanism can accurately reflect the goal achievement of both parties in the game, and through the 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 present application relates to a kind of main satellite of star companion under the spacecraft main satellite cooperative evasion control method, it is applied to the non-cooperative target such as space debris, malfunction spacecraft when facing unknown intention approach, task spacecraft is cooperated with star companion in the vicinity of orbit and carries out evasion in one pursuit one escape one guard scene.First, the dynamic model of relative motion state including non-cooperative target, guard satellite and task spacecraft is established;Then, the cost function of both sides of differential game is constructed using the idea of differential game;After that, the Hamilton-Jacobi method is used to solve Riccati equation, and the full-state linear feedback control solution of task spacecraft and satellite is obtained.The present application can avoid non-cooperative target approaching important on-orbit task spacecraft as much as possible, and assist task spacecraft to evade the potential threat of direct collision from the target through the active shielding of satellite, which is consistent with the engineering practice.

The invention discloses a guidance control method based on a particle optimization algorithm and a differential game in a tripartite game scene, which is based on the particle optimization algorithm and the differential game and combines the efficient global search capability of the particle optimization algorithm and the real-time decision capability of the differential game. And a more accurate and reliable solution is provided for guided missile guidance control in a tripartite game combat scene. Comprising the following steps: S1, building a missile six-degree-of-freedom model; s2, solving an optimal guidance strategy by a tripartite differential game problem; s2, introducing a particle optimization algorithm to iteratively update parameters of the three-loop autopilot in real time according to a performance index formula so as to track an optimal guidance strategy; and S4, missile strike simulation is carried out based on a particle optimization algorithm and a differential game guidance control method in a tripartite game combat scene. The method provides an efficient, reliable and accurate solution for guided missile guidance control in a tripartite game combat scene, and has important application value and prospect.

The present disclosure provides a spacecraft pursuit control method, device, computer equipment and medium, comprising: acquiring a reference orbit in a two-spacecraft pursuit process; determining a spacecraft on-orbit pursuit orbit dynamics model that meets perturbation factors in the two-spacecraft pursuit process based on the reference orbit in the two-spacecraft pursuit process; determining a spacecraft on-orbit pursuit differential game model based on the spacecraft on-orbit pursuit orbit dynamics model and preset countermeasure parameters in the two-spacecraft pursuit process; and determining a pursuit game control strategy of the two spacecrafts based on the spacecraft on-orbit pursuit differential game model. Thus, by constructing a spacecraft on-orbit pursuit orbit dynamics model that meets perturbation factors in the two-spacecraft pursuit process, a spacecraft on-orbit pursuit differential game model is determined, the operation efficiency of the spacecraft pursuit differential game is effectively improved, and the pursuit game control strategy of the two spacecrafts is easily formulated.