25results about How to "Improve task completion rate" patented technology

The invention relates to an Internet of Vehicles computing power scheduling method based on a graph neural network and deep reinforcement learning, and the method comprises the steps: S1, collecting the state information of network entities in the Internet of Vehicles and the association information between the entities in real time, and constructing a dynamic time-space attribute graph; s2, inputting the dynamic space-time attribute graph into a pre-trained graph neural network encoder, and outputting a node embedding feature set containing a high-order topological relation and global graph embedding features through multi-layer message passing and feature aggregation; s3, the node embedding feature set and / or the global graph embedding feature are / is used as the state of a deep reinforcement learning agent and input to a strategy network, the computing power scheduling action at the current moment is output, and the computing power scheduling action comprises the steps of assigning an unloading target node for a to-be-processed computing task and distributing corresponding computing and communication resources; s4, dispatching actions are distributed to the corresponding network entities to be executed, environment feedback is collected and used for model updating and next round of dispatching, and the method has the advantages of improving dispatching efficiency, adaptability and system energy efficiency and the like.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle target classification matching strike control method and system based on deep learning, and the method comprises the following steps: S1, collecting the original environment data of an unmanned aerial vehicle flight region, carrying out the preprocessing of the original environment data, and generating multi-mode perception data; and S2, inputting the multi-modal sensing data into a pre-constructed deep learning classification network, extracting multi-level depth features of the target through the deep learning classification network, carrying out classification identification on the target based on the multi-level depth features, and outputting target category information and target position information of the target. The multi-modal sensing data is input into the deep learning classification network for target recognition, the multi-modal fusion sensing mode can give full play to the complementary advantages of different sensors, high target recognition accuracy can still be kept in complex environments such as night, low illumination and severe weather, and the adaptive capacity of the system to environment changes is remarkably improved.

The invention discloses an RPA-based power dispatching service auxiliary method, system and device, and a medium. The method comprises the following steps: logging in a power dispatching OMS system by using an RPA automation technology; interactively triggering an operation management function through a target control, and sequentially executing UI element clicking, interface component activation operation and event distribution to a CLOW node to jump to a scheduling operation page; obtaining the number of executed work orders by adopting a selenium technology, triggering work order operation instructions in sequence, and extracting planned operation time, operation content and advance notice unit names in the work orders; on the basis of the extracted work order information, target grouping check and message content input and sending including operation content, time and ticket changing signature are completed in a message management module; generating massive controlled training sample sets through an RPA model, and performing parallel training on the sample sets by adopting a deep reinforcement learning algorithm; and carrying out adaptive process design based on a training result, and adjusting and perfecting a business process through iterative development testing and according to a deployment environment.

The invention relates to a dynamic environment autonomous navigation method and system based on sub-target generation hierarchical reinforcement learning. The method comprises the following steps: constructing a double-layer framework in which a top layer is responsible for target scheduling and a bottom layer is responsible for high-frequency execution; the top layer constructs a local occupation grid map according to the laser radar, constructs strip-shaped sub-target probability distribution based on a global reference path, and regulates and controls the entropy of the strip-shaped sub-target probability distribution and the commitment time limit between the top layer and the bottom layer through the environment self-adaptive exploration temperature; and the bottom layer follows the current sub-target within the committed time limit, so that the robot drives to the real-time sub-target. According to the method, strip-shaped probability sub-target distribution is constructed around a global reference path, the distribution entropy and the underlying committed time limit are regulated and controlled by utilizing environment feature-driven exploration temperature single knob linkage, and sub-target screening and issuing are completed in combination with safety expansion and reachability constraint; and a bottom-layer high-frequency closed-loop actuator follows within a commitment period and triggers advanced re-mining when the risk is increased, so that safe and smooth real-time arrival of a final target is realized.

The application relates to the technical field of path planning, in particular to a crowd-sensing cooperative optimization method and system based on evolutionary fusion two-stage mixing. The method comprises the following steps: constructing a multi-agent cooperative optimization model of a heterogeneous space based on a mobile crowd-sensing operation scene; adopting a stage-type evolutionary fusion strategy to deeply fuse MOPSO and NSGA-II, and constructing an EF-DH algorithm; using the EF-DH algorithm to perform unmanned aerial vehicle multi-target path planning based on the constructed multi-agent cooperative optimization model, including first-stage unmanned aerial vehicle cluster path optimization and second-stage ground operation personnel task optimization; and performing air-ground cooperative execution and dynamic re-optimization based on the path planning. The evolutionary fusion two-stage optimization algorithm fusing MOPSO and NSGA-II is constructed, and the global search capability and local convergence performance of the multi-target optimization problem are effectively improved.

This invention provides an image semantic transmission method and system for remote sensing small target detection. The invention constructs an end-to-end semantic transmission detection model comprising a multi-scale feature extraction backbone network, a JSCC feature transmission network, a feature fusion module, and a detector. This model is deployed on an unmanned aerial vehicle (UAV) platform, and the pre-trained end-to-end semantic transmission detection model is used to detect transmitted remote sensing images and output target detection results. This invention employs feature-level JSCC continuous symbol transmission, avoiding hard decision failures in discrete links at low SNR, thus significantly mitigating the cliff effect. Gradient-based channel importance prioritizes the retention of high-frequency and texture representations most critical for small target detection under a fixed CBR, reducing the bandwidth / power consumption of redundant background channels. Policy distillation transfers privileged gradient knowledge obtained during training to a lightweight statistical predictor, enabling the generation of high-quality masks at the edge even without ground truth values.

The invention relates to the technical field of unmanned aerial vehicle dispatching, and discloses an electric power construction-oriented heterogeneous unmanned aerial vehicle dynamic cooperative command and dispatching method and system. The method comprises the following steps: decomposing task levels according to an environment tolerance threshold value and constructing an unmanned aerial vehicle capability matrix; collecting real-time environment parameters, calculating an environment coupling degree index, and generating a degradation task package when the environment coupling degree index exceeds a threshold value; calculating a single machine matching degree index, and if not, selecting the collaborative machines with the maximum capability complementation index to form a virtual collaborative group and distributing roles; position and wind speed vectors are obtained, and the time difference of arrival is eliminated by delaying takeoff after the flight speed is corrected. According to the invention, the environment adaptability, task completion rate and execution efficiency of heterogeneous unmanned aerial vehicle cooperative operation in an electric power construction scene are improved.

The application provides a stamping line tail AGV scheduling system, relates to the field of intelligent manufacturing logistics scheduling, adopts a hierarchical centralized collaborative control architecture, sets a business layer module, a scheduling core layer module, an execution control layer module and a digital twin simulation layer module, internally arranges three customized strategies of main and auxiliary position dynamic priority distribution, flexible partition scheduling and double anti-locking traffic control in the scheduling core layer, realizes task differentiated priority distribution, operation area logical isolation control, space and time collaborative active anti-locking control, and completes strategy preposition verification and running state visual monitoring through digital twin simulation. The application can effectively adapt to the rigidity production rhythm of the stamping line tail, improve the multi-AGV collaborative scheduling efficiency and long-term operation stability, reduce the engineering landing debugging cost, and is suitable for the intelligent material carrying scene of the stamping workshop line tail.

The application discloses a kind of off-site computing power dynamic collaborative optimization methods based on multi-agent reinforcement learning, including the following steps: constructing resource topology diagram;Based on resource topology diagram, get node-level state feature vector and system-level state feature vector;Multi-agent environment is constructed;Local observation, global information and agent action set are input into improved CTDE model, output policy network parameters and value network parameters and construct training batch;Based on training batch, get converged policy network parameters and converged value network parameters;Get execution result;Obtain dynamically updated policy network parameters and dynamically updated value network parameters, realize the dynamic collaborative optimization of task acceptance, resource allocation, task migration, replica start-stop and bandwidth matching.

The application discloses a kind of based on edge cloud cooperation's inspection robot visual analysis task dynamic scheduling method and system, belong to inspection robot visual analysis and edge computing technical field.The method includes: real-time acquisition network state parameter and computing power resource information;Task feature vector is constructed to the visual analysis task and is extracted feature;Based on multi-objective optimization scheduling model, delay, energy consumption and accuracy index are determined optimal execution position by comprehensive evaluation;Task is distributed to corresponding node execution;Based on Q-learning reinforcement learning, scheduling weight is dynamically updated.The application can realize the optimal scheduling of visual analysis task under the dynamic change of network and computing power environment, compared with the scheduling efficiency of existing static allocation strategy is improved by more than 40%, and the comprehensive performance is significantly better than local execution, full cloud execution and static allocation scheme.

This invention relates to an AGV task allocation and path planning system and method, specifically a multi-AGV task collaborative time-series allocation and path planning system and method. It addresses the problems of inflexible deployment, high costs, and inability to accurately trigger tasks based on map point arrivals in complex time-series task collaboration among multiple AGVs due to reliance on a central server. The invention includes a scheduling decision module, a status management module, a task distribution module, and a path planning service module. The status management module continuously collects real-time status and location information of the AGVs. The task distribution module sends task instructions or scheduling instructions generated by the scheduling decision module to the corresponding target AGVs. Through establishing a map network and task flow timeline, path planning, status monitoring, task issuance and execution, and fault tolerance handling steps, automated operation of multiple AGVs is achieved.

The present application relates to the technical field of metering informationization and intelligent management system, in particular to a kind of inspection and calibration system and device of intelligent inspection platform;Including: customer inspection digital acceptance module, sample intelligent receiving tracking module, detection execution automation integrated module, report automatic generation management module, provincial measurement resource sharing analysis module;The present application realizes inspection whole-process digitization, automation and intelligent management by constructing wisdom measurement inspection and calibration platform, significantly improves customer handling efficiency, reduces communication cost, eliminates sample confusion loss risk, improves detection data accuracy and task completion rate, shortens report issuing cycle and enhances standardization and anti-fake ability, simultaneously creates new mode of centralized sharing of provincial measurement resources, breaks information silos, provides strong support for administrative supervision, agency optimization and customer traceability, overall reduces inspection cost, improves service quality, promotes high-quality development of measurement industry.

The application discloses a real-time dynamic task allocation and scheduling method for a multi-vehicle cluster, relates to the technical field of task scheduling, and comprises the following steps: acquiring real-time physical running state sequences of each vehicle node in the multi-vehicle cluster and global task distribution characteristic sequences of a task set to be allocated; constructing a space-time topology tensor with dynamic edge weights; performing node feature aggregation on the space-time topology tensor; calculating a state divergence degree between real-time local physical states of the vehicle nodes and expected states corresponding to an initial task allocation correlation matrix; extracting a sub-cluster topology tensor within the local reconstruction boundary; inputting the sub-cluster topology tensor into the task scheduling network for local deduction to generate a local correction allocation matrix; and updating a task execution sequence of the target vehicle node and associated vehicle nodes within the local reconstruction boundary by using the local correction allocation matrix. The application improves the stability and adaptability of cluster task scheduling.

The application discloses a task planning method and device, electronic equipment, medium and product. The method comprises the following steps: obtaining an interactive sentence input by a target user; performing intent recognition on the interactive sentence to obtain an intent candidate list; generating a structured intent object according to the intent candidate list and multi-round dialogue data of the target user; loading a corresponding business domain task graph according to the structured intent object; and performing task planning according to the business domain task graph to obtain a task execution sequence. The method can combine real-time interactive intent of a user with multi-round dialogue history to form a structured intent expression, and complete adaptive task planning based on a matched business domain task graph, thereby automatically generating a task execution sequence that meets current requirements without relying on a static preset process, and effectively improving dynamic adaptation capability and execution rationality of task planning.

The application discloses a multi-target equipment scheduling scheme generation method based on a genetic algorithm, takes the visible time window of an observation target of equipment as a resource, combines and integrates multi-target scheduling task characteristics into the genetic algorithm under the condition that the resource is constrained, more fully and comprehensively utilizes the resource, can provide more efficient and more reasonable scheduling schemes for scheduling work, and improves a task completion rate and a resource utilization rate.

The application is specifically an optimization method of an air unmanned aerial vehicle communication network based on AI, relates to the technical field of communication networks, and comprises intelligent sensing and data processing, an AI decision and optimization core, communication and task collaborative management, and resource and energy consumption optimization.In the application, a laser radar scans a canyon terrain in real time, a cutoff frequency is calculated in combination with a rectangular waveguide model, a transverse electric wave transmission mode is dynamically adjusted, terrain changes are predicted in advance and mode switching is triggered, a buffer interval and a rollback mechanism are matched, and signal interruption caused by a terrain-induced multipath trap can be avoided; when a canyon width suddenly changes, mode switching is started in advance, parameter updating is completed before the unmanned aerial vehicle reaches a critical area, the risk of communication interruption is reduced, the rollback mechanism can correct erroneous switching, and link continuity is ensured.

The invention discloses a multi-target scheduling system and a multi-target scheduling method for a continuous video stream super-gap scene, which are used for constructing a unified modeling framework integrating edge equipment, an acquisition device, a visual detection model and task parameters on the basis of a sensing-calculation cooperative computing architecture aiming at a typical application scene with limited inter-frame processing time. Core constraints are clearly defined, an improved fast non-dominated sorting multi-target cuckoo genetic algorithm is designed, and task efficient disassembly and edge resource collaborative scheduling are achieved with minimization of task latest completion time and minimization of the number of edge devices used as double optimization targets. According to the method, the task throughput rate and the equipment resource utilization rate of the edge end are improved, and the deployment number of the required edge equipment is greatly reduced while the high task completion rate and the system stability are guaranteed.

The present application relates to the technical field of unmanned aerial vehicle assisted edge computing, and particularly relates to a multi-unmanned aerial vehicle air-ground collaborative task offloading optimization method for smart agriculture. First, multiple system models are constructed to improve the task completion rate and reduce the system energy consumption. An improved particle swarm optimization algorithm with adaptive inertia weight and dynamic learning factor is used to realize the optimal initialization deployment of the unmanned aerial vehicle access position. Then, the task offloading is modeled as a multi-agent partially observable Markov decision process. A double-delay deep deterministic policy gradient algorithm is used to learn the collaborative offloading strategy. An attention mechanism, a preference prediction offloading prior guidance mechanism and a synchronous offloading constraint are integrated to avoid resource idleness and task blocking. Finally, according to the offloading target and mode, a multi-resource joint allocation is performed to generate a dynamic offloading and resource allocation strategy for the whole system running time, so that the efficient and stable execution of agricultural heterogeneous tasks is realized, and the task computing rate and overall operation efficiency of the smart agriculture system are improved.

This invention discloses a dynamic antenna resource scheduling method for low-Earth orbit (LEO) satellite communication, relating to the field of satellite communication technology. It aims to overcome the technical bottlenecks of traditional genetic algorithms and static scheduling methods. Addressing the specific constraints of LEO satellite communication scenarios, it designs a dynamic spatiotemporal quantum coding structure to support automatic reorganization of task units and multi-satellite collaborative parameter embedding. Furthermore, through relative time coding, dynamic grouping, and conflict prediction mechanisms, it solves the problems of short time windows and numerous conflicts in LEO satellite communication. Complementarily, it constructs a fitness function based on a hierarchical game architecture to perform multi-objective optimization of the antenna resource allocation scheme. By dynamically allocating task priorities and energy consumption weights through a Nash equilibrium model, it completes the improvement of the quantum coding and multi-objective game-based genetic algorithm, achieving efficient and robust dynamic antenna resource scheduling for task allocation and conflict optimization.

The invention belongs to the technical field of unmanned aerial vehicle obstacle avoidance, and discloses an unmanned aerial vehicle human intervention obstacle avoidance strategy generation method and system, and the method comprises the steps: obtaining the point cloud data of the current environment of an unmanned aerial vehicle when an unmanned aerial vehicle autonomous obstacle avoidance system fails, and building a three-dimensional environment model through the point cloud data; analyzing the course angle, speed and height of the unmanned aerial vehicle by using the three-dimensional environment model, and determining course angle adjustment amount, speed adjustment amount and height adjustment amount; determining the total safety distance of the unmanned aerial vehicle in the current flight state according to the predetermined unmanned aerial vehicle driver response distance and the unmanned aerial vehicle safety braking distance; safety obstacle avoidance is taken as a main target, and flight stability, energy consumption, flight rule adherence and environmental influence are taken as auxiliary targets. According to the invention, through multi-dimensional cooperative control and dynamic safe distance calculation, a scientific and quantitative operation basis is provided for a driver, the collision risk caused by inadequate experience or inadequate consideration is reduced, and the safety of the unmanned aerial vehicle is significantly improved.

The application provides an emergency scheduling method and system for agile satellite resources, and relates to the technical field of emergency task scheduling. By considering the insertion opportunity of the fixed elevation visible time window of the ground station in the scheduling algorithm, the fixed elevation visible time window of the appropriate ground station is inserted in the case that the agile satellite storage constraint is not met, so as to release the agile satellite storage data to the ground station, reduce the agile satellite imaging data load, provide more insertion opportunities for tasks, and improve the overall task completion rate. In the algorithm scheduling process, the emergency task emergency degree is calculated according to the designed emergency task emergency degree heuristic factor, the tasks with larger weight, less fixed elevation visible time window and closer to the completion deadline are preferentially arranged, the influence of the previously arranged tasks on the insertion opportunity of the subsequent tasks is reduced, and the task completion rate and overall scheme benefit are improved.

The invention discloses a client selection and resource allocation strategy for the Internet of Vehicles, which is based on the combination of a Deep Reinforming Learning algorithm and a Federated Learning algorithm. The client selection and resource allocation strategy for the Internet of Vehicles is used for the Internet of Vehicles. The method comprises the following steps: firstly, constructing a three-layer collaborative architecture consisting of a vehicle, an edge server and a cloud server; and secondly, proposing an FLMADDPG algorithm which is based on the combination of Deep Reinformation Learning and Federated Learning and is used for a client selection and resource allocation strategy of the Internet of Vehicles, wherein the FLMADDPG algorithm is used for the client selection of the Internet of Vehicles and the resource allocation strategy of the Internet of Vehicles. And then, obtaining a client selection and resource allocation strategy based on an FLMADDPG algorithm. And finally, when the client is selected to participate in federal aggregation, personalized differential privacy noise is added to the transmitted model parameters.

The present invention discloses a method, system and device for edge computing resource sharing based on coalition game, which includes that an edge server collects all device nodes and their task information in the network to form an edge computing network; setting a coalition structure and establishing an initial single-device coalition structure C i , where i ∈ 1, 2, …, n; modeling the utility function of the device nodes; setting the coalition game and the preference operation relationship, and selecting an improved coalition structure C j , and calculating the coalition revenue V(C j ); obtaining the stable coalition structure set C * , until no coalition structure changes, stop the iteration; reaching the stable coalition structure set C * , obtaining the overall system offloading strategy, and the game reaches equilibrium. It can maximize the task completion rate of all device nodes, solve the problem that all tasks are submitted to the edge server resulting in task blocking and processing failure, relieve the computing pressure caused by its resource limitation, and at the same time enable all device nodes to obtain the maximum benefit, improving the overall efficiency and benefit of the system as well as the satisfaction of all parties.

The invention discloses an intelligent transfer robot AGV path planning and control system based on a PC, and relates to the technical field of robot control, the system comprises a data monitoring module, an AI dynamic path planning model module, a task planning module, an AGV path traffic monitoring module and an AGV intelligent obstacle avoidance module; environment and state data of the AGV are obtained in real time through various sensors and a 5G network, a dynamic three-dimensional space map is constructed, and a data set is generated; performing dynamic planning and optimization on the path based on a convolutional neural network; task allocation and path planning are carried out by using an ant colony algorithm, and task execution capability is evaluated; the traffic condition is monitored in real time, the congestion risk is evaluated, and the path is adjusted; and an avoidance coefficient is calculated during congestion, so that the AGV can pass safely, and the path is re-planned dynamically. According to the system, the task scheduling and path planning of the AGV are optimized, and the task execution efficiency and safety are improved.

The invention belongs to the technical field of wireless sensor network application, and discloses a wireless sensor network coverage scheduling method and system oriented to energy harvesting, and the method comprises the steps: constructing an intra-day multi-scene power track according to the historical weather and photovoltaic data collected by each sensor, introducing tail risk measurement to evaluate the track, and carrying out the prediction of the track. Outputting a risk-controllable intra-day prediction power curve, and calculating the current-day energy budget of each sensor; dividing an intra-day time slot into different energy balance periods according to an intra-day prediction power curve, and establishing available energy constraints in the balance periods; gridding a preset monitoring area, and evaluating the collaborative detectable potential of a grid in combination with the energy budget of each sensor; grids meeting continuity constraints are selected from the preset space column structure, and a continuous target grid chain is formed; and the coverage quality, the energy utilization rate and the sustainability of the wireless sensor network are remarkably improved.