25results about How to "Reduce communication load" patented technology

A dynamic micro-service migration and route redirection method based on topology awareness comprises the following steps: S1, acquiring cloud computing data center and edge server resource information, an application request stream set and an initialized mirror image deployment matrix in a dynamic scene, and dividing time slot-based input request stream features; s2, in each time slot, a multi-mirror-image preliminary deployment scheme of the micro-service is obtained in a marginal gain optimal mode, and a node routing scheme is redirected according to a mirror image deployment matrix of a physical server node; and S3, according to the resource cost, the multi-mirror-image preliminary deployment scheme and the node routing scheme, screening out topology dependence pairs with abnormal bandwidths in a request stream, and based on topology perception, executing migration operation for micro-service mirror images on server nodes of links with abnormal bandwidths to obtain an optimal joint decision of micro-service deployment and request routing. According to the method, topology-aware micro-service mirror image migration and online redirection probability routing are realized in a dynamic cloud computing data center scene.

The invention provides a multi-axis mechanical arm model consistency control method based on neighbor event driving, and the method comprises the steps: firstly building a nonlinear multi-mechanical arm system and a dynamic model of a corresponding mechanical arm, which comprises a leader mechanical arm and a plurality of sub-mechanical arms; the sub-mechanical arm obtains state information of a neighbor sub-mechanical arm according to the neighbor relation; when the leader mechanical arm sends out a leader signal, the sub-mechanical arms obtain state information of the leader mechanical arm and state information of the neighbor sub-mechanical arms according to the state information of the sub-mechanical arms and the connection relation between the sub-mechanical arms and the neighbor mechanical arms, and then consistency error signals of the sub-mechanical arms are calculated; and a neighbor event triggering mechanism and a sub-mechanical arm consistency controller are designed, parameters of the sub-mechanical arm consistency controller are adjusted according to the consistency error signal based on the neighbor event triggering mechanism, and a control signal of the sub-mechanical arm is generated. According to the method, the consistency control problem of the multi-agent system can be solved, a neighbor event triggering mechanism is introduced to reduce the communication load, the communication burden of the system is reduced, and the communication efficiency of the system is improved.

The invention relates to the technical field of satellite system data transmission, in particular to a satellite system model training method and device, equipment and a medium. The method comprises the following steps: acquiring a distillation data set sent by each distillation satellite; performing centralized training on each distillation data set to obtain a first average gradient; gradient data sent by all the federated satellites are obtained, and a second average gradient of all the federated satellites is generated according to all the gradient data; determining the global gradient of each running satellite; updating the global model parameters according to the global gradient to obtain the global model parameters of the current round, and sending the global model parameters of the current round to the running satellite; and if the global model parameter of the current round does not meet the preset convergence condition, repeating the above steps until the target global model parameter is output to meet the preset convergence condition. According to the method and the device, the problems of overlarge communication load, relatively large privacy security problem and relatively low satellite data analysis capability in the prior art can be solved.

The invention provides a fully-distributed unmanned aerial vehicle robust formation control method based on minimum information amount, and the method comprises the following steps: employing a second-order system as a kinematic model of an unmanned aerial vehicle, and building a state space model of an unmanned aerial vehicle system; establishing a state space model of the virtual leader system to describe a global flight path vector of the unmanned aerial vehicle; describing a communication network of the unmanned aerial vehicle group by using a directed graph; designing an output-based adaptive distributed observer to estimate virtual leader system information required by each unmanned aerial vehicle; based on the output information of the observer, constructing an asymptotic internal model to process uncertain parameters in the virtual leader system; an asymptotic internal model is combined with a dynamic state feedback controller, an unmanned aerial vehicle robust formation flight controller is constructed, and the unmanned aerial vehicle robust formation flight controller and an output-based adaptive distributed observer jointly control robust formation flight of a full-distributed unmanned aerial vehicle group. And the robustness and the distributed performance of the formation flight of the unmanned aerial vehicles are improved.

The invention provides a technology for suppressing communication load between a vehicle and a server device. A server device is provided with: an acquisition unit that acquires use information relating to the time at which a vehicle has been used; a learning unit that learns on the basis of the use information and outputs a use pattern indicating the time of use of the vehicle; a specifying unit that specifies, on the basis of the usage pattern, a transmission timing at which vehicle data of the vehicle is transmitted to a prescribed server device; and an instruction unit that instructs the vehicle to transmit the vehicle data at the determined transmission timing.

The present application relates to a kind of multi-unmanned aerial vehicle narrow airspace cooperative path planning method based on distributed optimization, comprising the following steps: establishing the three-dimensional kinematic model of unmanned aerial vehicle, and defining narrow airspace boundary constraint;According to task demand, generate reference path for each unmanned aerial vehicle;Design distributed optimization controller based on local information;With reference path as tracking reference, use distributed optimization controller to solve control input vector in real time, to control the actual flight path of unmanned aerial vehicle.The method of the present application can realize completely distributed control, eliminate single point failure risk, and can ensure that unmanned aerial vehicle is collision-free and maintains desired formation, realizes speed consistency, improves cooperative efficiency.

The present invention discloses a method for dividing star clusters and a method for handing over slave satellites in collaboration with multiple star clusters, and relates to the field of space-based detection technology. The specific implementation of the method includes: for each slave star of any star cluster, calculating the first distance from the slave star to the central control center of each star cluster; comparing multiple first distances to determine the minimum value of the first distance; determining the star cluster to which the central control center corresponding to the minimum value of the first distance belongs as the target star cluster of the slave star. This implementation can compare the distances of each satellite in the star cluster relative to the center of each star cluster, determine the optimal star cluster to which the satellite belongs, thereby improving the satellite utilization rate during the execution of observation tasks, and rationally utilizing on-board resources, and the slave satellites between star clusters can be flexibly switched based on the distance relative to the center of each star cluster, so as to observe the observation target, so that the observation effect of the entire star cluster is optimal, and is suitable for various mission scenarios.

This application discloses a fall detection method based on MQTT-ROS communication bridging and multidimensional statistical features, comprising: acquiring data packets; decoding the data packets; if a human body is in an active state, sending continuous motion sensing states and active states to two sliding window queues of preset lengths respectively; determining whether the active state meets a first preset condition and / or a second preset condition, and simultaneously determining whether the motion sensing state meets a third preset condition and / or a fourth preset condition; if a fall event is determined, modifying the fall state field in the data packet to a valid state and adding a ROS system timestamp; based on the modified data packet, publishing alarm information to the ROS system through a ROS topic, triggering a single-trigger mechanism to shut down the ROS system node, thereby completing the abnormal event response process. This application effectively avoids false fall detections caused by historical data contamination and significantly improves detection accuracy in scenarios with frequent personnel entry and exit.

The invention provides a control method based on a charging station energy management system, and the system comprises an energy management host and a plurality of energy management sub-controllers which are in communication connection with the energy management host. Comprising the steps that S1, the energy management host receives state information sent by the multiple energy management sub-controllers respectively, and the state information comprises the state of charge SOC, the required power Np and the buffer distribution proportion B of the corresponding electric equipment; s2, the energy management host obtains the total power MPW of the charging station, obtains the total power APW of the upper limit value of the energy buffer area distributed to each energy management sub-controller, and calculates the residual distributable power RPW; in the charging peak period, when the multiple sub-controllers carry out rapid power adjustment at the same time, the response time can still be stably kept at the millisecond level, so that rapid suppression can be achieved during instantaneous load fluctuation, and the problems of transformer overload and power distribution imbalance are effectively prevented.

This invention belongs to the field of multi-agent cooperative intelligent control technology, specifically relating to a multi-manipulator cooperative control method for unknown disturbances. The method includes: establishing a dynamic model of a strictly feedback nonlinear multi-manipulator cooperative system; constructing the system's communication topology through a virtual coupling mechanism; building a unified system cooperative error model; introducing a preset time function and a globally preset performance function to map errors to an adjustable constraint performance space, ensuring dynamic convergence of all error signals within the user-defined performance range; designing task-level shape control forces and pose control forces within a distributed gradient descent framework to construct an overall task-level control force, driving the multi-manipulators to move cooperatively and maintain a stable formation in the task space; and designing a joint-level distributed adaptive control law under unknown disturbances using the passive characteristics of the nonlinear system. This invention enables multi-manipulator systems to achieve cooperative convergence and stable control within a preset time under unknown disturbances.

The application relates to the field of power system data acquisition and edge computing, and discloses a multi-source heterogeneous data pre-acquisition and edge intelligent processing method, which comprises the following steps: using a modal division mechanism, combining the operation state of power equipment, and performing condition correction on an adaptive sampling frequency to obtain a corrected sampling frequency, so as to acquire multi-source heterogeneous data; extracting fusion core features from multi-dimensional feature vectors; substituting the fusion core features into a general fault judgment model and a time sequence prediction model to respectively output equipment fault prediction results and time sequence prediction results, and obtaining early warning information; based on the fusion core features, the early warning information and the equipment fault prediction results of each acquisition node, and combined with the Lymerzi complexity theory, the spatio-temporal coupling law is identified; and based on the spatio-temporal coupling law and the time sequence prediction results, edge end decision suggestions are provided for source-grid-load-storage collaborative regulation. The application effectively improves the power system data processing efficiency and the power grid operation and maintenance regulation level.

This application provides a mobile robot task scheduling method and system based on a heterogeneous computing architecture, relating to the field of intelligent scheduling technology. The method includes: constructing a workspace geometric model based on the geometric structure of a planting row; obtaining the motion state parameters of the mobile robot using a first computing unit; determining the look-ahead compensation amount by combining the statistical value of the inference time of the target recognition task by a second computing unit; determining a spatiotemporal pre-aiming slice in the visual field region based on the look-ahead compensation amount and the workspace geometric model, and cropping image data located within the slice from the original image data; the second computing unit performing target recognition on the image data, generating a work instruction corresponding to the recognized target, and controlling the lateral actuator to perform the work according to the work instruction. This can alleviate the spatiotemporal asynchrony problem between perception results and execution position in a moving work scenario, reduce missed and misaligned tasks, and reduce the communication and computational load of the heterogeneous computing system.

The invention discloses a cooperative state estimation method and system based on Kalman filtering and deep learning, and relates to the technical field of cluster control, and the method comprises the steps: obtaining a state value of an intelligent agent at a (k-1) th sampling moment and a control value of the intelligent agent from the (k-1) th sampling moment to a kth sampling moment; according to the obtained state value and control value, a predictive state value and predictive uncertainty of the intelligent agent at the kth sampling moment are obtained by using a deterministic kinematics model and a process noise neural network; obtaining observation data of the intelligent agent on a neighbor intelligent agent, calculating a structured residual error based on the prediction state value, the observation data and the prediction state value of the neighbor intelligent agent, and inputting the prediction state value, the prediction uncertainty, the observation data and the structured residual error of the intelligent agent at the kth sampling moment into a posterior neural network to obtain a predicted state value of the intelligent agent at the kth sampling moment; and obtaining a posterior state value and posterior uncertainty of the intelligent agent at the kth sampling moment. According to the method, the state estimation precision and the robustness to environmental interference can be remarkably improved.

The invention provides a path memory method and device for an agricultural unmanned vehicle, a medium, a terminal and a program product, and the method comprises the steps: obtaining a plurality of motion parameters of the agricultural unmanned vehicle in a moving process, the motion parameters comprising unmanned vehicle position information, unmanned vehicle acceleration, unmanned vehicle speed and unmanned vehicle direction; the initial position of the agricultural unmanned vehicle is recorded as a path starting point, and whether the current position of the agricultural unmanned vehicle serves as a path node or not is judged based on a preset judgment rule according to the obtained unmanned vehicle position information, the unmanned vehicle acceleration, the unmanned vehicle speed and the unmanned vehicle direction; if it is judged that the current position of the agricultural unmanned vehicle serves as the path node, the current position is recorded as the next path node and stored; and if the current position of the agricultural unmanned vehicle is not used as the path node, discarding the related motion parameter data of the current position. According to the invention, storage occupation and communication load can be significantly reduced, so that data resource efficiency and communication stability are improved.

The invention discloses a circulation transmission method and system for sampling signals of an intelligent water station, and relates to the technical field of artificial intelligence, and the method comprises the steps: dividing the sampling signals into slow variables and fast variables according to the change rate of the sampling signals, and dividing the slow variables and the fast variables into signal subsets with different priorities according to the importance indexes of the sampling signals; for the signal subset of each variable category, defining a slow variable depth Q network state space, an action space and a reward function and a fast variable depth Q network state space, an action space and a reward function, and constructing a slow variable depth Q network and a fast variable depth Q network; the edge device receives the variable state of the signal subset, outputs a global sampling decision according to the slow variable depth Q network and the fast variable depth Q network, and executes the global sampling decision and collects sampling signals; and the edge device divides the sampling signals into data packets according to the priority, and performs grading processing on the data packets according to the variable category and the priority sequence.

A kind of multi-circle formation control method of non-holonomic constraint vehicle based on azimuth relative position observer belongs to the field of multi-vehicle cooperative control. A distributed scheme relying only on local observation of azimuth and light communication with neighboring vehicles is constructed to reduce the performance requirements of vehicle-mounted sensors. A basic formation controller based on relative position with input saturation characteristics is designed, which adapts to the physical limits of Ackerman chassis through parameter constraints. To overcome the limitation that relative position cannot be directly measured, an azimuth-based relative position observer is designed to adapt to different observation scenarios. An integrated formation strategy of observation and control is built, and the theoretical proof of controller closed-loop stability and observer global convergence is completed by combining Lyapunov stability theory and graph theory. The convergence performance and engineering advantages of the method are verified through numerical simulation and real vehicle experiment. The performance requirements of vehicle-mounted sensors and system deployment cost are greatly reduced, which is suitable for complex scenarios such as GPS denial and electromagnetic interference, and has strong engineering practicability.

The invention discloses a flow table state synchronization and packet processing method based on an intelligent network card, and belongs to the technical field of computer network communication, and the method comprises the steps: obtaining flow table item meta-information, local historical matching data and an external state prompt signal, and generating multi-dimensional prediction input; based on the multi-dimensional prediction input, a synchronization urgency index is obtained through calculation of a local flow table behavior predictor, and a precise synchronization request is generated and sent according to the synchronization urgency index; performing arbitration processing based on the local flow table mirror image and the shadow flow table area on the received data packet to generate a data packet processing result; and receiving pre-execution confirmation feedback generated based on the data packet processing result, and adjusting the calculation parameters of the local flow table behavior predictor according to the pre-execution confirmation feedback, so that the low-delay consistency synchronization of the flow table state and the continuous linear speed processing of the data packet can be realized while the control signaling overhead is remarkably reduced.

The application discloses a parameter updating method and system based on periodic check I / O board card parameters, a storage medium and an electronic device, which are used for performing parameter check in the process of real-time periodic communication between a controller and I / O, so as to guarantee the accuracy of parameters issued to the I / O board card. The method comprises the following steps: sequentially judging whether each I / O board card mounted by the controller is correctly configured and normally communicated, scanning and packing parameters to be updated and recording information for the I / O board card of which the configuration type is correct and the communication is normal, and performing parameter check and updating logic. The method can guarantee the accuracy of parameters issued to the I / O board card by the controller, and only update parameters to be updated in the host computer configuration software, without increasing the communication load between the controller and the I / O board card, so that the stable operation of the controller and the I / O board card is guaranteed.

The present disclosure provides a lighting device and a method and system for controlling the lighting device, wherein the lighting device control method is a method for controlling the lighting device by an effect application executed by at least one processor on a director terminal, comprising the steps of: generating a library setting a light emitting mode of a plurality of lighting devices; generating a scenario setting a light emitting mode of the plurality of lighting devices within a specified time; generating a performance preparation data including at least one of the library and the scenario; providing the generated performance preparation data to the plurality of lighting devices; transmitting a lighting device control signal to the plurality of lighting devices, the lighting device control signal commanding execution, interruption or termination of at least one of the library and the scenario within the provided performance preparation data; and implementing a comprehensive lighting control performance effect according to the transmitted lighting device control signal.

This application discloses a diagnostic data reading method, apparatus, device, and storage medium, relating to the field of vehicle diagnostic technology. The method includes: obtaining a target diagnostic file based on an initial data stream of a preset type in an initial diagnostic file; providing the target diagnostic file to the vehicle terminal in response to an acquisition command sent by the vehicle terminal; having the vehicle terminal send the target diagnostic file to the diagnostic terminal; and having the diagnostic terminal read diagnostic data based on the target diagnostic file. This effectively solves the problem of communication resource waste caused by non-separation of data streams, reduces the communication load of the electronic control unit, and greatly improves the data reading efficiency of diagnostic data.

The invention discloses an extensible video content transmission method and system based on a sliding window BATS code, and the method comprises the steps: segmenting a to-be-transmitted initial file according to a preset parameter, and obtaining a plurality of classified files; establishing a communication system, and acquiring request information and a channel state of a receiving end; solving an optimal parameter of a coding scheme according to the request information and the channel state of the receiving end; according to the optimal parameters, coding each classification file, and constructing a coding packet batch; transmitting the coding packets to each receiving end in batches through the communication system; and the receiving end receives the coding packet batch and decodes the received coding packet batch to obtain a file requested by the receiving end. The embodiment of the invention can effectively improve the transmission efficiency of a communication network, reduce the packet loss probability in the transmission process, improve the reliability of data transmission and reduce the communication load of the network, and can be widely applied to the technical field of network communication.

The application relates to the technical field of intelligent transportation, and discloses a multi-terminal cooperative online car-hailing order management path planning system, which comprises a vehicle-mounted terminal computing module, an edge aggregation and deduction module and a cloud parameter synchronization module. The vehicle-mounted terminal computing module generates a data tensor containing main and auxiliary channel trajectory records; the edge aggregation and deduction module receives the data tensor, detects the space-time intersection of multi-vehicle trajectories, quantifies a shadow price penalty parameter, and executes multi-terminal atomic switching decision operation and calculates a truncated counterfactual reward parameter according to the same; and the cloud parameter synchronization module updates global model weight parameters according to the reward parameter and synchronizes the same to the vehicle-mounted terminal computing module. The application places multi-vehicle trajectory conflict detection and cooperative decision and other high-frequency local tasks in edge processing, and keeps global model updating and other low-frequency optimization tasks in the cloud, thereby improving the decision speed and response capability of the system in processing large-scale terminal cooperation.

The application provides a self-evolution environment monitoring system based on digital twinning and causal perception, which is suitable for high-precision, high-timeliness, interpretable and sustainable evolution monitoring and management of complex dynamic environments such as urban atmosphere, industrial parks and ecological sensitive areas. The system adopts a three-level collaborative architecture of "edge-fog-cloud", which is composed of the following three parts: pulse edge intelligent micro station (PEIM): deployed on the ground of the target area, responsible for event-driven multi-modal environment perception; light-weighted environment digital twinning (LEDT): deployed on the edge gateway or fog computing node, constructs a local virtual environment field and performs anchor-free self-calibration; causal perception center (CACH): deployed on the regional server or cloud, fuses multi-source data to construct a dynamic environment causal graph, and outputs pollution tracing results and intervention suggestions.

The vehicle system (1) of the present invention comprises: sensors (61, 63) for detecting at least one of vibration of the track (4) in the inspection section (S3) and sound generated in the inspection section (S3); a storage unit (75A) for storing the detection results detected by the sensors (61, 63); an evaluation unit (75B) for evaluating the state of the target vehicle (6); and a communication unit (75C) for sending the evaluation results evaluated by the evaluation unit (75B) to a controller (90). The controller (90) controls multiple vehicles (6) in such a way that only one target vehicle (6) exists in the inspection section (S3), and makes the target vehicle (6) drive in a predetermined driving mode in the inspection section (S3). The evaluation unit (75B) evaluates the state of the target vehicle (6) based on the detection results of the target vehicle (6) stored in the storage unit (75A) and the detection results of other vehicles (6) besides the target vehicle.

The invention discloses a multi-time scale event trigger consistency control method for a discrete time multi-agent system. Aiming at the problem that a discrete time multi-agent system is insufficient in cooperative adaptability due to multi-time scale dynamic coupling, a singular perturbation theory is adopted to decompose the system into fast and slow subsystems, an adaptive dynamic event triggering mechanism is designed to realize on-demand triggering, and a distributed controller is designed based on a consistency theory. A closed-loop stability condition is established by constructing a discrete time Lyapunov function, and controller gain is solved by using a linear matrix inequality technology. According to the method, on the premise of ensuring the stability and consistency of the system, the communication and calculation burden is remarkably reduced, a multi-time-scale coupling scene is effectively adapted, and the system expansibility and robustness are high.