67 results about "Collaborative game" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

The invention discloses a game theory improved model-based cloud computing node storage calculation training resource competition scheduling method. The method comprises the following steps of S1, constructing a global resource scheduling system; s2, carrying out initial division on the cloud computing nodes in each logic partition; s3, executing a local resource competition game model; s4, the global resource scheduling system collects a local resource competition result of each logic partition and residual available resources in each logic partition; s5, executing the global collaborative game model; s6, dynamically updating the resource state information of the cloud computing node; s7, the global resource scheduling system performs adaptive adjustment on resource demand strategies in the local game model and the global cooperative game model; and S8, the global resource scheduling system stores and analyzes past resource competition results and task execution conditions, and each cloud computing node updates a resource demand strategy according to the data. According to the invention, the problems of resource bottleneck and overload in the prior art are solved, and the resource utilization efficiency and the scheduling flexibility are greatly improved.

The invention discloses an unmanned aerial vehicle multi-target task scheduling method based on a distributed cooperative game, and the method comprises the following steps: S1, defining the state information of an unmanned aerial vehicle, and generating basic data; s2, constructing a task competition game model; s3, individual task optimization is carried out, and an improved artificial protozoa optimization algorithm is adopted; s4, performing global task scheduling optimization by adopting an improved egret group optimization algorithm; s5, performing adaptive adjustment in a task execution process; and S6, completing the task scheduling method. According to the method, the resource utilization rate and global task income of the unmanned aerial vehicle are remarkably improved, and the method can be widely applied to unmanned aerial vehicle cluster task management scenes of multiple industries such as logistics distribution, disaster rescue, environment monitoring and military reconnaissance.

The invention relates to the technical field of land planning analysis, in particular to a coastal zone land utilization space planning method based on big data analysis, which comprises the following steps: acquiring a multi-time-sequence remote sensing image and environment monitoring data of a planning area, and calculating an ecological safety index of each space unit in the planning area; obtaining economic activity big data and infrastructure data of a planning area, and calculating a land development suitability index of each space unit in the planning area; and establishing a multi-target collaborative game model taking the land utilization demand and the occupancy identifier as constraint conditions, and finally generating and outputting an optimal coastal zone land utilization space planning scheme. According to the method, the real interpretability of the model is improved, rapid adaptation can be conveniently realized through parameter configuration in different regions and different policy tendencies, and the method has high transportability and engineering deployment value.

A method and system for collaborative game play or coaching game play in a virtual environment are disclosed. 【Solution means】The memory may store a content control profile regarding a set of control inputs associated with actions within the virtual environment of the digital content title. Requests may be received from a set of one or more users associated with different source devices regarding collaborative game play of the digital content title. In response to the requests, at least one virtual avatar may be generated for an interactive session of the digital content title. A plurality of control inputs may be received from a plurality of different source devices and combined into a combined set of control inputs. Generating the combined set of control inputs may be based on the content control profile. Virtual actions associated with the virtual avatar may be controlled within the virtual environment according to the combined set of control inputs.

The invention discloses a multi-energy balance margin adaptive regulation and control method and device, and relates to the technical field of quantum computing crossover, and the method comprises the steps: carrying out the optimization of a four-dimensional input matrix through a quantum encoder, executing quantum annealing calculation to generate a ground state solution, integrating an inertia demand increment with the ground state solution through quantum bit entanglement, and carrying out the optimization of a four-dimensional input matrix; calculating a comprehensive inertia gap value, and generating a hierarchical scheduling instruction by adopting a dynamic threshold hierarchical strategy; and according to the hierarchical scheduling instruction, a topology reconstruction instruction is generated through a virtual inertia dynamic algorithm, dynamic monitoring is performed by applying multi-agent collaborative game optimization, and an intelligent regulation and control report is generated. After three-dimensional fault features and space-time dimensions of a multi-energy fluctuation ratio are aligned based on a dynamic time warping-covariance matching method, dynamic fusion is carried out on standard field phase fiber bundles by adopting aged-Simons topology weaving, and self-adaptive regulation and control of a multi-energy balance margin are realized.

The invention discloses an electricity-gas comprehensive energy system planning method based on a cooperative game. The method comprises the steps that S1, a multi-main-body electricity-gas comprehensive energy system model comprising a carbon capture power plant, renewable energy sources and a carbon utilization unit is established; s2, constructing a distributed optimization objective function and an operation constraint of each main body model; s3, based on the cooperative game alliance, according to the distributed optimization objective function and the operation constraint, constructing a cooperative game model through distributed optimization; and S4, carrying out decomposition and collaborative solution on the collaborative game model by adopting an alternating direction multiplier method, and generating an electricity-carbon bilateral transaction volume and a dynamic negotiation price. According to the method, distributed decision behaviors among multiple subjects can be processed, the physical interaction relationship among multiple energy flows in an electricity-gas-carbon system can be truly described, the physical reliability and engineering applicability of the model are improved, the income of each subject can be maximized, the privacy protection demand and cooperation surplus value distribution are considered, and the method is suitable for popularization and application. And the operation efficiency and fairness of the system are improved.

The embodiment of the invention provides an optimal configuration method of a regional energy system. The method comprises the following steps: acquiring multi-dimensional basic data of the regional energy system; constructing a dynamic elastic layered optimization model; based on the multi-dimensional basic data, time scale elastic adjustment, space subarea energy mutual aid and dynamic weight adaptation of multi-main-body demands are carried out; determining collaborative optimization boundaries of energy storage and energy and collaborative optimization boundaries of energy storage and loads in a layered manner, and overall collaborative optimization targets of energy storage, energy and loads; a capacity configuration sub-model, a real-time scheduling sub-model and a game arbitration sub-model are introduced, decision bifurcation is made in combination with a multi-agent collaborative game mechanism, and parameters are dynamically adjusted to achieve Dapareto optimum; a multi-energy storage characteristic dynamic mapping mechanism is integrated into the hierarchical optimization model; and outputting a collaborative optimization configuration scheme of the stored energy, the energy and the load in the regional energy system according to a collaborative result. According to the embodiment of the invention, the dynamic adaptability of the optimal configuration scheme of the regional energy system is realized.

The invention provides a charging and storage integrated intelligent scheduling method and system based on multi-objective optimization, and relates to the technical field of power system scheduling, and the method comprises the steps: collecting operation data, dividing a scheduling scene into a plurality of micro-grid units, distributing virtual energy tokens, and recording charging and discharging behaviors through a block chain; constructing a charging scene characteristic spectrum and establishing a scene probability distribution matrix; and performing real-time scheduling decision by using a distributed collaborative game method, and finally determining a power distribution scheme. According to the invention, automatic cooperation of distributed scheduling is realized, the system operation efficiency is improved, and the energy cost is reduced.

The invention discloses an electric vehicle V2G intelligent power distribution method based on sequence control, and the method comprises the steps: collecting power grid operation data and vehicle state data, and constructing an energy state data set after preprocessing; the central scheduling layer generates a self-evolution sequence compiler, and optimizes and generates a sequence control byte code; the group coordination layer constructs an energy state coordination game mechanism and executes local coordination and Nash equilibrium search; the edge execution layer loads the three types of strategies and executes consensus consistency verification and correction; the charging pile terminal executes the S-policy script and collects and uploads execution feedback information; and the central scheduling layer retrains a compiler according to feedback, recompiles scripts and updates a control strategy. According to the method, the self-evolution sequence compiler, the energy state collaborative game mechanism and the multi-strategy consensus executor are introduced, so that dynamic scheduling, adaptive optimization and safe consistent execution of the V2G intelligent power distribution process of the electric vehicle are realized.

This invention discloses a collaborative game control method for swarm spacecraft based on a CTDE architecture, comprising the following steps: Step S1: Constructing a swarm-based relatively nonlinear dynamic model containing multiple tracking spacecraft; Step S2: Obtaining a configuration evaluation index for guiding a collaborative encirclement configuration; Step S3: Obtaining a Markov game environment framework; Step S4: Obtaining a dense reward signal for policy optimization within the Markov game environment framework; Step S5: Obtaining a convergent collaborative game control policy based on the CTDE architecture; Step S6: Training the collaborative game control policy using a phased learning mechanism. This invention achieves stable convergence in a multi-agent environment, provides dense reward signals to alleviate reward sparsity, forms a swarm encirclement configuration, smoothly integrates hard engineering constraints, and improves the robustness of the policy under high-dynamic adversarial conditions through phased learning.

This application discloses a digital twin control method for facility poultry farming environments based on deep spatiotemporal data fusion, relating to the field of poultry farming technology. It proposes a spatiotemporal sequence prediction model for environmental parameters using a bidirectional long short-term memory network (VMD-Attention-BiLSTM) that integrates variational mode decomposition and attention mechanisms, effectively overcoming the system's large time lag. A digital twin decision engine based on nonlinear model predictive control is constructed, and the optimal control sequence for energy consumption and environmental quality is solved under multiple constraints by establishing a joint state equation for thermodynamics and gas diffusion. Specifically, for complex winter conditions, a ventilation-heating collaborative game strategy based on real-time heat loss compensation is designed. Adaptive sliding mode control with radial basis function neural network compensation is introduced into the underlying execution unit, significantly enhancing the system's anti-disturbance capability. This achieves precise environmental control under complex conditions, improving poultry welfare and energy efficiency.

The invention relates to the field of carbon market policy design, and discloses a dynamically optimized carbon pricing strategy and budget allocation collaborative game model. Comprising a participant modeling module, a carbon pricing dynamic adjustment module, a budget allocation optimization module and a game evolution module. By establishing a cost function and a revenue function of multiple subjects such as a government and an enterprise and combining a dynamic carbon price function and a budget constraint condition, optimal resource allocation among multiple industries is realized. According to the method, a master-slave game method, a Nash equilibrium method and a reinforcement learning method are adopted, so that carbon pricing and budget allocation are co-evolved in multiple periods, and the dynamic regulation capability and excitation compatibility of the system are enhanced. Compared with the prior art, the policy execution consistency is improved, the total emission reduction cost is reduced, and the method is suitable for application scenes of regional carbon markets and multi-enterprise alliance.

A water resource optimal distribution method based on a reinforced game algorithm is characterized in that a multi-layer game system comprising three types of agents including a water resource management mechanism, a regional water resource management unit and a water user is constructed, and a near-end strategy optimization algorithm in reinforcement learning is utilized to realize collaborative game and strategy dynamic update among the multi-layer agents; and finally, generating an optimal water resource allocation strategy set suitable for different water consumption scenes. The method has the advantages of being high in resource allocation precision, high in feedback response speed, high in strategy updating stability and the like, the problems of a traditional system in the aspects of resource imbalance, response lag, conflict adjustment difficulty and the like can be effectively solved, and an extensible and generalizable intelligent optimization new normal form is provided for actual water resource allocation.

According to the heterogeneous resource-oriented virtual power plant hierarchical clustering-cooperative game aggregation and trusted interaction system provided by the invention, refined and structured representation of a strong heterogeneous resource set is realized by constructing a dynamic digital twinborn archive and adaptive clustering of resources, and a hybrid prediction model fusing physical rules and data driving is adopted, so that the reliability of the system is improved, and the reliability of the system is improved. Physical rationality of behavior prediction and quantitative capture of uncertainty are ensured; through a hierarchical distributed optimization architecture of a master-slave-alliance collaborative game, a global complex optimization problem is efficiently decoupled, and real-time collaborative optimal scheduling under large-scale resource access is realized while privacy and autonomy of a resource main body are protected; through an online rolling credibility regulation capability interval evaluation mechanism, a diversified capability vector with a confidence level is output, so that the virtual power plant becomes a reliable transaction object which is transparent to a power grid and has a risk known, and the interaction credibility and credit rating of the virtual power plant in the power market are fundamentally enhanced.

This invention relates to the field of industrial design and intelligent manufacturing technology, specifically disclosing a digital twin AI-based industrial design and production process collaborative optimization system. The system includes a multi-source heterogeneous industrial data spatiotemporal synchronous fusion module, a full-element dynamic digital twin construction module, a design domain multi-agent cluster, a process domain multi-agent cluster, a design-process collaborative game negotiation engine, and an edge-cloud collaborative real-time scheduling and execution module that communicates bidirectionally with all the aforementioned modules. This invention breaks through the technical barriers of traditional serial R&D models. Through the design-process collaborative game negotiation engine, it constructs a non-cooperative game optimization model between the design and process domains, achieving forward adaptation of design parameters to process solutions and reverse correction of process constraints to design optimization, forming a bidirectional closed-loop iterative optimization mechanism.

The invention discloses a self-adaptive control method and system for the gravel crushing production process, and belongs to the technical field of industrial production intelligent control, and the method comprises the steps of production data collection, material blocking risk assessment, production control optimization and self-adaptive control execution. According to the invention, the sandstone crushing and material blocking risk assessment method based on spatial-temporal feature hierarchical fusion is adopted, adaptive identification is carried out on equipment load abnormity under the conditions of feeding abrupt change and working condition switching, advanced assessment of the short-term evolution trend of the material blocking risk is realized, and a prospective risk constraint basis is provided for subsequent production control optimization; a multi-agent collaborative game production control optimization method based on risk awareness is adopted, and multi-device control behaviors such as feeding, crushing and screening are coordinated and adjusted under the blockage risk constraint condition, so that control strategies of all devices are dynamically converged to a safe, stable and efficient operation state along with risk changes; therefore, production continuity is guaranteed, the probability of occurrence of material blockage is reduced, and the overall operation effect is improved.

The invention provides a multi-agent collaborative game decision-making method and system, and relates to the technical field of electricity markets, and the method comprises the steps: obtaining perception data needed by decision making; a cooperative alliance is constructed by thermal power-energy storage-VPP multiple subjects and participates in frequency modulation market bidding as a whole, and under the bidding constraint, with maximization of the expected total income of the alliance as a target, an optimal bidding strategy is optimized through deep reinforcement learning of intelligent agents to assist alliance bidding; and after the alliance participates in the frequency modulation service and wins the bid to obtain the income, an optimal income distribution scheme is determined in combination with a cooperative game algorithm to assist alliance income distribution among alliance members. According to the invention, dispersed flexible resources (namely thermal power-energy storage-VPP) are aggregated into a whole (namely collaborative alliance) to participate in market competition of frequency modulation auxiliary services, and a scale effect and a complementary effect of '1 + 1gt; 2' are realized.

The invention provides a learning-based and easy-to-generalize large-scale multi-agent collaborative game method. The method comprises the following steps: S1, constructing a training sample and a newly added sample; s2, designing a prediction network for each defense agent based on a multi-layer perceptron; the network input is a newly added sample, and the output is a predicted game result; s3, designing a strategy network for each defense agent based on a graph neural network and a multi-layer perceptron; the network input is a prediction network output result, the position and speed information, sensed by each defense agent, of a part of attacker agent and the position and speed information of other parts of defense agents, and the network output is probability distribution of defense tasks of each defense agent; s4, on the basis of probability distribution, according to greedy sampling, a defense object for defending the subject is confirmed; and S5, realizing pursuit capture of the defense subject on the confirmed defense object based on sliding mode control. According to the invention, a large-scale multi-agent system collaborative game target representing a defense party can be realized.

The invention relates to a parking lot pricing prediction system and method based on artificial intelligence and big data, and belongs to the technical field of big data analysis. The system comprises a three-layer state sensing and coding module, wherein a graph loop fusion network, an attention scoring model and a user clustering model are arranged in the three-layer state sensing and coding module; the graph loop fusion network is used for encoding the network game layer data into graph embedding vectors; the attention scoring model is used for carrying out dynamic weight distribution and fusion according to the importance of the influence of the current moment on the parking demand; the user clustering model is used for clustering individual heterogeneous layer data into a plurality of typical user groups, and extracting group features to obtain user heterogeneous state vectors. According to the method, a'three-layer perception-collaborative game 'framework is constructed, a regional Pareto improved pricing strategy is generated by utilizing a multi-agent dynamic game, meanwhile, the method has real-time response and decision interpretation capabilities, and social benefits and collaborative commercial and public benefits are embedded in a reward function of the method.

This invention discloses a BIM and AI collaborative architectural design optimization method, relating to the field of architectural design, comprising the following steps: Step 1, two-layer intelligent agent modeling; Step 2, environmental constraint modeling; Step 3, two-layer reward mechanism design; Step 4, two-stage collaborative game optimization; Step 5, automatic generation of BIM model; and Step 6, performance feedback closed-loop iteration. This invention's BIM and AI collaborative architectural design optimization method addresses the core shortcomings of traditional design processes, such as low optimization efficiency, difficulty in balancing multiple objectives, and easy omission of regulatory constraints, by constructing a complete intelligent closed-loop system of "modeling-optimization-generation-feedback".

This invention provides a multi-source collaborative interaction optimization method and system based on capacity domain, comprising: constructing a capacity demand domain for renewable energy considering the total output deviation of renewable energy over continuous periods; transforming the adjustment capabilities of each flexible resource within the system into corresponding constraints to construct a capacity adjustable domain reflecting the total adjustment capability of flexible resources over multiple periods; constructing a grid-source cluster interactive optimization potential game model and solving it using a distributed optimization algorithm and parallel optimization process; taking the sum of the capacity adjustable domains of all flexible resources in the system being no less than the total capacity demand domain of all renewable energy as a global constraint, adding it to the potential game model in the form of a penalty function to make the decision result feasible in all scenarios; improving the multi-source collaborative game process into a parallel process, enabling all game subjects to participate in optimization synchronously, and achieving collaborative interaction among various game subjects in the power grid through the combination of adjustable power domain and improved potential game, thus unifying individual optimization and global optimization.

The present application relates to a kind of microgrid zero carbon scheduling method based on cold-heat-carbon potential space-time coupling and multi-agent game, the dynamic characteristics of cold and heat load are deeply coupled with the space-time field of power grid carbon potential, the collaborative game of source (green electricity, non-green electricity), storage (battery), load (cold load, heat load) is realized by multi-agent reinforcement learning, the green electricity consumption and carbon emission reduction benefit are maximized, while the interests of each subject are guaranteed.

A collaborative learning device and processes for groups to enable each person of the group to learn difficult subject matter by answering written questions, awarding points, revealing answers, and discussing answers and concepts. The device and process promotes technology, science, engineering and math courses, sometimes known as STEM disciplines, through collaborative learning centered on the devices of the invention. Devices of the collaborative game are a game board with markers and written questions associated with STEM subject indicators on the game board. Participants take turns selecting questions, answering individually, revealing and collaboratively discussing answers, and awarding points for correct answers. Participants award points to the most collaborative participant, which encourages participation and input from the participants.

The present application relates to bulk storage yard scheduling technical field, disclose a kind of based on the route scheduling method and system of multiple circular stacker-reclaimer of collaborative game, the method in the bulk storage yard of multiple circular stacker-reclaimer collaborative operation, when high priority emergency task appears and is accompanied by multiple operation restrictions, traditional collaborative game scheduling method cannot effectively handle upstream process continuity constraints and material cross-contamination risk, leading to scheduling deadlock, affect emergency demand response speed problem.

This disclosure is related to improved security authentication frameworks for crowdsourced gaming systems that facilitate collaboration among multiple virtual participants over a network. In certain embodiments, an electronic gaming platform includes a geolocation authentication system that authenticates, verifies, and authorizes virtual user participants prior to accepting submissions in virtual multi-participant contests hosted by the electronic gaming platform. The geolocation authentication system can transition collaborative gaming applications operated by virtual user participants between a restricted functionality state that locks one or more functionalities pertaining to the virtual multi-participant contests hosted by the electronic platform and an unrestricted functionality state that unlocks one or more functionalities pertaining to the virtual multi-participant contests.

The present application relates to a kind of load aggregator operation adjustment method considering environmental rights increment game, comprising: based on the coupling relationship of environmental rights value and electric energy value in green electricity trading market, environmental rights increment game model is built;Combining the technical characteristics and operating constraints of flexible resource, flexible resource operation model is built, and the flexible resource operation model includes equipment operation model and energy coupling model;Based on environmental rights increment game model and the flexible resource operation model, non-cooperative game and collaborative game method are fused, multi-agent game equilibrium is solved, and load aggregator initial operation adjustment strategy is obtained;According to the dynamic change of green electricity trading market scene, environmental rights weight coefficient and electric energy value weight coefficient are dynamically adjusted, and load aggregator optimal operation adjustment strategy is obtained by optimization.Compared with prior art, the present application has realized the collaborative maximization of load aggregator environmental rights increment and economic benefits and other advantages.

A cooperative game decision-making method for multi-intersection traffic signal lamp association control comprises the following steps: collecting traffic flow input and waiting vehicles omega of each phase of each traffic intersection in a region to obtain collected data; judging whether each traffic intersection reaches a congestion threshold value th or not, triggering a game mechanism to construct a double-layer cooperative game model, and respectively forming an upper-layer game matrix and a lower-layer game matrix; and using the state updating function Stn and the revenue function Fn to obtain revenue values F under different strategies, performing upper-layer game, and solving an equilibrium point to obtain a phase sequence strategy xn of the traffic intersection signal lamps. And using a state updating function Stn and a revenue function # imgabs0 # to obtain revenue values f under different strategies, performing lower-layer game, and solving an equilibrium point to obtain a green light duration strategy yn of each phase. According to the invention, a double-layer cooperative game model is used, the calculation complexity of the signal lamp phase sequence and the green light duration is reduced, the signal lamp control strategy can be adjusted by quickly responding to the congestion condition, the intelligent coordination control among the intersections in the area is realized, and the condition that the congestion of individual road sections is difficult to relieve is avoided.

The invention discloses a storage and calculation training AI server energy efficiency optimization method based on a space-time deep cooperative game. The method comprises the following steps: S1, obtaining a multi-task load information set of an AI server; s2, initializing a space-time deep collaborative game model based on the multi-task load information set; s3, arranging an intelligent cache unit on a path for transmitting data from the storage unit to the calculation unit and returning the data from the calculation unit to the storage unit; s4, generating a dynamic caching strategy; s5, optimizing a two-way transmission path of the data; s6, the cache position and path of the high-frequency access data are redistributed, so that the cache strategy adapts to the optimal data flow direction under the current load condition; s7, optimizing the energy efficiency performance of the data from the storage unit to the calculation unit and a return path; and S8, further analyzing a resource competition state between different task loads in the AI server under the action of a feedback adjustment mechanism of the space-time deep cooperative game model. According to the method, the professional and technical thresholds required by traditional modeling are reduced, and the universality and convenience of three-dimensional modeling are improved.

The application discloses a kind of human-computer collaborative game simulation decision-making methods based on linear temporal logic, comprising: using linear temporal logic expression to describe the task between man and machine, and the expression is converted into automaton;Formalized comprehensive modeling is carried out to unmanned aerial vehicle, and initial global path planning of unmanned aerial vehicle system is carried out using planning automaton;Unmanned aerial vehicle runs according to the initial global path planned, when being disturbed by enemy, dynamic situation is handled using man-machine enemy game algorithm.This method describes the task using linear temporal logic, can describe more complex timing task, effective interaction between man and machine;Linear temporal logic constructs controller by the way of synthesis automaton, does not need human intervention, and is guaranteed by mathematics to construct and correct, so that the system is more reliable;Zero-sum game of man-machine-enemy, consider multi-role arena game model, and construct game tree to obtain dynamic optimal strategy under alternation simulation.