48 results about "Resource allocation algorithm" patented technology

The invention discloses an AI digital human real-time rendering method based on GPU acceleration, and relates to the technical field of digital human rendering, and the method comprises the steps: S1, constructing a multi-GPU hardware feature and load monitoring module, and collecting hardware feature parameters and current load states of each GPU participating in collaborative rendering in real time; according to the method, by constructing the multi-GPU hardware feature and load monitoring module and combining a dynamic task allocation algorithm, dynamic allocation of the rendering sub-tasks to the optimal GPU is achieved, the problems of GPU performance bottleneck and resource idleness caused by traditional fixed task allocation are solved, the multi-GPU collaborative rendering efficiency is improved, and the multi-GPU collaborative rendering efficiency is improved. A scene complexity analysis module and a self-adaptive resource allocation algorithm are built, the proportion of GPU resources between an AI driving module and a rendering module is dynamically adjusted according to scene complexity, the AI digital human interaction naturalness is improved in a simple scene, the rendering frame rate and the picture quality are guaranteed in a complex scene, and the method is suitable for being used in a large-scale scene. And finally, dual optimization of rendering efficiency and picture quality in AI digital human real-time rendering is realized.

The invention provides a resource allocation method in task unloading, relates to the technical field of network and communication, and solves the technical problem of relatively high time consumption of joint optimization of channel resources, computing resources and power consumption control in a task unloading process. According to the technical scheme, the method comprises the following steps of S1, task unloading problem modeling; s2, realizing an unloading decision; and S3, designing and realizing a resource allocation algorithm. According to the allocation method, the search behavior can be dynamically adjusted according to the real-time performance of the particles, and exploration and development of resource allocation problem solving are dynamically balanced; according to the scheme, the time overhead performance of program calculation is reduced, and a high target utility function value can be maintained.

A multi-dimensional data processing system is disclosed for the automated ingestion, normalization, and computational mapping of project data across spatial, temporal, and regulatory dimensions. The system operates by ingesting diverse datasets—including two-dimensional schematics, three-dimensional data models, cost structures, and workforce logistics—and normalizing them within a unified schema. A dimensional calculations model enables the system to computationally align normalized data with spatial zones, project phases, and operational constraints, structuring it for further computational processing. By integrating this mapped data within a coordinated digital environment, the system applies task sequencing and resource allocation algorithms, generating optimized project plans that support complex, multi-dimensional data interoperability. The structured data is further configured for display through an interactive visualization interface, allowing computational resources to facilitate real-time analysis and adaptive data-driven project management.

The invention provides an intelligent resource dynamic sharing and task scheduling method based on a cloud desktop system. According to the method, a resource monitoring module is deployed on a local cloud desktop to monitor the utilization rate of key resources in real time, resources are requested from a cloud desktop system when the resources are insufficient, a cloud desktop resource management module selects a proper cloud desktop to provide the resources, and tasks are reasonably allocated by using a resource allocation algorithm. Meanwhile, efficient operation of the compute-intensive application in the cloud desktop system is realized through task scheduling and monitoring, data synchronization and a feedback adjustment mechanism. According to the method, the resource utilization rate is improved, the task execution time is shortened, the system stability is enhanced, the cost is reduced, and the problems that an existing cloud desktop system is unreasonable in resource allocation and low in task execution efficiency are effectively solved.

The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.

The application discloses a kind of outbound line decision-making method and system based on incoming call display perception, the method includes: determining outbound task and obtaining the number attribute of outbound line, the number attribute and outbound task are modeled to obtain number feature vector and outbound service feature vector;Based on the willingness prediction network of pre-set, according to number feature vector and outbound service feature vector, the willingness score sequence corresponding to outbound line is obtained by prediction;For any outbound line, according to the constraint condition of willingness score and line cost, the target outbound line executed by outbound task is determined by calculating through the pre-set resource allocation algorithm;The feedback sample of target outbound line corresponding outbound task is obtained, and the willingness prediction network and resource allocation algorithm are continuously updated by hybrid learning mechanism.It can be seen that the application can realize the intelligent selection and dynamic optimization of outbound line, so as to effectively improve the reach rate of intelligent outbound system.

This invention provides a method for generating and distributing inspection tasks for coal mine inspection robots, comprising: constructing an inspection space mapping model; analyzing the correlation between inspection nodes; clustering inspection nodes using a multi-density clustering algorithm to form multiple inspection node clusters; optimizing the multiple inspection node clusters using a multi-objective ant colony algorithm to obtain multiple inspection path task combinations; calculating the task adaptation analysis results between the inspection robot and the inspection path task combinations; and assigning inspection path task combinations to the coal mine inspection robot using a convex resource allocation algorithm to generate coal mine inspection task distribution results. This invention uses a multi-density clustering algorithm to cluster inspection nodes and generate node clusters, which can accurately adapt to the scene characteristics of large differences in the density and uneven spatial distribution of inspection nodes in underground coal mines; and by using a convex resource allocation algorithm to assign the optimal inspection robot to each inspection path task combination, it can achieve precise optimization of inspection resources.

The invention discloses an elastic anti-interference distributed power resource allocation method with preset time convergence. The method comprises the following steps of: 1, describing a power resource allocation problem as a distributed optimization problem with total load constraint, and constructing a mathematical model of power grid power dispatching adopted by a preset time distributed power resource allocation method; 2, designing a preset time distributed resource allocation algorithm with elastic anti-interference capability under total load constraint; 3, solving a distributed optimization problem under the constraint of a total load in the absence of a network attack scene, and realizing convergence of preset time kd; and step 4, solving a distributed optimization problem with total load constraint in a scene with network attack based on the condition that false data injection attack is carried out on the system by the outside, and finally realizing convergence of the system state to an optimal value through preset time. The method can realize preset time convergence, and has elastic anti-interference capability when the outside carries out false data injection attack on the system.

The invention discloses a fluid pulsation accelerator architecture for reinforcement learning of a large language model, and belongs to the technical field of artificial intelligence hardware acceleration. The system aims at solving the problems that when a current universal processor executes an RLHF working load, an instruction driving normal form is not matched with data flow calculation, fixed parallel granularity cannot adapt to a dynamic load, and the efficiency is low due to the fact that a framework does not sense data statistical characteristics. The core of the system is a liquid systolic array calculation fabric capable of being dynamically reconstructed, execution is triggered through data flow, and kernel boundaries and instruction overhead are eliminated. According to the system, a global lookup table subsystem is fused, and nonlinear calculation is optimized through merging query and parallel lookup by using data distribution prior; and a dynamic scheduling unit is configured, and elastic parallelism is realized by adopting a resource allocation algorithm supporting work stealing. According to the method, the throughput and the energy efficiency of RLHF training and a large language model reasoning stage can be remarkably improved, and a new design normal form is provided for a next-generation AI special computing architecture.

A real-time resource allocation method for IEEE 802.11be WiFi based on deep deterministic policy gradient includes the following steps: 1) establishing an IEEE 802.11be WiFi network model; 2) determining the mobility model, path loss model, and interference model adopted by the network; 3) deriving the network throughput expression; 4) proposing an optimization problem with maximizing the minimum throughput as the objective function. This optimization problem aims to optimize the allocation of power, channels, and resource units in real time, thereby improving the network's minimum throughput; 5) designing a real-time resource allocation algorithm based on deep deterministic policy gradient to solve the optimization problem, realizing real-time resource allocation for IEEE 802.11be WiFi. This invention can effectively improve the network's minimum throughput.

The invention discloses a 6G air-space-ground integrated network resource allocation device based on an evolutionary game, and belongs to the technical field of calculation, reckoning or counting. According to the device, the problems of unbalanced resource allocation, task processing delay increase and the like caused by the fact that a plurality of tasks fight for limited computing resources at the same time in the 6G space-air-ground integrated network are considered, and the device for task unloading and resource allocation of the edge server is provided so as to meet the requirements of ground users for the tasks. According to the device, firstly, a 6G air-space-ground integrated calculation unloading network model is constructed; secondly, defining an optimization target and a constraint condition for minimizing task execution time delay; and finally, a task unloading and resource allocation problem is solved by adopting a combined task unloading and resource allocation algorithm. A simulation result shows that the algorithm has good network performance.

The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.

The invention relates to the technical field of data processing, in particular to a high-speed mobile terminal routing addressing method and a communication device, and the method comprises the steps: collecting three-dimensional space coordinates, channel state indication information and adjacent node connection states, and constructing a network state data set with a timestamp; on the basis of node positions and connection states, a routing algorithm is combined with a topology prediction model to pre-judge a motion track, an optimal path containing a time effectiveness factor is calculated, and convergence delay is reduced through an increment updating mechanism; based on the path quality and the channel state, path stability and channel conditions are analyzed through a deep neural network, and a coding scheme is dynamically selected from a forward error correction coding library in combination with parallel simulation verification; evaluating energy efficiency of a resource allocation algorithm and a task scheduling algorithm in a digital twin simulation environment according to routing and coding requirements, and selecting a resource allocation strategy; through cross-layer optimization of an interface collaborative application path, a coding scheme and a resource strategy, a transmission index is monitored in real time and parameters are dynamically adjusted.

This invention provides a method and apparatus for multi-task scheduling in federated learning. The method involves acquiring a target federated learning task and its current events, wherein the target federated learning task includes multiple events, which are divided into stages based on their execution time and required resources. An event priority ratio is determined based on the attribute information of the target federated learning task and the type of the current event. A cumulative priority is determined based on the event priority ratio and the time the current event entered the scheduling queue. A predefined adaptive resource allocation algorithm is used to allocate computing nodes to the current event, and the current event is executed based on the computing nodes and according to the cumulative priority. This invention ensures fairness in task execution, reduces the overall task execution time of the system, and improves resource utilization.

Disclosed in the present invention is a feedback-free transmission and dynamic resource allocation method for a fully-decoupled space-air-ground integrated network, which provides a solution for resource management of space-air-ground multi-node cooperation. The method comprises: on the basis of a deep learning-based channel state information prediction model, replacing a traditional feedback signal by means of user geographic location information, so as to implement multiple-input multiple-output transmission; and designing a many-to-one matching model for enabling adaptive allocation of spectrum resource blocks between space-air-ground heterogeneous nodes, so as to ensure matching stability and have the characteristics of low complexity and fast convergence. For cooperative transmission of multiple nodes and resource allocation between the heterogeneous nodes, the spectrum efficiency of a system is improved by means of feedback-free channel state information prediction and a resource allocation mechanism. Compared with a single-connection mode and polling-based resource scheduling in a traditional network, the flexible resource allocation algorithm of the present invention significantly increases the network capacity and improves user communication quality.

The invention provides a heterogeneous resource pooling management-oriented equipment virtualization orchestration method, and relates to the technical field of equipment virtualization orchestration, and the method comprises the steps: carrying out the equipment virtualization of to-be-orchestrated equipment, unifying the to-be-orchestrated equipment into a same resource pool, obtaining a production resource formula of each production plan, and obtaining a production resource formula of each production plan; establishing a resource control atlas according to a historical production resource formula, obtaining a production resource formula of a future production plan as a demand resource, and arranging resources in a resource pool through a resource allocation algorithm according to the real-time digital label and the demand resource of each virtual device type, the method comprises the steps of arranging virtual devices, outputting a logic connection relation and arranged virtual device resources through a resource control atlas, marking timestamps on the arranged virtual device resources, obtaining the usage amount of resource types of each device type through the same time interval, and calculating a historical resource allocation equilibrium coefficient as an optimization target. And optimizing the single arrangement ratio, the weight adjustment coefficient and the fitting coefficient.

The invention provides a meta-task management method and device, and the method comprises the steps: S10, receiving a task list, extracting task information, and obtaining real-time available resources; s20, filtering real-time available resources, screening out all our roles and opposite roles for the current task list, and allocating the our roles and the opposite roles to each task by using a resource allocation algorithm in combination with the extracted task information to form a meta-task list; and S30, executing the meta-task list, and monitoring the execution condition at the same time. According to the method, real-time response to the dynamic environment is realized by fusing the global state and the historical execution information, local optimal traps caused by neglecting global state change and inter-task collaboration are avoided, and it is ensured that meta-task execution is consistent with a global target. By monitoring the execution condition of the meta-task, the monitoring result can be incorporated into a reference system for subsequent distribution, the strategy adjustment period is shortened, and the timeliness and accuracy of decision making are improved.

The invention relates to the technical field of cognitive Internet of Vehicles communication, in particular to a spectrum resource allocation algorithm considering reliable transmission. According to the scheme, firstly, channel historical transmission data including reliability indexes such as bandwidth, packet loss rate, bit error rate and transmission delay are collected, and a multi-dimensional channel reliability feature sequence is constructed; then, a lightweight Transform prediction model is constructed to predict the channel reliability states of multiple time slots of the channel in the future, modeling is carried out on channel time sequence correlation through a time sensing multi-layer perceptron, and a future channel reliability index prediction result is obtained through a dimension separable output structure; further performing normalization processing and index fusion on the prediction result, and constructing a channel reliability index; on the basis, a self-adaptive spectrum access mechanism based on reinforcement learning is introduced in combination with a network load state, and a self-adaptive spectrum access decision of the vehicle terminal is realized by dynamically selecting an overlay access mode or an underlay access mode. According to the method, collaborative optimization of channel reliability prediction and a spectrum access strategy can be realized in a high-mobility Internet of Vehicles environment, the system throughput and the spectrum utilization rate are improved, and the packet loss rate is reduced, so that the reliable transmission performance of Internet of Vehicles communication is improved.

The invention provides a fault-recoverable mobile edge computing-oriented robust dependent task scheduling method, which comprises the following steps of: acquiring mobile edge computing system parameters which comprise a dependent task set, an edge network, the number of available CPU (Central Processing Unit) kernels of a server, kernel processing capability and link bandwidth; calling an adaptive resource allocation algorithm based on the system parameters to dynamically determine the scheduling sequence of the sub-tasks, and allocating system resources required for execution to the sub-tasks to obtain a resource allocation matrix; executing the subtasks according to the resource allocation matrix; when a server fault occurs, calling a server fault rescheduling algorithm to update the resource allocation matrix; when a communication link fault occurs, calling a link fault rescheduling algorithm to update the resource allocation matrix; executing the subtasks according to the updated resource allocation matrix until all the outlet subtasks are executed; obtaining execution completion time frames of all the dependent tasks; according to the invention, the robustness and effectiveness of the mobile edge computing system can be improved.

Embodiments of the present application provide an attribute selection method and device, electronic equipment and storage medium. The method comprises: initializing a plurality of populations, each population comprising a plurality of simulation nodes; determining the fitness of each simulation node according to the values of delay, bandwidth, throughput, computing resources, storage resources, network resources and load balancing indicators contained in each simulation node, and a preset fitness function; determining the simulation node with the maximum fitness from the plurality of populations based on the fitness of each simulation node; taking the key attribute corresponding to the population to which the simulation node with the maximum fitness belongs as a target attribute, the target attribute being an input of a resource allocation algorithm in an actual application process, and obtaining a resource allocation result. The key attribute of the population to which the simulation node with the maximum fitness belongs is taken as an input of a resource allocation algorithm in an actual application, so that resource allocation is performed in the actual application through the key attribute and the resource allocation algorithm, and resource utilization efficiency is improved.

The invention belongs to the technical field of wireless communication, and particularly relates to an active reconfigurable intelligent surface enabled satellite-ground RSMA network resource allocation device and algorithm. The algorithm comprises the following steps: establishing a satellite-ground RSMA system model containing nonlinear energy collection; jointly optimizing satellite active beam forming, an ARIS reflection coefficient matrix and a user power distribution ratio, and constructing a system spectrum efficiency maximization optimization problem; the method comprises the following steps: aiming at satellite beam forming and power distribution ratio optimization sub-problems, fixing an ARIS reflection coefficient matrix, and solving through a convex optimization technology to obtain an optimal solution; and aiming at an ARIS reflection coefficient optimization sub-problem, satellite beam forming and a power distribution ratio are fixed, and a feasible solution is obtained through iterative solution by a positive semi-definite relaxation and penalty function method. A simulation result shows that the algorithm is obviously superior to a traditional multiple access technology and a passive RIS auxiliary scheme in the aspect of spectrum efficiency, and the effectiveness and superiority of the algorithm are verified.

The invention requests to protect a dynamic spectrum resource allocation algorithm for evaluating the risk probability of a vehicle terminal in a cognitive Internet of Vehicles. The invention provides a spectrum resource allocation algorithm which takes user risk probability as priori knowledge and takes priority as a medium. Firstly, binary modeling simplification is carried out on historical channel using behaviors of a user, then a risk assessment model of a vehicle terminal is constructed through a nonparametric Bayesian model, the risk probability of the vehicle terminal is iteratively updated, and priorities are divided according to the risk probability. Furthermore, a state space and a reward function are reconstructed by taking the maximum utilization efficiency of the spectrum and the collision probability between the vehicle terminals as guidance, and a dynamic spectrum resource allocation algorithm based on reinforcement learning is provided. Finally, the experimental result shows that the proposed algorithm can reduce the waste of spectrum resources from the source, and reduces the collision probability in the user access process while improving the utilization rate of the spectrum.

The invention discloses a passive positioning method, device and system for a base station, a medium and a product, and relates to the technical field of computers, and the method comprises the steps: dynamically adjusting the beam optimization parameter of each phased-array antenna through a beam optimization resource allocation algorithm; navigation signals from a communication satellite and reflection signals from a plurality of passive terminals are received according to the beam optimization parameters of the phased-array antennas; performing signal analysis on the navigation signal and each reflection signal to obtain a distance difference value of each passive terminal; and processing the distance difference value by using a distributed phased array passive positioning algorithm so as to position each passive terminal. According to the embodiment of the invention, the accuracy of passive positioning of the base station can be improved.

The invention relates to a protocol conversion method and system for an Autbus and FC equipment, and the method comprises the steps: S1, receiving an FC message, and carrying out the priority marking and traffic shaping according to the priority and data type of the FC message; s2, analyzing the FC message, and extracting the data load of the FC message; s3, according to the Autbus protocol and the traffic shaping result, splitting and packaging the data load of the FC message to form a packaged Autbus message; s4, according to the priority identifier, adding the Autbus message into a corresponding cache queue; s5, based on a dynamic time slot resource allocation algorithm, allocating a time slot for the Autbus message, and transmitting the Autbus message through an Autbus bus; s6, an Autbus message is received, and decoding, verification and analysis are carried out on the Autbus message; s7, according to FC protocol recombination data, FC message recombination is carried out on the analyzed Autbus message; s8, performing end-to-end integrity verification on the recombined FC message; and S9, carrying out flow control and rate adaptation based on the FC equipment capability and the network condition, and then sending the recombined FC message.

The invention discloses a network security integrated protection method. The method comprises the following steps: receiving resource requests sent by a plurality of slave gateway devices; performing conversion processing on the resource request by adopting a protocol adaptation model to obtain an analysis data packet; determining throughput corresponding to the analyzed data packet according to the total uplink bandwidth capacity, the emergency degree requested by the slave gateway and historical resource allocation information; according to a preset adjustment algorithm, resource allocation information corresponding to the throughput corresponding to the analyzed data packet is determined, and the preset adjustment algorithm at least comprises a maximum and minimum fairness algorithm or a resource allocation algorithm based on an auction model; and sending the resource allocation information to each slave gateway device, so that the slave gateway carries out resource allocation on each terminal according to the resource allocation information, thereby solving the problem of an unbalanced situation, and improving the network security collaborative protection and linkage sharing capability.

The invention provides a dynamic access method and system for a wireless ad hoc network system, and the method comprises the steps: obtaining a central control node through a preset election algorithm based on a plurality of pre-obtained node parameters; based on pre-acquired initial position information of each node, calibrating the initial position information of each node through a preset ranging algorithm, and constructing a node physical space topology; obtaining real-time traffic data of each node based on a preset statistical period, and obtaining predicted traffic data of each node through a preset prediction algorithm and the real-time traffic data; sending the node physical space topology and the predicted flow data to a central control node, and performing time slot allocation on node resources through a preset resource allocation algorithm to obtain a resource allocation strategy; and sending the resource allocation strategy to each node so as to control the dynamic access of the nodes according to the resource allocation strategy. According to the invention, the space position information of the node is fully considered to carry out time slot allocation, and the access reliability of the wireless ad hoc network is improved.

This invention discloses a time-frequency energy resource allocation system based on an intelligent reflective surface for an underground Internet of Things (IoT) architecture. Belonging to the field of resource allocation, this invention designs a frequency-division duplex IoT architecture for underground spaces by jointly using distributed energy access points and a Resource Allocation System (RIS). To achieve efficient utilization of channel and energy resources, based on this IoT architecture, this invention models the resource allocation problem as a joint allocation of time, spectrum, and energy to meet QoS requirements, along with RIS phase shift configuration. This problem is a mixed-integer nonlinear programming problem, difficult to solve in polynomial time. To reduce the complexity of the solution, this invention constructs a multi-agent collaborative resource allocation algorithm based on near-end policy optimization. Overall, the purpose of this invention is to build an efficient monitoring IoT in underground spaces, achieving an energy-saving and highly efficient resource allocation scheme.

This invention discloses a dynamic task orchestration system and method for telephone lead lifecycle management, relating to the field of data processing technology. The system includes a task orchestration module, a concurrency control module, a lead management module, and a line management module: the task orchestration module dynamically obtains load weights based on load assessment parameters of each service node, and dynamically executes task migration processes based on the obtained load weights and service node loads; the concurrency control module uses an adaptive resource allocation algorithm combining proportional allocation and heap optimization to achieve distributed atomic concurrent control; the lead management module manages the entire lead lifecycle based on a lead status transition mechanism and a three-layer deduplication mechanism; the line management module performs availability checks on candidate lines before outbound calls, implements intelligent allocation of line resources based on a priority-based line scheduling algorithm, and uses a sliding window rate limiting mechanism to achieve periodic frequency control of lines.

The invention relates to the field of digital management of social governance, in particular to a multi-platform social organization collaborative management method and system.The method comprises the steps that social organization basic data and collaborative task requirements in a multi-platform heterogeneous environment are obtained, and conflict feature vectors are determined through an adaptive conflict detection mechanism; real-time synchronous consistency processing is executed based on the conflict feature vectors, and a globally unified collaborative management data set is constructed; and dynamically scheduling the multi-main-body resources by adopting a refined resource allocation algorithm according to a collaborative task requirement, and generating a collaborative task execution instruction in real time. According to the method and the device, automatic and unified integration of cross-platform data is realized, semantic conflicts among the data can be accurately identified and quantified, fine scheduling of multi-subject resources is realized, and the resource matching efficiency and the task completion rate are improved.

The application provides an electromagnetic catapult scheduling method and system for take-off and landing field multi-aircraft cooperation, effectively solving the problem that the prior art cannot meet the operation rule that emergency critical materials must be absolutely prioritized when facing a sudden high-priority task. The method comprises the following steps: acquiring multi-source scheduling input data of at least one unmanned aerial vehicle, and preprocessing the multi-source scheduling input data to obtain a scheduling decision parameter set; processing the scheduling decision parameter set to generate a comprehensive scheduling sequence list of all unmanned aerial vehicles; combining the comprehensive scheduling sequence list with real-time state data of each catapult runway, assigning a target catapult runway to each unmanned aerial vehicle through a resource allocation algorithm, and planning the catapult timing on each runway to generate a final scheduling scheme; issuing the catapult runway corresponding to the final scheduling scheme and controlling the catapult runway to enter a ready state, so that the unmanned aerial vehicles are catapulted and taken off in turn, and the feedback results of the take-off and landing field are monitored in real time during the execution.