49results about How to "Save communication resources" patented technology

The invention belongs to the field of cooperative control of a multi-agent system, and the method aims at a fully heterogeneous linear multi-agent system under a directed topological graph, takes event-triggered communication transmission as a background, and aims at realizing stable formation control on the premise of utilizing a fully distributed control structure. And the formation control problem of the fully heterogeneous multi-agent system based on the mixed event triggering mechanism is researched. According to the method, a mixed event trigger communication strategy is used, network transmission bandwidth occupation can be reduced, and data transmission energy consumption is saved; designing a local state observer and a distributed event trigger state observer to estimate the states of the followers and the leader according to the conditions that the states of the followers are unknown and communication links do not exist between the leader and part of the followers; the designed distributed formation control protocol does not depend on global information, can ensure that followers realize ideal time-varying output formation configuration in a dynamic environment, and can accurately track a motion trail formed by a plurality of leaders at the same time.

The invention relates to the technical field of unmanned ship trajectory tracking, in particular to an unmanned ship trajectory tracking control method based on self-adaptive dynamic event triggering, and establishes a kinematic model, an expected trajectory and a position error dynamic equation of an unmanned ship. A dynamic event triggering mechanism and self-adaptive dynamic planning are combined, a cost function containing a dynamic event triggering control law and a Hamilton function are given, and resource consumption of an unmanned ship controller is reduced. And an unknown cost function and an optimal control law are approximately obtained by using the good nonlinear approximation capability of the neural network. And applying the calculated optimal control law to a control system of the unmanned ship, and driving the unmanned ship to perform accurate tracking according to a predetermined trajectory. The method provided by the invention can effectively improve the precision of trajectory tracking of the unmanned ship, and saves system calculation and communication resources.

The invention relates to the technical field of multi-agent control, in particular to a second-order multi-agent system control method based on dynamic event triggering, and the method comprises the steps: building a kinetic model of a leader-follower second-order multi-agent system considering unknown external interference, and designing a distributed fixed time observer on the basis, so as to achieve the control of the leader-follower second-order multi-agent system. State information of the leader is estimated in real time; introducing a fixed time extended state observer to estimate external disturbance; proposing a sliding mode surface based on the observation error; a distributed fixed-time sliding mode consistency tracking control protocol is designed, so that tracking errors are accurately converged within fixed time, an ideal control effect can be achieved even if external interference exists, the communication burden is effectively reduced through introduction of a dynamic event triggering mechanism, and the sesame phenomenon is avoided; tracking consistency control of the multi-agent system can be realized in a fixed time, the robustness and reliability of the system are improved, and communication resources are saved.

The present invention proposes a communication-sensitive multi-agent collaboration method, which is applied to the field of vehicle network communication technology. The method of the present invention includes: the agent uses an information filtering module to encode the locally observed message into a hidden state vector, that is, a compressed filtered message, and before sending the message, the shared medium access control module is used to score the message, and the edge computing node sorts the message value according to the score, and sends a confirmation message to the agent with the topK message value. After the agent receives the confirmation, it sends the message; the edge computing node receives all messages, uses the message distillation module to extract valid information, and summarizes and distributes it to each agent; in the process of summarizing the agent messages, the graph convolution aggregation method based on the graph information bottleneck is used to retain the agent topology structure information, and the summarized message is distilled. The present invention reduces the transmission of redundant and invalid information between agents, saves communication resources, and improves the decision-making ability and collaboration efficiency of multiple agents.

The invention provides a mine video semantic communication transmission method and system based on deep learning, which can realize the recovery of observable data and the execution of pragmatic tasks. According to the method, a semantic communication system model under a mining working face is constructed, the semantic communication system model comprises a semantic encoder and a semantic decoder which are realized based on a deep neural network, and the semantic encoder is located at a transmitting end and used for extracting data containing observable data and semantic information; the semantic decoder is located at the receiving end and used for reconstructing data related to empirical data and pragmatic tasks. According to the method, a semantic encoder and a semantic decoder are trained based on a DL-SC algorithm, image restoration and pragmatic task execution are considered in the aspect of design of a loss function, appropriate distortion measure functions are selected for the image restoration and pragmatic task execution respectively, and then the weighted sum of the distortion measure functions is solved to form a semantic distortion measure function to serve as the loss function. According to the invention, more efficient information interaction can be realized with lower data transmission quantity.

The invention provides an under-actuated unmanned ship trajectory tracking control method, which is used for under-actuated unmanned ship trajectory tracking control with input quantization and output limitation based on an event trigger mechanism, and comprises the following steps: acquiring sea condition information of a surrounding environment and other ships, and establishing a trajectory tracking control model of an unmanned ship; a composite quantizer is adopted to quantize control input of a control system, and a linear analysis model is used to describe an input quantization process; designing a barrier Lyapunov function on the basis of an output limited theory, compensating uncertain items and external interference in the under-actuated unmanned ship model by using a neural network system, and obtaining a trajectory tracking controller and an adaptive rate of the under-actuated unmanned ship; an event triggering mechanism is introduced, and the control rate of the under-actuated unmanned ship system is updated based on an event triggering strategy; it is proved that the stability of the designed under-actuated unmanned ship trajectory tracking control system is finally bounded when priori information of parameters does not need to be quantized, and all signals are finally bounded, so that Zeno behaviors are avoided.

The invention belongs to the technical field of multi-agent formation control, and particularly relates to a multi-agent formation control method of a finite time event trigger control strategy. Comprising the following steps: constructing a network communication topological graph of the multi-agent system; establishing an agent position state model under an event triggering mechanism according to the communication topological graph; setting a target position of the intelligent agent; and designing a control law and an event triggering condition of a controller according to the target position, sampling the position state of the intelligent agent by using a finite time formation control algorithm under an event triggering mechanism, and realizing formation control of the intelligent agent arriving at the target position within finite time. According to the method disclosed by the invention, a large amount of communication resources can be saved, the system can converge in finite time, the flexibility is higher, and the method is more in line with engineering practice.

The invention relates to an event-triggered neural network backstepping control method for a double-Duffing MEMS gyroscope with state constraint, and belongs to the technical field of MEMS gyroscope control. The method comprises the following steps: carrying out mathematical modeling on the dual-Duffing MEMS gyroscope, and introducing a digital signal processing (DSP) method to carry out dynamic analysis on the dual-Duffing MEMS gyroscope; in an event-triggered neural network backstepping controller, a tracking error, an acceleration function and an acceleration error of the event-triggered neural network backstepping controller are defined; designing a plurality of integral obstacle Lyapunov candidate functions, the control input of a controller and the adaptive rate according to the tracking error, the acceleration function and the acceleration error, estimating a nonlinear unknown function through a type 2 sequence fuzzy neural network, and solving the problem of virtual control rate differential item explosion through an acceleration second-order tracking differentiator; and establishing an event triggering mechanism based on a switching threshold strategy to assist the controller in performing event triggering neural network backstepping control. According to the method, the problems of chaotic oscillation, state constraint, uncertain interference, communication burden and the like in the system are solved.

The invention discloses a fault-tolerant state quantitative control method based on a flexible joint mechanical arm, and relates to the field of state quantitative control of flexible joint mechanical arms. Comprising the steps that a, a state space model of a flexible joint mechanical arm system is established; b, defining a radial basis function neural network of an approximate unknown term; c, defining a uniform quantizer; d, error transformation of an unquantized flexible joint mechanical arm system is defined; e, deriving an unquantized error variable; f, constructing an unquantized virtual controller and an adaptive rate; g, designing an unquantized actual anti-interference fault-tolerant controller; and h, designing a quantized controller and an adaptive rate. According to the controller designed by the invention, the tracking performance of the flexible joint mechanical arm with a quantitative state can be ensured, fault-tolerant control is realized, and the system efficiency is improved.

The invention discloses a data uploading method, device and equipment, a readable storage medium and a vehicle, and the method comprises the steps: obtaining a target signal of the vehicle in a driving process, the target signal being used for representing a running state of the vehicle; performing feature extraction on the target signal under a plurality of different dimensions to obtain a plurality of target feature values corresponding to the plurality of different dimensions; determining that the target signal is abnormal under the condition that at least two target characteristic values in the plurality of target characteristic values are in abnormal deviation; according to the number of times of exception occurring in the preset time period, the probability that the target component breaks down is calculated; and under the condition that the probability is greater than a preset threshold value, uploading the target signal within a preset time period. According to the method, whether the target component is abnormal or not is judged according to the multiple target feature values, and the target signal is uploaded to the cloud platform only when the target component has the fault, so that the uploaded data volume can be effectively reduced, and communication resources are saved.

An iterative learning fault-tolerant consistency control method for a multi-flexible-mechanical-arm system comprises the following steps that S1, a directed communication topological structure is constructed, flexible mechanical arms serve as followers, and a virtual leader is established; a mathematical model of the multi-flexible mechanical arm system is established based on the Hamiltonian principle; s2, constructing a finite time observer for estimating the angular displacement and the angular velocity of the virtual leader for each follower of the multi-flexible mechanical arm system; s3, designing an iterative learning rule, a parameter adaptive rule and a control signal of the multi-flexible mechanical arm system based on the virtual leader state estimated by the finite time observer in combination with an actuator fault model and an event triggering rule based on sampling data; according to the control method, fault-tolerant consistency control design based on a sampling data event triggering mechanism is carried out in combination with an estimated value of a finite time observer, so that communication resources are saved, and meanwhile, consistency control of the system is realized.

The present invention discloses a spacecraft formation attitude cooperative control method based on event triggering, comprising: establishing a spacecraft formation system attitude dynamics model considering external disturbance and model uncertainty; setting cooperative error auxiliary variables according to attitude and angular velocity information of formation spacecraft and communication relationship between formation spacecraft; setting measurement error variables according to cooperative error auxiliary variables; determining event-triggered attitude cooperative control law and event triggering conditions according to cooperative error auxiliary variables and measurement error variables; controlling the attitude and angular velocity of formation spacecraft according to the event-triggered attitude cooperative control law, and judging whether the event triggering conditions are met, if so, updating the event-triggered attitude cooperative control law corresponding to the formation spacecraft, and sending the state information of the formation spacecraft to adjacent formation spacecraft. The present invention can significantly save the energy, communication and computing resources of the formation system while ensuring that the formation system has good control performance.

The present invention belongs to the field of smart grid energy-saving and environmental protection economic dispatch, and specifically relates to a distributed economic dispatch method for smart grid based on dynamic event triggering. The method comprises: first, designing a finite time consistency economic dispatch algorithm based on multi-agent consistency, and designing dynamic event triggering conditions according to the finite time consistency economic dispatch algorithm. Secondly, when the dynamic event triggering conditions are met, the finite time consistency economic dispatch algorithm is used to update the incremental cost of the generator and calculate the output power. Finally, an auxiliary variable is introduced, and the finite time consistency economic dispatch algorithm is used to update the auxiliary variable, and the incremental cost is updated with the auxiliary variable, and the output power is calculated so that the output power meets the generator power output power limit. The present invention can perform economic dispatch on the smart grid within a limited time and save communication resources.

The present disclosure provides a time division duplex based semantic data transmission method and related equipment. The method comprises: a base station modulating and processing first user data corresponding to semantic data to obtain a first modulation sequence; the base station determining a first number of time-frequency resource blocks required for mapping the first modulation sequence to a first time-frequency resource; the base station judging whether the mapping of the first modulation sequence to the first time-frequency resource spans a time slot to obtain a first time slot span judgment result; the base station mapping the first modulation sequence to a first time-frequency resource with a first number of time-frequency resource blocks based on the first time slot span judgment result to obtain a first time-frequency signal, and sending the first time-frequency signal to a user terminal through a downlink; and the user terminal mapping the second modulation sequence to a second time-frequency resource with a second number of time-frequency resource blocks based on a second time slot span judgment result to obtain a second time-frequency signal, and sending the second time-frequency signal to the base station through an uplink.

The application relates to a data processing system, method and device. Each first intermediate result output by each map node is aggregated by an aggregation module to obtain an aggregation file containing a plurality of second intermediate results. Then, a reduce task is generated according to the second intermediate results in the aggregation file, and the reduce task is distributed to a reduce node. For the reduce node, one communication is needed to obtain one reduce task, so that the communication resource of the reduce node can be effectively saved.

The invention discloses a distributed fixed time robust formation trajectory tracking control method for a heterogeneous cluster system, and belongs to the technical field of formation control, and the method comprises the following steps: S1, building kinematics and dynamics models of a six-degree-of-freedom four-rotor unmanned plane and kinematics and dynamics models of a three-degree-of-freedom unmanned ship; s2, determining a formation tracking error and a speed tracking error of the air-sea heterogeneous cluster system; s3, generating an error conversion function; s4, determining a fixed time robust virtual control law and a distributed fixed time robust controller; s5, determining a distributed fixed time robust controller under the dynamic time triggering mechanism; and S6, completing trajectory tracking control. On the basis of dynamic event triggering, the distributed fixed-time robust H-infinity formation control method based on dynamic event triggering is determined, the updating frequency of the controller is reduced, and communication resources are saved.

The invention discloses a dynamic event triggering preset time fault-tolerant control method considering a non-affine fault. The method comprises the following steps: carrying out online approximation on an unknown nonlinear term in a dynamic decoupling model by adopting a fuzzy logic system to obtain an optimized system model; constructing a segmented preset time performance function, and introducing an instruction filter based on a dynamic surface control technology to process a virtual control signal of the constructed adaptive virtual control law so as to obtain a filtering error; constructing a dynamic signal compensation mechanism based on the filtering error in combination with a sectional preset time performance function, and obtaining a compensated final tracking error; and constructing a dynamic event triggering mechanism to obtain a final fault-tolerant controller, and realizing dynamic event triggering preset time fault-tolerant control considering the non-affine fault according to the final fault-tolerant controller. According to the method, the technical problems of uncontrollability of convergence time, influence of actuator faults and nonlinearity on control performance and limitation of communication and computing resources in the existing method are solved.

The application discloses a kind of concealed communication detection methods based on federal learning, including the following steps: the sample data transmitted by several time slots antennas is stored in the form of matrix;Determine global model, center server initializes global model;Center server distributes initialized global model to user;Participating node downloads global model from center server, and uses local data to train and update local model with downloaded global model as local model;Participating node uploads local model to center server, and center server receives all local models and carries out model aggregation update, forms new global model;Center server distributes new global model to all training nodes, and carries out iterative training;User detects local data with the final updated global model, judges whether concealed communication data is contained in physical layer signal, and completes classification detection task.The application saves communication resources and protects data security.

The application discloses a method and device for determining training data, and relates to the technical field of computers. The method comprises the following steps: acquiring a training data set; for each training party, based on the identification of the data, the data is cyclically distributed to each computing node in the training party; based on the preset data amount and the total number of computing nodes contained in the training party, the data amount of each computing node in the training party to acquire data in each round of training is determined; for each computing node in each training party, based on the preset data amount, the total number of computing nodes contained in the training party and the data index identification adopted by the computing node in the last round of training, the data index identification adopted by the computing node in the current round of training is determined; based on the determined data amount and the adopted data index identification, the training data used by the node in the current round of training is determined. The method can ensure the accuracy of vertical federated learning.

The invention relates to the technical field of parking, in particular to a parking automatic timing system and method. The system comprises a compass sensor, a display, a communication module and a control module, wherein a timing unit is arranged in the control module; the control module is used for receiving the direction change signal and judging whether the vehicle is in a stopped state or not, if yes, the display is converted and controlled to display parking time information according to the current time calculated by the timing unit and preset rule requirements, and the whole process is automatically detected by the compass sensor; the user does not need to carry out other operations when getting off the vehicle, so that the conditions that the parking time is wrongly updated, the parking time is not updated or a time notice board is forgotten after the user parks the vehicle are avoided.

The invention discloses an automatic driving vehicle anti-disturbance event trigger control method which comprises the following steps: a, establishing a mathematical model of a nonlinear man-machine cooperation automatic driving vehicle; b, designing a disturbance observer and an adaptive estimator; c, defining error conversion of the man-machine collaborative automatic driving vehicle system; d, solving the derivative of the error; e, constructing a virtual controller; and f, constructing a dynamic event triggering controller. The controller designed by the invention not only can ensure the tracking capability of the vehicle and resist the influence of external disturbance, but also can reduce the communication burden and save communication resources.

The application provides a processing method and device in a virtual scene, electronic equipment, a computer readable storage medium and a computer program product; the method comprises: displaying a virtual scene; in response to triggering an operation of a first virtual object holding a virtual weapon, controlling the first virtual object to hold the virtual weapon, and displaying a sight of the virtual weapon in the virtual scene; in response to the sight moving from outside a first adsorption area to inside the first adsorption area, automatically adjusting a shooting direction of the virtual weapon, so that the sight of the virtual weapon is automatically adsorbed onto a second adsorption area in a second virtual object; wherein the first adsorption area is an area surrounding the second virtual object, and the second adsorption area is bound to the second virtual object along a direction penetrating at least part of a skeleton of the second virtual object. Through the application, the efficiency of human-computer interaction in the virtual scene can be improved.

The application discloses a kind of communication link perception multi-agent dynamic event trigger consistency methods, comprising S1: the communication network topology graph of information exchange between multi-agent is established;S2: determine the dynamics equation of multi-agent system;S3: introduce adaptive control, design complete distributed consistency control input strategy based on event trigger;S4: the solution of algebraic riccati equation obtains feedback gain matrix;S5: based on Lyapunov theorem and channel capacity parameter, design communication link perception dynamic event trigger control strategy;S6: reach multi-agent consistency by complete distributed control input strategy and communication link perception dynamic event trigger control strategy;The communication link perception dynamic event trigger mechanism of the application is completely distributed, through adaptive coupling gain, control input adaptive adjustment, without relying on global information related to network topology, realize and apply in large networked multi-agent system.

The invention provides a charging control method and charging equipment. The charging control method provided by the invention comprises the following steps: when request charging information is received, sending a charging strategy request to cloud equipment according to the request charging information; determining reference charging power of the charging equipment based on a charging strategy fed back by the cloud equipment; acquiring actual grid-connected power between the alternating-current bus and the power grid and actual charging power of the charging equipment through the intelligent electric meter; determining redundant power according to the actual grid-connected power and the actual charging power; determining target charging power of the charging equipment according to the reference charging power and the redundant power; and generating a control signal according to the target charging power and sending the control signal to the charging equipment, wherein the control signal is used for adjusting the actual charging power of the charging equipment. According to the charging control method provided by the invention, the charging cost can be better reduced while the charging requirement of a user is met.

The invention relates to the field of unmanned aerial vehicle control methods, and particularly discloses a continuous time unmanned cluster system anti-synchronization optimization control method, which comprises the following steps: constructing a topological structure and a Laplacian connection matrix of an unmanned cluster system according to connection conditions and cooperative competition conditions among unmanned aerial vehicles of the unmanned cluster system; designing a strategy gradient algorithm fusing event triggering and a priority experience playback mechanism; the method comprises the following steps: constructing an event-critic neural network based on event triggering; the network determines the updating moment of the network weight by utilizing an event triggering mechanism, so that a non-periodic learning process is realized; the Critic network evaluates the approximate control action of the Actor network, the Actor network adjusts the control action according to the evaluation of the Critic network, and the whole process is updated by gradient descent.

The present disclosure provides a task processing method, device, electronic device and computer-readable storage medium, including: obtaining a target computing task; determining that the target computing task corresponds to a target model image, the target model image includes an operating environment configuration file, a model image slice description file, and multiple model slices, the multiple model slices include a target model slice, and the multiple model slices are obtained after slicing the target model; pulling the operating environment configuration file and the model image slice description file from a non-model file, and starting a target container according to the operating environment configuration file and the model image slice description file; determining that the target model slice is used to process the target computing task; pulling the target model slice from the target model image, and running the target model slice in the started target container to perform the target computing task.

This invention discloses a method for implementing a fault-tolerant satellite attitude tracking controller based on event-triggered learning, comprising the following steps: S1, establishing a mathematical model of satellite attitude actuator failure; S2, defining a tracking error vector and designing a satellite angular velocity command signal based on the established mathematical model; S3, designing an interference observer based on external interference, and then designing a controller and adaptive law; S4, for cases where the additive failure of the actuator is unknown, introducing a fuzzy logic system to approximate it; and designing event triggering conditions, satisfying the following condition: the weights of the fuzzy logic system are only updated when the additive failure of the actuator triggers the designed event triggering conditions. This invention can save communication resources from ground base stations to satellites and reduce the communication burden.

The embodiment of the present application provides a method and system for designing a fault-tolerant controller for a quad-rotor drone. The method includes building a target motion model for reflecting the flight status of the drone in combination with the fault distribution and the rotor speed of the drone, taking into account the impact of the fault on the rotor; setting a relative threshold and a fixed threshold that change synchronously with the control input of the model; adjusting the control input of the model through the relative threshold and the fixed threshold in combination with the event triggering characteristics, so that the flight trajectory of the drone approaches the preset target flight trajectory; in the adjustment process, the backstepping method is used to recursively design the corresponding virtual control law and the actual control law, so that the model approaches asymptotic stability. The implementation of this method can ensure accurate tracking control of the quad-rotor drone when multiple faults occur simultaneously.

The present invention discloses an unmanned ocean ship event trigger output control method based on a fuzzy model, including: establishing an unmanned ocean ship system T-S fuzzy model and its system model parameters; designing a periodic dynamic event trigger controller that meets the state stability and dissipation performance of the unmanned ocean ship system, and obtaining an unmanned ocean ship closed-loop system according to the designed controller; using matrix inequality and other technologies to establish a method for solving the controller gain; and using the designed controller and the obtained controller gain to safely control the unmanned ocean ship system. The present invention establishes a safety control method for ensuring the unmanned ocean ship system, which can effectively resist the adverse effects brought by external disturbances and provide key technical support for the safe and stable operation of the unmanned ocean ship system.

The present invention relates to a network path planning optimization method considering multiple types of transmission constraint models, comprising the following steps: step S1, a satellite acquires environmental information, and generates a semantic vector based on the environmental information; step S2, in any communication cycle, the generated semantic vector is transmitted to an adjacent satellite; step S3, when a satellite has a service requiring a path planning decision, the semantic vectors from the adjacent satellite enter a semantic aggregation module to generate an aggregated semantic vector; step S4, based on the aggregated semantic vector, a strategy vector is output to formulate a next-hop decision for the satellite's service. The present invention can efficiently utilize communication resources, avoid local optimality, and achieve more global and accurate path planning decisions.