70 results about "Cloud scheduling" patented technology

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention relates to the technical field of industrial vehicle intelligence, in particular to an intelligent flexible forklift system based on multi-mode sensing and self-adaptive path planning. The intelligent forklift comprises an intelligent forklift body, a vehicle-mounted integrated control system, a multi-mode sensing and navigation module, a self-adaptive decision-making and path planning module, a cooperative communication and task management module and a cloud scheduling and management platform, and the intelligent forklift body is provided with a pallet fork mechanism with a 3D camera and a force sensor; the vehicle-mounted integrated control system serves as a core to achieve multi-module data interaction and instruction output, the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion, the self-adaptive decision-making and path planning module completes path planning, obstacle avoidance and pre-verification, the cooperative communication and task management module achieves multi-vehicle cooperation and task disassembly, and the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion. And the cloud scheduling and management platform realizes global monitoring and management. According to the method, the adaptability to complex scenes and the operation safety are improved, and accurate forking and flexible path planning are realized.

The invention relates to the technical field of industrial network control, in particular to a side-cloud collaborative software-defined industrial control system, which comprises a side control action configuration module, a multi-source signal collection module, a state mutation recognition module, a cloud scheduling module and a collaborative control generation module. In the invention, through decoupling control action configuration, multi-channel signal acquisition, data label construction, sudden change state identification and a remote scheduling execution process and realizing standardized processing, an edge control basic instruction structure body can be quickly constructed based on an equipment state and a unified time sequence data label mechanism is established; in combination with the dynamic sensing ability of a sudden change state and a feedback point identification mode, efficient discrimination and rapid classification of key control points can be realized, task chain execution can be rapidly mapped to a key path and real-time regulation and control are implemented, rapid response to industrial field multi-source change and accurate issuing of a control instruction are realized, and the task chain execution efficiency is improved. And the real-time performance and the collaboration of the control process are greatly improved.

The invention relates to the technical field of photovoltaic power generation cloud platforms, and discloses a multi-site energy storage cluster cloud scheduling platform based on photovoltaic power generation prediction, and the platform employs a real-time data collection and space-time synchronization module to carry out the timestamp alignment and space grid processing of multi-source heterogeneous data. In combination with a dynamic priority scheduling mechanism of a prediction task distribution module based on volatility and a power grid security situation, the effects of cooperative execution of prediction tasks and priority guarantee of key tasks are achieved; through containerized parallel reasoning and consistency Hash load balancing technology of a distributed prediction calculation module, time consistency of a multi-node prediction result is ensured, and a rolling optimization algorithm and a time sequence constraint embedding strategy of a unified scheduling decision module are adopted. Accurate calculation of power generation unbalanced power and time sequence consistency generation of an energy storage strategy are achieved, and through instruction verification and conflict detection and filtering frequency reduction processing of a smoothing module, the effects of prolonging the service life of equipment and guaranteeing stable operation of a power grid are achieved.

The application relates to a cloud service-based model inference service system and method capable of on-demand scaling. The system comprises a gateway module, a model inference service management module and a request distribution module. The gateway module is used for receiving a user request and extracting heterogeneous features representing the request's demand for computing resources. The model inference service management module is used for generating resource scheduling instructions for each inference service instance based on the multi-dimensional indicators of the deployed inference service instances, in combination with the resource cost and availability information of each cloud resource pool. The request distribution module is used for distributing the user request to the inference service instance that is adapted to the heterogeneous features according to the resource scheduling instructions. The system can solve the problems of low utilization rate of computing resources, poor request adaptability and lack of cross-cloud scheduling in the prior art.

This invention discloses a non-embedded UAV information acquisition device and method. The non-embedded UAV information acquisition device includes a shell assembly, a three-mode adapter structure, a standardized physical interface, a main control module, a communication module, a positioning and sensing module, and a power management module. The internal cavity wall of the shell assembly is fitted with an anti-magnetic shielding layer made of permalloy. The three-mode adapter structure is integrated into the bottom of the shell assembly and includes a magnetic suction component, a suspension component, and a screw fixing interface. The standardized physical interface is located on the surface of the shell assembly and uses a gold-plated gold finger contact structure, which is electrically connected to the main control module. This invention enables deployment without disassembly of the UAV through a non-intrusive design while maintaining the UAV's airworthiness. It utilizes edge computing to alleviate cloud scheduling pressure and reduce task latency, and with independent power supply, achieves long endurance and high reliability, making it widely adaptable to various UAV models and harsh operating environments.

The invention provides a UV printer intelligent control system and method based on remote task scheduling, and the system comprises a cloud scheduling center module which is used for global task planning and distribution, network state monitoring, equipment state monitoring, and data storage and analysis; the edge gateway node module is used for caching the task data issued by the cloud scheduling center module, serving as a local scheduling center when the network is interrupted, and performing cooperative control and fault diagnosis and early warning on a plurality of devices at the same time; and the UV printer terminal module is used for collecting state data of printing equipment in real time, receiving and executing tasks, locally caching the data and performing man-machine interaction. By combining cloud global optimization, edge local collaboration and terminal offline autonomy, the dependence on a single network is reduced, the overall production efficiency is improved through multi-device collaboration and load balancing, ink waste and invalid loss of devices are reduced based on a prediction model, a printing path is optimized, and energy consumption is reduced.

The invention discloses a warehouse logistics scheduling method and system based on cloud computing, and relates to the field of warehouse logistics management, and the method comprises the steps: collecting to-be-processed cargo information and available robot state information in a warehouse management system in real time based on a cloud scheduling platform; carrying out the clustering of the to-be-processed goods, carrying out the calculation and evaluation of a clustering result, and obtaining an optimal task partition division scheme; obtaining the shortest collision-free path from each available robot to each task partition, and calculating the path length of the shortest collision-free path; calculating path suitability, load suitability and electric quantity sufficiency between each available robot and each task partition; determining an optimal allocation scheme between the available robots and the task partitions based on the three features; and scheduling the available robots based on the optimal allocation scheme and monitoring the operation states of the robots in real time. According to the invention, real-time, efficient and balanced global integrated scheduling can be carried out on logistics robots and order tasks, and the collaborative operation capability of a robot cluster is improved.

The present application relates to the technical field of computer, specifically relates to a kind of dynamic cloud scheduling method and system of artificial intelligence workflow;The method of the present application includes defining standardized AI-Workflow IR intermediate representation, convert native workflow into the workflow of standardized AI-Workflow IR intermediate representation, build calculation dependency graph, obtain the key path node in calculation dependency graph by CPA algorithm, form key path, mark the subgraph in key path, carry out subgraph segmentation and subgraph optimization to the workflow of standardized AI-Workflow IR intermediate representation using GRR algorithm, based on deep reinforcement learning algorithm, the matching scheduling of Agent is carried out to subgraph;The present application supports the conversion of workflow of different frameworks, optimizes scheduling strategy with environmental change, JIT-Agent generates specialized service on demand, and realizes the distribution of workflow reasonably.

The invention relates to a model reasoning service system and method for on-demand capacity expansion and contraction based on cloud service. The system comprises a gateway module, a model reasoning service management module and a request distribution module, and the gateway module is used for receiving user requests and extracting heterogeneous features representing the requirements of the requests for computing resources; the model reasoning service management module is used for generating a resource scheduling instruction for each reasoning service instance based on the multi-dimensional index of the deployed reasoning service instance in combination with the resource cost and availability information of each cloud resource pool; and the request distribution module is used for distributing a user request to the inference service instance matched with the heterogeneous characteristics according to the resource scheduling instruction. By adopting the system, the problems of low computing resource utilization rate, poor request adaptability and cross-cloud scheduling deficiency in the prior art can be solved.

The invention provides an intelligent calculation center cloud scheduling system, and relates to the technical field of artificial intelligence and big data, and the system comprises a GPU cluster which is formed by the mixed deployment of at least a first type of GPU cards and a second type of GPU cards; and the scheduler is connected with the GPU cluster, a plurality of scheduling strategies are configured in the scheduler, and the scheduler is used for carrying out hybrid scheduling on the first type of GPU cards and the second type of GPU cards based on the scheduling strategies. The method has the beneficial effects that multi-strategy hybrid scheduling of different types of GPU cards is effectively realized, the use efficiency of computing power resources is improved, the operation cost is reduced, the operation efficiency of a system is improved, and the system risk is reduced.

The invention relates to the technical field of computers, in particular to a dynamic cloud scheduling method and system for an artificial intelligence workflow. The method comprises the steps of defining a standardized AI-Workflow IR intermediate representation, converting an original workflow into a workflow of the standardized AI-Workflow IR intermediate representation, constructing a calculation dependency graph, obtaining key path nodes in the calculation dependency graph through a CPA algorithm, forming a key path, marking sub-graphs in the key path, and calculating the workflow of the standardized AI-Workflow IR intermediate representation. Subgraph segmentation and subgraph optimization are carried out on the workflow represented in the standardized AI-Workflow IR by adopting a GRR algorithm, and Agent matching scheduling is carried out on subgraphs based on a deep reinforcement learning algorithm; according to the method, workflow conversion of different frameworks is supported, the scheduling strategy is optimized along with environment changes, the JIT-Agent generates special and precise services according to needs, and reasonable workflow distribution is achieved.

The invention provides a scheduling test method based on a vehicle and road cloud scheduling system and an electronic device, the method is applied to a cloud side device in the vehicle and road cloud scheduling system, and the method comprises the following steps: in response to a scheduling test instruction, receiving to-be-tested vehicle information, to-be-tested object information and a test type carried in the scheduling test instruction; according to the to-be-tested vehicle information and the to-be-tested object information, calling a scheduling model to generate to-be-tested scheduling information; performing a corresponding scheduling test on the to-be-tested scheduling information according to the test type to obtain a test result corresponding to the test type; when it is determined that the to-be-tested scheduling information is abnormal according to the test result, updating the scheduling model to obtain an updated scheduling model; and according to the to-be-tested vehicle information and the to-be-tested object information, calling the updated scheduling model to generate new to-be-tested scheduling information, and carrying out scheduling test on the new to-be-tested scheduling information according to the test type until a test result of the new to-be-tested scheduling information does not have abnormity.

The specification discloses a multi-screen content scheduling system based on multi-dimensional behavior data, which comprises an edge computing device and a cloud processing module. The edge computing device is configured on the side of the screen and comprises a data acquisition and preprocessing unit and a scheduling execution unit. The data acquisition and preprocessing unit acquires multi-dimensional behavior data on the spot of each screen, performs preprocessing and constructs a behavior feature vector. The scheduling execution unit receives and executes a content resource scheduling strategy issued by the cloud processing module. The cloud processing module comprises a cloud database unit and a cloud scheduling unit. The cloud database unit stores data. The cloud scheduling unit performs calculation based on a rule engine and a reinforcement learning network model according to the behavior feature vector, generates a content resource scheduling strategy containing a play content list of each screen, adopts a terminal and cloud cooperation architecture, realizes unified resource scheduling of multiple screens according to different behavior data, and realizes content resource scheduling across time periods and devices.

A method for scheduling edge nodes and a cloud scheduling platform are disclosed, relating to the field of computer technology. This method is applied to a cloud scheduling platform. After receiving a DNS request from a target user, the cloud scheduling platform determines the IP address of the first edge node corresponding to the target access information indicated in the DNS request, based on edge node scheduling information, and returns the IP address of the first edge node to the target user. Since the first edge node scheduled by the cloud scheduling platform has fewer ASs on its network link to the target AS indicated by the target access information than other edge nodes, when the target user accesses the first edge node corresponding to the target domain name through the target AS, the number of AS hops on the network link is reduced compared to accessing other edge nodes corresponding to the target domain name. This reduces access latency and improves network communication quality.

The invention discloses a flexible regulation and control system and method based on distributed photovoltaic power generation, and belongs to the technical field of photovoltaic energy storage regulation and control, and the system specifically comprises a power grid cloud scheduling platform, a meteorological sensing module, a load prediction module, a photovoltaic power generation prediction module, a net load evaluation module and an energy storage distribution scheduling module. The method comprises the steps of obtaining a net load curve based on a future generating capacity curve and a future load curve, introducing a net load reference line to compare and mark a charging window and a discharging window, and decomposing the net load curve into a high-frequency component and a low-frequency component by adopting a frequency domain decomposition technology; then different battery scheduling sub-modules are introduced to respond to the high-frequency component and the low-frequency component respectively, an optimal charging and discharging instruction in a future period is formulated based on net load absolute values and battery characteristic parameters under a charging window and a discharging window, and through distributed dynamic flexible energy storage scheduling, rapid charging and discharging response is ensured, and the charging and discharging efficiency is improved. And a peak clipping and valley filling plan can be stably executed.

This invention belongs to the field of digital cloud scheduling technology for road networks, specifically relating to a method and system for digital cloud scheduling of road networks based on vehicle-road cooperation. The method includes: collecting traffic flow data using roadside units and identifying vehicle information; analyzing the vehicle information to obtain the vehicle spatial interference index and vehicle behavior interference index of unconnected vehicles, and combining these to generate a penetration impact index for unconnected vehicles; using the penetration impact index of unconnected vehicles to obtain a green wave attenuation index through green wave traffic interference analysis; using the green wave attenuation index to obtain road saturation through road capacity assessment; and invoking the corresponding green wave coordination strategy based on the road saturation. This invention achieves quantitative and dynamic differentiated scheduling of interference from unconnected vehicles, effectively improving the efficiency of green wave traffic and the overall operational efficiency of the road network in mixed traffic flow environments.

This invention discloses a method and system for vehicle access edge cloud scheduling based on multi-dimensional scenarios. The method includes: a scheduling scenario configuration step, which predefines multiple scheduling scenarios and configures corresponding scheduling strategies for each scenario; a vehicle request processing step, which receives and verifies the access request from the vehicle terminal and extracts the vehicle location and service requirements; a scheduling decision step, which determines the target scheduling scenario based on the service requirements and selects the optimal target edge cloud node based on the scenario's strategy, vehicle location, and the real-time resource status of each edge cloud node; and a service redirection step, which returns the target node information to the vehicle, enabling it to directly access and obtain services. This invention achieves intelligent and dynamic vehicle scheduling through scenario-based strategies and multi-factor decision-making, effectively improving the system's ability to cope with tidal loads, ensuring low-latency services, and optimizing resource utilization efficiency.

The invention provides a strategy migration method and system applied to a cross-domain unmanned system, and relates to the technical field of strategy migration, and the method comprises the steps: obtaining a task instruction, platform information and environment information of the cross-domain unmanned system; computing platform-independent ownership potential representations and serializing the ownership potential representations to obtain an ownership token sequence; processing the task instruction and the token sequence with the body through a pre-constructed model to obtain a high-level strategy token sequence; the high-level strategy token sequence is converted into a high-level strategy; based on a cross-domain strategy distillation loss processing high-level strategy, obtaining a root domain high-level strategy and a target domain high-level strategy after alignment; receiving a high-level strategy and a real-time platform state detected by a state space through a pre-constructed lightweight control head, generating a bottom-layer control instruction and outputting the bottom-layer control instruction; and detecting and generating an execution result of the underlying control instruction, and adaptively updating the model and the lightweight control head. According to the invention, a unified deployment framework is constructed, and plug-and-play cross-platform deployment is realized through modes of cloud scheduling and end-side loading.

The invention provides a vehicle lightering auxiliary system and method in a closed area and electronic equipment. The vehicle lightering auxiliary system comprises a cloud server and vehicles located in the same closed area. The first vehicle obtains environment sensing data and vehicle motion state data in the driving process in the closed area; a memory path from a lightering starting point to a lightering ending point in the closed area is generated based on the environment perception data and the vehicle motion state data, and the memory path is uploaded to a cloud scheduling system; the cloud server generates a customized lightering path of the closed area based on the memory path uploaded by the first vehicle; when a lightering trigger instruction sent by a second vehicle in the closed area is received, issuing the customized lightering path of the closed area to the second vehicle; the second vehicle performs an autonomous lightering operation based on the customized lightering path. According to the invention, signal delay and error risks caused by multi-system docking are effectively solved, and the overall collaboration and stability of autonomous lightering of vehicles in a closed area are improved.

The invention relates to the technical field of power systems and smart power grids, and discloses a smart power grid energy management method and system driven by the Internet of Things. According to the method, power grid data are collected in real time through an Internet of Things sensing terminal, millisecond-level electromagnetic transient simulation is executed on the edge side, and a thermoelectric coupling model and a cumulative damage theory are combined; quantifying the equipment life loss caused by the transient event, and mapping the equipment life loss into monetization health loss cost; the cost is embedded into a cloud hour-level economic dispatching objective function, an optimal dispatching instruction considering short-term economy and long-term asset health is generated, and closed-loop control is formed. The system comprises a sensing terminal array, an edge computing cluster, a health loss cost mapping module, a cloud scheduling platform and an instruction synchronization module. According to the invention, full life cycle cost optimization is realized, the power grid safety margin is effectively improved, and the service life of equipment is effectively prolonged.

The invention provides an AI intelligent management system based on multi-cluster heterogeneous computing power scheduling. Relates to the technical field of artificial intelligence computing and resource management, and comprises an edge cloud scheduling module which comprises an edge node and a cloud cluster, executes task switching between the edge node and the cloud cluster based on a cross-domain scheduling result, and executes the task switching through an intelligent decision mechanism to generate an edge cloud scheduling result. According to the AI intelligent management system based on multi-cluster heterogeneous computing power scheduling, efficient scheduling and optimal distribution of heterogeneous computing resources are realized through feature recognition of a deep learning algorithm, cross-domain scheduling and edge cloud collaborative scheduling technologies. Specifically, the feature recognition unit generates a computing resource feature vector set, optimizes allocation of computing resources through an adaptive scheduling mechanism in combination with task scheduling parameters, and forms a cross-platform adaptive scheduling scheme, so that the task processing efficiency and the utilization rate of the computing resources are remarkably improved.

The present invention discloses an intelligent triage and real-time vital sign monitoring transfer stretcher system, belonging to the technical field of intelligent medical devices. The present invention combines the advantages of hardware adaptability, intelligent triage and monitoring, and cloud scheduling integration, greatly improving the overall efficiency and safety of casualty transfer and first aid. The stretcher hardware adopts lightweight composite materials and is equipped with a multi-functional design, adapting to different casualties and scenarios. Multiple sensors achieve high-precision continuous collection of vital sign and environmental data, dynamically adjust the body temperature of the casualty, and ensure transfer safety at the basic level. Through the trained injury triage model, four-level accurate classification of injuries is achieved, and the monitoring threshold can be dynamically adjusted according to the injury condition. Early warnings are given for abnormal vital signs in a timely manner. Relying on cloud-based multi-mode wireless transmission, data interconnection in the whole scenario is realized, docking with the hospital information system, completing multi-terminal linkage scheduling for the entire first aid process, optimizing transfer specifications through data correlation analysis, rationally allocating first aid resources, and achieving seamless connection between pre-hospital and in-hospital first aid.

The invention aims to provide the V2G vehicle cluster ordered charging and discharging scheduling system based on multi-objective optimization, the response rate is high, the carbon emission reduction is high, the cycle life of the battery side is prolonged, the load peak-valley difference of the power grid side is relatively low, and the annual charging and discharging cost of the user side is relatively low. The system comprises a cloud scheduling platform, a vehicle end response module, a third-party SDK aggregator and an incentive mechanism platform, the cloud scheduling platform is in communication connection with the third-party SDK aggregator, and the cloud scheduling platform is in communication connection with the vehicle end response module through a plurality of edge computing nodes. The vehicle end response module is in communication connection with the incentive mechanism platform, the cloud scheduling platform is provided with a five-dimensional target optimization engine, a GNN-MOPSO module and a DDPG real-time engine, and the incentive mechanism platform is in communication connection with a carbon exchange and an electricity market. The method is applied to the technical field of power grid dispatching.

The invention provides a multi-energy flow collaborative park virtual power plant hierarchical control system, which adopts a cloud-side-end three-level hardware architecture, and realizes physical interconnection based on an alternating current and direct current hybrid micro-grid taking an electric energy router as a core. The system comprises a cloud scheduling platform deployed in a remote data center, a plurality of edge cooperative controllers deployed in a park, and various terminal execution devices connected to the AC / DC hybrid microgrid. The AC / DC hybrid micro-grid takes the electric energy router as a core, integrates various terminal execution devices through a multi-stage DC bus, and realizes DC routing and collaborative management of energy. Wherein the cloud scheduling platform, each edge cooperative controller and each terminal execution device are in communication connection in sequence to form a hierarchical information flow channel; and the electric energy router, the multi-stage direct current bus and each terminal execution device are physically connected in sequence to form a graded energy flow channel. The system is clear in structure, and the energy efficiency of the park energy system and the cooperative response capability to the power grid regulation instruction are improved.

This application discloses a control method and device for a logistics intelligent mobile body, belonging to the field of logistics automation technology. It includes: acquiring task instructions, which include a combined loading scheme consisting of multiple standardized cargo boxes of different specifications, determined by a cloud scheduling module based on cargo attributes, and the location of the pickup point; navigating to the pickup point; upon arrival, controlling the hatch to open, and performing automatic loading operations on each standardized cargo box through a composite mechanical actuator, including controlling a vertical lifting mechanism to support the two side wings of the cargo box and raise it a preset distance to detach it from the support surface, and then controlling a horizontal moving mechanism to move the cargo box to a designated position within the cargo hold; after loading is completed, executing the transportation task and providing status feedback. This invention, through the coordination of cloud-based on-demand combination of cargo boxes and specific linked loading and unloading actions at the mobile body end, achieves variable transportation volume and fully automated loading and unloading, solving the problems of fixed cargo hold capacity and reliance on manual loading and unloading in existing technologies, thus improving logistics efficiency and scenario adaptability.

The application relates to the technical field of unmanned aerial vehicle automation application, and discloses an unmanned aerial vehicle channel automatic inspection system based on an AI large model, which comprises a cloud scheduling platform that extracts structured task parameters of a natural language inspection instruction through a large language model and issues a three-dimensional waypoint sequence in combination with a three-dimensional GIS database. An unmanned aerial vehicle platform is equipped with a multi-light pod for taking off, and an on-board edge platform runs a target detection algorithm to process image streams and track target entities. In combination with an RTK absolute coordinate, an inertial measurement unit, a multi-light pod attitude matrix and a laser ranging value, a smooth absolute coordinate sequence of the target entity is calculated and filtered. The system extracts static ground feature data to construct a three-dimensional bounding box set, calculates and predicts a trajectory, determines a hovering observation point coordinate when the bounding box is crossed to cause occlusion, and controls the unmanned aerial vehicle to hover until the target entity passes through and visual tracking is restored. The application solves the problem of tracking interruption caused by obstacles in a complex channel, and maintains the continuity of the inspection closed loop.

This invention discloses a charging pile energy scheduling method based on cloud-edge collaboration, comprising the following steps: Step 1: Collecting station-side energy operation data within the target charging station and generating a unified time-based state sequence; Step 2: Establishing edge connections between different energy nodes and calculating edge weights to generate an intra-station energy relationship graph; Step 3: Uploading the intra-station energy relationship graph to a cloud scheduling platform and generating a regional-level cloud energy scheduling graph; Step 4: Inputting the regional-level cloud energy scheduling graph into an improved Graphormer model to generate cloud-based regional scheduling results; Step 5: Splitting the cloud-based regional scheduling results according to the charging station number and distributing station-level scheduling benchmarks to the corresponding edge gateways; Step 6: Obtaining real-time power allocation results within the station based on the station-level scheduling benchmarks and the unified time-based state sequence. This invention achieves intelligent scheduling of charging energy through an improved Graphormer model.

The present application relates to unmanned aerial vehicle battery replacement technical field, disclose a kind of mobile autonomous battery replacement system and control method for agricultural unmanned aerial vehicle, system includes plant protection unmanned aerial vehicle, mobile battery replacement trolley, cloud scheduling platform and slide bar adjusting part;Mobile battery replacement trolley is equipped with omnidirectional mobile chassis, battery replacement guide platform, mobile station, battery replacement mechanical arm and modular battery compartment;Control system uses bottom layer double MCU hard real-time control, heterogeneous sensing redundancy and edge computing three-layer architecture.The method comprises: cloud scheduling platform assigns idle trolley based on nearest neighbor matching algorithm;Trolley autonomously navigates to the meeting point;Unmanned aerial vehicle is landed to battery replacement guide platform by visual guidance.The present application solves the problem that fixed energy station leads to too long invalid range of unmanned aerial vehicle, and low accuracy of single calibration method in complex environment, realizes energy with high robustness battery replacement, and is suitable for large-scale agricultural plant protection operation.

This application discloses a method, system, and storage medium for controlling and operating unmanned aerial vehicles (UAVs) for beyond-line-of-sight (BLS) power transmission line inspections via cloud computing. The method includes: integrating 5G / 5G-A, BeiDou positioning, and self-organizing network technologies through a multi-mode communication unit; constructing a dedicated transmission channel for power line inspection based on 5G network slicing to achieve adaptive switching of communication links and low-latency transmission under obstructed environments; preprocessing and real-time transmission of high-definition video and infrared thermal imaging data collected by the UAV through a data processing unit; remotely issuing autonomous driving routes and assigning multi-UAV tasks through a collaborative management platform built by a cloud scheduling unit; and automatically detecting and issuing early warnings of potential hazards using a power transmission defect AI identification model; and monitoring and alarming intruding UAVs through an airspace electronic fence configured by a security protection unit, combined with 5G-A sensing technology. This invention can extend the inspection distance per flight, improve data processing efficiency and remote command and coordination capabilities, and enhance the intelligence and reliability of power transmission line inspections.