53 results about "Strategy selection" patented technology

The invention belongs to the technical field of network security, and discloses a countering measure strategy construction method and system based on attack intelligence. According to the method, firstly, multi-source attack intelligence is fused, and a causal time sequence attack knowledge graph is constructed; then, mapping the atlas on a digital twinborn model based on the IEC 62443 standard, constructing a Bayesian attack graph, and quantifying the risk of each attack path; then, taking the risk, the cost and the operation influence as multiple targets, and adopting an NSGA-II algorithm to generate a Pareto optimal countering strategy set; further, an optimal robust strategy is selected from the strategy set by solving the Stackelberg safety game model; and finally, explaining the decision process by using an interpretable AI technology. According to the method, the problems of passive threat intelligence analysis, static risk assessment, sub-optimal strategy selection and opaque decision-making process in the prior art are solved, and active, quantitative, optimal and credible defense decision-making for the key infrastructure is realized.

The invention provides a multi-intention processing method and device for a smart home, electronic equipment and a storage medium, and relates to the technical field of man-machine interaction. The smart home system can first judge whether intention conflicts exist among the different intentions, and if the intention conflicts exist, the smart home system can first calculate a first priority score corresponding to the intention map and a second priority score corresponding to the secondary intention; the method comprises the following steps: acquiring a first priority score and a second priority score, then performing strategy selection according to the first priority score and the second priority score, determining a conflict resolution strategy for the deliberate map and the secondary intention, and then executing a control instruction corresponding to the conflict resolution strategy, thereby improving intention understanding accuracy and response efficiency, and improving intention understanding experience. And the capability and decision of the system on complex intention conflicts are improved, the timeliness and flexibility of control instruction response are guaranteed, and the accuracy of system response is improved.

The invention discloses an efficiency self-adaptive optimization method and system for data retrieval, relates to the technical field of data management, and discloses an efficiency self-adaptive optimization system for data retrieval. Comprising a mode acquisition module, a task evaluation module, a cost prediction module, a strategy selection module, a consensus retrieval module, an execution decision module, a cache management module and a model updating module. Risk perception support is provided for strategy selection and execution modes; according to the method, resource consumption, execution time delay and path complexity under a plurality of standard retrieval strategies are predicted by using a query cost prediction model, and a confidence score is output, so that prospective evaluation of a strategy execution effect is realized.

The invention provides a dynamic path planning method for an automatic pomelo picking front end. The method comprises the steps that an initial track is generated according to the target pomelo position and the current position of the picking front end; moving along an initial track, and detecting a path obstacle in real time; dynamically selecting a rigid obstacle avoidance strategy or a flexible penetration strategy based on physical characteristics of an obstacle; and circularly executing detection and strategy selection until the target position threshold range is reached. The picking efficiency can be greatly improved, and the computing power is saved.

The embodiment of the invention discloses a project strategy optimization method and system and a storage medium, and the method comprises the steps: building a multi-objective optimization model which is a constraint model of cost, time and residual risk; an improved NSGA-II algorithm is utilized to solve the multi-objective optimization model, a Pareto optimal strategy set is generated, and the improved NSGA-II algorithm comprises an elitism strategy and a Gaussian disturbance variation strategy; a screening threshold value is dynamically adjusted according to the project real-time progress deviation, and the screening threshold value is used for controlling selection of strategies in the Pareto optimal strategy set so as to adapt to the change of the project progress; the method improves the adaptability and flexibility of strategy selection, and is suitable for a dynamic strategy optimization scene under the constraint of complex project resources.

The invention provides a dynamic coding and decoding negotiation method based on link state quality. The dynamic coding and decoding negotiation method comprises the following steps: grouping reference station RTCM data and determining the priority of the RTCM data according to the message ID of the RTCM data; based on the determined first strategy selection mode, the RTCM data is subjected to check code generation, character coding and encryption in sequence, and the strategy selection mode of the base station is synchronized to a strategy selection module of the moving station; after the moving station receives the data, decryption, character decoding and verification are sequentially carried out on the encrypted data based on the information synchronized by the strategy selection module; meanwhile, the moving station counts the packet loss rate and the verification failure rate of the RTCM data in real time; and adjusting the strategy selection mode of the next group of data according to the quality of the current link, the system load and the priority of the RTCM data until all grouped data are sent. According to the invention, a real-time closed loop is provided, and the link quality is evaluated more comprehensively.

The invention discloses a military strength resource scheduling method based on multi-objective optimization, which comprises the following steps: analyzing a strategy selection problem in a confrontation environment through a game theory model according to a resource demand matching degree and an enemy action prediction result, and if an enemy threat mode changes, triggering a self-adaptive adjustment mechanism specified by scheduling, dynamically correcting resource allocation strategy parameters by adopting a reinforcement learning algorithm to obtain an optimal scheduling rule adapted to the current confrontation situation; and calculating a resource consumption rate and supplement demand prediction according to an accurate resource allocation result, predicting a consumption trend of various resources in a future time period through a time sequence analysis algorithm, if the predicted consumption rate exceeds the supplement capability, triggering a resource allocation plan in advance, and determining an optimal resource reserve and supplement strategy by adopting an inventory optimization model. According to the method, the military strength resource scheduling efficiency and the combat effectiveness in a complex confrontation environment are effectively improved.

The invention relates to an intelligent agent automatic detection system and method based on a large model, and the system comprises a data collection module which collects multi-source heterogeneous data monitored in real time in an automatic detection process; the quantification module is used for carrying out confidence coefficient coding on the preprocessed multi-source heterogeneous data by utilizing a pre-trained large model to obtain a confidence coefficient score of an abnormal event; the matching module is used for carrying out multi-dimensional priority ranking on the abnormal events based on the confidence score; the strategy generation module is used for simulating a strategy selection scene in a dynamic environment by adopting a reinforcement learning algorithm according to the sorted abnormal priority list so as to obtain an optimized dynamic strategy set; and the detection processing module is used for selecting the highest matching item from the optimized dynamic strategy set and carrying out corresponding processing on the detected abnormity. According to the method, the accuracy, efficiency and adaptability of intelligent agent automatic detection are effectively improved, and a reliable anomaly detection and response mechanism is provided for complex systems such as network security and industrial monitoring.

The invention discloses a self-adaptive process scheduling method and system based on real-time state perception, and the method comprises the steps: firstly carrying out the low-overhead real-time monitoring of a system operation state through an extended Berkley data packet filter technology, and collecting multi-dimensional original data; secondly, identifying a current system load mode by using a pre-trained gradient boosting decision tree model, and performing stable prediction on a load mode of a next decision period in combination with a historical window mechanism based on time weighted voting so as to smooth instantaneous fluctuation; then, querying a'load mode-target scheduler 'mapping table which is constructed offline in advance according to a prediction result to determine a target scheduling strategy; and finally, dynamically and safely switching to a target scheduling strategy through the extensible scheduler class framework, and realizing self-adaptive adjustment of system scheduling. According to the method, macroscopic strategy selection and microcosmic scheduling decision are decoupled, high real-time inference overhead is avoided, safety and stability of the system are guaranteed by utilizing a kernel native mechanism, and the system operation efficiency under diversified loads is remarkably improved while low performance overhead, high safety and easy deployment are kept.

The application discloses a Text-to-SQL intelligent agent coordination system and method for perceiving potential intention of a user in a multi-round context. The system comprises a memory bank, a prompt strategy pool, a strategy recommender, an SQL generator, an SQL refiner and a strategy optimizer. The memory bank is used for storing system running data. The prompt strategy pool is used for storing prompt strategies. The strategy recommender is used for receiving user information and strategy information of the prompt strategies, identifying the current potential intention of the user based on the user information and the strategy information, and matching the optimal strategy from the prompt strategy pool according to the potential intention to constrain the generation of an SQL query statement. The SQL generator generates an SQL query statement based on the current natural language query of the user, the system running data and the optimal strategy. The SQL refiner is used for adjusting and iteratively updating the SQL query statement. The strategy optimizer is used for updating the prompt strategies. The application increases strategy selection before SQL generation, so that the SQL generator can generate an SQL statement meeting the user demand at one time with a higher probability, and the cost is reduced.

The present application relates to a kind of multi-stage self-defined combination strategy warehouse picking optimization method, device and medium, wherein, method includes collecting order data to be picked and preprocessing generation structured data set;Support user self-defined configuration wave division dimension and weight, complete order clustering and generate picking wave with unique identification;Divide the whole process of picking into four core stages, provide each stage strategy selection and combination interface, generate compatible multi-stage strategy combination scheme;Correlation scheme and wave task are executed in stages, real-time acquisition of field data and dynamically correct abnormal strategy;After completing picking, the core index is counted, report is generated and review output optimization suggestion, the present application solves the problem that traditional picking strategy is poor in adaptability, low efficiency, can be flexibly adapted to multiple warehouse scene, improve picking efficiency and accuracy, reduce resource consumption, and the practical value is remarkable.

The invention discloses an automatic marketing strategy generation system based on intelligent service, and relates to the technical field of intelligent marketing, and the system comprises a basic template layer, a dynamic adaptation layer, a creative generation layer, and a multi-target strategy selection layer. Through a constructed hierarchical strategy engine of a basic template layer, a dynamic adaptation layer, a creative generation layer and a multi-target strategy selection layer, a single strategy generation mode of a traditional fixed template is broken through, the basic template layer uses an industry marketing knowledge base to provide a standardized strategy generation starting point, the dynamic adaptation layer accesses multi-source heterogeneous data in real time, and the multi-target strategy selection layer is used for providing a standardized strategy generation starting point. According to the method, a creative generation layer is established, strategy parameters are dynamically corrected, market changes can be quickly responded, the creative generation layer automatically generates personalized marketing content by means of a multi-modal generation technology, and a gradient type strategy generation mode is formed from basic framework establishment to real-time parameter adjustment to creative content output through the dynamic layered architecture. And a brand new technical path is provided for marketing strategy generation.

The application discloses a planning calibration method and system for an information analysis intelligent assistant, and comprises the following steps: in each planning round, a candidate strategy set is generated by a planning agent, the candidate strategy set contains an information retrieval target, a retrieval keyword combination and an information processing flow; the candidate strategy set is evaluated by multiple agents with heterogeneous private memories, a strategy selection module based on the agents is constructed for information consistency, and a strategy with the most stable evaluation result is selected as an optimal strategy of the round; a difference between a self-selected strategy of the planning agent and the selected optimal strategy of the round is recorded, a calibration constraint module guided by consistency is constructed, and the difference is converted into a cognitive calibration constraint and stored in the memory of the planning agent; strategy selection optimization is realized in single-round planning by relying on the strategy selection module based on information consistency; and the cognitive calibration constraint generated by the calibration constraint module guided by consistency is combined, so that the constraint is transmitted between different rounds and gradually accumulated, and a constraint is formed on strategy generation of the planning agent; and the trained agent is applied to an open domain webpage retrieval task to generate accurate retrieval results.

This invention relates to the field of wireless body area network (VBA) technology, and particularly to a method for selecting routing strategies for vehicular network (V2N) communication based on multiple communication state indicators. The method includes: S0, setting a strategy set; S1, obtaining communication demand features ( ) and network state feature vectors; S2, filtering to obtain a candidate strategy set P'; S3, constructing a strategy selection input description, associating each candidate strategy ∈ P' with a path optimization method type; S4, selecting the optimal strategy based on the calculated matching score of each candidate strategy; S5, generating a path control scheme; and S6, executing the path control scheme. This method can reduce the coupling between routing planning methods and specific implementations, improving the robustness and adaptability of V2N communication.

The application discloses a method for optimizing infrastructure network game strategy based on linear constraint, which comprises the following steps: obtaining the topology structure of the infrastructure network, determining the strategy set of the attack side and the defense side, and constructing a basic model of complex network game; determining an index representing network connectivity, calculating the income of the attack side and the defense side under various strategy profiles in the complex network game model, and thus obtaining an income matrix; adding a restriction condition to the strategy selection of the attack side and the defense side, and thus obtaining the strategy constraint set of the attack side and the defense side; converting the solution of the basic model of complex network game into the solution of a linear programming problem, obtaining a mixed Nash equilibrium solution, and thus obtaining the strategy optimization result of the attack side and the defense side. The method can greatly widen the application of complex network game research in practice by introducing the constraint matrix countermeasure into the infrastructure complex network game.

The invention relates to an analysis and decision-making method for channel selection of a double-channel closed-loop supply chain, and belongs to the technical field of supply chain management. According to the method, a demand and profit function is established based on a consumer net utility theory to construct a game model comprising three modes of manufacturer network direct sales (M strategy), retailer traditional retail (R strategy) and double-channel parallel (D strategy), and a reverse induction method is adopted to solve equilibrium decisions and optimal performance under each strategy. According to the method, the balance results of all strategies are compared and analyzed through the system, and key market parameters, namely acceptance of consumers on reprocessed products and acceptance of the consumers on channel transfer, and decisive influences on profits of all parties and optimal strategy selection, are revealed. According to the specific levels of the parameters, a scientific and quantitative decision basis is provided for a manufacturer to select an optimal channel strategy considering individual profit and system total benefit, and the adaptability and competitiveness of an enterprise in a complex market environment are effectively improved.

The invention discloses a port resource collaborative scheduling method, system and device and a medium, and the method comprises the steps: obtaining a strategy library and historical resource data of a target port; the strategy library comprises a plurality of different first solving strategies; performing example generation on the historical resource data to obtain a plurality of port examples; according to each first solving strategy in the strategy library, performing example solving on all the port examples to obtain a plurality of example training groups; and according to all the example training groups, performing parameter updating on the initialized strategy selection model to obtain the trained strategy selection model. According to the method, the target solving strategy is determined through the trained strategy selection model, and the to-be-solved resource example is solved, so that the efficiency and effect of port resource collaborative scheduling can be improved. The invention relates to the technical field of port scheduling.

The application discloses a kind of partition division strategy selection method and its related components based on two-way tracking, it is related to partition division field, obtains the train running time of track line in the same direction Same multiple one-way partition division strategies, respectively determine the train tracking interval time of these strategies in two driving directions, retain the one-way partition division strategy in the train tracking interval time of two directions is less than the preset tracking interval time on the direction pair, and delete other strategies, in the retained one-way partition division strategy, according to the specific partition quantity in one-way partition division strategy and train tracking interval time determine the final two-way partition division strategy. By selecting the strategy that train tracking interval time is relatively short in two directions in multiple partition strategies, a partition division strategy with higher regional carrying capacity and train operation efficiency can be selected under the premise of ensuring the safety of train operation.

The application discloses an automatic marketing strategy generation system based on intelligent service, and relates to the technical field of intelligent marketing.The system comprises a basic template layer, a dynamic adaptation layer, a creative generation layer and a multi-target strategy selection layer.A hierarchical strategy engine is constructed by the basic template layer, the dynamic adaptation layer, the creative generation layer and the multi-target strategy selection layer, thereby breaking through the single strategy generation mode of a traditional fixed template.The basic template layer utilizes an industry marketing knowledge base to provide a standardized strategy generation starting point.The dynamic adaptation layer accesses multi-source heterogeneous data in real time and dynamically corrects strategy parameters, and can quickly respond to market changes.The creative generation layer automatically generates personalized marketing content by means of a multi-modal generation technology.This dynamic layered architecture forms a gradient strategy generation mode from the construction of a basic framework to the real-time parameter adjustment and then to the creative content output, thereby providing a brand-new technical path for marketing strategy generation.

The invention relates to the technical field of air combat intelligent aid decision making, in particular to an aid decision making recommendation model adaptive method and system based on a Bayesian neural network, and is particularly suitable for man-machine collaborative decision making in a 2V2 air combat game scene. By designing a self-adaptive framework and an incremental learning algorithm, the model can be dynamically updated based on real-time data in the use process of a user, so that the recommendation accuracy and robustness are improved. The method comprises the following steps: constructing an incremental learning algorithm of a Bayesian neural network, analyzing the time complexity and the space complexity of the incremental learning algorithm, evaluating the real-time performance of the incremental learning algorithm in an online reasoning process, and researching the robustness performance under the condition of data uncertainty. The method can be widely applied to strategy selection in an intelligent recommendation system, an auxiliary decision-making platform and a multi-agent game.

The present application relates to the technical field of network security, in particular to a network attack and defense strategy selection method and system based on three-party evolutionary game, a three-party evolutionary game model is constructed by an attacker, a defender and a user as network attack and defense confrontation participants based on evolutionary game theory, the participants of the three-party evolutionary game model imitate the advantageous strategy in the evolutionary game and adjust their own strategy selection under the driving of game income; the stability of the three-party evolutionary game model is solved by using a three-party behavior strategy selection dynamic equation, the equilibrium point of the three-party evolutionary game model is obtained, and the best selection strategy of each subject of the participants is obtained according to the equilibrium point of the three-party evolutionary game model. The present application models and describes the real attack and defense scene more accurately by constructing a game model with the participation of the attacker, the defender and the user, and provides technical support for solving the network information security problem.

The invention discloses a new energy automobile charging mode analysis method based on an evolutionary game theory, and the method comprises the steps: firstly, determining a government, an automobile manufacturing enterprise and a consumer as game participants, and defining a strategy set and influence factor variables of the game participants; based on this, a payment matrix coupling benefits of three parties is constructed, and then a replication dynamic equation is established by taking the strategy selection probability of each party as a state variable; a system equilibrium point is obtained by solving an equation, stability analysis is carried out by using a Jacobian matrix eigenvalue method, and a stability condition for enabling the equilibrium point to become an evolution stability strategy point is deduced; and finally, based on the condition analysis, factor variables needing to be adjusted for realizing the expected stable strategy are analyzed, and a decision support result is generated. According to the method, the defect that the prior art lacks a dynamic view angle, a system framework and a quantitative model is overcome, the three-party strategy interaction evolution process can be quantitatively depicted, the stability condition is determined, and a scientific and quantitative basis is provided for industrial policy making and enterprise strategic decision making of a battery charging and replacing mode.

The present application belongs to the technical field of supply chain decision optimization, and particularly relates to a supply chain evolution game decision optimization method and system fusing multi-source data. The present application firstly calculates strategy distribution entropy based on the strategy selection proportion of each subject, then calculates external disturbance factors based on external environment indexes in the global trusted state data, secondly calculates comprehensive game state entropy based on the calculation results of strategy distribution entropy and the calculation results of external disturbance factors, and finally sets a game imbalance early warning threshold to pre-judge potential risks based on the calculation results of comprehensive game state entropy, and automatically generates a regulation and control strategy to intervene in the game process when the early warning is triggered, thereby guaranteeing the stable operation of the supply chain. This progressive calculation method makes the entropy value not only reflect the change of the internal game state, but also perceive the disturbance of the external environment, thereby realizing accurate risk measurement combining internal and external factors, and being more scientific and authentic than single-dimensional entropy value calculation.

The application discloses a multi-strategy negotiation intelligent customer service method for a complaint scene, which comprises the following steps: complaint receiving and multi-dimensional feature fusion analysis, four-dimensional features of emotion, appeal, historical interaction and current dialogue are fused by adopting a cross attention mechanism; user game type identification, four game types of cooperation type, competition type, compromise type and avoidance type are identified by a gating recurrent unit; negotiation strategy selection and initialization; multi-round negotiation interaction and state tracking, three game signals of attitude change, appeal adjustment and emotion fluctuation are detected; gradual concession and dynamic strategy adjustment, concession control based on emotion sensitivity and strategy switching driven by game signals; dynamic compensation scheme generation and agreement reaching, the optimal scheme is searched in a multi-dimensional compensation space based on a Nash bargaining solution. The application changes complaint processing from passive response to active negotiation, and effectively improves complaint processing efficiency and user satisfaction.

This invention discloses a 5G-based data security sharing method, device, and system, relating to the field of information security technology. The method includes: acquiring network slice status and user context data to construct a state vector; performing inverse game theory based on the state vector to deduce the attacker's optimal path and calculate the game payoffs for both attacker and defender; generating multiple policy individuals based on the game payoffs and constructing a payoff matrix to generate guiding variables; constructing a fitness function by combining the guiding variables and game payoffs, and outputting the fitness score of each policy individual; performing sensitivity evaluation on policy parameters based on the fitness scores and calculating the mutation rate; performing parameter selection, crossover, and mutation based on fitness and mutation rate to obtain an updated set of policy parameters; selecting the policy parameter group with the highest fitness and configuring encryption methods, access rules, and scheduling strategies. This improves the efficiency of encryption strategy selection, enhances the accuracy of access control, and mitigates the risks of secure data sharing.

Safety reinforcement learning is widely applied to the fields of games, robot control and the like. However, the traditional method often causes efficiency and stability problems due to limitation of an optimization strategy and a projection technology. In order to solve the problems, the invention provides a new algorithm which is named as an OSAPS (One-step Anticipative Policy Selector), and the new algorithm is an OSAPS (One-step Anticipative Policy Selector). The OSAPS improves the efficiency and safety of agent behaviors through integrating strategy selection, single-step planning and a self-adaptive safety threshold mechanism. The core of policy selection is a conditional dual-path policy network architecture, which comprises a vertex policy network and a basic policy network. In addition, a single-step planning mechanism of the OSAPS enables the intelligent agent to perform prospective action adjustment in a safe range, and simulates a human decision process. The self-adaptive safety threshold mechanism further improves the balance and safety between exploration and utilization. A robot simulation experiment shows that the OSAPS improves the time efficiency by 18.52% in a Pendula environment; the performance is improved by 7.67% in a Hovertrap environment, and meanwhile, the stability and the safety are kept.

The application relates to a kind of energy storage cost dredging evolutionary game method, comprising: (1) collecting the corresponding information of target area power supply side, grid side, user side main body, and establishing the evolutionary game matrix for energy storage cost dredging;(2) based on the evolutionary game matrix, calculate the expected income of power supply side main body strategy selection and its replication dynamic equation;(3) based on the evolutionary game matrix, calculate the expected income of grid side main body strategy selection and its replication dynamic equation;(4) based on the evolutionary game matrix, calculate the expected income of user side main body strategy selection and its replication dynamic equation;(5) carry out evolutionary game, analyze the scene of evolutionary game;(6) combined with the policy that may be taken for the result of each evolutionary game, consider the change of user demand response willingness, simulate the probability change of user willingness by Markov chain, and return to step (1) to continue evolutionary game.The method is beneficial to accurately analyze the possibility of each main body bearing energy storage cost.

The application discloses a driving strategy selection method and device based on human-computer interaction, equipment and medium. The method comprises the following steps: generating the outflow of the strategy scheme according to the comprehensive priority of the strategy scheme relative to the reference scheme and the inflow generation model; generating the inflow of the strategy scheme according to the comprehensive priority of the strategy scheme relative to the reference scheme and the preset outflow generation model; determining the net flow corresponding to the strategy scheme according to the outflow and the inflow; and selecting the strategy scheme corresponding to the maximum net flow as the optimal driving strategy. The application collects and learns the decision characteristics and behavior patterns of people in various interactions of intelligent agents represented by intelligent vehicles by constructing a human behavior model, introduces the prospect theory in the traditional algorithm, improves the accuracy of evaluating and selecting driving decisions, helps to provide a driving strategy that is more in line with the decision-making preference of people in the intelligent driving process, and further improves the user experience in the whole human-computer interaction process.

The invention discloses a debugging control and execution management method based on a UI automatic test platform, and relates to the technical field of software testing, and the method comprises the following steps: providing flexible debugging starting point control capability, realizing strategy selection and scheduling by a front end, and sending an application case to a server executor; the method comprises the following steps: transplanting a UI automatic test platform executor to a local server, locally starting a special controlled browser instance by virtue of Playwright to realize real-time monitoring of case execution, and constructing a breakpoint mechanism based on a producer-consumer model, so that the server executor realizes queue execution control by depending on a front-end strategy; a dynamic breakpoint adding mechanism is provided, and breakpoints are allowed to be inserted when the use cases are executed, so that repeated operation of executed use case segments is avoided; in UI automatic testing, thread management supports checking and modifying of executor thread variables so that page data can be obtained and stored into the variables to be used in subsequent steps, and debugging is convenient. According to the invention, the efficiency of compiling and maintaining the test script by the UI automatic test platform can be improved.

The invention provides an official document writing optimization method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: extracting a state feature of a current writing task from writing demand information of a user; inputting the state features into a strategy selection model, obtaining a writing strategy output by the strategy selection model, and generating a first document according to the writing strategy; generating and fusing different types of reward signals according to the interactive behavior data of the user for the first document of the official document to obtain a comprehensive reward signal; and updating parameters of the strategy selection model by using the comprehensive reward signal to complete online optimization of the writing strategy. The writing strategy is dynamically selected through the state characteristics of the writing task, and the parameters of the strategy selection model are optimized in real time based on user interaction feedback, so that the problems that an existing official document writing system is fixed and rigid in strategy and cannot adapt to a dynamic scene are effectively solved; dynamic matching of strategy selection and scene requirements and optimization of strategy performance are realized.