196 results about "Distributed decision" patented technology

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

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

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention relates to the technical field of energy scheduling, and discloses an intelligent optimization scheduling method of an informatization energy management system, which comprises the following steps of: data acquisition and fusion: acquiring real-time data of each node in an energy system through distributed Internet of Things equipment and a multi-mode sensor network, and fusing and processing the acquired data to obtain a data fusion result; a high-quality data set in a unified format is generated; and prediction modeling and demand response: based on user behavior analysis and a machine learning algorithm, establishing a demand response model, and constructing a multi-stage energy demand and supply prediction model including short-term, medium-term and long-term time dimensions. Through combination of a collaborative optimization algorithm and a distributed decision-making mechanism, a global optimal energy scheduling scheme is realized on the basis of local optimization of each distributed energy unit, high collaboration and optimal resource allocation among the energy units in a complex energy network are ensured, and the energy scheduling efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the whole system are improved.

The invention relates to the technical field of robot scheduling, in particular to a distributed robot collaborative scheduling system and method in a dynamic environment, and the system comprises an environment sensing layer which is used for collecting environment dynamic data in real time; the distributed decision-making layer comprises local task scheduling modules of a plurality of robots; the cooperative communication layer is used for realizing task state synchronization and conflict detection among the robots based on a low-delay communication protocol; the dynamic weight calculation module is used for generating a real-time optimization weight according to the task emergency degree, the robot energy consumption and the path risk factor; according to the invention, by using a completely distributed collaborative scheduling architecture, through a decentralized task distribution mechanism and a distributed consensus protocol, a single-point fault risk existing in a traditional centralized scheduling system is thoroughly eliminated, and even if a part of robot nodes have faults or communication is interrupted, the system can work normally. And the system can still run continuously through autonomous negotiation of the remaining nodes, so that the reliability of the system in a complex environment is remarkably improved.

The embodiment of the invention provides a multi-platform e-commerce order management method and system based on cloud data analysis, and the method comprises the steps: carrying out the analysis and standardization processing of order data through employing a dynamic format conversion channel, constructing a dynamic priority evaluation matrix in combination with real-time logistics load and historical aging data, and carrying out the analysis and standardization of the order data. A geographic position weight model is constructed based on equipment fingerprint identification and address similarity calculation, an order topology aggregation scheme is generated through a spatial incidence matrix, a distributed decision tree is constructed according to inventory fluctuation prediction and supplier response rate, a picking path is optimized, and finally logistics node data is integrated to construct a visual tracking interface. According to the technical scheme, abnormal order real-time early warning and compensation path planning are achieved through a self-repairing mechanism, logistics state holographic projection is generated, the flexibility and efficiency of the multi-platform e-commerce order management system are improved, and the problems of high complexity, poor expansibility, performance bottleneck and the like existing in a traditional system are effectively solved.

The invention discloses a multi-aircraft distributed decision-making swarm route avoidance cooperative control method and system, relates to the technical field of unmanned aerial vehicle formation control, and is used for multi-aircraft flight formation. Each unmanned aerial vehicle is provided with an intelligent decision-making module with environment sensing, communication and path planning capabilities; the method comprises the steps that each unmanned aerial vehicle generates a preliminary avoidance path by adopting an improved genetic algorithm based on acquired local environment information and received state information of neighborhood unmanned aerial vehicles in combination with a preset avoidance rule and a preset multi-objective optimization function; the optimization objectives of the preset multi-objective optimization function comprise an obstacle avoidance safety distance, an energy consumption coefficient, a formation retention degree and task timeliness; space-time conflict detection is executed based on the multiple preliminary avoidance paths, the preliminary avoidance paths are optimized based on conflict detection results, a final conflict-free optimal path is obtained and broadcasted to other unmanned aerial vehicles in the formation, and distributed collaborative decision making is achieved; according to the invention, the control efficiency of multi-unmanned aerial vehicle formation control is improved.

The invention relates to an interference power distribution method based on multi-agent deep reinforcement learning, and belongs to the technical field of wireless mobile communication. The method comprises the following steps: S1, in a many-to-many communication confrontation scene, establishing a cooperative interference model of multiple interference devices confronting multiple unmanned aerial vehicle targets; s2, constructing a distributed local observable Markov decision process (De-POMDP), and modeling an interference power distribution problem into a multi-agent complete cooperation model; s3, designing a'decentralized training, distributed decision '(DTDE) multi-agent framework, and reducing the collaborative decision dimension of interference power resources; s4, an extractor is designed, intelligent agent input state information is preprocessed, and the training efficiency is improved; and S5, realizing efficient exploration of a power distribution strategy based on a discrete flexible actor-commentator (DSAC) algorithm. According to the method, the interference power distribution of the multi-interference equipment during confrontation of multiple unmanned aerial vehicle targets can be realized based on multi-agent deep reinforcement learning, the interference resource utilization rate is effectively improved, and the method has good expansibility in a complex communication confrontation scene.

The invention belongs to the technical field of mobile communication, and particularly relates to a task unloading and resource allocation method for edge computing. According to the method, a three-layer network structure is established, a distributed decision framework is constructed through reinforcement learning, the task emergency degree is dynamically evaluated, and computing resources are distributed in a differentiated mode; and task unloading and resource allocation are optimized in combination with an edge-cloud collaborative architecture, so that calculation load balancing is realized. According to the method, aiming at a cloud edge-end collaborative edge calculation model, the total cost of a system is defined as a joint optimization problem of task unloading time delay and energy consumption, the problem model is converted into a Markov decision process, and multi-agent and multi-user oriented deep reinforcement learning algorithm agent near-end strategy optimization (MAPPO) is designed; and obtaining an optimal unloading decision through mutual learning among multiple agents. According to the method, the total cost of the system can be effectively reduced, the rationality of edge computing task unloading and resource allocation decision is realized, and meanwhile, the use experience of a user can be improved.

The invention provides a production line process scheduling optimization method and system based on an agent cluster. The method comprises the steps that the agent cluster comprising a plurality of distributed decision agent nodes is configured according to a production line process scheduling instruction; based on the production line architecture feature graph, a communication protocol between agent nodes is generated through node association relationship analysis, and a cooperation rule set is constructed in combination with a process priority rule and a resource constraint condition; the intelligent agent cluster is instructed to perform scheduling according to a communication protocol and a cooperation rule set, process tasks are dynamically allocated and adjusted through real-time state interaction among nodes, scheduling behavior logs are obtained, a reinforcement learning training sample set is constructed in combination with expected labels, decision parameters of intelligent agent nodes are optimized by using a multi-intelligent-agent reinforcement learning algorithm, and a reinforcement learning training result is obtained. According to the method, the dynamic response capability and the cooperation efficiency of automatic production line scheduling can be improved, and the method is suitable for efficient process scheduling in a complex production environment.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent dynamic pilot frequency networking system and method for an image transmission module MESH ad hoc network. The method is based on spectrum fingerprint features, combines a real-time interference topological graph constructed by interaction of neighborhood nodes, identifies a conflict frequency set needing to be evaded through multi-dimensional weighted evaluation, and dynamically generates a pilot frequency strategy table containing main and standby frequencies and a switching threshold value by using a frequency hopping decision engine according to the conflict frequency set and a preset legal frequency pool. Synchronizing the pilot frequency strategy table to an associated relay node, generating a link stability evaluation matrix and feeding back the link stability evaluation matrix to a frequency hopping decision engine; and correcting a frequency switching threshold value and an alternative frequency weight in the pilot frequency strategy table based on the link stability evaluation matrix. According to the invention, the spectrum utilization efficiency of the unmanned aerial vehicle image transmission system can be improved, the stability and reliability of a multi-hop link are ensured, the networking time delay is reduced through a distributed decision-making mechanism, and an effective solution is provided for high-quality wireless transmission in a dynamic topology environment.

The invention discloses a distributed energy system source load coordinated optimization method based on a quasi-potential game method, and the method comprises the steps: constructing a distributed energy system model, inputting system parameters, and predicting renewable energy power generation and initial load demands. In a source side optimization stage, a leader layer potential function of a quasi-potential game is established by taking minimization of source side cost # imgabs0 # as a target, and an initial power generation plan is generated by comprehensively considering economical efficiency and carbon emission constraints; and then, based on a scheduling result, calculating a carbon potential epsilon t of each node and a dynamic carbon emission factor # imgabs1 # of each stage, optimizing a load side response based on an LCDR scheme, constructing a local potential function of a follower layer, reflecting a relationship between a user profit maximization target and carbon emission, and adjusting user behaviors through a distributed decision. And the updated load demand is fed back to the source side, and the source side optimizes the output plan of each unit based on the updated load, so that an iterative process of source side potential function optimization-load side equilibrium response is formed, and an optimal scheduling strategy and scheduling result of the energy supply side are obtained. According to the framework, the global consistency requirement of a traditional potential game is relaxed, independent optimization of source-load two sides under the guidance of respective potential functions is allowed, and a Nash equilibrium state is finally achieved only by ensuring monotonous convergence of total potential energy of a system in an iteration process. According to the method, the convergence advantage of the potential game is reserved, the method is also adapted to the characteristics of a source-load heterogeneous decision subject, efficient consumption of renewable energy and collaborative optimization of carbon emission are realized through bidirectional transmission of the carbon potential signal, and the overall efficiency of the system is remarkably improved.

The invention provides a vehicle-road cooperative control method and system for a double-layer ecological city in a mixed traffic flow environment, and the method comprises the steps: taking a signal lamp at each intersection as an intelligent agent, and obtaining the real-time driving information of a vehicle through infrastructure communication; the intelligent agent calculates the vehicle number and the driving speed of each motorcade according to the headstock distance between the vehicles; the upper perceptron predicts the time required by the motorcade to arrive at an intersection stop line by stacking a full-connection layer and a long-short-term memory network; the predicted time information is input into a strategy network and a value network of the intelligent agent to generate actions, and the strategy network is updated through delayed feedback rewards; the optimized suggested driving speed is sent to the head vehicle of the motorcade so as to optimize the driving track of the motorcade, relieve road network congestion and reduce pollutant emission; according to the invention, cooperative control is carried out through distributed decision making and cooperative learning by the intelligent agent, and the vehicle-road cooperative control capability and the traffic passing efficiency of the urban road network are improved.

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

The invention relates to the field of multi-source data management, in particular to an intelligent security collaborative management system based on multi-source perception and a large language model. Comprising a multi-source data acquisition module which is accessed to various intelligent monitoring devices and is output and converted into a unified standard tuple format through a mapping function; the information fusion module is used for screening a candidate alarm set according to the space-time tolerance, and generating composite alarm information by adopting confidence ranking and semantic embedding weighted averaging; the RAG knowledge base module is used for generating a composite event description through a large language model and vectorizing the composite event description as a retrieval index; the intelligent retrieval module is used for acquiring related historical events by adopting a mixed retrieval strategy and constructing structured cue words; the LLM decision module is used for outputting root cause analysis and classification disposal suggestions based on the composite alarm and the priori knowledge; the double-path response module is used for distributing decision suggestions to management personnel and an agent system to realize collaborative execution; and the feedback optimization module is used for collecting disposal data and adjusting the weight of the knowledge base and the decision template to realize continuous optimization.

The invention provides a circuit breaker control method and system based on wireless communication, and the method comprises the steps: obtaining a network environment through employing a safety authentication wireless network with a self-adaptive spectrum sensing capability, and carrying out the control of a circuit breaker through employing a zero-knowledge proof algorithm based on an advanced encryption standard according to an operation request sent by a user through a mobile terminal, the method comprises the following steps: generating an encryption control command, obtaining an operation instruction based on a Hash chain security protocol and a digital signature technology, executing a corresponding preset action according to the operation instruction to obtain execution state information, generating a log entry according to an advanced encryption standard feedback mechanism, and storing the log entry by utilizing a block chain technology. The operation consistency between systems is coordinated through a consensus algorithm under a distributed decision framework, and non-tampering and distributed maintenance and control records are generated; according to the technical scheme provided by the invention, the communication flexibility, reliability and security are enhanced, the risk of unauthorized access is reduced, and the accuracy and reliability of the operation instruction are ensured.

The invention discloses a distributed collaborative decision-making system based on multi-modal data driving and an implementation method thereof, and relates to the technical field of group intelligence and distributed decision-making, and the system comprises a user end interaction module which provides a multi-modal interaction and decision-making scheme visual interface; the distributed node management module comprises a main node and an edge node, and the main node manages node registration, state monitoring and task distribution; the information fusion and preprocessing module is used for processing multi-source heterogeneous data; the decision analysis module is used for carrying out clustering analysis on the opinions and generating candidate schemes in combination with domain knowledge; the domain knowledge graph module is used for constructing a domain entity relationship network; the consensus mechanism and credit evaluation module determines multiple rounds of interaction rules, calculates a user credit value and influences an opinion weight; and the decision result output and feedback module is used for collecting user feedback for system optimization. According to the method, the stability, the response speed and the load balancing capacity are improved, the multi-source information processing and opinion aggregation quality is optimized, and efficient and reliable support is provided for distributed collaborative decision making.

The invention provides an active power distribution network multi-time scale rolling optimization method driven by multi-agent graph reinforcement learning. The method comprises the following steps: dividing slow time scale equipment and fast time scale equipment; the slow time scale is optimized and modeled into a multi-agent Markov game, and a global state space and a combined action space corresponding to switch actions, reactive compensation equipment gears and voltage regulation equipment gears are defined; switching action candidate vectors are generated based on a basic loop of the power distribution network, and action space dimensions are compressed through a scheme of excluding conflicting actions of the same common branch; fitting an action value function of each agent by adopting a graph neural network, generating a dimensionality-reduced discrete action strategy according to the global state space, and carrying out collaborative optimization through a distributed decision and a global reward mechanism; and on the basis of the slow time scale optimization result, the fast time scale optimization problem is converted into a mixed integer second-order cone programming model, and an instruction is solved in a rolling manner.

The invention discloses a source network load storage real-time scheduling method for MADRL-GAN collaborative optimization, and the method comprises the steps: firstly constructing a carbon pollution collaborative optimization model, and converting the model into a solvable convex problem through function linearization and mixed integer conversion; further mapping the model into a multi-agent reinforcement learning model, dividing agents according to an electrical coupling degree, and designing a state and action space containing a dual-Critic reward mechanism; thirdly, learning system uncertainty distribution by using a generative adversarial network, generating diversified scenes to train a reinforcement learning model, and obtaining a preliminary strategy; and finally, correcting the strategy through the generative adversarial network, generating a final scheduling action through a strategy mixing mechanism, and realizing distributed real-time optimal scheduling. According to the method, the problems of multi-target collaboration, high-uncertainty scheduling and real-time distributed decision making in a high-proportion renewable energy system are effectively solved.

The invention discloses a frequency control method and system based on multi-agent deep reinforcement learning, and relates to the technical field of power system frequency control, and the method comprises the steps: constructing a power system frequency response model; a training framework of centralized training and distributed decision making is introduced into a depth deterministic policy gradient (MADDPG) algorithm; a power system environment is modeled as a Markov decision process based on a depth deterministic policy gradient (MADDPG) algorithm. The invention provides a multi-region power system frequency cooperative control method based on an MADDPG algorithm. The defects of a traditional control method in a multi-region complex environment are overcome. A centralized training and decentralized execution framework is adopted, so that an intelligent agent can realize cooperative work in a multi-region power system, and the flexibility and adaptability of a control strategy are improved.

The invention provides an intelligent management system and method based on multi-agent cooperation. The intelligent management system comprises a data acquisition module used for acquiring production field data in real time; the data processing module is used for data cleaning; the large model module is used for performing semantic analysis on a task input by a user based on a large model; the agent module is used for performing task scheduling based on a TSQlearning scheduling algorithm; and the RAG knowledge base module is used for constructing an RAG knowledge base and providing knowledge retrieval based on hierarchical indexing and a mixed retrieval mechanism. According to the method, user requirements are analyzed through the large model module, the task comprehensive scheduling capability is improved through the distributed decision of the agent module and the TSQlearning algorithm, the resource utilization rate is improved, the information retrieval accuracy is improved from 70% to 92% through the hierarchical indexing and mixed retrieval mechanism of the RAG knowledge base, and the management efficiency and the intelligent level of the smart factory are improved.

The invention relates to the technical field of unmanned aerial vehicle cluster energy management, and discloses an unmanned aerial vehicle ad hoc network-based autonomous inspection and charging nest system, which comprises the following steps that: each unmanned aerial vehicle in a cluster calculates and broadcasts a time dynamic scalar capable of being freely controlled in real time, and broadcasts a task and an energy opportunity packet when the scalar of a certain unmanned aerial vehicle is lower than a threshold value; according to the invention, through a distributed decision-making mechanism driven by an energy potential difference, cluster energy is enabled to dynamically flow according to a minimum resistance path like liquid, internal energy consumption caused by a fixed return path in traditional inspection is avoided, and the reliability of the unmanned aerial vehicle is improved. And meanwhile, by combining dual-channel communication arbitration and a mechanical locking unit, reliable energy cooperative transmission can still be maintained in a complex environment.

The invention discloses an artificial intelligence content generation task multi-server collaborative optimization method based on deep reinforcement learning, and relates to the crossing field of edge computing and artificial intelligence. According to the method, a user equipment-base station-edge server-core network four-layer architecture is constructed, distributed decision is realized through a deep Q network model, and the method comprises the following steps: defining a task and time delay model; a deep reinforcement learning framework is designed, a decision model is constructed through a state space, an action space and a reward function, and training stability is improved by adopting experience playback and a target network slow update mechanism; and providing an adaptive multi-server selection and load distribution strategy, and solving an optimal distribution proportion based on a time delay equality principle. According to the method, the average unloading time delay of the AIGC tasks is remarkably reduced, the task failure rate is reduced, the dynamic environment adaptability and the extreme scene robustness are improved, and the method is suitable for scenes with strict requirements for time delay and reliability.

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.

The invention provides a distributed task reconstruction method based on historical information backtracking, and relates to the technical field of distributed decision making. The method comprises the following steps: establishing a distributed multi-unmanned aerial vehicle cooperative task allocation model according to constraint conditions of an information interaction architecture; generating a preliminary decision-making scheme, wherein the preliminary decision-making scheme comprises two links of task construction and task negotiation; in the task negotiation link, designing an information backtracking mechanism for the failed unmanned aerial vehicle; constructing a task priority chain type storage structure according to an information backtracking result; and carrying out quantitative analysis on the distribution model containing the random node failure, determining the failure position of the unmanned aerial vehicle, and executing task reconstruction. The invention provides a method for realizing dynamic redistribution after task distribution aiming at the problem of complex optimization of task distribution of multiple unmanned aerial vehicles. And distribution and reconstruction of tasks are jointly determined through communication and cooperation between the unmanned aerial vehicles, so that the flexibility and robustness are further improved.

The invention belongs to the technical field of collaborative unmanned aerial vehicle cluster reinforcement learning, and relates to an unmanned aerial vehicle cluster dynamic strategy optimization method based on a super network and a diffusion model. A dynamic weight generator (super network) is embedded in a diffusion strategy model (MADiff) framework, a differentiated Actor network weight is generated for each agent, and strategy personalized customization is realized, so that the problems of cooperation failure and training oscillation caused by strategy homogenization in a traditional method are solved. The dynamic strategy optimization normal form is especially suitable for continuous operation of a large-scale unmanned aerial vehicle cluster in a non-stationary environment, and a reinforcement learning framework with environment perception-strategy prediction-dynamic adjustment closed loop is constructed. The performance of the system in the aspects of dynamic task collaboration, non-stationary strategy optimization, distributed decision consistency and the like is remarkably improved, and key technical support is provided for application of an intelligent unmanned cluster in a complex scene.

The invention relates to a heterogeneous satellite cluster autonomous measurement and control resource allocation method, system and device. The problem of multi-target balance of task integrity rate, resource utilization rate and cost-efficiency ratio is effectively solved through a two-stage decomposition strategy. In the first stage, a genetic algorithm is adopted to complete satellite-task-time window three-dimensional matching, and optimal distribution of limited visible windows is achieved. In the second stage, a distributed decision-making mechanism is implemented based on an improved Improved-MADDPG framework, optimal dynamic configuration of measurement and control resources is achieved through global information sharing and local strategy iteration, and key calculation indexes such as an average reward value are remarkably improved.

The invention discloses an unmanned aerial vehicle autonomous cluster cooperative control system based on swarm intelligence, and belongs to the technical field of unmanned aerial vehicle control. The control system comprises an unmanned aerial vehicle cluster which is composed of a plurality of unmanned aerial vehicles with autonomous flight capability; each unmanned aerial vehicle is provided with a sensing unit, a navigation and flight control unit, a communication unit and a cluster cooperative control unit; a group intelligent decision module based on an ant algorithm is arranged in the cluster cooperative control unit and comprises a virtual pheromone generation and updating sub-module, a local decision sub-module and a task planning and cooperative sub-module; the system provided by the invention takes swarm intelligence as a core, realizes distributed decision-making through a virtual pheromone field, avoids communication and computing power bottlenecks of centralized control, and also can enable a cluster to autonomously emerge a global optimization behavior. Through dynamic role allocation, task decomposition and priority scheduling mechanisms, resources can be adaptively allocated along with task progress; and the collaborative behavior pheromones strengthen the efficient path, so that the execution efficiency of the cluster in the complex task is remarkably improved.

The invention relates to the technical field of fluid machinery control, and discloses an intelligent vibration reduction control method and system for a centrifugal pump, and the method comprises the following steps: obtaining vibration signal data to obtain frequency spectrum characteristics; when the spectrum feature exceeds a threshold value, abrupt change parameters are extracted, pipeline system coupling data are fused, and a multi-pump cooperation state vector is generated; generating a distributed decision instruction sequence according to the state vector classification; fusing the feedback information according to the instruction sequence to obtain an initial adaptive strategy parameter; the operation parameters are adjusted according to the initial parameters, the efficiency is calculated, loop iteration optimization is carried out until the efficiency reaches the standard, and final self-adaptive strategy parameters are obtained; and the stability is verified after the final parameters are deployed, and loop optimization is returned if the verification fails. Through multi-pump cooperation and double closed-loop optimization, the stability, efficiency and reliability of cooperative operation of the pump set can be improved.

The invention discloses a multi-robot control system, and relates to the field of robot path planning. According to the system, in a robot path driving task, candidate path nodes are selected by randomly switching target guidance and a global exploration mode, feasible nodes and path segments are determined by combining historical path nodes, and the path nodes are expanded after collision detection of a static obstacle and a dynamic robot. The system also dynamically adjusts a driving step length based on a load rate, sets a safety margin including load compensation, and realizes multi-machine cooperation through local path topological optimization and distributed decision. According to the method, the multi-robot path conflict and collision risk is effectively reduced, the heavy-load working condition motion stability and the system cooperation efficiency are improved, and the method is suitable for a multi-robot cooperation operation scene.