510 results about "Swarm control" patented technology

The invention discloses an unmanned aerial vehicle cluster intelligent cooperative control method, and the method comprises the steps: optimizing a network topology through heterogeneous unmanned aerial vehicle cluster dynamic networking and a dynamic clustering algorithm, and guaranteeing the reliability of a communication link; a layered hybrid decision architecture is designed to improve the task allocation rationality and the dynamic adaptability of the unmanned aerial vehicle cluster; a distributed control strategy network is trained by using a multi-agent near-end strategy optimization MA-PPO algorithm, and unmanned aerial vehicle cluster behavior collaboration is ensured in combination with space-time consistency constraint; an asynchronous incremental consensus protocol AICP is provided, the data transmission amount is reduced, and topology reconstruction is accelerated; real-time three-dimensional environment reconstruction and dynamic threat prediction are realized based on a neural radiation field NeRF technology; a lightweight anti-interference communication middleware is developed, and the instruction transmission stability is enhanced by adopting a space-time coding diversity technology. The method solves the problems of high delay of centralized control of the unmanned aerial vehicle cluster, poor convergence of a distributed algorithm and the like, and is suitable for high-dynamic task scenes such as urban street battle and complex terrain search.

The invention provides an unmanned aerial vehicle collaborative wind power plant inspection route planning method and system, and relates to the technical field of wind power plant inspection, and the method comprises the steps: constructing a wind power plant three-dimensional environment model comprising a plurality of features according to the regional topographic surveying and mapping data of a wind power plant, and then building an unmanned aerial vehicle cluster collaborative architecture; determining unmanned aerial vehicle performance, task matching and communication specifications, generating an initial collaborative inspection path set based on the wind power plant three-dimensional environment model and the unmanned aerial vehicle cluster collaborative architecture, including independent inspection paths, intersection coordinates and a task allocation list, and performing collaborative optimization processing on the initial collaborative inspection path set to obtain an optimization scheme; and finally, the optimization scheme is converted into an unmanned aerial vehicle cluster control instruction set containing flight parameters, task processes and coordination rules, and efficient and safe routing inspection of the wind power plant is realized.

The invention discloses an unmanned aerial vehicle cluster interaction method and system based on an ad hoc network, and belongs to the technical field of unmanned aerial vehicle communication and cluster control. The method comprises the following steps: acquiring positioning information, energy parameters and task types of an unmanned aerial vehicle group to generate a clustering node set; predicting a cluster moving direction based on the motion acceleration data of the clustering node set, and generating a dynamic path planning instruction; allocating a main communication channel and a standby channel according to the dynamic path planning instruction, and generating a channel allocation result; monitoring a signal quality parameter of the main communication channel according to a channel allocation result, and triggering a link switching instruction when interference is detected; and constructing an interference thermodynamic diagram based on the topological relation of the clustering node set, and generating a power adjustment instruction. According to the invention, through dynamic clustering, mobile prediction, adaptive channel management, interference sensing switching and topology power control, the communication stability, reliability and resource efficiency of the unmanned aerial vehicle cluster in a complex environment are improved.

The invention relates to the technical field of unmanned aerial vehicle cluster control, and particularly discloses an intelligent cooperative flight path planning method and system for an unmanned aerial vehicle cluster system, and the method comprises the steps: firstly constructing a dynamic environment perception model, collecting data through all unmanned aerial vehicle sensors, and carrying out the preprocessing, attention feature extraction and federal learning fusion, and generating a global environment situation map; planning and screening a candidate track set by adopting a particle swarm-genetic hybrid optimization algorithm for adaptive weight adjustment on the basis of the image; a global optimal track consensus is achieved through an improved consensus algorithm and conflict resolution through a distributed collaborative negotiation mechanism and no human-computer interaction evaluation indexes; and finally, monitoring the environment in real time during execution, triggering dynamic re-planning when the environment is abnormal, and ensuring track adaptation through multi-level threshold and incremental planning. According to the method, the unmanned aerial vehicle cluster can quickly respond to the environment change and adjust the flight path, so that the task execution efficiency and success rate of the unmanned aerial vehicle cluster in the complex dynamic environment are improved.

The invention relates to the field of unmanned aerial vehicle cluster control, in particular to a distributed unmanned aerial vehicle protective net dynamic generation optimization method and system. The method comprises the following steps: acquiring real-time environment sensing parameters and unmanned aerial vehicle real-time monitoring images; performing dynamic environment situation evolution on the unmanned aerial vehicle based on the real-time environment perception parameters, performing distributed cluster simulation, and constructing a distributed unmanned aerial vehicle cluster model; performing multi-target visual detection on the real-time monitoring image of the unmanned aerial vehicle, predicting the moving trend of each target one by one, and constructing a dynamic prediction path of each target; identifying an unmanned aerial vehicle communication module, and obtaining real-time communication network state parameters of the unmanned aerial vehicle cluster; and carrying out dynamic communication parameter optimization on the real-time communication network state parameters, carrying out instant communication topology adjustment, and constructing an instant communication optimization strategy. Through dynamic cooperative control of the unmanned aerial vehicle cluster, the anti-interference and fault-tolerant capabilities of the unmanned aerial vehicle cluster are enhanced, and the protection efficiency of the unmanned aerial vehicle protection net is improved.

The invention discloses an unmanned aerial vehicle cluster cooperative control system and method, and relates to the technical field of unmanned aerial vehicle cluster control, and the system comprises a multi-source sensing unit, an integrated laser radar, an RGB-D camera and a millimeter wave radar array, and is used for generating a centimeter-level precision three-dimensional map and tracking a dynamic obstacle in real time; the collaborative decision-making unit comprises an artificial intelligence prediction module which is used for predicting a collision point based on the movement state of the obstacle; the safety space construction module is used for generating a dynamic safety sphere according to the formation envelope size and the obstacle volume; the artificial intelligence correction module is used for constructing a safety section and generating an optimization path; and the dynamic communication unit is used for carrying out cluster data interaction by adopting hybrid networking of 5G and LoRa adaptive switching. The unmanned aerial vehicle cluster can be used as a whole to avoid obstacles, the original forms of the unmanned aerial vehicles are synchronously kept, and it is guaranteed that multiple unmanned aerial vehicles in the cluster do not collide.

The invention discloses a target intelligent cooperative identification method based on an unmanned aerial vehicle cluster, and belongs to the field of unmanned aerial vehicle cluster control and computer vision. According to the method, cluster networking and model initialization are realized through a dynamic heterogeneous federated learning architecture; a space-time attention mechanism is adopted to optimize task allocation, and a deformable network is utilized to extract multi-view target features; a cascade characteristic distillation fusion strategy is provided, and modal compression and cross-modal gating fusion are carried out on multi-source data such as multispectral data and laser radar data; an anti-interference elastic communication mechanism based on meta-learning is designed, and the system robustness is enhanced by combining space-time confrontation detection and a dynamic spectrum sensing technology; an unsupervised federal incremental learning system is established, and online evolution of the model is realized through momentum weighted aggregation. According to the method, the target identification accuracy is improved by 35% in a complex environment, the time delay is reduced to 200 ms, and high-precision real-time identification support is provided for military reconnaissance, disaster rescue and other scenes.

The invention discloses a hierarchical multi-agent robot cluster control method and system, and aims to solve the problems of partial observability and environment non-stability of a multi-agent system in a complex environment. According to the method, a three-layer layered reinforcement learning architecture is constructed, a high-layer strategy is responsible for global task decomposition and role allocation, a middle-layer strategy converts tactical intention into a cooperative behavior mode, and a low-layer strategy executes accurate motion control; a graph neural network is adopted for cluster modeling, global graph representation and local neighborhood features are extracted in parallel through graph convolution and an attention mechanism, and hierarchical decision making is supported; a centralized graph enhancement evaluation network is designed to be combined with an MAPPO algorithm for collaborative optimization, and dynamic adversarial training is introduced to improve strategy robustness. According to the method, effective decoupling of global planning and local control is realized, and the cluster cooperation efficiency, the strategy interpretability and the adaptive capacity in a dynamic environment are improved.

The invention provides a server power consumption optimization method and system, electronic equipment and a storage medium. A data acquisition module acquires running state data of each server in a server cluster; the power consumption prediction module inputs the operation state data into a power consumption prediction model, predicts power consumption change data of each server in a preset duration, and generates a power consumption prediction result of each server; the dynamic scheduling engine establishes a cluster energy consumption cost model based on a power consumption prediction result, performs multi-objective optimization solution in combination with task priority constraints, and generates a resource scheduling strategy of each server; and the cluster controller performs resource scheduling on each server in the server cluster according to the resource scheduling strategy so as to adjust and optimize the power consumption of each server. Compared with the prior art, the cluster energy consumption cost model is established through the power consumption prediction result, and multi-objective optimization solution is performed in combination with task priority constraints; the global energy consumption can be calculated; and cluster global energy efficiency optimization can be realized.

The invention discloses a social intelligent agent device and a cluster control method and device thereof, and the method comprises the steps: carrying out the statistics of different groups of Internet platform users, and constructing corresponding user feature portraits; according to the internet user feature portraits, user portraits are formed for the intelligent agents deployed to the cluster, and the intelligent agents with preferences are obtained; creating an information consumption activity task for the agent based on a BS architecture; information consumption data resulting from agent activity is collected. According to the invention, through agent portrait formation and task scheduling optimization, the simulation degree and cluster control efficiency of the social agent are improved. The behaviors of the intelligent agent are more diversified and natural, and user interaction can be simulated more truly. The cluster control mechanism is rapid and flexible in response, data synchronization and task distribution are more efficient, the problems that in the prior art, intelligent agent behaviors are single, cluster control is slow, data processing is complex, and real-time feedback cannot be achieved are solved, and the information spreading efficiency and the activity degree of the social platform are improved.

The invention discloses an unmanned aerial vehicle cluster control method and system based on artificial intelligence, and relates to the technical field of unmanned aerial vehicle cluster control, and the method comprises the steps: obtaining global cooperative situation data through a sensor, and carrying out the preprocessing; calculating control output of each unmanned aerial vehicle by using a fuzzy C-means clustering algorithm and an analytic hierarchy process, generating a preliminary flight path, and dynamically adjusting the flight path according to real-time environment feedback through a reinforcement learning algorithm; the unmanned aerial vehicle cluster shares the adjusted flight path and global environment information through a 5G network, analyzes the speed, course angle, height, acceleration gradient and height change rate of each unmanned aerial vehicle by using a distributed control algorithm, and generates cooperative control parameters; according to the invention, by combining the fuzzy C-means clustering algorithm, the analytic hierarchy process, the reinforcement learning algorithm, the distributed control algorithm and the genetic algorithm, efficient cooperative control of the unmanned aerial vehicle cluster in a complex environment is realized.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to an unmanned aerial vehicle cluster control method and system, and the method comprises the steps: predicting the remaining flight time of an unmanned aerial vehicle cluster for safely arriving at a destination in a current carrying state; calculating a cluster energy consumption deviation degree according to the current energy consumption parameter, predicting the remaining endurance time of each unmanned aerial vehicle, and determining the minimum predicted endurance time; dynamically generating a safe flight time threshold according to the minimum predicted endurance time and a preset safety margin; if the remaining flight time is smaller than the safe flight time threshold value, a cooperative control instruction is generated according to the offset, the offset speed and the cluster energy consumption deviation degree; and distributing the cooperative control instruction to the corresponding unmanned aerial vehicle in the unmanned aerial vehicle cluster. And the flight endurance crisis caused by non-uniform energy consumption in the irregular article carrying process is effectively solved.

The invention provides a fire extinguishing unmanned aerial vehicle cluster control method for new energy station fire emergency, and the method comprises the steps: dynamically sensing the fire information of a new energy station through a three-dimensional fire scene model, and the fire information comprises the size of a fire, the position of an ignition point and meteorological information; based on the fire behavior size, the ignition point position and the meteorological information, multi-target optimization task distribution is realized for the unmanned aerial vehicle cluster; based on the distributed multi-objective optimization task, planning a three-dimensional path for the unmanned aerial vehicle cluster through a composite artificial potential field; a fire extinguishing instruction is sent to an unmanned aerial vehicle cluster by using a dual-mode redundancy transmission and distributed instruction storage mechanism, the unmanned aerial vehicle cluster is controlled to carry out fire extinguishing of different target optimization tasks according to a three-dimensional path, and fire extinguishing is carried out by using the three-dimensional path produced in a mode of a composite artificial potential field and a three-dimensional fire scene model. The unmanned aerial vehicle cluster fire extinguishing method closely fits the environment information of a new energy station, and compared with a pure unified control mode, the fire extinguishing efficiency of the unmanned aerial vehicle cluster is effectively improved in a fire extinguishing mode based on the distributed tasks.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to an unmanned aerial vehicle cluster distributed optimal formation control method considering multiplicative noise and a computer readable medium, and the method comprises the following steps: constructing a communication topological graph of an unmanned aerial vehicle cluster, each edge represents a communication relationship between two adjacent unmanned aerial vehicles; constructing a Laplacian matrix of the communication topological graph; establishing an unmanned aerial vehicle kinetic equation considering the multiplicative noise influence; defining an objective function needing to be optimized and constraint conditions; constructing a Hamilton function of the target function; and designing a distributed optimal control strategy by applying a random optimal control theory, setting operation time, system parameters and initial state information of the unmanned aerial vehicle cluster, and obtaining a formation operation track and a final formation state of the unmanned aerial vehicle cluster under optimal control. According to the invention, the global optimality and robustness of the unmanned aerial vehicle cluster control algorithm are ensured.

The invention discloses a multi-target bridge unmanned aerial vehicle inspection path optimization method and system, and relates to the technical field of crossing of bridge operation and maintenance and unmanned aerial vehicle cluster control. The method comprises the following steps: constructing a bridge three-dimensional point cloud model, extracting key inspection points, and establishing a multi-objective optimization function including path length, time consumption, coverage rate, risk coefficient, cluster safety and formation retention; a multi-target reward mechanism is fused through a double-delay depth deterministic strategy gradient network, and a deep learning model is constructed and used for generating an unmanned aerial vehicle cluster obstacle avoidance strategy; training a deep learning model; and based on real-time data, performing multi-target bridge unmanned aerial vehicle routing inspection path optimization by using the trained deep learning model, and adjusting a weight coefficient of a multi-target optimization function through an attention network dynamic weight distribution technology in the optimization process. According to the invention, multi-objective optimization of routing inspection safety, efficiency, quality and cluster cooperation is realized, and the method is suitable for automatic routing inspection of various complex steel structure bridges.

The invention discloses a hybrid attack defense control method and system for a heterogeneous cluster in an urban confrontation environment, terminal equipment and a medium, and relates to the technical field of unmanned platform cluster control, and the method comprises the steps: constructing an ideal system model of an unmanned platform cluster comprising a leader and a plurality of followers, constructing an information physical hybrid attack model based on the model; using the attack model to simulate an attack behavior of an attacker on an ideal system to obtain an attacked system model; based on an attacked system model, constructing a distributed elastic security estimator with a compensation mechanism, compensating attack influence for each follower and estimating an expected position; and based on the expected position of the follower, constructing a distributed elastic safety controller with a compensation mechanism, and controlling the follower. By designing the estimator and the controller, attack interference is counteracted, control input is generated, and safe collaboration of the heterogeneous nonlinear unmanned cluster under the cyber-physical hybrid attack is guaranteed.

The invention discloses an unmanned autonomous cluster flight control method based on a bionic alert mechanism, which is applied to the field of unmanned autonomous cluster control, and is characterized in that a W-MSR algorithm and a dynamic weighted bionic alert mechanism are fused, the W-MSR filters and eliminates extreme values of neighbor individuals, the dynamic weighted bionic alert mechanism strengthens input from informed individuals, and the dynamic weighted bionic alert mechanism is used for improving the robustness of the unmanned autonomous cluster flight control. And meanwhile, the influence of suspicious individuals is inhibited, so that the local information aggregation degree is effectively improved, and group splitting can be prevented. Different from a scheme depending on explicit attacker recognition, the method can improve the motion accuracy and connectivity of the unmanned autonomous cluster in a confrontation environment by probabilistically suppressing the influence of suspicious individuals and amplifying a consistent signal, provides a new idea for improving the security of a swarm intelligence system, and has a wide application prospect. Damage of malicious individuals to the cluster is effectively defended, and robustness and recovery capability of the cluster in a complex environment are improved.

The invention relates to the technical field of intelligent power distribution network control, in particular to a variable grid group control optimization method, system and device based on cloud edge collaboration and a medium. Collecting real-time operation data of the distributed resources and performing state information processing to generate structured state information reflecting a relationship between a physical state and a network structure; the topological similarity and the operation characteristic difference degree are integrated through a clustering algorithm, self-adaptive division is conducted on the structured state information, and a plurality of variable grid groups and topological boundary information are generated; taking each variable grid group as an independent game main body, and combining a revenue function and a constraint condition to construct an initial strategy set of each variable grid group; performing multiple rounds of game interaction and income evaluation through a game algorithm to obtain an optimal variable grid group strategy; edge-side collaborative scheduling and resource allocation are carried out based on the optimal strategy, and an edge control result is obtained; an edge control result is fed back to the cloud for global coordination and integration, and a consistency strategy is generated, issued and executed.

The invention provides an unmanned aerial vehicle cluster formation control method and system based on a multi-mode large language model, and the method comprises the steps: obtaining an environment image shot or received by an unmanned aerial vehicle array, and analyzing the environment image through a first large language model to generate an environment description; acquiring a user instruction and environment data, analyzing the user instruction and the environment data based on a second large language model, and generating command semantics in combination with the environment description; and acquiring a preset formation image, calling the first large language model to analyze the preset formation image, generating a formation instruction in combination with the command semantics, and controlling the unmanned aerial vehicle array to complete cluster formation according to the formation instruction. According to the invention, through the multi-modal large language model and the supervised and trained large language model, real-time unmanned aerial vehicle cluster control based on multi-modal image acquisition and identification is realized.

The invention discloses a hydro-junction gate group intelligent cooperative control method based on digital twinning, and relates to the technical field of water conservancy control, and the method comprises the steps: taking a water conservancy dynamic digital twinning model as a reinforcement learning environment, setting gate group control parameters and performance indexes, carrying out the training under diversified hydrological conditions, and generating a preliminary control strategy; inputting the preliminary control strategy into a water conservancy dynamic digital twinborn model, executing rollback simulation covering multiple working conditions, and obtaining simulation responses of the corresponding working conditions; scheduling performance indexes of simulation response are analyzed, high-quality control strategies are screened, scheduling conflict identification and conflict resolution among gate groups are carried out, and an optimal control strategy set is output; according to the method, by executing rollback simulation covering multiple working conditions and automatically identifying and eliminating scheduling conflicts among gate groups, the adaptability and accuracy of a scheduling strategy are improved, conflicts and risks are reduced, and efficient and safe operation of the hydro-junction is ensured.

The invention discloses an intelligent networked unmanned ship cluster control method and system, and relates to the technical field of intelligent control and cluster collaboration, and the method comprises the steps: constructing a multi-layer distributed unmanned ship cooperative control network structure, and dividing nodes according to the task attributes of unmanned ships. The method comprises the steps of establishing a connection topological graph in a cluster according to initial function distribution, calibrating nodes through the connection topological graph, collecting the energy state, the load capacity, the communication quality and the task pressure of each unmanned ship, establishing a multi-dimensional state matrix, and calculating a comprehensive decision value of each node according to the multi-dimensional state matrix. And switching node operation through an adjacent topological relation and a comprehensive decision value, synchronizing the updated node state and the connection diagram to an unmanned ship cluster, and carrying out self-adaptive distribution and network structure optimization. According to the method disclosed by the invention, the management orderliness of a cluster structure and the task cooperation efficiency among the nodes are improved by constructing a three-layer cooperative network of the control nodes, the execution nodes and the sensing nodes.

A drone system for synthetic aperture radar (SAR) operation to control and operate an aerial vehicle mounted with an SAR may comprise: a flight control module configured to control a low level of the aerial vehicle; a high-level control module configured to perform communication for swarm control of the aerial vehicles, receive flight information from the flight control module, and transmit a command to the flight control module; a link module configured to link the flight control module and the high-level control module; and a data acquisition board connected to the high-level control module and configured to store a flight log from the high-level control module and radar data from a radar module provided with the SAR.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to a multi-mode sensing fusion unmanned aerial vehicle cluster control method, device and equipment based on deep learning and a storage medium. According to the invention, gesture, voice, vision and touch data are collected in parallel through a multi-mode perception access layer and are preprocessed and synchronized; utilizing a special deep network to extract high-level features rich in cluster semantics; through cross-modal semantic alignment, graph neural network association learning and a multi-dimensional conflict detection and intelligent resolution mechanism, deep fusion and accurate intention analysis of four modals in a cluster control semantic space are realized; high-level instructions are efficiently converted into single-machine executable tasks and coordination strategies by adopting hierarchical task decomposition, virtual structure formation control and a multi-objective optimization algorithm; state feedback and adaptive parameter optimization are carried out in real time, and the accuracy, reliability, environmental adaptability and man-machine interaction efficiency of large-scale unmanned aerial vehicle cluster control are improved.

The invention provides a reasoning service system. A global controller is used for dividing machine resources into an interactive resource pool and a batch processing type resource pool in advance; when a reasoning request is received, determining a service level target type of the reasoning request, and performing resource expansion and contraction on the interactive resource pool and the batch processing type resource pool according to the service level target type; the cluster controller is used for respectively dividing the interactive resource pool and the batch processing type resource pool into a cue word resource pool and a token resource pool in advance; aiming at a cue word stage and a token stage of the reasoning request, respectively distributing corresponding cue word resources and token resources from a cue word resource pool and a token resource pool, and carrying out resource expansion and contraction on the cue word resource pool and the token resource pool; and the machine controller is used for adjusting the batch processing size for processing the reasoning request according to the service level target type and the use state of the affiliated machine. And on the premise of meeting the service level target, the resource utilization rate and throughput performance of the inference service system are improved.

The invention discloses an unmanned aerial vehicle cluster control platform, and the platform comprises a user interface module which is used for task configuration, real-time monitoring and flight path visualization; the control and management module supports cooperative control and dynamic path planning of three-dimensional and two-dimensional scenes; the data processing module is used for real-time data collection and analysis and global situation awareness; the hardware collaboration interface layer communicates with a flight control node of the unmanned aerial vehicle and an airborne computer in real time to realize instruction issuing and data synchronization; the flight task scheduling module is used for dynamically adjusting task distribution according to task priorities and environmental conditions; the unmanned aerial vehicle cluster flight management module is used for coordinating multi-aircraft cooperative flight, obstacle avoidance and state monitoring; the platform controls unmanned aerial vehicle clusters in a three-dimensional space scene and a two-dimensional plane scene at the same time, and dynamic path planning, obstacle avoidance and multi-vehicle task collaboration are achieved through a hardware collaboration mechanism. The problems that a traditional unmanned aerial vehicle control platform cannot perform multi-scene comprehensive control and is low in hardware cooperation efficiency can be solved.

The invention discloses a reinforcement learning-based cluster spacecraft multi-target intelligent cooperative tracking method. The method comprises the steps of establishing a relative motion relationship between a tracking spacecraft and a target spacecraft through a nonlinear orbit kinetic equation; designing a double-layer game framework, constructing a non-zero sum game in the cluster to drive cooperation, and introducing a zero and maximum and minimum game between the cluster and a target to describe a confrontation relationship; based on communication topology and relative state information, defining a local error term and a performance index function of the tracking spacecraft and the target spacecraft, and defining an optimization target of each agent under cooperation and confrontation; and iteratively updating the value function and the control law. According to the invention, collaborative tracking can be realized without defining a system model. The method can be widely applied to the field of cluster control.

The invention discloses a robot cluster cooperative control method and device based on indoor positioning and virtual-real fusion simulation, equipment and a medium, and relates to the technical field of robot cluster control, and the method comprises the steps: building a unified coordinate system through arranging a plurality of optical dynamic capture cameras indoors, obtaining the three-dimensional space coordinates and attitude information of a target object, and obtaining the three-dimensional space coordinates and attitude information of the target object; based on the data, data fusion display synchronized with the actual environment is realized in the virtual simulation environment. And then, a cluster control algorithm is utilized to generate a cooperative control instruction, and the cooperative control instruction is sent to the robot cluster through wireless communication to execute a specific task, so that high-precision positioning of the robot cluster is realized, and the task execution efficiency and cooperative control capability of the robot cluster in an indoor environment are greatly improved.

The present invention provides a cooperative cluster control method, device, equipment and storage medium for unmanned aerial vehicles. In the initial stage, initial information of N unmanned aerial vehicles in the cluster is obtained, and the leading weight value of each unmanned aerial vehicle is calculated based on the initial information, wherein the initial information includes the ammunition load, combat radius and endurance time; then, the leading unmanned aerial vehicle is determined according to the leading weight value of each unmanned aerial vehicle, and the communication delay between the leading unmanned aerial vehicle and other unmanned aerial vehicles in the cluster is calculated; finally, other unmanned aerial vehicles in the cluster are clustered and grouped according to the communication delay, a secondary leading unmanned aerial vehicle is randomly selected from each group, and a communication routing table is generated and synchronized to each unmanned aerial vehicle in the cluster, wherein the secondary leading unmanned aerial vehicle is used to communicate with the unmanned aerial vehicles and the leading unmanned aerial vehicle in the group. The problem that the existing communication method between the leading unmanned aerial vehicle and the cluster is prone to cluster out-of-control is solved.

The invention discloses a cooperative control method and system for a seabed intelligent underwater vehicle and a mother boat, and relates to the technical field of underwater intelligent control and multi-boat cooperative navigation, and the method comprises the steps: collecting multi-source marine environment parameters to construct a dynamic environment perception model, and building a propulsion energy consumption estimation map according to three-dimensional space nodes. And constructing a multi-agent state-action structure, and executing path selection and cooperative route planning of the mother boat-underwater vehicle through reinforcement learning. And error judgment is executed according to the propulsion energy consumption data returned by the underwater vehicle, and an online increment mechanism is called to dynamically update the energy consumption model and the path diagram structure. According to the method disclosed by the invention, an underwater cluster control whole-process system from sensing to planning to feedback optimization is completed. Each step has independent value and supports each other, finally, a high-adaptability, high-efficiency and intelligent cooperative seabed operation control system is constructed, the dependence of the prior art on communication, a high-precision model and cluster control capability is broken through, and the method is suitable for deep sea complex application scenes.

The invention relates to the technical field of unmanned aerial vehicle cluster control and intelligent scheduling, and discloses an industrial unmanned aerial vehicle cluster intelligent scheduling management method based on multi-data analysis, and the method comprises the steps: extracting a quantitative risk feature vector based on multi-source operation data; constructing and updating a four-dimensional dynamic risk field; constructing a rolling time domain optimization model to generate a cooperative scheduling scheme including adaptive formation adjustment; decomposing and issuing a scheduling instruction; and carrying out dynamic risk field updating based on the real-time returned data, and triggering the compensatory adjustment. According to the method, real-time sensing and quantitative evaluation of multi-source and dynamic risks in a complex industrial environment can be realized, flight safety and task efficiency are dynamically balanced through rolling optimization and an elastic compensation mechanism, and robustness and intelligence of overall operation of a cluster are improved.