236 results about "Game strategy" patented technology

The invention discloses a control method and system based on multi-unmanned aerial vehicle cooperative game confrontation, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: S1, game environment modeling and simulation, S2, game strategy generation and optimization, S3, dynamic task allocation and cooperative control, S4, communication and information interaction optimization, and S5, efficiency evaluation and adaptive adjustment. On the basis of realizing cooperative game confrontation of multiple unmanned aerial vehicles, task allocation can be efficiently carried out on the unmanned aerial vehicles, and the autonomous decision-making capability of the unmanned aerial vehicles can be improved.

The invention provides a tunnel fan group cooperative control dynamic optimization method and system. According to the method, dynamic data of vehicles in a tunnel are tracked through the Leiyu fusion technology, and a multi-dimensional game parameter set is constructed in combination with edge computing node analysis data and environment monitoring indexes. Based on a dynamic game theory model, fan cooperative response intensity is dynamically adjusted, smoke exhaust efficiency and evacuation channel wind speed constraint are balanced, fan control priority is calculated, differential rotating speed and deflection angle control is driven, and game strategy weight is iteratively optimized after environment feedback data is generated. And realizing rolling time domain control until the vehicle movement and the environment index reach a stable interval. According to the technical scheme provided by the invention, dynamic collaborative optimization of tunnel smoke exhaust and evacuation safety is realized, and the ventilation response efficiency and the evacuation channel safety in an emergency scene are improved.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

The invention provides a game strategy retrieval method and device based on event-driven knowledge graph embedding. The method comprises the following steps: performing structured analysis on strategy data, and updating entities and relationships; performing graph embedding calculation on entities and relationships in the knowledge graph to obtain vector representation among the entities, and constructing a searchable vector index; vectorizing a strategy query request of a user, and performing similarity retrieval on the strategy query request and the vector index to obtain a first candidate entity set similar to the query vector; performing relation reasoning by taking the first candidate entity set as a starting point in the knowledge graph to obtain a second candidate entity set in semantic association with the first candidate entity set, and combining the second candidate entity set with the first candidate entity set to form a target entity set; and performing correlation sorting on the target entity set to obtain a strategy result. According to the method, the strategy data can be automatically and structurally managed, and deep semantic retrieval is supported, so that the accuracy of game strategy content retrieval is improved, and the dynamic expansion capability is achieved.

The invention relates to a Markov game-based satellite cluster observation resource allocation method and system. Through a two-stage decomposition strategy, a multi-target balance problem of a task integrity rate, a resource utilization rate and a cost-efficiency ratio is effectively solved. In the first stage, a genetic algorithm (GA) is adopted to complete satellite-task-time window three-dimensional matching, and optimal distribution of limited visible windows is achieved. And in the second stage, a distributed decision-making mechanism is implemented based on an improved Improved-MADDPG framework, and after an intelligent agent adopts a random game strategy to execute exploration in a distributed manner, optimal dynamic configuration of observation resources is achieved through global information sharing and local strategy iteration, and key calculation indexes such as an average reward value and the like are remarkably improved.

The invention discloses an adversarial game cooperative control method and system for a heterogeneous unmanned ship cluster, and the system comprises a hierarchical game strategy engine, a role dynamic adaptation module, a distributed balance solver, a communication optimization module, an adversarial strategy selection module, and unmanned ships. Wherein the hierarchical game strategy engine is used for coordinating differentiation strategies of heterogeneous unmanned ships; the role dynamic adaptation module is respectively connected with the hierarchical game strategy engine and the distributed equilibrium solver; the distributed equilibrium solver integrates a mixed solving architecture of Nash equilibrium and Stackelberg equilibrium, and supports a fast game decision; the communication optimization module is respectively connected with the distributed equilibrium solver, the confrontation strategy selection module and the unmanned ship; the confrontation strategy selection module is used for generating a forgery strategy signal according to a preset probability. The technical blank of efficient cooperative control of the heterogeneous unmanned ship cluster in a dynamic confrontation scene is filled, and core support is provided for actual deployment of an intelligent maritime combat system.

The invention discloses an automatic driving vehicle longitudinal speed control system and method based on a game theory, and the system comprises a self-vehicle game strategy module which is used for constructing a first strategy set related to a self-vehicle longitudinal driving game, and calculating a first game income corresponding to a self-vehicle longitudinal driving game strategy; the manual driving vehicle game strategy module is used for constructing a second strategy set about the manual driving vehicle lane changing game and calculating a second game revenue corresponding to the manual driving vehicle lane changing game strategy; the game solving module is used for planning the optimal longitudinal driving track of the vehicle based on a master-slave game strategy according to the first strategy set, the second strategy set, the first game income and the second game income; according to the method, driver behavior characteristics, environmental constraints and multi-objective optimization can be comprehensively considered, game optimization between a self-driving vehicle and a manual driving vehicle is carried out, the decision robustness of a mixed driving environment is improved, unreasonable cut-in behaviors are effectively inhibited, and the collision risk is reduced.

The invention provides a complex event analysis processing method and device for a multi-agent cooperative game, relates to the technical field of artificial intelligence, and aims at solving the technical problems that an existing complex event analysis method is insufficient in the aspects of event element extraction accuracy, a verification feedback mechanism and a multi-agent cooperative game strategy. The method comprises the following steps: acquiring to-be-processed complex event data; extracting key element features and interrelation features in the complex event data by using a first agent to obtain structured event information; performing verification processing on the context semantic relationship and the logic relationship of the structured event information by utilizing a second intelligent agent, and generating a feedback instruction according to a verification result; according to the feedback instruction, guiding the first intelligent agent to carry out correction processing on the structured event information; and repeating the cooperative game process of the first agent and the second agent until the first agent and the second agent reach a consensus, and outputting corresponding structured event information.

The invention discloses a multi-agent cluster cooperative attack and defense game method based on near-end strategy optimization, and relates to the technical field of automatic control, and the method comprises the steps: constructing a partial considerable Markov decision process, and generating a multi-agent decision model; establishing a cooperative attack and defense game simulation framework including an attacker agent, a defender agent, a target area and irregular obstacles in the preset task area; parallel trajectory sampling is executed, an observation-action-reward sequence of each agent is collected, and a training data set is generated; a centralized state is constructed by cascading and splicing local observation of each agent, a time sequence difference error and a dominant function are calculated, network parameter updating is carried out by adopting cutting strategy gradient and value function regression, and a trained cooperative attack and defense game strategy network is generated when a reward curve converges. And the multi-agent cluster realizes intelligent cooperative attack and defense game and autonomous obstacle avoidance in a task environment with irregular obstacles.

The invention provides a cross-domain attack defense method based on a game strategy. The method comprises the following steps: constructing a joint state space, and discretizing a joint norm of the joint state space; designing action spaces of an attacker and a defender, and performing factorization strategy design on the action spaces of the attacker and the defender; designing a revenue function for calculating the revenue obtained by the defender for defending; establishing a cross-layer coupled state transition model based on the combined residual variation trend of the combined state space; and formalizing the state transition model into a zero sum game, solving Nash equilibrium of attacker income and defender income, and outputting a defender strategy during Nash equilibrium as a defense strategy. When the method is applied to cross-domain attack defense, the design of the security state, defense strategy and action space of the information physical system can be clearly quantified by unified state modeling while the cascade reaction is considered, and the revenue function is designed to quantitatively guide the optimal defense decision, so that the effectiveness and robustness of cross-layer defense are improved.

The invention belongs to the field of mobile edge computing, and particularly relates to a resource pricing problem based on a Stackelberg game in mobile edge computing. The method comprises the following steps: constructing a mobile edge computing model, wherein the mobile edge computing model comprises a server base station, task equipment and cooperative equipment; in a task execution period, for a server base station profit maximization problem of resource pricing based on a Stackelberg game in mobile edge computing, delay brought by task unloading is fully considered in problem modeling, and the delay requirement of a task is met to the maximum extent; analyzing and establishing a dynamic punishment and redistribution mechanism for task unloading failure of the cooperative equipment, wherein the mechanism comprises a punishment cost calculation module, a task value reevaluation module and a game strategy updating module; and obtaining an unloading strategy of the task equipment, a task allocation strategy of the server base station and a resource pricing strategy after the server base station and the cooperative equipment carry out the Stackelberg game by utilizing a greedy strategy, thereby obtaining a final task allocation scheme for maximizing the profit of the server base station. According to the method, the optimal task execution and resource allocation strategy of the server base station is formulated, and the relationship among the task value, the resource cost and the additional resource purchase cost is balanced as required, so that more flexible and efficient task scheduling service can be provided.

The invention discloses a heterogeneous agent distributed multi-alliance game control method, device and equipment and a medium, and relates to the field of multi-agent system cooperative control and game theory cross application, and the method comprises the steps: dividing agents in a heterogeneous unmanned cluster system into a plurality of alliances; establishing a high-order heterogeneous linear state dynamical model for the intelligent agent in each alliance; constructing an internal and external double-layer alliance communication topological graph according to an actual communication condition; and on the basis of the model and the topological graph, through a distributed Nash equilibrium search algorithm containing a search layer and an output adjustment layer, performing iterative optimization on an agent decision state to obtain a multi-alliance game strategy, and controlling the agent according to the strategy. According to the method, the cooperative control problem of the heterogeneous agents in a multi-alliance game scene can be effectively solved, the Nash equilibrium point is quickly searched, the decision state is adjusted to be optimal, and the cooperative control performance and task execution efficiency of a multi-agent system are improved.

The invention discloses an incomplete information game intelligent processing method and system based on secure multi-party computing, a terminal and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a game participant set, an initial game state, a historical action set, a terminal historical set, and a preset game strategy based on a secure near-end strategy optimization algorithm, an action set, an information set and a revenue function corresponding to each participant are obtained; according to the participant set, the initial game state, the historical action set, the terminal historical set, the game strategy, and the action set, the information set and the revenue function corresponding to each participant, carrying out modeling based on a modeling framework of an extended game tree, and constructing a security extended game model; and according to a preset optimization target and leaf nodes in the security extension type game model, determining a game result between the participants and outputting the game result. Therefore, the data security can be improved, and the security of the game process can be improved.

The invention discloses an Internet of Things monitoring management method and system based on deep learning, and relates to the related technical field of Internet of Things monitoring management, and the method comprises the steps: reading node multi-source data through an intelligent agent, constructing a deep causal structure field through cross-modal deep causal reasoning, and revealing the relationship between environmental inducements, state paths and risk propagation; constructing a space-time causal tensor representing dynamic risk distribution of a monitoring area, quantitatively evaluating monitoring income, energy consumption cost and risk exposure, executing game analysis under a multi-party game strategy, and establishing an intelligent agent behavior strategy; and updating the monitoring network topology, executing deep space-time-cause and effect joint reasoning, generating an exception explanation result, and performing exception report management. According to the invention, the technical problems of lack of causal interpretability, resource allocation rigidness and insufficient abnormal response capability of Internet of Things monitoring in the prior art are solved, and the technical effects of accurate abnormal management of Internet of Things nodes and improvement of resource utilization efficiency and risk decision interpretability are achieved.

The invention relates to a partially observable unmanned aerial vehicle cluster collaborative pursuit game decision-making method. The method comprises the following steps: acquiring local observation information of a to-be-decided unmanned aerial vehicle; obtaining a global observation value in the to-be-decided unmanned aerial vehicle cluster; motion information in the local observation information is transmitted to the stable matching model in a communication mode, and target information that the unmanned aerial vehicle pursues or escapes preferentially is obtained; and generating a strategy of the unmanned aerial vehicle for preferentially chasing or preferentially escaping the target. According to the invention, a lightweight dominant communication mechanism is utilized, the individual state information and local sensing information of the unmanned aerial vehicles are shared in the unmanned aerial vehicle cluster, and effective screening and fusion of the sensing information among the unmanned aerial vehicles are realized, so that each unmanned aerial vehicle can make an optimal decision according to the current environment state and behaviors of teammates and opponents. In addition, an unmanned aerial vehicle confrontation game strategy based on a stable matching theory is introduced, the cluster confrontation decision process is effectively simplified, and the robustness and the reaction speed of the whole system are further enhanced.

The invention provides a game assisting method and device.The method comprises the steps that in the process that a user plays a game, voice of the user is recognized, the voice is converted into a text, and a game image corresponding to the text is matched and recorded as a target game image; retrieving a preset game strategy knowledge base by utilizing the target game image to obtain target game strategy information; the target game image, the text and the target game strategy information are submitted to the large-scale multi-mode model for analysis, the instructive text is generated, the instructive text is synthesized into the voice and played, and a game assisting scheme which does not affect the game experience of the user in the game playing process of the user and efficiently and effectively guides the user is provided.

The invention discloses a game strategy model generation method, a game agent behavior strategy generation method, corresponding devices, electronic equipment and a storage medium. The game strategy model generation method comprises the steps of obtaining a preset data set, wherein the preset data set comprises a first state feature sequence of a game agent in a game and a second state feature sequence of a player character; according to the first state feature sequence and the second state feature sequence, training a to-be-trained reward model until a preset training ending condition is met, and obtaining a trained reward model; inputting the target state feature of the game agent into the trained reward model to obtain first reward data corresponding to the game agent; and training a to-be-trained strategy model according to the first reward data, wherein the strategy model is used for generating a behavior strategy for a game agent in the game. According to the method, the stability and scene adaptability of the generated game strategy model can be improved.

The invention provides a game strategy planning method and system, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, the method is applied to multiple agents, and the multiple agents comprise at least one unmanned aerial vehicle and at least one unmanned ship. According to the method, environment data, attitude data, position data and game party data of a target game region are acquired in real time, an accurate environment model and a state and action space are constructed, an objective function of a current task is efficiently solved by applying a Monte Carlo algorithm and a deterministic strategy gradient algorithm, a game countering strategy set is generated, and the game countering strategy set is calculated. And based on the game countering strategy set, the motion planning data of each agent for executing the current task is determined, each agent is controlled to execute the task according to the plan, and the game countering strategy generation accuracy, the task allocation accuracy and the task execution efficiency of the multiple agents are remarkably improved.

The invention discloses a global intelligent marketing method based on multi-modal information fusion. The method comprises the following steps: firstly, collecting multi-source heterogeneous data, extracting text, vision and behavior characteristics by using deep learning, and mapping the text, vision and behavior characteristics to a public semantic space for fusion through an attention mechanism; and then, constructing a dynamic user portrait based on the fusion features and identifying a user intention stage. On the basis, the click rate is estimated by using a double-tower network, and a dynamic bidding strategy is generated in combination with a reinforcement learning DQN model and PID control. And finally, aiming at customer inquiry, generating a quoted price in combination with a cost model or performing automatic negotiation by using a time-dependent game strategy, and updating model parameters in real time through online incremental learning. Through full-link intelligent closed-loop control, accurate user intention recognition, efficient advertisement putting and automatic business negotiation are realized, the marketing conversion rate is effectively improved, and the operation cost is reduced.

The invention provides a giant disaster reinsurance contract optimization method, system and device and a medium, and belongs to the field of insurance. The method comprises the following steps: constructing multiple agents; designing a dynamically optimized reinsurance contract mechanism by adopting a mathematical optimization model and combining core terms in a reinsurance contract so as to optimize the terms of the reinsurance contract; and designing a cooperation and game mechanism among the agents, and dynamically optimizing a game strategy by adopting a multi-agent reinforcement learning method so as to adjust contract terms in real time. And adopting a strategy of combining game and reinforcement learning to optimize contract terms between the insurance company and the reinsurance company. The game mechanism helps the intelligent agent to find the optimal balance between competition and cooperation, and reinforcement learning enables the intelligent agent to continuously adjust the strategy in the dynamic market. And it is ensured that reinsurance contract terms can be automatically optimized when the market changes, disaster events and supervision requirements change. And the intelligent agent optimizes contract terms through a real-time feedback mechanism, so that the balance between risk sharing and profit is ensured.

The invention relates to a multi-modal sentiment classification method based on a dynamic game strategy, and the method comprises the steps: obtaining multi-modal data which contains an image, a text and voice and is to be subjected to sentiment classification, and carrying out the feature extraction of the multi-modal data, thereby obtaining a multi-modal feature; performing redundancy elimination processing on the multi-modal features to obtain a preliminary feature subset; in each round of game of the dynamic game strategy, compensating the contribution of each feature in the preliminary feature subset through a high-order synergistic effect compensation mechanism based on tensor decomposition to obtain the compensation contribution degree of each feature; performing weight updating on each feature based on the compensation contribution degree of each feature to obtain a feature weight after each feature is updated; screening the preliminary feature subset based on the updated feature weight to obtain an optimized feature subset; multi-round gaming is carried out until a convergence condition is reached, and a final feature subset is obtained; and inputting the final feature subset into a multi-modal sentiment classification model, and outputting a predicted sentiment category.

The invention discloses a supply chain supply and demand dynamic matching method based on a multi-agent game, and relates to the technical field of supply chain management, and the method comprises the following steps: constructing a multi-agent system, dividing agents, carrying an edge module, interacting with an MQTT protocol through a block chain, and encrypting sensitive data; sensing supply and demand information in real time, acquiring parameters according to dynamic frequency, and synchronizing the parameters to a sharing middle station after filtering and cleaning; generating a game strategy, constructing a model by taking Nash equilibrium as a target, and iteratively generating an initial strategy; dynamic matching is executed, requirements are distributed according to priorities, deviation is monitored, and negotiation and adjustment are carried out; evaluating a matching effect in multiple dimensions, and calculating a comprehensive score by using an analytic hierarchy process; and iteratively optimizing a strategy, adjusting parameters according to an evaluation result, and updating a strategy library to adapt to dynamic change. The method breaks the supply chain information island, balances the interests of all participants to achieve overall optimization, improves the supply and demand matching precision and dynamic adaptation capability, and enhances the efficiency and stability of the supply chain.

The invention provides a game strategy retrieval method and device based on knowledge graph structured management. The method comprises the following steps: reading a structured file which is uploaded by a user and contains game strategy information, analyzing fields in the structured file, and mapping the fields into corresponding entry data; writing the entry data into a database, constructing an entity object and a semantic relationship corresponding to the entry data, and packaging the entity object and the semantic relationship into an update request message; in response to the update request message, performing incremental update on the knowledge graph data according to a predefined knowledge graph mode; and based on the knowledge graph data, in combination with keywords input by a user and screening conditions, performing semantic hierarchy analysis, determining a retrieval target entity and related entries, and outputting game strategy contents and associated information related to keyword semantics. According to the method, the retrieval efficiency and the accuracy of the retrieval result can be improved, and the systematic management and structuring degree of the game strategy content can be improved.

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 multi-chess game robot control method and system based on behavior and emotion adaptive game difficulty adjustment. The method comprises the following steps: initializing a multi-chess game scene; interaction object behavior and emotion perception; calculating the instantaneous state time sequence of the interaction object; performing two-way mapping calculation on the difficulty coefficient to obtain a final difficulty coefficient; and according to the output final difficulty coefficient state, a game strategy of a preset AlphaZero reinforcement learning model and kinematics parameters of the mechanical arm are adjusted, so that dual anthropomorphism of the game level and the action performance of the robot is achieved. Executing control and safety detection; according to the method, behaviors and emotions of the interaction object are sensed in real time, self-adaptive adjustment of the game difficulty is achieved in the two dimensions of the game strategy and the physical action, and the interaction experience and the intelligent level of man-machine game are effectively improved.

The invention discloses a zero-sum game-based two-degree-of-freedom helicopter safety controller design method, which relates to the technical field of two-degree-of-freedom helicopter control, and comprises the steps of constructing a system equation, introducing an attack signal and a preset performance function, decomposing into two stages of subsystems, and designing a virtual controller, an adaptive law and a self-triggering mechanism. And solving a Hamiltonian-Jacobian-Exophone equation by using a zero-sum game theory, and finally verifying the stability of the system through a Lyapunov function. According to the invention, it can be ensured that the tracking error converges to the steady-state error interval within the predetermined time, and the transient and steady-state performance of the system is significantly improved; and meanwhile, a Nussbaum function and a zero-sum game strategy are introduced to improve the attack resistance, the communication burden and actuator abrasion are reduced through a self-triggering mechanism, and the sesame effect is avoided.

The invention belongs to the field of feature selection and machine learning, and discloses a data feature dimensionality reduction selection method based on dynamic gaming, which comprises the following steps: for an input initial data set, calculating individual contribution degree based on a Shapley value, evaluating linear and nonlinear redundancy of a hybrid kernel HSIC, and then utilizing a dynamic bidding-elimination gaming mechanism to obtain a dimensionality reduction selection result; and the purpose of feature dimension reduction is efficiently and uniformly completed. The method is characterized in that a dual-objective optimization framework is constructed to balance feature contribution and redundancy, a sub-sampling and in-situ centralization algorithm is adopted to reduce calculation complexity, and feature importance-redundancy self-adaptive balance is realized through a dynamic bidding-elimination game strategy. The technology has important practical significance and application value in the aspects of improving large-scale data feature selection efficiency, model generalization ability and edge equipment applicability.

The invention provides an intelligent network connection vehicle confluence area cooperative control system and method, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: collecting vehicle trajectory data in a confluence area, and carrying out conflict risk quantitative evaluation and risk grade division based on TTC and PET composite indexes; a maximum value selection operator of a traditional game enhanced DQN is replaced by mixed strategy Nash equilibrium distribution, and multi-agent collaborative decision optimization is realized; predicting a vehicle trajectory based on a space-time attention mechanism, and generating a control scheme, a vehicle control command and an execution time sequence in combination with a risk level and a game strategy; and carrying out real-time interaction on information of the road side unit and the CAV vehicle, and pushing a control scheme, a vehicle control command and an execution time sequence to complete cooperative control of the intelligent network connection vehicle confluence area. The method overcomes the defects that in the prior art, vehicle control is conducted only according to a traditional reinforcement learning algorithm, learning is unstable, and strategy conflicts exist in the multi-agent environment.

The invention provides an adversarial game strategy real-time optimization method and system based on reinforcement learning. The method comprises the following steps: acquiring historical game situation characteristics; capturing dynamic motion parameter data of the target object in real time to generate a physical dynamic vector; inputting the physical dynamic vector into a preset real-time data compression circuit for high-dimensional signal processing, and generating a low-dimensional electrical response signal; performing correlation analysis on the historical decision characteristics and the historical game situation characteristics of a preset to-be-optimized confrontation game strategy to generate strategy evolution characteristics; the strategy evolution characteristics and the time sequence continuity characteristics of the low-dimensional electrical response signals are fused, and dynamic confrontation environment state data are generated; and inputting the dynamic confrontation environment state data into a preset reinforcement learning model to generate an optimized confrontation game strategy. According to the method, cross-domain collaborative closed loop from physical motion data to the decision strategy is realized, the response delay bottleneck of traditional game strategy optimization is broken through, and millisecond-level updating of the confrontation strategy along with the motion state of the target object is ensured.

The invention provides a game-based electric vehicle price guidance and space regulation and control method and device, and belongs to the field of electric vehicle charging. The method comprises the following steps: establishing a response model of the electric vehicle user for charging price adjustment by collecting charging related historical data of the electric vehicle user, so as to obtain a selection probability prediction result of the user for each charging station; based on the selection probability prediction result, a master-slave game model of a charging station operator and an electric vehicle user is constructed, the charging station operator is a leader, and the electric vehicle user is a follower; and based on the master-slave game model, game strategy optimization among the charging station operators is carried out, and space regulation and control of charging station selection are realized by optimizing the charging price. Through price optimization and space regulation and control, space distribution optimization of the charging load is realized, the resource utilization rate of the charging station is improved, and power grid congestion is relieved.