223 results about "Q-learning" patented technology

The invention provides a reinforcement learning-based database adaptive data flow acquisition optimization method and system, and relates to the technical field of databases, and the method comprises the steps of constructing a distributed acquisition proxy network, obtaining database data flow information, and carrying out multi-modal feature decomposition and fusion. And calculating an optimal initial acquisition parameter combination by using the fusion feature vector and combining a genetic algorithm, constructing a double-layer deep Q learning network structure, performing hierarchical training, and generating a hierarchical acquisition strategy model. The model adaptively adjusts a global acquisition strategy and parameters according to a characteristic drift degree and a performance index so as to realize continuous optimization of the acquisition strategy. According to the method, through multi-modal feature fusion, reinforcement learning and a self-adaptive tuning mechanism, the data flow collection efficiency is effectively improved, the collection delay is reduced, and the resource utilization rate is optimized.

Techniques for Meta-Q-Learning (MQL) are described. A method of MQL may include receiving a request from an agent to perform adaptation based at least on task data associated with a new task collected by the agent, identifying a subset of meta-training data corresponding to the task data in a replay buffer, and adapting a policy using the subset of meta-training data and the task data to generate an adapted policy, wherein the adapted policy is used identify a next action for the agent to perform.

The invention relates to the technical field of intelligent power energy monitoring, in particular to a power grid monitoring method. The method comprises the following steps: obtaining power grid monitoring nodes, and carrying out monitoring node graph structure processing to construct a physical feature fusion network graph; performing graph structure knowledge enhancement on nodes in the physical feature fusion network graph to construct a semantic enhancement physical association graph; performing composite diagram operation processing of fault propagation risk assessment and root cause analysis based on the semantic enhanced physical association diagram, and constructing four types of intelligent agents of monitoring, coordination, analysis and decision to obtain a hierarchical collaborative decision network; and executing a local strategy learning process based on Q learning based on the hierarchical collaborative decision network so as to realize agent reinforcement learning optimization and end-to-end deployment implementation. According to the method, automatic and end-to-end construction of the intelligent agent is realized through graph structure semantic fusion and multi-stage reinforcement learning, and the intelligent level of power grid monitoring is remarkably improved.

The invention provides a feeding bin intelligent management method and system based on self-adaptive control, and relates to the technical field of feeding bins, and the method comprises the steps: collecting the data of a feeding bin group through multiple sensors, inputting the data into a prediction model to obtain a state prediction result, and correcting the deviation through a Kalman filtering algorithm; a reinforcement learning environment is constructed based on the corrected data, and an optimal control strategy is obtained through deep Q learning network training; the distributed control system generates a control instruction sequence based on a consistency protocol and a model prediction control algorithm to realize cooperative control. According to the invention, intelligent management of the stock bin group is realized, the control precision and the system stability are improved, and the manual intervention cost is reduced.

The invention relates to the technical field of personalized pushing, in particular to a personalized music teaching content pushing system. The system comprises a user ability evaluation module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module and a personalized recommendation module. According to the invention, a clustering algorithm, a support vector machine, a random forest algorithm, accurate assessment of learner ability, K-means clustering and an analytic hierarchy process are adopted, teaching content and ability matching, a Dijkstra algorithm and dynamic planning are realized, a personalized learning path, a Q learning and strategy gradient method are designed, and a push strategy, a long and short term memory network and a seasonal decomposition time sequence are optimized. Learning requirements are predicted, the project reaction theory, the Markov decision process and the Bayesian network are utilized, feedback adjustment precision is improved, deep Q network and Monte Carlo tree search are carried out, personalized recommendation is optimized, and teaching efficiency and achievements are enhanced.

The invention discloses an energy system fault prediction method based on a knowledge graph, and relates to the technical field of graph neural networks, and the method comprises the steps: collecting new energy real-time data, and carrying out the preprocessing of the real-time data; constructing a dynamic graph model based on the preprocessed data; outputting a result based on the dynamic graph model, and identifying abnormal data by using a graph neural network; the dynamic graph model is used again for analysis and fault prediction through the identified abnormal data; according to the historical data and the real-time data, a fault judgment result after dynamic optimization is obtained through an adaptive fault threshold adjustment algorithm; according to the method, the action value is estimated through the two independent Q networks, the over-estimation problem in traditional Q learning is effectively relieved, and therefore the stability and accuracy of decision making are improved.

The invention relates to the technical field of computers, and discloses a server cluster security control method combined with multi-level encryption strategy joint scheduling, which comprises the following steps: S1, carrying out sensitivity evaluation on calculation tasks and data in a cluster, and classifying the data into low-sensitivity data, medium-sensitivity data and high-sensitivity data according to a preset rule; s2, encrypting the low-sensitivity data by adopting a first encryption algorithm, encrypting the medium-sensitivity data by adopting a second encryption algorithm, and encrypting the high-sensitivity data by adopting a homomorphic encryption algorithm; and S3, based on a joint scheduling mechanism of reinforcement learning, calculating task requirements and data sensitivity information by acquiring the load of the cluster in real time. Through a multi-level encryption strategy, dynamic resource scheduling based on deep Q learning and a security protection mechanism combining a zero-trust architecture and deep learning, the problems that cluster resource utilization efficiency is low, data security is insufficient and security threats cannot be responded in real time are solved.

The invention discloses a robot skill learning method and system for text large model assisted reinforcement learning, and a terminal. The method comprises the following steps: representing an environment by adopting a scene graph; analyzing a task instruction and an environment by utilizing a text large model; taking the large text model as a guide agent, taking a reinforcement learning agent as an exploration agent, combining jump reinforcement learning and conservative Q learning, and training the reinforcement learning agent by using a data set generated by interaction of the guide agent and the exploration agent with the environment to obtain a trained reinforcement learning agent; and selecting a target observation object for the trained reinforcement learning agent by using the text large model, and outputting a target action sequence by the trained reinforcement learning agent. According to the method, language instructions are understood through a large model technology, feasible guide strategies are generated, so that random exploration steps are reduced, parameterized skills are introduced, the training process of reinforcement learning is accelerated, the efficiency is high, and the method does not depend on the data volume of a skill library.

The invention provides a dynamic spreading factor anti-interference method based on annealing Q learning. The method comprises the following steps: determining an information transmission rate according to a spreading factor; determining system throughput and an optimization target according to the signal-to-noise ratio of the receiving end; determining a state space, an action space, a state transition probability matrix and rewards in the decision process; calculating a reward obtained by the selection action according to a broadband spectrum sensing result; the interference condition at the current moment is obtained through broadband spectrum sensing; deciding a spreading factor of the next time slot according to the Q value table; the user selects the action of the next time slot according to the decision information and updates the state; the award obtained by selecting the corresponding action is calculated according to the broadband spectrum sensing result; selecting the spreading factor of the next time slot according to the Q value table, and updating the Q value of the selection action in the state; and the user makes the most anti-interference strategy in a dynamic interference environment. According to the method, the system throughput is maximized, and time delay and overhead caused by channel switching are avoided.

The invention relates to the technical field of video source switching, and discloses an intelligent video source switching system and method for a director switching station. The method comprises the following steps: acquiring an input video stream of a current video source through a director switching station, and performing N-gram feature vector calculation and inter-frame difference analysis to obtain video frame segmentation data; performing scene analysis on the video frame segmentation data to obtain a scene feature signature; performing spatial-temporal feature extraction and fusion on each video frame in the input video stream according to the scene feature signature to obtain a scene spatial-temporal fusion feature set; inputting the scene space-time fusion feature set into an LSTM-based lightweight time sequence convolutional network model to carry out video switching behavior prediction so as to obtain video switching behavior prediction data; and inputting the video switching behavior prediction data into a double-depth Q learning model based on a Markov decision process to carry out video source switching decision to obtain a video source switching decision result, thereby improving the intelligent video source switching accuracy of the director switching station.

The invention belongs to but is not limited to the technical field of port loading and unloading operation, and discloses a hyper-heuristic dry bulk cargo cabin cleaning and ship unloading equipment cluster cross-cabin collaborative operation method and system based on large language model-Q learning dual drive. Acquiring information such as historical data and equipment attributes of cabin clearing and ship unloading operation of the dry bulk cargo port; determining the cabin cleaning and ship unloading operation process and task information of the dry bulk cargo port; establishing a cabin-cleaning and ship-unloading equipment cluster cross-cabin collaborative operation model for determining an equipment collaborative operation plan; acquiring and utilizing a large language model expansion training sample; training and adjusting a Q learning model based on the sample; based on a hyper-heuristic algorithm, LLM and QL are fused, an LQ-HH algorithm is designed and used for optimizing and solving the equipment collaborative operation model, and the operation sequence and starting time of cabin cleaning and ship unloading equipment are obtained. According to the method, the operation efficiency, the equipment cooperation capability and the anti-interference capability can be effectively improved, and intelligent transformation and upgrading of the dry bulk port can be promoted.

The invention relates to the technical field of intelligent agriculture and Internet of Things control, and discloses an intelligent greenhouse control method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-factor environment state vector; strawberry image features are extracted based on YOLOv5; inputting a strawberry growth stage prediction model in combination with the time sequence to determine target environment parameters; constructing an environmental parameter prediction model; constructing a dual error term, and inputting the dual error term into a fuzzy controller to generate a preliminary control instruction; optimizing and outputting an optimization control instruction by the Q learning network; compared with a greenhouse control method which mostly depends on a fixed threshold value or a static rule in the prior art, the technical problem that dynamic target setting and self-adaptive adjustment of environmental parameters are difficult to realize especially under the condition that demands of strawberry crops in different growth stages change rapidly is solved. Intelligent greenhouse environment control in combination with the growth stage of strawberry crops is realized, and the adaptability of the greenhouse to crop growth conditions is improved.

The invention belongs to the technical field of power system protection and control, particularly relates to a short-circuit simulation test method and system for a subway direct-current power supply system, and aims to solve the technical problems of insufficient flexibility, difficulty in adapting to complex actual working conditions and lack of adaptivity in the prior art. The method comprises the following steps: presetting and adjusting various short-circuit fault parameters to generate a short-circuit working condition; determining a transient fluctuation separation result based on the short-circuit working condition; constructing a system topology model by utilizing a graph neural network, generating a long-term protection plan in combination with an optimized double-Q learning strategy, and generating an initial risk assessment result by adopting a neural network based on physical information and Monte Carlo simulation; constructing a hybrid risk assessment model in combination with a Bayesian network and fuzzy logic to obtain a target risk assessment result, and generating an emergency response plan based on the target risk assessment result by using a natural language generation technology and a planning algorithm; according to the invention, the precision and reliability of the short-circuit simulation test of the subway direct-current power supply system are improved.

The invention discloses a double-capacity water tank control method based on multi-target reward Q learning, and belongs to the technical field of machine learning and automatic control. The method comprises the following steps: firstly, establishing a discretization mathematical model of a double-capacity water tank liquid level control system, and designing a multi-target reward function; secondly, empirical data are stored only when the liquid level tracking error exceeds the limit or the control quantity suddenly changes, and sampling weights are distributed according to award absolute values; and finally, updating the Q table based on the time difference error, and performing closed-loop real-time control on the double-tank water tank according to the dynamically adjusted PID parameter. According to the method, a five-dimensional reward function is designed, and multi-index dynamic balance is realized through linear weighting; the empirical tuple is stored only when the liquid level tracking error exceeds the limit, and the quick response requirement of the dynamic time-varying scene of the double-capacity water tank is met; dimensionality reduction is performed on a continuous state space by adopting a non-uniform grading strategy, so that the calculation complexity is remarkably reduced; a perception-decision-execution integrated framework is constructed, and the dependence of a traditional control method on a mathematical model of the water tank is abandoned.

The invention provides a tunnel anomaly detection and response method and device based on an orbital robot, and the method comprises the steps: obtaining scoring and grading data from a quantitative scoring model through a preset model optimization module, carrying out the dynamic adjustment of the parameters of the scoring model through a deep Q learning network in combination with historical response records, optimizing a grading threshold value to match an actual threat level, and carrying out the detection of an actual threat level. Transmitting the adjusted hierarchical data to a response scheduling system; and according to the adjusted hierarchical data, automatically matching a preset response strategy combination, if at least one selectable strategy combination exists, determining an optimal strategy combination through a weighted optimization algorithm, a comprehensive response speed, a resource utilization rate and a disposal cost, and issuing an execution instruction to a related execution unit.

The invention provides an outbound routing method and system based on multi-dimensional index coupling and large model decision, and the method comprises the steps: carrying out the preprocessing of the historical data of each line provider, predicting the future performance index through an LSTM neural network, and optimizing the prediction result through a Kalman filter, and eliminating the noise. In the route decision-making link, the system calculates the Q value of each line provider based on the deep Q learning network, comprehensively considers the call completing rate, average cost, negative feedback rate and other factors of each line provider, and intelligently selects the optimal line provider in combination with the user portrait, scene recognition and real-time load state. The system also constructs a complete feedback closed loop, continuously optimizes the model by collecting call result data, and automatically adjusts decision parameters. The call completing rate and the service quality of the outbound service are improved, and the communication cost is reduced.

The invention provides an integrated feature selection method and product based on quantum computing and Bayesian optimization, and relates to the technical field of data processing. According to the embodiment of the invention, an original mathematical integration model is converted into a QUBO model which can be solved by quantum calculation, and the QUBO model is decomposed into a joint optimization discretization step length optimization sub-problem and a parameterization QUBO sub-problem. In a mixed quantum classical optimization algorithm framework, a self-adaptive Q learning model is designed on the upper layer, and proper sub-problems can be dynamically selected in the search process. In the lower layer, a dropout Bayesian optimization algorithm is provided for effectively optimizing the high-dimensional discretization step length in each iteration. A CIM-based quantum computing method is adopted, and a parameterized QUBO sub-problem under the given discretization step length is efficiently solved. According to the method provided by the embodiment of the invention, the feature selection problem can be successfully and efficiently solved, and the selected features of the credit classification problem and the credit classification model for classification based on the selected features can be obtained.

The invention relates to a novel pulse neural network synaptic weight adjustment method, and belongs to the field of artificial intelligence. Firstly, a traditional pulse time sequence dependence plasticity rule is improved based on the distribution trend of a Gaussian function, so that the weight change corresponding to the optimized synaptic plasticity rule when the time interval tends to 0 is larger, and when the time interval is gradually increased, the weight change is in an accelerated descending trend; the method comprises the following steps: firstly, performing synaptic weight local regulation on a neuron model to improve the local regulation capability of the synaptic plasticity of the neuron, then introducing reinforcement learning, associating Q learning with the synaptic plasticity in the neuron model, and realizing the global regulation and control of the plasticity on the basis of the synaptic weight local regulation. According to the above synaptic plasticity rule, the spiking neural network constructed based on Leaky Integrating and Fire is subjected to weight adjustment, so that the spiking neural network has better performance.

The invention discloses a waypoint file-oriented unmanned aerial vehicle flight route generation method, which comprises the following steps of: acquiring a waypoint file comprising a waypoint to be flied of an unmanned aerial vehicle, analyzing the waypoint file to obtain latitude and longitude information and flight height information of the waypoint to be flied, and generating a flight route of the unmanned aerial vehicle; the latitude and longitude information and the flight height information of the waypoint to be flied are converted into three-dimensional space information under a Cartesian coordinate system, the three-dimensional space information of the waypoint to be flied is obtained, a waypoint sequence is generated by using a self-organizing mapping neural network model or in a Q learning mode, and a flight route file of the unmanned aerial vehicle is generated based on the waypoint sequence. According to the invention, automation of unmanned aerial vehicle flight route generation is realized.

The invention provides a joint channel and route selection cross-layer decision-making method based on Q learning. The method comprises the following steps: acquiring coordinates; each routing node periodically broadcasts messages used for discovering, establishing and keeping alive neighbor relations; after receiving the Hello packet, the neighbor node feeds back an acknowledgement message and establishes a neighbor relationship; when data needs to be transmitted in the source node, the source node selects a next-hop node and a channel transmission data packet according to a selection strategy, and the data packet comprises the address of the selected next-hop node; the neighbor point judges whether the data packet should be forwarded by the neighbor point; the next-hop node replies to the source node through the control link, wherein reply information comprises the estimated signal-to-interference ratio and the latest Q table of the next-hop node; the source node calculates a reward value according to the feedback signal-to-interference ratio and the position obtained in the earlier stage; and the source node updates the Q table and the node selection strategy according to the reward value. According to the method, the network transmission rate and the anti-interference performance are improved, and the end-to-end time delay and hop count are reduced.

The invention relates to the technical field of intelligent traffic, and discloses an end-to-end vehicle group collaborative decision-making method based on mixed deep reinforcement learning in a mixed traffic flow environment. According to the method, a graph structure modeling method aiming at a complex traffic scene is designed, a communication mechanism between traffic participants is realized from a bottom layer, then, a space-time encoder based on a graph convolution method realizes effective embedding of space-time information in environment observation, and the space-time information in the environment observation is effectively embedded. According to the method, the precondition is realized for a system to understand space-time coupling information in an environment, finally, an act-critic architecture and a Q learning architecture are deeply coupled, a novel reinforcement learning method suitable for a mixed action space is constructed, after model training is completed based on the method, model deployment can be realized by storing and loading model parameters, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. And the deployed model can output the collaborative decision of the controlled vehicle group by inputting environment observation data.

The invention discloses a foot type gait and form co-evolution system integrating reinforcement learning and evolutionary computation, and relates to the technical field of robots and the field of artificial intelligence. Comprising the steps that a real-time motion control strategy learning module is realized based on a dynamic mask integrated double-Q learning method; the gait and form collaborative optimization module optimizes key gait and form parameters influencing the global motion performance of the foot robot based on an evolutionary computation theory; the training environment adaptive enhancement module comprises an automatic domain randomization module and a course learning module; the parameterized trajectory generator module is used for providing structured motion prior for the real-time motion control strategy learning module; the robot simulation environment is used for simulating the physical effect of the robot and the environment in the computer; according to the invention, the high-speed, stable and efficient autonomous movement capability of the foot robot in a complex and changeable unstructured environment is realized.

The invention discloses an aircraft carrier ship surface unmanned equipment intelligent scheduling method considering security equipment degradation. The method comprises the steps of collecting related information of ship-based aircraft deck operation unmanned scheduling, wherein the related information comprises deck layout, position coordinates of working equipment matched with a ship-based aircraft, the number of ship surface unmanned equipment and the operation process of the ship-based aircraft; assumed conditions for guaranteeing degradation of the equipment and constraint conditions of the unmanned ship surface equipment are set, and a mathematical model of the unmanned ship surface equipment is established; based on a Markov decision process model, under a Markov decision framework, an epsilon-greedy exploration strategy in Q learning is improved, and exploration efficiency and strategy adaptability are enhanced; and selecting the state and the action by adopting the finally iterated Q value table to obtain an optimal scheduling sequence of unmanned scheduling of the shipboard aircraft deck operation considering the degradation of the support equipment.

The invention discloses an elevator dispatching method and system, electronic equipment and a storage medium, and the method comprises the steps that an elevator dispatching environment is constructed according to the elevator state and calling information, and the elevator dispatching environment comprises a state space, an action space and a reward function; reinforcement learning processing is conducted on the elevator dispatching environment according to a double-Q learning model, and an initial dispatching strategy is obtained; performing global optimization processing on the initial scheduling strategy according to a simulated annealing algorithm to obtain a target scheduling strategy; and operation control is conducted on the elevator system according to the target scheduling strategy, and the double-Q learning model is updated according to an operation result. The embodiment of the invention can improve the elevator dispatching efficiency, and can be widely applied to the technical field of elevators.

The invention discloses a heterogeneous cluster task scheduling fusion method based on Q learning and a genetic algorithm, and belongs to the technical field of intelligent scheduling. Collaborative optimization is carried out through a dynamic feedback mechanism of Q-Learning and the global search capability of the genetic algorithm: in an initialization stage, a cluster state space and an action space are defined, and a multi-Q-value table and an initial population are generated; during task scheduling, nodes are selected based on a # imgabs0 #-greedy strategy, reward values are calculated, and a Q value table is updated; meanwhile, the scheduling scheme is coded into chromosomes, a new population is generated through roulette selection, crossover and variation, and a Q value table is optimized; in the iteration process, the convergence is judged according to the Q value change or the population fitness change, and the strategy is dynamically adjusted. According to the method, the advantages of double algorithms are fused, local optimum is avoided, the task processing efficiency and the resource utilization rate are remarkably improved, adaptive strategy updating during cluster state change is supported, and the method is suitable for an efficient task scheduling scene of a large-scale heterogeneous cluster.

The invention discloses a data security management method and system based on a cloud computing platform, and relates to the technical field of data security management, and the method comprises the steps: collecting original data of the cloud computing platform in real time, and carrying out the preprocessing; constructing a discrimination model based on a generative adversarial network (GAN) to generate a forged data sample, taking the forged data sample and a real data sample as input data, optimizing the discrimination model by using the input data based on an extreme learning machine, and discriminating attack data; and constructing a security situation model by using a hidden Markov model, optimizing parameters through a cluster particle algorithm, and outputting a predicted security situation based on real-time attack data. A discrimination model is optimized through a generative adversarial network and an extreme learning machine, the discrimination precision of attack data is effectively improved, a security situation model is established through a hidden Markov model, model parameters are optimized in combination with a cluster particle algorithm, a predicted attack path is combined with a deep Q learning network, and the security risk of attack data is improved. And attack path prediction is carried out through the dual deep Q network, and a defense strategy is adjusted in real time.

The invention discloses a limited block length optimization method and system of a cellular-free communication perception integrated system, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing a cellular-free multi-station cooperative communication perception integrated architecture, and establishing a channel estimation and data transmission model; deducing a closed expression of the communication transmission delay, the decoding error probability and the sensing precision Cramer-Rao bound under the limited block length; analyzing a Pareto optimal boundary of communication and sensing performance by adopting a non-dominated sorting genetic algorithm, and determining a block length feasible region; and designing a dynamic optimization algorithm based on a double-depth Q learning network, and realizing collaborative optimization of the communication performance by adaptively adjusting the block length. The method solves the problem that high communication reliability, low time delay and high sensing precision are difficult to consider in a dynamic environment, the overall performance of the system is remarkably improved, and the method is suitable for high-requirement scenes such as automatic driving and industrial Internet of Things.

The invention provides a linear motor temperature control method and system based on air cooling heat dissipation, and relates to the technical field of motor temperature control, and the method comprises the steps: dividing a linear motor into a plurality of temperature control regions, and configuring an independent control execution unit for each region; predicting a temperature trend based on the LSTM; a multi-agent reinforcement learning control network is constructed, and each agent calculates an optimal control strategy based on deep Q learning; generating an overall airflow regulation strategy through a collaborative optimization algorithm; dynamically adjusting the opening of the electric adjusting valve and the rotating speed of the fan. Accurate temperature control is achieved, the heat dissipation efficiency is improved, and the service life of equipment is prolonged.

The invention relates to the technical field of reinforcement learning, motor driving and the like, provides a reinforcement learning-based adaptive search self-tuning method for a motor driving algorithm, and realizes an optimal decision of an individual search behavior by performing adaptive switching among various heuristic search operators by introducing a Q learning mechanism. And meanwhile, in combination with a neighborhood search strategy based on differential evolution, the local search capability is enhanced, and the global optimization performance is improved. The method is applied to the setting problem of key parameters of the motor driving system, the I TAE performance index serves as an optimization target, the search behavior is dynamically adjusted through a self-adaptive strategy, and therefore better control performance is obtained. Compared with an existing method, the method has the remarkable advantages in the aspects of convergence speed, self-adaptive capacity and anti-interference performance.

The invention discloses a two-stage excitation method based on multi-agent deep reinforcement learning, relates to the technical field of agent deep reinforcement learning, and aims to solve the problems that an excitation method in the prior art is too tedious in process and poor in effect. Through a simulation experiment in a specific area, taxies are used as participants to simulate real perception task geographical distribution and trajectory planning, comparison mechanisms such as deep Q learning and historical participation rate optimization are adopted, and multi-dimensional indexes such as average participant utility, platform utility and task completion rate are combined, so that the adaptability of the scheme in a dynamic environment is verified, and the accuracy of the scheme is improved. Cost is quantified by combining different sensor characteristics, participants can accurately evaluate energy consumption of themselves, platform utility is maximized under budget constraint, meanwhile, through bidirectional counterbalance of budget and task coverage, it is ensured that'minimum feasibility coverage 'of tasks and budget are not hyperbranched, actions of other agents can be observed during training to optimize cooperation, and the training efficiency is improved. The method only depends on a local state during execution, and adapts to a multi-participant competition scene.