Scheduling method, digital twin system, unmanned aerial vehicle and base station

Abstract

The invention provides a scheduling method, a digital twin system, an unmanned aerial vehicle and a base station. The scheduling method comprises the following steps: receiving service data and service intention of a target unmanned aerial vehicle sent by a first base station by a digital twin system; the digital twin system generates optimized route information about the target unmanned aerial vehicle according to the service data and the service intention, and sends a flight command of flight based on the optimized route information to the target unmanned aerial vehicle; in response to the fact that the utilization rate of a first resource in a service resource unit corresponding to the service intention on the first base station is larger than a target utilization threshold value, the digital twin system determines whether a first target resource unit used for scheduling the first resource for the service resource unit exists on the first base station, and in response to the fact that the first target resource unit exists on the first base station, the service intention is scheduled. And the digital twin system sends a first allocation command for scheduling the first resource in the first target resource unit to the service resource unit according to the preset capacity reduction proportion of the first resource margin to the first base station.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and more specifically, to a scheduling method, a digital twin system, a drone, and a base station. Background Art

[0002] With the expansion of the general aviation industry chain and the generation of new "drone +" business models, a new economic model is being created in the economic field - the low-altitude economic model. Low-altitude airspace is divided into available space and unavailable space due to its proximity to the surface and the influence of low-altitude obstacles and their spacing standards. Among them, available space refers to airspace without obstacles and not affected by the geographical environment, and unavailable space refers to airspace that becomes unusable due to obstacles or geographical environment. Under the same flight altitude and air traffic flow conditions, the available space of low-altitude airspace in areas with complex low-altitude obstacles is smaller than that in areas with simple low-altitude obstacles. Therefore, there is a need to optimize the utilization rate of low-altitude airspace. Summary of the invention

[0003] A brief overview of the disclosure is given below in order to provide a basic understanding of some aspects of the disclosure. However, it should be understood that this overview is not an exhaustive overview of the disclosure. It is not intended to identify the key or important parts of the disclosure, nor is it intended to limit the scope of the disclosure. Its purpose is simply to give some concepts of the disclosure in a simplified form as a prelude to a more detailed description given later.

[0004] One of the purposes of the present disclosure is to provide a scheduling method, a digital twin system, a drone and a base station to perceive the business intentions of the drone in real time, schedule airspace resources, optimize the business execution of the drone, and improve airspace utilization.

[0005] According to a first aspect of the present disclosure, a scheduling method is provided, comprising:

[0006] The digital twin system receives the business data and business intention of the target UAV sent by the first base station;

[0007] The digital twin system generates optimized route information about the target UAV according to the business data and the business intent, and sends a flight command to the target UAV to fly based on the optimized route information;

[0008] In response to the utilization rate of the first resource in the business resource unit corresponding to the business intent on the first base station being greater than the target utilization threshold, the digital twin system determines whether there is a first target resource unit on the first base station for scheduling the first resource to the business resource unit, and, in response to the existence of the first target resource unit on the first base station, the digital twin system sends a first allocation command to the first base station to schedule the first resource in the first target resource unit to the business resource unit according to a preset reduction ratio of the first resource margin, wherein a business application related to the business intent is deployed on the business resource unit, and the first resource is used by the business resource unit to process the business application according to the business data.

[0009] According to a second aspect of the present disclosure, a digital twin system is provided, comprising:

[0010] A first receiving module, wherein the first receiving module is configured to receive the service data and service intention of the target UAV sent by the first base station;

[0011] A generating module, wherein the generating module is configured to generate optimized route information about the target UAV according to the business data and the business intent;

[0012] a determination module, the determination module being configured to determine whether there is a first target resource unit on the first base station for scheduling the first resource to the service resource unit in response to a utilization rate of the first resource in the service resource unit corresponding to the service intent on the first base station being greater than a target utilization threshold, wherein a service application related to the service intent is deployed on the service resource unit, and the first resource is used by the service resource unit to process the service application according to the service data; and

[0013] A first sending module, wherein the first sending module is configured to:

[0014] In response to the first target resource unit existing on the first base station, sending a first allocation command to the first base station to schedule the first resource in the first target resource unit to the service resource unit according to a preset reduction ratio of the first resource margin; and

[0015] A flight command based on the optimized route information is sent to the target UAV.

[0016] According to a third aspect of the present disclosure, a scheduling method is provided, including:

[0017] The base station receives the service request of the target UAV;

[0018] The base station identifies the service intention of the target UAV according to the service request, and receives the service data sent by the target UAV; and

[0019] The base station sends the business intention and business data of the target UAV to the digital twin system so that the digital twin system can generate corresponding scheduling commands;

[0020] In response to receiving a scheduling command sent by the digital twin system to the base station, the base station executes scheduling corresponding to the scheduling command.

[0021] According to a fourth aspect of the present disclosure, a base station is provided, including:

[0022] a second receiving module, wherein the second receiving module is configured to receive a service request of a target UAV and service data sent by the target UAV;

[0023] an identification module, the identification module being configured to identify the service intention of the target drone according to the service request; and

[0024] A second sending module, wherein the second sending module is configured to send the business intention and business data of the target UAV to the digital twin system so that the digital twin system generates a corresponding scheduling command;

[0025] A first execution module, wherein the first execution module is configured to execute scheduling corresponding to the scheduling command in response to receiving a scheduling command sent by the digital twin system to the base station.

[0026] According to a fifth aspect of the present disclosure, a scheduling method is provided, including:

[0027] The drone sends a first service request and first service data to the first base station, so that the first base station identifies the first service intention according to the first service request; and

[0028] In response to receiving a scheduling command sent by the digital twin system to the drone, the drone executes scheduling corresponding to the scheduling command, wherein the scheduling command is generated by the digital twin system based on at least the first business intent and the first business data.

[0029] According to a sixth aspect of the present disclosure, there is provided a drone, comprising:

[0030] a third sending module, the third sending module being configured to send a first service request and first service data to the first base station, so that the first base station identifies a first service intention according to the first service request; and

[0031] A second execution module is configured to execute scheduling corresponding to the scheduling command in response to receiving a scheduling command sent by the digital twin system to the drone, wherein the scheduling command is generated by the digital twin system at least based on the first business intent and the first business data.

[0032] According to a seventh aspect of the present disclosure, a scheduling system is provided, wherein the scheduling system comprises:

[0033] Digital twin systems as described above;

[0034] A base station as described above; and

[0035] A drone as described above.

[0036] According to an eighth aspect of an embodiment of the present disclosure, a scheduling device is provided, wherein the scheduling device includes:

[0037] Processor; and

[0038] A memory, wherein instructions are stored in the memory, and when the instructions are executed by the processor, the operation of the scheduling method as described above is implemented.

[0039] According to a ninth aspect of an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein instructions are stored on the non-transitory computer-readable storage medium, and when the instructions are executed by a processor, the operations of the scheduling method described above are implemented.

[0040] According to a tenth aspect of an embodiment of the present disclosure, a computer program product is provided, wherein the computer program product includes instructions, and when the instructions are executed by a processor, the operations of the scheduling method as described above are implemented.

[0041] Other features and advantages of the present disclosure will become more apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The foregoing and other features and advantages of the present disclosure will become apparent from the following description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are incorporated herein and form a part of the specification, and are further used to explain the principles of the present disclosure and enable those skilled in the art to make and use the present disclosure. Among them:

[0043] Figure 1 A schematic diagram showing a flow chart of a scheduling method according to an exemplary embodiment of the present disclosure is shown;

[0044] Figure 2 A schematic diagram of network self-optimization and management based on a digital twin system in a specific example is shown;

[0045] Figure 3 A schematic diagram of an intent network architecture in a specific example is shown;

[0046] Figure 4 A schematic diagram of ONAP architecture deployment in a specific example is shown;

[0047] Figure 5 A schematic diagram of an ONAP random graph network double closed-loop architecture in a specific example is shown;

[0048] Figure 6 A schematic diagram of an end-to-end slice closed loop structure in a specific example is shown;

[0049] Figure 7 A schematic diagram of an intent instance management model based on the ONAP open source architecture in a specific example is shown;

[0050] Figure 8 A schematic diagram of an intent instance management method in a specific example is shown;

[0051] Fig. 9 A schematic block diagram of a digital twin system according to an exemplary embodiment of the present disclosure is shown;

[0052] Fig.10 A schematic diagram showing a flow chart of a scheduling method according to another exemplary embodiment of the present disclosure is shown;

[0053] Fig.11 A schematic block diagram of a base station according to an exemplary embodiment of the present disclosure is shown;

[0054] Fig.12 A schematic flow chart of a scheduling method according to another exemplary embodiment of the present disclosure is shown;

[0055] Fig.13 A schematic block diagram of a drone according to an exemplary embodiment of the present disclosure is shown;

[0056] Figures 14 to 23 A schematic diagram of a flow chart of a scheduling method according to a specific example of the present disclosure is shown;

[0057] Fig.24 A schematic block diagram of a scheduling system according to an exemplary embodiment of the present disclosure is shown;

[0058] Fig.25 A schematic block diagram of a scheduling device according to an exemplary embodiment of the present disclosure is shown.

[0059] For ease of understanding, the position, size, range, etc. of each structure shown in the drawings and the like may not represent the actual position, size, range, etc. Therefore, the present disclosure is not limited to the position, size, range, etc. disclosed in the drawings and the like. DETAILED DESCRIPTION

[0060] Various exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0061] The following description of at least one exemplary embodiment is in fact merely illustrative and is in no way intended to limit the present disclosure and its application or use. That is, the structures and methods herein are shown in an exemplary manner to illustrate different embodiments of the structures and methods in the present disclosure. However, those skilled in the art will appreciate that they merely illustrate exemplary ways of the present disclosure that can be implemented, rather than exhaustive ways. In addition, the drawings need not be drawn to scale, and some features may be enlarged to illustrate the details of specific components.

[0062] In addition, technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0063] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0064] Low-altitude airspace refers to the space below 1,000 meters above the earth's surface where general-purpose aircraft can fly. It is an important part of the airspace and an important strategic resource. The capacity of low-altitude airspace refers to the maximum number of aircraft that can be safely accommodated in a unit of time, also known as the air traffic service volume of low-altitude airspace, and the capacity of low-altitude airspace is limited. With the vigorous development of communication technology, more and more user-end devices need to use airspace resources to perform corresponding services, but the space and capacity of airspace are limited. Therefore, it is necessary to optimize the utilization of low-altitude airspace to meet the business needs of more user-end devices.

[0065] To this end, the present disclosure proposes a scheduling method, which generates a more targeted scheduling strategy based on the business intentions and business data of the UAV through a digital twin system, and sends scheduling commands to corresponding network entities (such as base stations and UAVs) so that the network entities can execute corresponding scheduling according to the scheduling commands, thereby improving the utilization rate of low-altitude airspace.

[0066] According to one aspect of the present disclosure, a scheduling method that can be performed by a digital twin system is provided. Figure 1As shown, in an exemplary embodiment of the present disclosure, the scheduling method 100 may include:

[0067] Step S110: The digital twin system receives the business data and business intention of the target drone sent by the first base station.

[0068] In some embodiments, urban airspace (including aircraft and meteorological information) and ground building and road models can be constructed in the digital twin system, and an airspace management coordinate system can be established to obtain the location of each entity in the physical network.

[0069] Figure 2 FIG. 1 shows a schematic diagram of network self-optimization and management based on a digital twin system in a specific example. Figure 2 As shown in the figure, the digital twin system can create a virtual twin corresponding to the entity of the physical network in a digital way, and the physical network and the virtual twin can interact in real time. For example, the digital twin system can collect information about the physical network in real time, and based on the virtual twin simulation, the corresponding optimization and management can be achieved in the physical network entity. The digital twin system can be used for network operation, management, and optimization. By building a network virtual twin that can be interactively mapped in real time, the optimization management of the physical network can be achieved. The digital twin network system can achieve efficient analysis, diagnosis, simulation, and control of the physical network through high-fidelity real-time interaction between virtual and real. With the support of large-scale information infrastructure of operators, a large-scale low-altitude airspace digital twin system is built to realize the digital reconstruction and expression of large-scale airspace in the information space, so as to carry out mapping management and use control of airspace.

[0070] In the digital twin system, it is possible to realize visual analysis of airspace, and establish an airspace system diagram based on the visualized airspace to accurately grasp the actual use and allocation of airspace and the flight status of drones, and provide support for the dynamic management and configuration of airspace and drones based on consistent cognition. In the digital twin system, it is also possible to realize measurable processing of airspace, and establish numerical calculation methods for airspace performance and state evaluation based on big data methods for airspace spatiotemporal management to measure the traffic performance of the entire airspace, laying the foundation for low-altitude drone traffic control and combat airspace use. In the digital twin system, it is also possible to realize computable decision-making of airspace. By developing a set of computational decision-making models based on digital twin airspace, it lays the foundation for the coordinated management of airspace traffic flow control, and provides support for the optimization and operation control of low-altitude drone airspace based on model algorithms.

[0071] In some embodiments, the business intent of the target UAV can be identified by the first base station based on the business request of the target UAV. In some embodiments, the first base station can use an intent network to identify the business intent of the target UAV based on the business request of the target UAV. In some embodiments, the first base station can also send the business data and business intent of the target ground user terminal to the digital twin system. Among them, the business intent of the target ground user terminal can be identified by the first base station based on the business request of the target ground user terminal. Similarly, the first base station can also use an intent network to identify the business intent of the target ground user terminal based on the business request of the target ground user terminal.

[0072] Intent-based networks, also known as intent-based networks (IBN), can achieve translation and verification, automated implementation, network status awareness, assurance, and automated optimization / repair functions. For translation and verification functions: Intent-based networks can obtain higher-level business policies from user-end devices and convert them into necessary network configurations, generate and verify the final design and configuration to ensure correctness. For automated implementation functions: Intent-based networks can configure appropriate networks or make network changes on existing network infrastructure, and configurations and changes can be completed through network automation or network orchestration. For network status awareness functions: Intent-based networks can obtain real-time network status under their management control. For assurance and automated optimization / repair functions: Intent-based networks can continuously verify whether the original business intent is achieved, and can take corrective measures when the desired intent cannot be achieved.

[0073] In a specific example, the intent network may include an intent management module, an intent translation module, an intent verification module, and an intent analysis module. Among them, the intent management module can decompose, analyze, and extract key information from the intent, and transmit it to the intent translation module. The intent translation module can convert the received information into a corresponding strategy (e.g., network configuration), and transmit the strategy to the intent verification module for verification. The intent analysis module can analyze the user's descriptive language (high-level abstract intent), and train an artificial intelligence (AI) auxiliary model based on natural language processing (NLP) to translate and split the semantics of the intent requirements, so that the intent translation module can translate and decompose according to a specific model, wherein the analysis results based on the AI ​​auxiliary model can be used as a reference for the intent translation module to improve the accuracy of the intent management module in decomposing, analyzing, and extracting key information. In a specific example, the intent network may also include a policy model (also known as a policy library). The process of intent translation can refer to the mapping relationship between intent and configuration in the policy library. In some embodiments, the mapping relationship between intent and configuration can also be obtained through a machine learning (ML) model and added to the policy library.

[0074] SUPA (Simplified Use of Policy Abstractions) is a policy-based management framework that defines data models for modeling configuration and status data. SUPA data models can represent policies at a high level of network scope (e.g., more complex networks). SUPA data models can be input into network management functions, which can configure and monitor network elements and network services based on these policies.

[0075] Figure 3 FIG. 1 shows a schematic diagram of an intent network architecture in a specific example. Figure 3 As shown in the figure, the intent network architecture can be divided into the intent layer (also known as the intent orchestration layer), the control layer, and the network layer. The intent orchestration layer includes intent management (also known as intent collection), conversion, verification, and decision-making, and cooperates with the configuration distribution and network status collection of the control layer to achieve a complete closed-loop operation process. The intent network can convert human business intentions into configuration information for a specified infrastructure through artificial intelligence technology or intelligent software, thereby achieving the goal of closed-loop operation of the intent network.

[0076] In some embodiments, the intent network can be based on the Open Network Automation Platform (ONAP) architecture. In a specific example, the digital twin system can also be based on the ONAP architecture and can be combined with the intent network (also referred to as an intent-driven digital twin system in this article). It should be understood that the embodiments of the present disclosure can be applied to any other suitable platform / architecture currently and in the future.

[0077] Figure 4 A schematic diagram of ONAP architecture deployment in a specific example is shown. ONAP is an open source architecture solution integrated platform for network and edge computing service orchestration, management and automation for network operators, cloud storage and enterprises. ONAP provides a unified operating framework to manage large-scale loads and services through policy-driven design, implementation, analysis and lifecycle. ONAP's core capabilities are to achieve network agility, elasticity and self-healing capabilities, perceive and respond to business demands in real time, and facilitate near real-time reconfiguration of the network, orchestration of services and expansion and contraction of capacity. ONAP mainly provides general functions for building management mechanisms such as data collection, closed-loop control, metadata process creation, and policy / process distribution.

[0078] ONAP can include design time and run time functions, as well as functions for managing ONAP itself. The design time environment is used to launch services and resources to ONAP and design new services based on them. The runtime environment provides a model and policy-driven orchestration and control framework for the automated instantiation and configuration of services and resources. ONAP's Shared Services manage more complex and optimized topologies and provide shared functions for ONAP modules. ONAP Optimization Framework (OOF) can provide a declarative, policy-driven approach to creating and optimizing applications, such as Homing / Placement, change management scheduling optimization, etc.

[0079] Figure 5 FIG. 1 shows a schematic diagram of a dual closed-loop architecture of an ONAP random graph network in a specific example. Figure 5 As shown in the figure, the dual closed-loop architecture of ONAP random graph network can include the user side and the network side, and the two sides are isolated by end-to-end slices to realize the mutual conversion of intent instances and cloud network slices. By inputting user intent and operation and maintenance intent, business requirements can be converted into intent instances, achieving the translation of intent input and the update of feedback, and realizing the closed-loop interaction of user intent. By converting slice parameters and network configurations to each other, intent monitoring analysis and decision execution are realized, thereby realizing the closed-loop guarantee of network intent for mobile networks and cloud networks.

[0080] Figure 6 FIG. 4 shows a schematic diagram of an end-to-end slice closed loop structure in a specific example. Figure 6 As shown, the interaction process of end-to-end slicing can include the 5G network sending frequency modulation (FM) data (e.g., error data) and phase modulation (PM) data (e.g., performance data) to the Data Collection, Analytics, and Events (DCAE) subsystem to monitor the network status. When the network status cannot meet user needs, a command to modify the network policy configuration can be triggered. In a specific example, an intent-based vertical industry-oriented smart slicing framework can modify a slice instance or a policy to modify slice parameters. The modification request is executed through service orchestration (SO), and then the 5G network is controlled in the software defined network controller (SDN-C) to make some modifications to the network status. Among them, SO can be used to manage intent instances, receive and translate natural language input by users, read and create new intent scenario use cases, and store user intent instances in an instance warehouse (Active and Available Inventory, AAI). AAI can be used to maintain services and resources in the network in real time, and to store, maintain and manage intent instances. SDN-C can be a network controller that controls and configures the underlying network through software.

[0081] Figure 7 A schematic diagram of an intent instance management model based on the ONAP open source architecture in a specific example is shown. Figure 7As shown, closed-loop perception feedback of user intentions can be achieved through intent closed-loop management and network status monitoring. The user intention can be stored in the intent instance of AAI through the use case user interface (UUI) or SO responsible for user interaction, and then DCAE calls the intent instance in AAI through the corresponding interface. DCAE can receive user intention information when monitoring the underlying network data, and realize real-time perception interaction and guarantee of user intentions through the existing network closed-loop monitoring system, and realize closed-loop monitoring and analysis of end-to-end slice services. The intent monitoring and analysis module is used to feedback network status information to the user through the slice KPI monitoring interface, and obtain the network intention requirement update information input by the user or the acceptance instruction for the network status information. UUI is responsible for user interaction interface and intent translation. The service orchestration SO is responsible for scenario use case management, and the scenario use case in SO is responsible for reading the user interaction interface to translate the intent information and storing it in the instance warehouse AAI.

[0082] Figure 8 FIG. 1 is a schematic diagram showing a method for managing intent instances in a specific example. Figure 8 As shown, the intent instance management method includes creating an intent instance, updating an intent, and closing an intent instance.

[0083] When creating an intent instance, UUI binds the user intent to the newly created intent instance ID and sends it to the scenario use case in SO. The scenario use case binds the intent instance to the use case service ID and stores it in AAI. AAI provides a user intent monitoring interface for DCAE to monitor the latest user intent in real time. Figure 8 As shown, creating an intent instance may include: step S811, inputting user intent through the intent instance ID; step S812, saving the intent through the intent instance ID and the use case service ID; step S813, providing the user intent for monitoring.

[0084] In the update intent, UUI sends the user intent update to the scenario use case in SO, and the scenario use case binds the new use case service ID to the user intent and stores it in AAI to associate it with the existing intent instance. Figure 8 As shown, updating intent may include: step S821, updating user intent; step S822, updating the user's new intent through the use case service ID; step S823, providing the user's new intent for monitoring.

[0085] In the shutdown intent instance, UUI sends the user's service stop request to the scenario use case in SO, and the scenario use case shuts down the intent service to AAI. Figure 8 As shown, closing the intent instance may include: step S831, closing the intent service; step S832, closing the intent service through the intent instance ID.

[0086] For the mapping relationship between intent and related information in the data table, the intent instance ID is used as the key ID to associate the related information of all mapped user intents. The network status changes of the same user can be used to evaluate, predict and optimize the future network's support for user intent. In the process of intent instance management, desensitized data summary and export can be provided.

[0087] In some embodiments, the first base station may receive a network access request from a target user terminal device, where the network access request may include a service request and a geographic location of the target user terminal device.

[0088] The user terminal device may be a drone or a ground user terminal, that is, the target user terminal device may include a target drone and / or a target ground user terminal. The first base station may receive a network access request from a target drone or a network access request from a target ground user terminal.

[0089] The ground user terminal can be, for example, a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA) and other electronic devices.

[0090] In some embodiments, the first base station can identify the business intention of the target user terminal device based on the business request of the target user terminal device via the corresponding interface / module, and feed back the business intention of the target user terminal device to the digital twin system.

[0091] In some embodiments, the first base station can parse and translate the user intent through the UUI interface, and parse the business intent of the target user terminal device to the digital twin system (e.g., an intent-driven digital twin system).

[0092] In some embodiments, the existing use case service ID can be read through the UUI or the business intent of the target user terminal device can be bound to a newly created intent instance ID and sent to the scenario use case in the SO.

[0093] In some embodiments, the scenario use case can bind the intent instance with the use case service ID in SO and then store it in AAI, or it can be associated with an existing intent instance and store the read use case service ID in AAI.

[0094] In some embodiments, the first base station may feed back the service data of the target user terminal device to the digital twin system via a corresponding interface / module. In a specific example, the first base station may send the service data of the target user terminal device to the digital twin system via DCAE.

[0095] In some embodiments, DCAE can call the intent instance corresponding to the business intent of the target user-end device from AAI through the corresponding intent monitoring interface, monitor the business data of the target user-end device (for example, business execution data of the drone and ground user terminal, geographic location data, wireless network status data, power and storage data of the drone, etc.), and feed back the business data to the digital twin system.

[0096] return Figure 1 In an exemplary embodiment, the scheduling method 100 may further include:

[0097] In step S120, the digital twin system generates optimized route information about the target UAV based on the business data and business intent, and sends a flight command based on the optimized route information to the target UAV.

[0098] The digital twin system can determine the scheduling requirements of the drone based on the received business data and business intentions of the drone, and can also determine the scheduling requirements of the ground user terminal based on the received business data and business intentions of the ground user terminal, and then generate corresponding scheduling strategies and corresponding scheduling commands. In some embodiments, the digital twin system can perform resource scheduling in dimensions such as drones, routes, networks, and computing (cloud network resources) based on the collected business data and business intentions, and generate corresponding scheduling strategies and corresponding scheduling commands. Among them, the digital twin system can simulate the movement path of the UAV, and optimize the business execution of the UAV by adjusting the position and path of the UAV; the UAV networking is carried out according to the UAV data backhaul requirements and the coverage of the ground base station (such as the first base station), the UAV battery life and other data; according to the number of business executions in different areas, the location where the UAV hangar should be built is planned to facilitate the dispatch of UAVs; according to the intent instance and its number of users, the business priority and experimental requirements are determined (for example, the 5G Quality of Service (QoS) identifier (5G QoS Indicator, 5QI) can be referred to), the corresponding business applications are deployed, and the computing business corresponding to the UAV's business intent is offloaded to the server of the ground base station (such as the first base station) to which the UAV is connected, and then the cloud network resources are dispatched.

[0099] Drones are being widely used in industrial and agricultural operations, consumer entertainment, logistics and transportation, urban management and other industries, while the space and capacity of the airspace where drones are located are limited. In some embodiments, the capacity and space of urban low-altitude airspace can be evaluated to determine the available space and capacity of the airspace, laying the foundation for generating optimized route information for drones. Specifically, the available space in urban low-altitude airspace can be identified, and drones can be enabled to fly effectively according to the characteristics of the airspace. In addition, it is also possible to find the optimal flight trajectory of drones in the city and establish safe and effective low-altitude routes for drones. In a specific example, the low-altitude airspace capacity assessment method may include calculating a threshold value for the number of drones that can be accommodated in the urban low-altitude airspace.

[0100] The dispatch command may include a flight command. When the target UAV receives the flight command, it can fly according to the optimized route information so that the target UAV can better perform related business. This is conducive to increasing the threshold number of UAVs that can simultaneously perform related business in the airspace, thereby improving the utilization rate of the space and capacity of the airspace where the UAV is located.

[0101] In some embodiments, the target location for building a drone hangar can be determined based on the number of times the business is executed in different areas to facilitate drone scheduling.

[0102] Based on the modeling of the city by the digital twin system, if there are existing infrastructure such as ground base stations (for example, the first base station), tram charging piles, express delivery points, etc. near the common routes of drones, then drone hangars (also known as hangar management systems) can be planned and constructed based on these infrastructures. For example, drone hangars can be built near these infrastructures to facilitate drone maintenance, charging, networking, or pickup.

[0103] The digital twin system can monitor the parking status of the apron in the drone hangar, including the number of available charging positions, the condition of parts available for maintenance, the number of various types of business drones, the condition of the cargo hold (used for loading and unloading drones), etc.

[0104] In some embodiments, the service data can be classified based on the service intention of the drone, and the classified service data can be respectively classified into corresponding resource units (Pods). Each resource unit Pod can correspond to a type of service, and each resource unit Pod can deploy one or more containers to deploy one or more corresponding service applications on the resource unit Pod of the corresponding service, so that the computing task of the drone can be offloaded to the corresponding resource unit Pod of the ground base station (e.g., the first base station) to which it is connected for processing.

[0105] In a specific example, the inspection business data of a drone used to perform inspection services (hereinafter referred to as the inspection drone) can be included in a resource unit Pod used to perform inspection-related computing tasks (hereinafter referred to as the inspection resource unit Pod, or simply the inspection Pod); the base station business data of a drone used to perform base station services (hereinafter referred to as the base station drone) can be included in a resource unit Pod used to perform computing tasks related to base station services (hereinafter referred to as the base station resource unit Pod, or simply the base station Pod); the logistics business data of a drone used to perform logistics services (hereinafter referred to as the logistics drone) can be included in a resource unit Pod used to perform logistics-related computing tasks (hereinafter referred to as the logistics resource unit Pod, or simply the logistics Pod); the spraying business data of a drone used to perform spraying services (hereinafter referred to as the spraying drone) can be included in a resource unit Pod used to perform computing tasks related to spraying services (hereinafter referred to as the spraying resource unit Pod, or simply the spraying Pod). Among them, the inspection business data may include, for example, inspection video data, the base station business data may include, for example, the business data of the user-end device connected to the base station drone, the logistics business data may include, for example, the load data of the logistics drone, and the spraying business data may include, for example, the remaining data of the spraying agent of the spraying drone.

[0106] In some embodiments, the first base station identifies the business intention of the target drone, which may include identifying the target drone as an inspection drone, a base station drone, a logistics drone, or a spraying drone, so as to classify the business data of the target drone into a corresponding business resource unit Pod (for example, an inspection Pod, a base station Pod, a logistics Pod, and a spraying Pod), and allocate computing network slice resources for performing business-related computing tasks to the corresponding business resource unit Pod.

[0107] In some embodiments, business applications related to the business intent of the target drone can be deployed on the resource unit Pod. Business applications related to the business intent of the target drone may include, for example, applications for processing inspection video streams, applications for initiating alarms, logistics sorting applications, drone cargo load monitoring applications, bandwidth allocation for base station drones, spectrum allocation applications, drone power monitoring applications, drone route monitoring and scheduling applications, and other applications related to the business intent of drones. In some embodiments, business applications related to the business intent of the target ground user terminal may also be deployed on the resource unit Pod, for example, applications for processing game, video, call and other data. In addition, the supervision system of the digital twin system and the management system of the drone hangar can be deployed as business applications in the corresponding resource unit Pod.

[0108] In some embodiments, preprocessing can be performed for each resource unit Pod. For example, computing network slice resources (such as computing resources and wireless resources) can be pre-configured for each resource unit Pod in order to execute corresponding business applications.

[0109] The business resource unit Pod can process related business applications according to the corresponding business data, so as to generate data related to the business data (for example, the processing result data of the inspection video stream, the bandwidth allocation result data of the base station drone), and feed it back to the digital twin system. In some embodiments, the first base station can transmit the generated data related to the business data to the digital twin system. In some embodiments, the business data of the drone may include the actual flight path and endurance. The business data business resource unit Pod can feed back all drone-related information including the actual flight path and endurance of the drone to the digital twin system. The digital twin system can calculate the scheduling scheme, simulate the scheduling results of the scheme and optimize it according to the actual scheduling requirements, combined with the actual data (for example, the business data of the target drone and the data related to the business data), so as to generate the corresponding scheduling command, and send the scheduling command to the actual physical network. In some embodiments, the digital twin system can send the scheduling command to the physical network for execution via the corresponding interface / module (for example, SO).

[0110] The following describes some embodiments of the digital twin system generating optimized route information about drones in combination with the working processes of business drones (base station drones, inspection drones, spraying drones, logistics drones) and the business resource unit Pods corresponding to the business (base station Pods, inspection Pods, spraying Pods, logistics Pods).

[0111] The user terminal device (including the drone and the ground user terminal) can be connected to the base station drone in communication and transmit business data to the base station drone. Hereinafter, the user terminal device connected to the ground base station (e.g., the first base station) is referred to as the first user terminal device, and the user terminal device connected to the base station drone is referred to as the second user terminal device. The base station drone can transmit the business data received from the second user terminal device to the ground base station (e.g., the first base station). The base station drone can also pre-process the business data received from the second user terminal device (e.g., denoising and classification, etc.).

[0112] The base station Pod on the server of the ground base station (for example, the first base station) can perform preprocessing based on the received business data (including the business data of the first user terminal device and the business data of the second user terminal device). In a specific example, it can be classified by business type (for example, you can refer to 5QI business, such as video, game, call, etc.) to obtain user terminal (User Equipment, UE) business data. For example, with reference to 5QI, the business data of the first user terminal device and the business data of the second user terminal device can be classified into UE business data such as video data, game data, and call data. In addition, the ground base station can transmit the UE business data to the corresponding resource unit Pod (also referred to as UE business resource unit Pod in this article, UE business Pod for short), wherein the UE business resource unit Pod can process the corresponding business application (for example, cloud application deployment related to games, videos, virtual reality (Virtual Reality, VR), etc.), and can transmit the processing results to the base station Pod.

[0113] The base station Pod can allocate bandwidth to the user terminal device according to the service type, the number of user terminal devices (including the first user terminal device and the second user terminal device) and the connection status of each user terminal device (for example, QoS), and send the data to the base station drone and the ground base station (for example, the first base station) according to the current connection status of the user terminal device to the ground base station (for example, the first base station) and the base station drone, and send it to the first user terminal device through the ground base station and to the second user terminal device through the base station drone. In some embodiments, considering the displacement of the user terminal device, the cellular and the connected base station may be switched, and the data can be sent according to the current connection status of the user terminal device.

[0114] In some embodiments, the digital twin system generates optimized route information about the target UAV based on business data and business intent, which may include: in response to the target UAV performing base station business (i.e., the target UAV is a base station UAV), the digital twin system determines the optimized position of the target UAV in the next time slot based on the data transmission rate of all user-end devices connected to the target UAV in the current time slot and the flight speed of the target UAV, so as to generate optimized route information.

[0115] In a specific example, the digital twin system can calculate the average data transmission rate of the base station drone based on the data transmission rate of all user-end devices connected to the target drone in the current time slot, and at the same time, based on the flight speed of the drone, infer the possible location of the base station drone in the next time slot, and then calculate the access number, spectrum efficiency, transmission speed and bandwidth allocation of user-end devices connected to the base station drone in the next time slot. Based on the calculation results, the digital twin system plans and selects the optimal location of the base station drone in the next time slot, thereby generating the optimized route information of the base station drone.

[0116] In some embodiments, determining the optimal position of the target drone in the next time slot may include determining the optimal position according to the number of accesses of the user terminal device to the target drone at different positions in the next time slot of the target drone, so that when the target drone is located at the optimal position in the next time slot, the number of accesses of the user terminal device to the target drone can meet the first preset requirement, thereby enabling more user terminal devices to access the target drone. In some embodiments, determining the optimal position of the target drone in the next time slot may include determining the optimal position according to the average data transmission rate of the target drone, so that when the target drone is located at the optimal position in the next time slot, the average data transmission rate of the target drone meets the second preset requirement, thereby enabling the user needs of the user terminal device to be processed at a higher average data transmission rate. In some embodiments, determining the optimal position of the target drone in the next time slot may include determining the optimal position according to the number of accesses of the user terminal device to the target drone at different positions in the next time slot of the target drone and the average data transmission rate of the target drone, so that when the target drone is located at the optimal position in the next time slot, the number of accesses of the user terminal device to the target drone can meet the first preset requirement and the average data transmission rate of the target drone meets the second preset requirement, thereby enabling more user terminal devices to access the target drone, and the average data transmission rate of the target drone is higher.

[0117] In a specific example, taking the user terminal connected to the base station drone as a ground user terminal as an example, the number of ground user terminals accessing the base station drone in the next time slot N can be calculated based on the following process: j Average data transmission rate DR between base station and drone j .

[0118] According to the three-dimensional coordinates of the drone (x j ,y j ,h j ), the coordinates of all ground user terminals (x i ,y i ), determine the horizontal distance r between the user terminal device and the drone ijand the distance d between the ground user terminal and the base station drone ij ,in:

[0119]

[0120] exist In the case where the ground user terminal cannot receive the signal from the UAV base station, the signal coverage range of the base station UAV can be determined, where θ B is the half-power beamwidth of the base station drone.

[0121] Number of ground user terminals accessing N j =∑ i∈U I(α i =j), where and:

[0122]

[0123] according to Calculate Line of Sight (LoS) transmission path loss Among them, f c is the operating frequency of the base station drone, γ is the path loss index, c is the speed of light, η LoS is the average additional loss in the LoS scenario.

[0124] according to Calculate Non Line of Sight (NLoS) transmission path loss Among them, η NLoS is the average additional loss in the NLoS scenario.

[0125] The LoS scenario probability can be calculated according to the following formula

[0126]

[0127] Among them, α and β are environmental parameters.

[0128] Average path loss Signal power received by ground user terminal P T is the transmission power of the base station drone, where:

[0129] exist In the case

[0130] exist In the case of G ij =0.

[0131] The data transmission rate of the ground user terminal is:

[0132] Where N0 is the noise power and W is the channel bandwidth.

[0133] Average data transmission rate of base station drone

[0134] In this way, after calculating the current average data transmission rate and coverage of the base station drone, the average data transmission rate and coverage of the base station drone at different coordinates can be estimated based on the distribution of ground user terminals. According to the distribution of ground user terminals, the base station drone is dispatched to a location with higher coverage and higher data transmission rate, for example, the base station is dispatched to the center of the ground user terminal cluster, and its height and coordinates are adjusted according to the data transmission rate and coverage. Calculate the average data transmission rate DR of the base station drone in each time slot j changes until DR j and N j The balanced maximum value is obtained, that is, the number of ground user terminals accessing the target UAV can meet the first preset requirement and the average data transmission rate of the target UAV can meet the second preset requirement.

[0135] The inspection resource unit Pod can determine whether a crisis situation (such as a forest fire) occurs in the area where the inspection drone is located based on the inspection video data of the inspection drone and the current location of the inspection drone, thereby triggering an alarm. In some embodiments, the digital twin system generates optimized route information about the target drone based on business data and business intent, which may include: in response to the target drone performing the inspection business (that is, the target drone is a patrol drone), the digital twin system determines the position of the target drone in the next time slot based on the position of the target drone in the current time slot, the clarity of the captured image, and the shooting angle, and simulates the flight path of the target drone to generate optimized route information. In some embodiments, the generated optimized route information may include the optimized shooting position of the target drone and / or the optimized shooting angle of the target drone. In some embodiments, the optimized route information may include the route information of the target drone returning when the inspection business is completed, or the route information of the target drone heading to the next area to be inspected when the inspection of the current area is completed.

[0136] In some embodiments, the digital twin system can determine whether the inspection mission of the inspection drone needs to go to an area that is not covered by the network signal of the ground base station (for example, the first base station) based on the location and destination of the inspection drone and the distribution of ground base stations.

[0137] If the inspection drone is still within the area covered by the network signal of the ground base station, the digital twin system can calculate and simulate the location and flight path of the next time slot inspection drone based on the current picture clarity, drone location, shooting angle and other data. For example, the location of the next time slot inspection drone and the flight path of the inspection drone can be determined based on whether the drone has completed the inspection and needs to return, whether the shooting range is too narrow and needs to be raised, whether the shooting clarity is poor and needs to be lowered and the shooting angle adjusted (for example, the shooting angle can be adjusted by adjusting the pitch angle of the drone), and whether the current area has been inspected and the location of the next area to be inspected needs to be determined.

[0138] In some embodiments, optimized route information can be generated based on factors such as the pitch angle, moving direction, flight speed, geographic location, shooting clarity, power consumption, etc. of the inspection drone. For example, a proportional parameter can be calculated based on the current pitch angle, height, and monitoring width of the inspection drone, and the pitch angle and three-dimensional position of the inspection drone can be adjusted using the proportional parameter and the shooting clarity to calculate the inspection route. In some embodiments, the relationship between the drone's elevation angle, height, and monitoring range can be:

[0139] h = k*w;

[0140]

[0141] Among them, h is the height of the inspection drone. The appropriate flight height can take into account both the overall picture and specific situation of the detection range, k is the proportional parameter, w is the monitoring width, and β d is the pitch angle (usually between 30 and 60 degrees).

[0142] In some embodiments, the next time slot position of the drone can be calculated based on the monitoring width (also known as the inspection width of the inspection drone). For example, the three-dimensional coordinates of the position of the inspection drone in the current time slot are (x j,t ,y j,t ,h j,t ), then the coordinates of the next time slot position of the drone (x j,t#1 ,y j,t#1 ,h j,t#1 )=(x j,t +w,y j,t +w,h j,t ). If the image clarity is not good, you can also lower the height of the drone appropriately.

[0143] In some embodiments, in response to the target drone going to a target area that cannot be covered by the signal of the first base station, the digital twin system determines the first drone hangar within a first preset distance from the target area, and sends a dispatch command to the first drone hangar to dispatch a relay drone for the target drone to transmit back to the first base station. Among them, the first drone hangar can be a drone hangar that is closest to the target area and can dispatch relay drones. In this way, the target drone can continue to communicate with the first base station via the relay drone, so that it can continue to perform the corresponding business. The dispatch command includes a dispatch command, and when the first drone hangar receives the dispatch command, the relay drone can be dispatched.

[0144] In some embodiments, in response to detecting that the data transmission rate of the signal received by the target drone from the first base station is less than the preset transmission rate requirement of the corresponding service (for example, the video data transmission rate is less than 100KB / s), the digital twin system determines a second drone hangar within a second preset distance range from the target drone, and sends a dispatch command to the second drone hangar to dispatch a relay drone for the target drone to transmit back to the first base station. Among them, the second drone hangar can be the drone hangar closest to the target drone and can dispatch relay drones. When the second drone hangar receives the dispatch command, a relay drone can be dispatched. In this way, the target drone can transmit data back to the ground base station (such as the first base station) through the relay drone for processing by the corresponding resource unit Pod of the ground base station (such as the first base station), thereby improving the data transmission rate.

[0145] If the inspection drone is about to go to a wilderness area such as a woodland or river channel where the network signal of the ground base station (for example, the first base station) cannot cover, the digital twin system can dispatch the corresponding resource unit Pod (for example, the drone hangar management system resource unit Pod and the inspection drone management resource unit Pod), read the location data of the drone hangar and the inventory data of the drone, and dispatch a relay drone from the drone hangar closest to the inspection target area as a relay base station for the inspection drone to transmit data back to the ground base station, and go to the operation area together with the inspection drone. The inspection drone and the relay base station drone can form an aerial local area network, and always maintain a connection with the ground base station (for example, the first base station) during the execution of the task.

[0146] Similarly, when the base station drone is about to go to a target area that is not covered by the network signal coverage of the ground base station, a relay drone can be dispatched from the drone hangar closest to the target area. The inspection drone and / or base station drone can first send the data stream to the relay drone, which then forwards it back to the inspection resource unit Pod and / or base station resource unit Pod of the ground base station for processing.

[0147] Spraying drones can perform pesticide spraying in agricultural or forestry scenarios, and the spraying resource unit Pod can monitor the spraying drone's remaining agent and the completion of the spraying business (such as how many acres of farmland or trees in the target area have been sprayed). In some embodiments, the digital twin system generates optimized route information about the target drone based on business data and business intent, which may include: in response to the target drone performing the spraying business (that is, the target drone is a spraying drone), the digital twin system determines whether the target drone needs to replenish the agent based on the remaining agent and the completion of the spraying business, and in response to the target drone needing to replenish the agent, the digital twin system generates optimized route information based on the remaining power of the target drone and the location of the target agent replenishment point within a preset distance range from the location of the target drone. In some embodiments, the target agent replenishment point is the agent replenishment point closest to the target drone when the inventory can meet the replenishment demand and the remaining power of the target drone can reach it.

[0148] In a specific example, the digital twin system can determine whether the remaining amount of medicine of the spraying drone can complete the spraying business and whether it needs to return to replenish the spraying agent based on the spraying drone's medicine dosage status and the completion status of the spraying operation. The digital twin system can also calculate the flight path for the spraying drone that needs to return to the nearest agent supply point (for example, a drone hangar with pesticide reserves) to generate optimized route information.

[0149] The corresponding resource unit Pod can monitor the amount of medicine used for spraying in each drone hangar and the remaining amount of medicine in the spraying drone. If the remaining amount of medicine in the spraying drone is insufficient, the digital twin system will calculate its range based on its remaining power, and query the drone hangar with stored medicine within the range (if the remaining amount of medicine in the hangar is not enough to replenish the needs of a spraying drone, it is considered to be out of stock), and sort them according to the distance between them and the spraying drone, and select the drone hangar closest to the spraying drone to replenish the medicine. If the drone hangar has an idle charging pile, it can charge the spraying drone.

[0150] The logistics resource unit Pod can monitor the operating status of the logistics drone (for example, remaining power, load, storage space, operating power, etc.) and the cargo storage and delivery needs of each warehouse. It can also monitor the cargo capacity of the drone based on the load weight limit and storage space limit of the logistics drone.

[0151] In some embodiments, the digital twin system generates optimized route information about the target UAV based on business data and business intent, which may include, in response to the target UAV performing logistics business (i.e., the target UAV is a logistics UAV), allocating a first cargo task to the target UAV based on cargo demand, the cargo capacity of the target UAV, the remaining power, and the current location, and generating optimized route information based on the first cargo task.

[0152] In some embodiments, the first cargo mission assigned to the target drone can be determined by the digital twin system from cargo missions that can be assigned to the target drone based on cargo demand, cargo capacity, remaining power and current location of the target drone.

[0153] In some embodiments, the power of the target drone can be calculated according to the mass of the cargo and the current maximum range of the target drone (for example, the maximum range of the target drone when taking off at full power) can be calculated, and the logistics range between the starting point and the end point of the cargo task to be assigned and the maximum range can be used to determine whether the cargo task to be assigned can be assigned to the target drone. In a specific example, whether the cargo task to be assigned to the target drone can be determined based on whether the current power of the target drone can support the flight energy consumption based on the cargo task to be assigned. In a specific example, the flight time and power of the target drone can be calculated based on the logistics range of the cargo task to be assigned, the cargo weight, and the flight speed of the target drone, thereby determining the flight energy consumption of the target drone based on the cargo task to be assigned.

[0154] Among them, in some embodiments, the starting point and end point of the freight task to be assigned can both be drone hangars (that is, the customer goes to the first drone hangar to ship the goods and picks up the goods at the second drone hangar). In response to the first logistics range between the starting point and the end point of the freight task to be assigned not exceeding the maximum range of the target drone, the digital twin system can determine that the freight task to be assigned is a freight task that can be assigned to the target drone.

[0155] In some embodiments, at least one of the starting point and the end point of the freight task to be assigned may not be in the drone hangar, and in response to the second logistics range of the freight task to be assigned being less than or equal to the current maximum range of the target drone, the digital twin system determines that the freight task to be assigned is a freight task that can be assigned to the target drone. In a specific example, when the starting point and the end point of the freight task to be assigned are not located in the drone hangar, the second logistics range may be the sum of the range from the drone hangar where the target drone is located to the door-to-door pickup point, the range from the door-to-door pickup point to the door-to-door delivery point, and the range from the door-to-door delivery point to the drone hangar closest to the door-to-door delivery point.

[0156] In some embodiments, the first cargo mission may be a cargo mission for the target drone during the flight from the drone hangar in the first area to the drone hangar in the second area. When the target drone completes the first cargo mission, in response to the drone hangar where the target drone is currently located being able to charge the target drone, the digital twin system may send a charging command to the target drone to charge in the drone hangar where it is currently located. The dispatch command includes a charging command, and when the target drone receives the charging command, it may charge in the drone hangar where it is currently located.

[0157] In this way, drone a flying from area A to area B will no longer take on cargo missions from area B to area A after completing the cargo mission assigned to area A. Instead, it will stay in area B to charge, and area B will send drone b to perform cargo missions to area A. This method can improve the reliability of logistics drones (fully charged drones have longer endurance than under-charged drones and are more reliable when encountering failures), optimize the execution of logistics operations and the flight paths of drones, and reduce the situation where drones repeatedly go back the same way.

[0158] In some embodiments, when the target drone completes the first cargo mission, in response to the drone hangar where the target drone is currently located being unable to charge the target drone, the digital twin system may assign the target drone a second cargo mission that meets the cargo capacity and remaining power of the target drone, and generate optimized route information based on the second cargo mission. The second cargo mission may be determined by the digital twin system from the cargo missions that can be assigned to the target drone based on cargo demand, the cargo capacity, remaining power, and current location of the target drone.

[0159] In some embodiments, in response to the difference between the remaining power of the target drone and the preset alarm power being less than or equal to the preset alarm threshold, or receiving an abnormal signal from the target drone indicating that the target drone has encountered an emergency, the digital twin system may send a pause command to the target drone to suspend the execution of the service. The scheduling command may include a pause command, and the target drone may suspend (or terminate) the execution of the service when the target receives the pause command. The abnormal signal may be issued by the target drone when the target drone encounters an emergency, or it may be issued by the corresponding resource unit Pod on the ground base station (e.g., the first base station) connected to it for communication after corresponding processing to find that the target drone has encountered an emergency. The abnormal signal can be fed back to the digital twin system via the corresponding interface / module so that the digital twin system generates a corresponding scheduling strategy and a corresponding scheduling command. An emergency is, for example, an event in which extreme weather or force majeure (such as a failure) prevents the drone from completing its mission.

[0160] In some embodiments, the digital twin system can generate optimized route information based on the current location of the target drone and the location of the target supply warehouse. The preset warning power is the power required for the target drone to fly to the rechargeable target supply warehouse closest to the current location. In this way, when the remaining power is close to the preset warning power, regardless of whether the drone can continue to complete the mission, it can suspend the execution of the business and fly to the target supply warehouse for resupply, reducing the possibility of the drone falling due to insufficient power.

[0161] In some embodiments, in response to the difference between the remaining power of the target drone and the preset warning power being greater than a preset warning threshold, the digital twin system can generate optimized route information based on the remaining power and the execution status of the current business.

[0162] Specifically, in some embodiments, in response to the target UAV performing a spraying service, the digital twin system can determine a first remaining flight time T0 based on the flight speed of the target UAV, the power consumption of performing the spraying service, and the remaining power, and generate optimized route information based on the first remaining flight time T0 and the remaining amount of medicine of the target UAV.

[0163] In a specific example, the weight of the spraying agent of the spraying drone will gradually decrease as the spraying progresses. The mass of the spraying agent of the spraying drone can be expressed as Among them, m1 is the weight of the agent, § is the spraying speed of the agent, rl is the total range of the spraying drone to perform the spraying business, and ve is the flight speed. Then the power consumption P1 of the spraying drone to perform the spraying business can be calculated.

[0164] in, m d is the drone’s own weight, a e is a constant, δ is the propeller power transmission efficiency, γ e is the lift-to-drag ratio of the UAV, and e is the energy consumption of the UAV’s electronic components.

[0165] In a specific example, the first remaining battery life Among them, E is the remaining power of the drone.

[0166] In some embodiments, generating optimized route information based on the first remaining endurance time T0 and the remaining amount of medicine of the drone may include: when the remaining amount of medicine is less than or equal to the preset medicine remaining threshold, the digital twin system determines the first supply warehouse that can replenish medicine within the flight range of the target drone based on the first remaining endurance time T0, and determines the time T2 required for the target drone to reach the first supply warehouse; when T0≥T2, the digital twin system generates optimized route information based on the current position of the target drone and the position of the first supply warehouse. In some embodiments, the preset remaining threshold may be zero. In a specific example, in response to the remaining amount of medicine being zero, the digital twin system sends a pause command to the target drone. As described above, since a preset alarm power is set, T0<T2 does not usually occur.

[0167] In a specific example, the coordinates (x′) of all drone hangars that can replenish medicines near the target drone can be used. k ,y′ k ,h′ k ) to determine the coordinates (x′) of the first supply warehouse K ,y′ K ,h′ K ).

[0168] For example, That is, the first supply warehouse is the drone hangar closest to the target drone where medicine can be replenished.

[0169] In a specific example, the time required for the target drone to reach the first supply warehouse

[0170] In some embodiments, in response to the target drone performing logistics services, the digital twin system can determine a second remaining flight time T0' based on the flight speed of the target drone, the power consumption in performing the logistics services, and the remaining power, and determine the time T1 required for the target drone to reach the logistics destination based on the location of the logistics destination, the current location and flight speed of the target drone, and generate optimized route information based on the second remaining flight time T0' and the time T1 required for the target drone to reach the logistics destination.

[0171] In a specific example, the power consumption of executing logistics business Among them, m d is the weight of the drone, m1 is the weight of the cargo carried by the drone (m1=0 when the drone is empty), ve is the flight speed, a e is a constant, δ is the propeller power transmission efficiency, γ e is the lift-to-drag ratio of the drone, and e is the energy consumption of the drone's electronic components. In a specific example, the second remaining flight time Where E is the remaining power of the drone. In a specific example, the drone coordinates (x j ,y j ,h j ) and the destination coordinates (x d ,y d ,h d ) Calculate the time required for the target drone to reach the logistics destination

[0172] In some embodiments, generating optimized route information based on the second remaining flight time T0' and the time T1 required for the target UAV to reach the logistics destination may include: when T0'>T1, it means that the UAV does not need to be charged at this time, and the digital twin system can generate optimized route information based on the current position of the target UAV and the position of the logistics destination.

[0173] In some embodiments, generating optimized route information based on the second remaining endurance time T0' and the time T1 required for the target drone to reach the logistics destination may include: when T0'≤T1, the digital twin system determines a rechargeable second supply warehouse within the flight range of the target drone based on the second remaining endurance time T0' and determines the time T2' required for the target drone to reach the second supply warehouse, and when T0'≥T2', the digital twin system generates optimized route information based on the current position of the target drone and the position of the second supply warehouse. As described above, since a preset alarm power is set, the situation of T0'<T2' will not usually occur.

[0174] In a specific example, the coordinates (x k ,y k ,h k ) to determine the coordinates of the second supply warehouse (x K ,y K ,h K ),For example, That is, the second supply warehouse is the rechargeable drone hangar closest to the target drone.

[0175] In a specific example, the time required for the target drone to reach the second supply warehouse

[0176] When generating optimized route information for logistics drones, the digital twin system can consider that the flight path of logistics drones can satisfy point-to-point straight-line flight as much as possible to reduce detours. Base station drones and inspection drones usually have no cargo load, so the remaining flight time of base station drones and inspection drones can be calculated based on mechanical power and transmission power, so that the digital twin system can determine whether it needs to be charged or the mission is terminated.

[0177] In some embodiments, the digital twin system may determine whether the target drone meets the preset flight condition based on at least one of the preset route, flight speed, and data storage space margin of the target drone. In response to the target drone not meeting the preset flight condition, the digital twin system may send a hovering command to the target drone to hover at the current location. The dispatch command may include a hovering command, and the target drone may hover at the current location when receiving the hovering command.

[0178] In some embodiments, the preset flight conditions include at least one of the following: there is no collision risk for the target drone; the target drone does not deviate from the preset route; the target drone data storage space margin is greater than or equal to the preset storage space threshold. The digital twin system can monitor the preset routes and speeds of each drone, calculate the intersection between the preset routes of each drone and the time difference between the corresponding drone flying to the intersection, so as to determine whether the drone has a collision risk and whether it deviates from the route preset by the digital twin system, and then correct the route or adjust the flight speed of the drone, thereby reducing the possibility of a collision. In some embodiments, the digital twin system generates optimized route information about the target drone based on business data and business intent, which may include: when the target drone has a collision risk and / or deviates from the preset route, the digital twin system corrects the preset route and / or adjusts the flight speed of the target drone based on the current position and current flight speed of the target drone to generate optimized route information.

[0179] In some embodiments, the target drone may include a first drone and a second drone, and the digital twin system may determine whether the target drone meets the preset flight condition based on at least one of the preset route, flight speed, and data storage space margin of the target drone, and may include: in response to the first preset route of the first drone and the second preset route of the second drone having an intersection and the time difference between the time when the first drone flies to the intersection and the time when the second drone flies to the intersection is less than a preset time threshold, there is a collision risk between the first drone and the second drone, and a hovering command may be sent to one of the first drone and the second drone. In a specific example, the preset time threshold may be set to be less than 1s, such as 0.1s.

[0180] The digital twin system may determine that the drone with a lower priority in the service performed by the first drone and the second drone does not meet the preset flight condition, and the digital twin system may send a hovering command to the drone with a lower priority in the service performed by the first drone and the second drone. In a specific example, the priority of the inspection service is greater than the priority of the base station service, and the priority of the base station service is greater than the priority of the logistics service.

[0181] In some embodiments, the digital twin system determines whether the target drone meets the preset flight conditions based on at least one of the preset route, flight speed and data storage space margin of the target drone, which may include: when the data storage space margin of the target drone is less than the preset storage margin threshold, determining that the target drone does not meet the preset flight conditions. Then the digital twin system can send a hover command to the target drone to make the drone hover. Further, in response to the target drone not meeting the preset flight conditions, the digital twin system can send a return command to the target drone to transmit all data in the data storage space back to the first base station. The scheduling command may include a return command, and the target drone may transmit the data in the data storage space back to the first base station when receiving the return command.

[0182] In some embodiments, in response to the target drone having transmitted all data of the data storage space of the target drone back to the first base station, the digital twin system generates a first release command for the target drone to release the data storage space. The scheduling command may include the first release command, and the target drone releases the data storage space when receiving the first release command.

[0183] In some embodiments, the digital twin system generates optimized route information about the target drone based on business data and business intent, which may include the digital twin system generating optimized route information based on the energy consumption of the target drone and the signal coverage distribution of the base station, wherein the optimized route information satisfies the minimum number of base stations passed through when the energy consumption of the target drone can meet the executed business. The minimum number of base stations passed through can mean that the target drone enters the minimum number of signal coverages of different base stations, so as to perform as few cellular switching as possible, and reduce the interference and resource loss caused by switching the base station connected to the communication.

[0184] In some embodiments, when the target UAV switches from a communication connection with a first base station to a communication connection with a second base station, the digital twin system may send a second allocation command to the second base station to allocate wireless resources for the target UAV. The scheduling command may include the second allocation command, and the second base station may allocate wireless resources to the target UAV when receiving the second allocation command, so that the target UAV can connect to the second base station.

[0185] In some embodiments, in the process of allocating wireless resources for the target drone by the second base station, the first base station can reserve wireless resources for the target drone, that is, the target drone can maintain connection with the first base station, and the drone target will maintain connection with both base stations at the same time for a short time, thereby realizing soft switching, improving the continuity of drone communication, reducing the call drop rate during the switching process, and improving the reliability of switching. In a specific example, it can be pre-set on the first base station so that the wireless resources for the target drone can be reserved for a short time in the process of the connected user terminal device switching to other base stations (for example, in the process of the second base station allocating wireless resources for the target drone). In another specific example, the first base station can also reserve wireless resources for the target drone when receiving a corresponding scheduling command from the digital twin system. For example, the digital twin system can send a reservation command to the first base station to reserve wireless resources for the target drone in the process of the second base station allocating wireless resources for the target drone. The scheduling command may include a reservation command, and the first base station can reserve wireless resources for the target drone when receiving the reservation command.

[0186] In some embodiments, in response to the second base station completing the allocation of wireless resources for the target drone, the digital twin system sends a second release command to the first base station to release the wireless resources for the target drone. The scheduling command may include the second release command, and the first base station may release the wireless resources for the target drone upon receiving the second release command to disconnect the communication connection with the target drone.

[0187] In some embodiments, in response to the first delay of data generated on the first base station and related to business data being forwarded to the second base station via the first base station and then forwarded to the target drone by the second base station being less than or equal to the preset delay threshold, the digital twin system may send a retention command to the first base station to retain the stored business data and the generated data related to the business data. The scheduling command may include a retention command. When the first base station receives the retention command, it may retain the stored business data and the generated data related to the business data, that is, it does not forward the stored business data and the generated data related to the business data to the second base station. The business-related calculation of the target drone can still be performed on the first base station, and the calculation results on the first base station can be forwarded to the second base station and then forwarded to the target drone by the second base station. Then, the corresponding resource unit Pod on the second base station may not deploy the corresponding business application, and the related computing tasks do not need to be completed on the second base station, so that the computing resources can be reasonably utilized, the resource configuration is optimized, and the resource loss of data transmission is reduced. In some embodiments, the preset delay threshold can be determined according to the business needs of the target drone. In some implementations, the data related to the business data may be generated by the business resource unit Pod on the first base station according to the corresponding business data processing-related business applications. The data related to the business data may include, for example, the processing result data of the inspection video stream, the bandwidth allocation result data of the base station drone, etc.

[0188] In some embodiments, in response to the first delay of data generated on the first base station and related to business data being forwarded to the second base station via the first base station and then forwarded to the target drone by the second base station being greater than a preset delay threshold, the digital twin system sends a migration command to the first base station to forward the stored business data and the generated data related to the business data to the second base station. The scheduling command may include a migration command, and the first base station may forward the stored business data and the generated data related to the business data to the second base station upon receiving the migration command, so that the computing tasks related to the target drone can be completed on the second base station.

[0189] If the target drone conducts a long-distance flight and needs to switch the cell where the base station connected to the target drone is located multiple times, the data transmission delay in response to the target drone flying to the cell where the next base station is located will not be able to meet the business data processing requirements. The digital twin system can send a migration command to the base station to which the target drone is currently connected to forward the business data to the corresponding business resource unit Pod on the next base station it is about to connect to for processing.

[0190] In some embodiments, when the base station drone acts as a relay base station (the base station drone is also referred to as a relay drone in this article) to establish an aerial network with the drone in front and the ground base station behind (for example, the target drone transmits data back to the first base station via the relay drone as described above), the base station drone can simultaneously transmit data with the drone in front and the ground base station behind.

[0191] In the case of a drone self-organizing network, the data transmission between the relay drone and the drone in front and the data transmission between the relay drone and the ground base station behind can be considered as line-of-sight LoS transmission, so Calculating Line-of-Sight Path Loss Among them, d jk is the distance from UAV j to the backhaul base station k (e.g., relay UAV / ground base station), and the coordinates of UAV j are (x j ,y j ,h j ), the coordinates of the transmitted base station k are (x k ,y k ,h k ),but γ is the path loss exponent, c is the speed of light, η LoS is the average additional loss in the LoS scenario. The horizontal distance between drone j and the backhaul base station k The number of user-end devices N that are accessed by the backhauled base station k k =∑ i,j∈U I(α i,j = k), where:

[0192]

[0193] Return signal power

[0194] Return data rate

[0195] As described above, the target drone can transmit data back to the first base station through the relay drone. At this time, the relay drone will receive signals from both the target drone and the first base station, causing interference between the signals of both parties. In some embodiments, the digital twin system can generate corresponding interference processing strategies and send corresponding scheduling commands to one or more of the target drone, the relay drone and the first base station to reduce the impact of signal interference and improve the efficiency of signal transmission. Similarly, as described above, in the process of switching from the first base station to the second base station, the target drone will receive signals from both the first base station and the second base station at the same time, causing interference between the signals of both parties. In some implementations, the digital twin system can also generate corresponding interference processing strategies and corresponding scheduling commands, and send corresponding scheduling commands to one or more of the target drone, the first base station and the second base station to reduce the impact of signal interference and improve the efficiency of signal transmission.

[0196] Continue to refer to Figure 1 In an exemplary embodiment, the scheduling method 100 may further include:

[0197] Step S130, in response to the utilization rate of the first resource in the business resource unit corresponding to the business intention on the first base station being greater than the target utilization threshold, the digital twin system determines whether there is a first target resource unit on the first base station for scheduling the first resource to the business resource unit, and, in response to the existence of the first target resource unit on the first base station, the digital twin system sends a first allocation command to the first base station to schedule the first resource in the first target resource unit to the business resource unit according to a preset reduction ratio of the first resource margin.

[0198] The business resource unit Pod is deployed with business applications related to the business intent of the target user terminal device (for example, the target drone and / or the target ground terminal), and the first resource is used by the business resource unit Pod to process the business application according to the business data.

[0199] If the first resource of the business resource unit Pod of the first base station is insufficient (for example, the first resource target utilization threshold may be 90%), feedback may be given to the digital twin system so that the digital twin system generates a corresponding scheduling command. Among them, the first resource may include wireless resources for accessing the network and / or computing resources for performing corresponding computing tasks. The wireless resource may be, for example, a wireless slice resource block (RB), such as the bandwidth of a wireless slice. The computing resources may be, for example, a CPU, memory, and the like.

[0200] The scheduling command may include a first allocation command. When the first base station receives the first allocation command, it may schedule the first resource in the first target resource unit Pod of the first base station to the service resource unit Pod according to the preset reduction ratio of the first resource margin. In a specific example, the target utilization threshold may be 90% to 100%, and the preset reduction ratio may be 80% to 90%.

[0201] In some embodiments, determining by the digital twin system whether there is a first target resource unit Pod on the first base station for scheduling the first resource to the service resource unit Pod may include traversing by the digital twin system each resource unit Pod on the first base station in order from low to high according to the corresponding service priority to determine whether there is a first target resource unit on the first base station. In a specific example, the service priority can be determined according to the 5QI service priority (for example, the priority of the voice service is less than the priority of the game service, and the priority of the game service is less than the priority of the video service), or it can be pre-specified.

[0202] In some embodiments, determining by the digital twin system whether there is a first target resource unit Pod on the first base station for scheduling the first resource to the service resource unit Pod may include determining by the digital twin system from the various resource unit Pods on the first base station a resource unit Pod whose utilization of the first resource is less than a first utilization threshold, so as to generate a first set, wherein the first utilization threshold is less than or equal to the target utilization threshold; and determining by the digital twin system whether there is a first target resource unit Pod on the first base station according to whether the first set is an empty set. In a specific example, the first utilization threshold may be 30%.

[0203] In some embodiments, the digital twin system determines whether there is a first target resource unit Pod on the first base station according to whether the first set is an empty set, which may include: in response to the first set being a non-empty set, the digital twin system determines the resource unit Pod with the lowest service priority in the first set as the first target resource unit Pod. In this way, when there are multiple resource unit Pods in the first set, it can be prioritized to ensure that the resource unit Pods with higher priorities have sufficient resources to execute corresponding service applications.

[0204] In some embodiments, determining by the digital twin system whether there is a first target resource unit Pod on the first base station based on whether the first set is an empty set may include: in response to the first set being a non-empty set, determining by the digital twin system that the resource unit Pod with the largest first resource margin in the first set is the first target resource unit Pod.

[0205] In some embodiments, the digital twin system determines whether there is a first target resource unit Pod on the first base station according to whether the first set is an empty set, which may include: in response to the first set being an empty set, the digital twin system determines that there is no first target resource unit Pod on the first base station. Alternatively, in some embodiments, the digital twin system determines whether there is a first target resource unit Pod on the first base station according to whether the first set is an empty set, which may include: in response to the first set being an empty set (that is, there is no resource unit Pod on the first base station whose utilization rate of the first resource is less than the first utilization threshold), when the first utilization threshold is less than the target utilization threshold, the digital twin system determines the resource unit Pod whose utilization rate of the first resource is less than the second utilization threshold from the various resource unit Pods on the first base station to generate a second set, wherein the second utilization threshold is greater than the first utilization threshold, and the second utilization threshold is less than or equal to the target utilization threshold; in a specific example, the second utilization threshold can be 50%. Further, the digital twin system determines whether there is a first target resource unit Pod on the first base station according to whether the first set is an empty set, which may also include: the digital twin system determines whether there is a first target resource unit Pod on the first base station according to whether the second set is an empty set.

[0206] In some embodiments, determining by the digital twin system whether there is a first target resource unit Pod on the first base station based on whether the second set is an empty set may include: in response to the second set being a non-empty set, the digital twin system determines that the resource unit Pod with the lowest service priority in the second set is the first target resource unit Pod. In this way, when there are multiple resource unit Pods in the second set, it can be prioritized to ensure that the resource unit Pods with higher priorities have sufficient resources to execute corresponding business applications. Alternatively, in some embodiments, determining by the digital twin system whether there is a first target resource unit Pod on the first base station based on whether the second set is an empty set may include: in response to the second set being a non-empty set, the digital twin system determines that the resource unit Pod with the largest first resource margin in the second set is the first target resource unit Pod.

[0207] In some embodiments, determining by the digital twin system whether the first target resource unit Pod exists on the first base station based on whether the second set is an empty set may include: in response to the second set being an empty set (i.e., there is no resource unit Pod on the first base station whose utilization of the first resource is less than the second utilization threshold), the digital twin system determines that the first target resource unit Pod does not exist on the first base station. Alternatively, in some embodiments, determining by the digital twin system whether the first target resource unit Pod exists on the first base station based on whether the second set is an empty set may include: when the second utilization threshold is less than the target utilization threshold, the digital twin system determines, from the various resource unit Pods on the first base station, a resource unit Pod whose utilization of the first resource is less than a third utilization threshold, to generate a third set, wherein the third utilization threshold is greater than the second utilization threshold, and the third utilization threshold is less than or equal to the target utilization threshold. In a specific example, the third utilization threshold may be 70%.

[0208] Further, the digital twin system determining whether there is a first target resource unit Pod on the first base station based on whether the second set is an empty set may also include, in response to the third set being a non-empty set, determining by the digital twin system the resource unit Pod with the lowest service priority in the third set as the first target resource unit Pod. In this way, in the case where there are multiple resource unit Pods in the third set, it can be prioritized to ensure that the resource unit Pods with higher priorities have sufficient resources to execute corresponding business applications. Alternatively, in some embodiments, the digital twin system determining whether there is a first target resource unit Pod on the first base station based on whether the third set is an empty set may include: in response to the third set being a non-empty set, determining by the digital twin system the resource unit Pod with the largest first resource margin in the third set as the first target resource unit Pod.

[0209] In some embodiments, determining by the digital twin system whether there is a first target resource unit Pod on the first base station based on whether the second set is an empty set may also include determining by the digital twin system that there is no first target resource unit Pod on the first base station in response to the third set being an empty set.

[0210] In some embodiments, one or more other utilization thresholds greater than the third utilization threshold and less than the target utilization threshold may be set and the above process may be referred to to determine whether the first target resource unit Pod exists on the first base station.

[0211] In some embodiments, the above process of determining the first target resource unit Pod and scheduling the first resource in the first target resource unit Pod to the business resource unit Pod according to a preset reduction ratio of the first resource margin can be repeated until the utilization rate of the first resource of the business resource unit Pod can be maintained within a range less than the target utilization threshold.

[0212] In some embodiments, in response to the absence of a first target resource unit Pod on the first base station, the digital twin system determines whether a second target resource unit Pod exists from a target base station that meets the preset delay requirements and is different from the first base station. Among them, the second target resource unit Pod is of the same category as the business resource unit Pod and the utilization rate of the first resource in the second target resource unit Pod is less than the fourth utilization threshold, and the fourth utilization threshold is less than or equal to the target utilization threshold. In a specific example, the fourth utilization threshold may be 30%. In response to the existence of the second target resource unit, the digital twin system sends a forwarding command to the first base station to forward the business data to the target base station where the second target resource unit Pod is located. The scheduling command may include a forwarding command. Upon receiving the forwarding command, the first base station may forward the business data of the target user terminal device to the target base station where the second target resource unit Pod is located, so that the second target resource unit of the target base station can process related business applications according to the business data of the target user terminal device.

[0213] In some embodiments, in response to the delay of data being forwarded from the first base station to the target base station and then being backtransmitted from the target base station to the first base station being less than or equal to a preset delay threshold, it can be determined that the target base station meets the preset delay requirement. In response to the delay of data being forwarded from the first base station to the target base station and then being backtransmitted from the target base station to the first base station being greater than a preset delay threshold, it can be determined that the target base station does not meet the preset delay requirement. The preset delay threshold can be determined based on the service requirements of the target user terminal device.

[0214] In some embodiments, in response to the absence of the second target resource unit Pod, the digital twin system sends a rejection command to the first base station to reject the service request of the target user terminal device. The scheduling command may include a rejection command, and the first base station may reject the service request of the target user terminal device when receiving the rejection command.

[0215] In some embodiments, in response to the fact that a service application related to the service intention of the target user terminal device is not deployed on the second target resource unit Pod, the digital twin system sends a deployment command to the target base station to deploy the service application on the second target resource unit Pod. The scheduling command may include a deployment command, and the target base station may deploy the relevant service application on the second target resource unit Pod when receiving the deployment command.

[0216] In some embodiments, determining by the digital twin system whether there is a second target resource unit Pod from a target base station that meets the preset latency requirement and is different from the first base station may include: determining by the digital twin system from the target base station a resource unit Pod whose utilization rate of the first resource is less than a fourth utilization threshold in a resource unit Pod of the same category as the business resource unit Pod, to generate a fourth set. In some embodiments, in response to the fourth set being a non-empty set, the digital twin system determines that the resource unit Pod with the largest margin of the first resource among the resource unit Pods of the fourth set is the second target resource unit Pod.

[0217] In some embodiments, in response to the fourth set being an empty set, it can be determined that the second target resource unit Pod does not exist in the target base station. In other embodiments, in response to the fourth set being an empty set, one or more other utilization thresholds greater than the fourth utilization threshold and less than the target utilization threshold can be set and the above process can be referred to to determine whether the second target resource unit Pod exists. For example, the fifth utilization threshold can be set to 50% and the sixth utilization threshold can be set to 70% to determine whether the second target resource unit Pod exists.

[0218] In a specific example, if the wireless resources of a resource unit Pod of a local node (base station) are insufficient, the wireless resources can be scheduled to the resource unit Pod from a resource unit Pod with a lower service priority and relatively abundant wireless resources according to the 5QI service priority; if the computing resources of the resource unit Pod are insufficient, the resource unit Pod with the least service load and the most computing resource surplus can be compressed to expand the capacity of the resource unit Pod. When the local resource scheduling cannot meet the wireless resource requirements of a resource unit Pod, the access of users whose service intention is the service corresponding to the resource unit Pod can be blocked. When local computing resources cannot be allocated to meet the demand, the forwarding delay of all forwardable base stations can be calculated and the available resources of all base stations can be counted. The service deployment request can be forwarded to the base station with the most abundant resources or the lowest forwarding delay to other nearby base stations. If the forwarding delay and the resources on the base station at the forwarding destination can meet the service requirements (for example, the delay of the processed data from the destination to the local and then forwarded to the user does not exceed the service delay requirements, and the computing and wireless resources at the destination can meet the service operation and data forwarding), the service is forwarded in the physical network, processed at the destination, and then sent back to the local, and the processing results are sent to the user. If the forwarding processing delay exceeds the service delay requirements, or the resources of the nearby base stations cannot meet the demand, the user can be directly refused to serve, the request in the physical network is not forwarded, and the user connection is disconnected.

[0219] Normally, a drone hangar will not store a large number of drones for a certain business due to high demand for that business during peak periods, in order to avoid drones for a certain business occupying a large number of slots in the drone hangar and reduce the inventory of drones for other businesses. When the quantity of a certain business is insufficient, drones can be dispatched from other drone hangars as needed.

[0220] In some embodiments, in response to the number of service drones in the first target drone hangar for executing the target service being less than a preset number, the digital twin system sends a drone dispatching command to the second target drone hangar that can dispatch service drones to the first target drone hangar. The dispatching command may include a drone dispatching command, and the second target drone hangar may dispatch the corresponding service drone to the first target drone hangar upon receiving the drone dispatching command.

[0221] In some embodiments, the second target drone hangar is a drone hangar that is within the maximum flight range of the business drone from the first target drone hangar and has the largest number of business drones in the hangar.

[0222] In some embodiments, the preset number is determined according to a preset ratio of the number of business drones performing the target business in the first target drone hangar during the target period. In a specific example, the target period may be a peak period of the target business, wherein the peak period may be determined according to the historical number of business drones performing the target business in each period over a period of time in the past. For example, the peak period of the target business may be the period with the largest number of business drones performing the target business in each period, and the preset number may be the historical number of business drones performing the target business during the peak period. The preset ratio may be 20%.

[0223] In some embodiments, when the number of inspection drones in drone hangar A is lower than 20% of the number of drones performing inspection services during the peak business period in the area where it is located, a scheduling request will be initiated. The digital twin system can first calculate how many inspection drones drone hangar A needs to add (i.e., the difference between the number of idle inspection drones and 20% of the number of drones performing inspection services during the peak business period in the area where drone hangar A is located), screen out all drone hangars within the maximum range of the inspection drones and screen out drone hangars that can dispatch inspection drones (i.e., the current idle inspection drones minus 20% of the number of drones performing inspection services during the peak business period is greater than 0). If a drone hangar has 20 inspection drones in idle state at this time, and the number of drones performing inspection services during the peak business period in this area is 50, then the number of drones that can be dispatched is 20-50*20%=10 (units). In some embodiments, the drone hangars that can dispatch inspection drones are sorted according to the number of idle inspection drones and the range. Furthermore, scheduling can be performed first based on the quantity sorting. If a certain drone hangar can meet the scheduling requirements at this time, it will be scheduled from this hangar (if the drones in the hangar are not performing tasks, they are fully charged by default). If no single hangar can meet the scheduling requirements, it will be sorted based on the flight range. First, all the idle inspection drones in the nearest drone hangar B will be transferred to drone hangar A. Then, the demand difference will be scheduled from the second nearest drone hangar C (the demand difference is the number of inspection drones that need to be supplemented in drone hangar A minus the number of inspection drones transferred from the nearest drone hangar B). If the scheduling volume of drone hangar C still cannot meet the demand, that is, the number of idle inspection drones in drone hangar B plus the number of idle inspection drones in drone hangar C is less than the demand of drone hangar A, it can be scheduled from the third nearest drone hangar D, and so on, until the scheduling requirements of drone hangar A are met.

[0224] According to another aspect of the present disclosure, a digital twin system is also provided. Fig. 9 As shown, in an exemplary embodiment, the digital twin system 900 may include a first receiving module 901 , a generating module 902 , a determining module 903 and a first sending module 904 .

[0225] The first receiving module 901 can be configured to receive the business data and business intention of the target drone sent by the first base station.

[0226] The generation module 902 may be configured to generate optimized route information about the target UAV according to the business data and business intent.

[0227] The determination module 903 can be configured to determine whether there is a first target resource unit Pod on the first base station for scheduling the first resource to the business resource unit Pod in response to the utilization rate of the first resource in the business resource unit Pod corresponding to the business intent on the first base station being greater than the target utilization threshold, wherein a business application related to the business intent is deployed on the business resource unit Pod, and the first resource is used by the business resource unit Pod to process the business application according to the business data.

[0228] The first sending module 904 can be configured to: in response to the existence of the first target resource unit Pod on the first base station, send a first allocation command to the first base station to schedule the first resource in the first target resource unit Pod to the business resource unit Pod according to a preset reduction ratio of the first resource margin; and send a flight command to the target UAV to fly based on the optimized route information.

[0229] In some embodiments, the digital twin system 900 may execute the scheduling method 100 as described above.

[0230] According to another aspect of the embodiments of the present disclosure, a scheduling method that can be performed by a base station is also provided. Fig.10 As shown, in an exemplary embodiment, the scheduling method 200 may include:

[0231] Step S210, the base station receives a service request from the target drone;

[0232] Step S220, the base station identifies the service intention of the target UAV according to the service request, and receives the service data sent by the target UAV;

[0233] Step S230, the base station sends the business intention and business data of the target UAV to the digital twin system, so that the digital twin system can generate corresponding scheduling commands;

[0234] Step S240, in response to receiving a scheduling command sent by the digital twin system to the base station, the base station executes scheduling corresponding to the scheduling command.

[0235] In some embodiments, the scheduling method 200 may also include receiving, by the base station, a service request of the target ground terminal; identifying, by the base station, the service intention of the target ground terminal based on the service request of the target ground terminal, and receiving service data sent by the target ground terminal; and sending, by the base station, the service intention and service data of the target ground terminal to the digital twin system, so that the digital twin system can generate corresponding scheduling commands.

[0236] In some embodiments, the scheduling command may include a second release command, wherein, when the target drone switches from a communication connection with a base station to a communication connection with other base stations different from the base station, and the other base stations complete the allocation of wireless resources to the target user terminal device, the scheduling corresponding to the scheduling command executed by the base station may include the base station releasing the wireless resources used for the target drone.

[0237] In some embodiments, the scheduling command may also include a reservation command, and the scheduling corresponding to the scheduling command executed by the base station may include the base station reserving wireless resources for the target drone during the process of other base stations allocating wireless resources to the target drone.

[0238] In some embodiments, the scheduling command may further include a retention command, and the base station executing scheduling corresponding to the scheduling command may include the base station retaining the stored service data and the generated data related to the service data.

[0239] In some embodiments, the scheduling command may further include a migration command, and the base station executing scheduling corresponding to the scheduling command may include the base station forwarding the stored service data and the generated data related to the service data to other base stations.

[0240] In some embodiments, the scheduling command may include a second allocation command, wherein, when the target drone switches from a communication connection with a base station other than the base station to a communication connection with the base station, the base station executing scheduling corresponding to the scheduling command may include allocating wireless resources to the target drone by the base station.

[0241] In some embodiments, the scheduling command includes a first allocation command, wherein, when the utilization rate of the first resource in the business resource unit Pod corresponding to the business intention of the target drone on the base station is greater than the target utilization threshold, and there is a first target resource unit Pod on the base station for scheduling the first resource to the business resource unit Pod, the base station executes scheduling corresponding to the scheduling command, which may include the base station scheduling the first resource in the first target resource unit Pod to the business resource unit Pod according to a preset reduction ratio of the first resource margin.

[0242] In some embodiments, the scheduling command includes a forwarding command. When there is no first target resource unit Pod on the base station, and there is a second target resource unit Pod in a target base station different from the base station that meets the preset delay requirement, wherein the second target resource unit Pod is of the same category as the business resource unit Pod and the utilization rate of the first resource in the second target resource unit Pod is less than a fourth utilization threshold, and the fourth utilization threshold is less than or equal to the target utilization threshold, the scheduling corresponding to the scheduling command executed by the base station may include forwarding the business data to the target base station where the second target resource unit Pod is located.

[0243] In some embodiments, the business intent of the target drone is identified by the base station based on the intent network.

[0244] According to another aspect of the present disclosure, a base station is also provided. Fig.11 As shown, in an exemplary embodiment, the base station 1100 may include a second receiving module 1101 , an identifying module 1102 , a second sending module 1103 and a first executing module 1104 .

[0245] The second receiving module 1101 may be configured to receive a service request from a target UAV and service data sent by the target UAV. The second receiving module 1101 may also be configured to receive a service request from a target ground terminal and service data sent by the target ground terminal.

[0246] The identification module 1102 may be configured to identify the service intention of the target UAV according to the service request of the target UAV. The identification module 1102 may also be configured to identify the service intention of the target ground terminal according to the service request of the target ground terminal.

[0247] The second sending module 1103 can be configured to send the business intention and business data of the target UAV to the digital twin system so that the digital twin system can generate corresponding scheduling commands. The second sending module 1103 can also be configured to send the business intention and business data of the target ground terminal to the digital twin system so that the digital twin system can generate corresponding scheduling commands.

[0248] The first execution module 1104 can be configured to execute scheduling corresponding to the scheduling command in response to receiving a scheduling command sent by the digital twin system to the base station.

[0249] In some embodiments, the base station 1100 may execute the scheduling method 200 described above.

[0250] According to another aspect of the embodiments of the present disclosure, a scheduling method that can be performed by a drone is also provided. Fig.12 As shown, a scheduling method 300 according to an exemplary embodiment of the present disclosure may include:

[0251] Step S310: The drone sends a first service request and first service data to the first base station, so that the first base station identifies the first service intention according to the first service request;

[0252] Step S320, in response to receiving a scheduling command sent by the digital twin system to the drone, the drone executes scheduling corresponding to the scheduling command, wherein the scheduling command is generated by the digital twin system based on at least the first business intention and the first business data.

[0253] In some embodiments, the scheduling method 300 according to an exemplary embodiment of the present disclosure may also include: in response to the drone receiving a second service request from a user-end device, the drone sends the second service request and second service data to the first base station, so that the first base station identifies the second service intention of the user-end device based on the second service request, wherein the scheduling command is generated by the digital twin system based on the first service intention, the second service intention, the first service data and the second service data.

[0254] In some embodiments, the user terminal device can be another drone connected to the drone, or it can be a ground user terminal connected to the drone.

[0255] In some embodiments, the dispatch command may include a flight command, and the dispatch corresponding to the dispatch command executed by the drone may include the drone flying based on the optimized route information.

[0256] In some embodiments, the dispatch command may include a charging command, and the drone executing the dispatch corresponding to the dispatch command may include the drone charging in a drone hangar where it is currently located.

[0257] In some embodiments, the dispatch command may include a pause command, and the execution of the dispatch corresponding to the dispatch command by the drone may include the drone suspending the execution of the service.

[0258] In some embodiments, the dispatch command may include a hover command, and the dispatch corresponding to the dispatch command executed by the drone may include the drone hovering at its current location.

[0259] In some embodiments, the scheduling command may include a first release command, and the execution of the scheduling corresponding to the scheduling command by the drone may include the drone releasing data storage space.

[0260] According to another aspect of the present disclosure, a drone is also provided. Fig.13 As shown, the drone 1300 may include a third sending module 1301 and a second execution module 1302. The third sending module 1301 may be configured to send a first service request and first service data of the drone to the first base station, so that the first base station can identify the first service intention of the drone according to the first service request. The second execution module 1302 may be configured to respond to receiving a scheduling command sent by the digital twin system to the drone and execute a scheduling corresponding to the scheduling command, wherein the scheduling command is generated by the digital twin system at least according to the first service intention and the first service data.

[0261] In some embodiments, the drone 1300 may execute the scheduling method 300 described above.

[0262] In an exemplary embodiment of the present disclosure, Figures 14 to 23 As shown, the scheduling method may include steps S1401 to S1454.

[0263] like Fig.14 As shown, the scheduling method may include:

[0264] Step S1401, constructing scene models such as airspace and ground buildings, roads, forests, and rivers in the intent-driven digital twin system;

[0265] Step S1402: The ground base station receives network access requests from the UAV and the ground user terminal, where the network access requests include the service requests from the UAV and the ground user terminal and the geographic locations of the UAV and the ground user terminal.

[0266] Step S1403: The ground base station analyzes the user intentions of the ground user terminal and the UAV through the UUI, and sends the service intentions of the UAV and the ground user terminal to the intent-driven digital twin system;

[0267] Step S1404, UUI sends the business intent to SO, and SO binds the business intent according to the existing intent instance ID, or binds the business intent to the newly created intent instance ID and sends it to the scenario use case in SO;

[0268] Step S1405, the scenario use case of the SO binds the intent instance ID to the new scenario use case ID and stores it in AAI, or reads the scenario use case ID associated with the existing intent instance and stores it in AAI;

[0269] In step S1406, AAI monitors the business execution data, geographic location data, wireless network status data, power and storage data of the drone and ground user terminals to DCAE through the intent monitoring interface, and feeds back to the digital twin system. The digital twin system determines the scheduling requirements of the drone and ground user terminals based on the received data.

[0270] like Fig.14 and Fig.15 As shown, after step S1406, the scheduling method may further include:

[0271] Step S1407: The digital twin system monitors the power consumption and remaining power of the drone in real time, determines whether the drone has enough remaining power to fly to the nearest hangar for charging, and sets a power alarm line and the best recharging point;

[0272] Step S1408: If the drone encounters extreme weather or force majeure (failure) that prevents it from completing its mission, it will temporarily hover and provide feedback to the digital twin system;

[0273] Step S1409, the digital twin system monitors the cache and data storage of the drone;

[0274] Step S1410: If the digital twin system receives feedback that the drone's mission is blocked or determines that the remaining power of the drone has triggered the power alarm line, the drone is ordered to fly to the nearest rechargeable hangar for inspection and charging;

[0275] Step S1411: When the drone continues to fly and a collision is inevitable or the data storage is about to be full, the digital twin system will hover the drone and release the data storage space of the drone after the drone transmits all the data back. After determining that the drone's route and storage status meet the flight conditions, the drone's flight will be resumed.

[0276] like Fig.14 and Fig.16 As shown, after step S1406, the scheduling method may further include:

[0277] Step S1412, the digital twin system simulates the flight path of the drone and optimizes business execution by adjusting the position and path of the drone;

[0278] Step S1413, the digital twin system monitors the route and speed of each drone, determines whether there is a collision risk, whether it deviates from the route, and then corrects the route;

[0279] Step S1414, the digital twin system plans the location of the drone hangar according to the number of business executions in different areas;

[0280] Step S1415, the digital twin system conducts drone networking based on the demand for drone data transmission, the coverage of the ground base station, the drone endurance power and other data;

[0281] Step S1416: If there are existing infrastructure such as ground base stations, electric vehicle charging piles, and express delivery points near the common routes, drone hangars are built using or close to these infrastructures to facilitate the maintenance, charging, and networking of drones;

[0282] Step S1417: The digital twin system monitors the downtime status of the drone hangar, including the number of available charging positions, the number of parts available for maintenance, the number of various types of business drones, the cargo hold status (used for loading and unloading of logistics drones), and the business allocation status, etc. At the same time, it receives dispatch requests and dispatches drones with required services to the target area;

[0283] Step S1418: When the number of drones for a certain business is lower than the set safety business lower limit (such as 20% of the number of drones performing such business during business peak hours), drones will be transferred from the hangar with the largest number of idle drones for such business and within the drone's endurance range.

[0284] Among them, Fig.15 and Fig.16 As shown, after step S1417, step S1410 may be executed.

[0285] like Fig.14 and Fig.17 As shown, after step S1406, the scheduling method may further include:

[0286] Step S1419: The digital twin system determines the service priority and experimental requirements (refer to 5QI) according to the intent instance and its number of users, deploys the corresponding service applications, and offloads the computing service of the drone to the server of the base station to which it is connected, thereby scheduling cloud network resources.

[0287] Step S1420: Preliminarily classify the computing services of the drones according to their business data (such as video business data of inspection drones, base station business data of base station drones, load data of logistics drones, etc.), and assign them to corresponding Pods. Each Pod corresponds to a type of business, and pre-configures computing network slice resources for it, deploys related applications, and performs pre-processing.

[0288] Step S1421, the pre-processed base station service data received by the base station drone and the base station service data received by the ground base station are classified according to the service type to be classified into UE service data (such as video data, game data, call data, etc., refer to 5QI service) and then output to the corresponding service Pod;

[0289] Step S1422, deploy relevant business applications on the Pod according to business needs, configure wireless networks and computing resources to execute business, and feed back the data processed by the Pod to the digital twin system. The digital twin system monitors the data of all drones, including flight paths and endurance. All execution strategies are issued through SO and policy issuance execution interface for execution, and the cloud network resource allocation and drone route scheduling of the low-altitude airspace network are controlled in the domain controller SDN-C.

[0290] Among them, Fig.16 and Fig.17 As shown, after step S1417, step S1422 may also be executed.

[0291] like Fig.17 and Fig.18 As shown, after step S1422, the scheduling method may further include:

[0292] Step S1423, the base station Pod receives data from the base station drone and the ground base station, and performs classification and pre-processing according to the business;

[0293] Step S1424, the base station Pod sends the classified data to the corresponding UE service Pod, and receives the data fed back to the user terminal device by the service Pod;

[0294] Step S1425, the UE service Pod processes service applications (such as cloud application deployment, such as games, videos, VR);

[0295] Step S1426, the base station Pod allocates bandwidth to each base station drone and user terminal device according to the service type, the number of connections of the user terminal device and the connection status QoS;

[0296] Step S1427, the data to be sent to the base station drone and the ground base station are sent to each base station drone and the ground base station according to the current connection status of the user terminal device, and then sent to the user terminal device;

[0297] Step S1428, according to the flight speed of the UAV, the possible position of the next time slot is calculated, and then the access number of user terminal devices, spectrum efficiency, data transmission rate and bandwidth allocation of the next time slot are calculated;

[0298] Step S1429, calculating the average data transmission rate of the base station drone according to the data transmission rates of all connected user terminal devices;

[0299] Step S1430, select the optimal position of the base station drone in the next time slot and calculate the flight path.

[0300] like Fig.17 and Fig.19 As shown, after step S1422, the scheduling method may further include:

[0301] Step S1431, the inspection Pod receives the inspection video data, determines whether a crisis situation (such as a forest fire) occurs in the area and triggers an alarm, or determines whether the inspection mission of the drone needs to go to an area that cannot be covered by the ground base station;

[0302] Step S1432: If the drone is still within the area covered by the network, the position and flight path of the drone in the next time slot are calculated based on the current picture clarity, drone position, and shooting angle;

[0303] Step S1433: If the inspection drone or base station drone is about to go to a wilderness area such as a forest or river that cannot be covered by the ground base station to perform operations, an air networking demand is initiated to a hangar near the area;

[0304] Step S1434: The digital twin system dispatches a base station drone from the hangar closest to the target area as a relay base station for data backhaul, and the drone goes to the operation area together with the operation drone;

[0305] Step S1435, the drones form an aerial local area network and always maintain connection with the ground base station during the service execution process.

[0306] like Fig.17 and Fig. 20 As shown, after step S1422, the scheduling method may further include:

[0307] Step S1436, the spraying Pod monitors the amount of medicine and the completion of the operation of the drone;

[0308] Step S1437, judging whether the remaining amount of medicine of the drone can complete the spraying business according to the amount of medicine of the drone, and judging whether the drone needs to return to replenish the spraying medicine;

[0309] Step S1438, calculate the path to the nearest medicine replenishment point (a drone hangar with medicine reserves) for the drone that needs to return.

[0310] like Fig.17 and Fig.21 As shown, after step S1422, the scheduling method may further include:

[0311] Step S1439, the logistics Pod monitors the operation status of the logistics drone and the storage and delivery needs of each warehouse;

[0312] Step S1440, monitoring the cargo capacity of the drone according to the load weight limit and the accommodation space limit of the drone;

[0313] Step S1441, according to the cargo capacity, power limit and current location of the drone, combined with the cargo demand of the warehouse, cargo tasks are assigned to the drone, the flight start and end points, flight path and cargo distribution of the drone are planned, the drone path is optimized, and repeated backtracking is reduced.

[0314] like Fig.17 and Fig. 22 As shown, after step S1422, the scheduling method may further include:

[0315] Step S1442: When a certain dimension of resources of a base station Pod is insufficient (such as wireless slice bandwidth, CPU, memory), feedback is given to the digital twin system;

[0316] Step S1443, the digital twin platform preferentially schedules resources locally based on the resource conditions of the local node;

[0317] Step S1444: If the wireless resources of the Pod are insufficient, wireless resources are allocated to the Pod from a Pod with a lower service priority and relatively abundant wireless resources according to the 5QI service priority;

[0318] Step S1445, if the local wireless resources cannot meet the needs of the Pod, then new user terminal devices of this type of service are blocked from accessing;

[0319] Step S1446: If the computing resources of the Pod are insufficient, the Pod with the least local business load and the most free computing resources is compressed to expand the capacity of the Pod;

[0320] Step S1447: if the local computing resources cannot be allocated to meet the Pod requirements, the service application deployment request is forwarded to other nearby base station servers;

[0321] Step S1448: If the forwarding processing delay exceeds the service delay requirement, or the resources of the nearby server cannot meet the requirement, then the service for the user terminal device is rejected and the connection with the user terminal device is disconnected;

[0322] Step S1449: If the forwarding delay and the server resources at the forwarding destination can meet the service requirements, the service is forwarded, processed on the destination server, and then sent back to the local server, and the processing results are sent to the user terminal device.

[0323] like Figures 18 to 21 and Fig.23 As shown, after step S1430, step S1432, step S1438 or step S1441, the scheduling method may further include:

[0324] Step S1450: If the flight route of the drone passes through more than two base stations, the digital twin system comprehensively considers the energy consumption and the locations of the base stations passed through, and comprehensively plans the flight route of the drone;

[0325] Step S1451, when the drone switches the base station to which it is connected, a soft switching method is used;

[0326] Step S1452, when the UAV switches from base station A to base station B, base station A reserves the wireless network resources of the UAV during the process of base station B allocating wireless network resources to the UAV and before establishing a connection. After base station B completes the wireless network resource allocation, base station A releases the wireless network resources of the UAV.

[0327] Step S1453, when the drone switches from base station A to base station B, if the delay of data generated by the service application of the drone deployed on base station A, which is forwarded from base station A to base station B and then to the drone, can meet the drone service requirements, the service data stored in the drone will not be migrated;

[0328] Step S1454: If a long-distance flight is performed, the cell where the drone is located is switched multiple times, and the transmission delay can no longer meet the processing requirements of the business data, the business data of the drone is forwarded to the Pod of the corresponding business deployed on the base station to which it is currently connected.

[0329] According to another aspect of the present disclosure, a scheduling system is also provided. Fig.24 As shown, the scheduling system 1500 may include a digital twin system 900 , a base station 1100 , and a drone 1300 .

[0330] According to another aspect of the present disclosure, a scheduling device is provided, such as Fig.25 As shown, an exemplary scheduling device 1600 according to an embodiment of the present disclosure may include a processor 1601 and a memory 1602. The memory 1602 stores instructions, which, when executed by the processor 1602, implement the scheduling method described in any of the foregoing embodiments of the present disclosure.

[0331] The processor 1601 may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component to implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc., and may be an X86 architecture or an ARM architecture, etc.

[0332] Memory 1602 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronously linked dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0333] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which instructions are stored, and when the instructions are executed by a processor, the scheduling method described in any of the foregoing embodiments of the present disclosure can be implemented. The computer-readable storage medium of the present disclosure can be a non-transitory computer-readable storage medium.

[0334] The non-transitory computer-readable storage medium in the embodiments of the present disclosure may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. It should be noted that the non-transitory computer-readable storage medium described herein is intended to include but is not limited to these and any other suitable types of memory.

[0335] According to another aspect of the present disclosure, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the scheduling method described in any of the foregoing embodiments of the present disclosure can be implemented.

[0336] The instructions may be any set of instructions to be executed directly by one or more processors, such as machine code, or any set of instructions to be executed indirectly, such as a script. The terms "instructions", "applications", "processes", "steps" and "programs" (including "computer programs") herein may be used interchangeably herein. The instructions may be stored in an object code format for direct processing by one or more processors, or as a script or collection of independent source code modules in any other computer language, including those interpreted on demand or compiled in advance. The instructions may include instructions that cause one or more processors, such as to act as the various neural networks herein. The functions, methods and routines of the instructions are explained in more detail elsewhere herein.

[0337] According to some embodiments of the present disclosure, the drone business and user business intentions can be summarized based on intent-driven and digital twin technology, use cases can be generated, drone route planning, drone scheduling, and related wireless and computing resource scheduling can be completed. According to some embodiments of the present disclosure, through the intent network, drone resources, computing resources, wireless resources and other resources can be dynamically allocated according to business intention requirements, and drone routes can be planned to avoid collisions, thereby optimizing the airspace management efficiency under the condition of multiple business drones in parallel. According to some embodiments of the present disclosure, while optimizing airspace resources, the digital twin technology is used to optimize the business execution efficiency of various types of drones and monitor the status of drones, thereby optimizing the execution and replenishment of services. According to some embodiments of the present disclosure, in the multi-business drone flight scene of low-altitude economic development, real-time perception and dynamic interaction of drone intentions are realized. For low-altitude economic scenes with personalized needs, based on the differentiated needs of drone services, drone scheduling, networking, operation efficiency, route planning, and allocation of related computing network resources can be optimized according to the business intentions and business data of drones, thereby realizing the integrated scheduling and scheduling of drone routes and airspace cloud network resources, improving the utilization rate of airspace resources, optimizing the business execution of drones, and improving the efficiency of drone business execution.

[0338] The words "left," "right," "front," "back," "top," "bottom," "up," "down," "high," "low," and the like, if any, used herein are used for descriptive purposes and are not necessarily used to describe an unchanging relative position. It should be understood that the words so used are interchangeable under appropriate circumstances, such that the embodiments of the present disclosure described herein, for example, are capable of operation in other orientations than those shown or otherwise described herein. For example, when the device in the accompanying drawings is turned over, features previously described as "above" other features may now be described as "below" the other features. The device may also be otherwise oriented (rotated 90 degrees or at other orientations), and relative spatial relationships will be interpreted accordingly.

[0339] In this document, when an element is said to be "above", "attached" to, "connected" to, "coupled" to, or "in contact with" another element, the element may be directly above, directly attached to, directly connected to, directly coupled to, or directly in contact with another element, or one or more intermediate elements may be present. In contrast, when an element is said to be "directly" "above", "directly attached" to, "directly connected" to, "directly coupled" to, or "in direct contact with" another element, there will be no intermediate elements. In the specification and claims, a feature is arranged "adjacent" to another feature, which may refer to a feature having a portion that overlaps with an adjacent feature or a portion that is above or below an adjacent feature.

[0340] As used herein, the word "exemplary" means "serving as an example, instance, or illustration" rather than as a "model" to be exactly copied. Any implementation described as an example herein is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, the present disclosure is not to be limited by any expressed or implied theory given in the technical field, background, summary, or detailed description.

[0341] As used herein, the term "substantially" is intended to include any minor variations due to design or manufacturing imperfections, device or component tolerances, environmental influences, and / or other factors. The term "substantially" also allows for deviations from a perfect or ideal situation due to parasitic effects, noise, and other practical considerations that may exist in actual implementations.

[0342] In addition, the terms "first", "second" and the like may also be used herein for reference purposes only and are not intended to be limiting. For example, the terms "first", "second" and other such numerical terms referring to structures or elements do not imply a sequence or order unless the context clearly indicates otherwise.

[0343] It should also be understood that when the term “include / comprises” is used in this document, it indicates the presence of the specified features, integers, steps, operations, units and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, units and / or components and / or their combinations.

[0344] In this disclosure, the term "provide" is used in a broad sense to cover all ways of obtaining an object, and thus "providing an object" includes but is not limited to "purchasing", "preparing / manufacturing", "arranging / setting up", "installing / assembling", and / or "ordering" an object, etc.

[0345] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. The terms used herein are for the purpose of describing specific embodiments only and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0346] Those skilled in the art will appreciate that the boundaries between the above operations are merely illustrative. Multiple operations can be combined into a single operation, a single operation can be distributed in additional operations, and operations can be performed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a specific operation, and the order of operations may be changed in various other embodiments. However, other modifications, variations, and replacements are equally possible. Aspects and elements of all embodiments disclosed above may be combined in any manner and / or in combination with aspects or elements of other embodiments to provide multiple additional embodiments. Therefore, this specification and accompanying drawings should be considered illustrative, not restrictive.

[0347] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. The various embodiments disclosed herein may be combined in any manner without departing from the spirit and scope of the present disclosure. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A scheduling method, characterized in that: The scheduling method comprises: The digital twin system receives the business data and business intention of the target UAV sent by the first base station; The digital twin system generates optimized route information about the target UAV according to the business data and the business intent, and sends a flight command to the target UAV to fly based on the optimized route information; In response to the utilization rate of the first resource in the business resource unit corresponding to the business intent on the first base station being greater than the target utilization threshold, the digital twin system determines whether there is a first target resource unit on the first base station for scheduling the first resource to the business resource unit, and, in response to the existence of the first target resource unit on the first base station, the digital twin system sends a first allocation command to the first base station to schedule the first resource in the first target resource unit to the business resource unit according to a preset reduction ratio of the first resource margin, wherein a business application related to the business intent is deployed on the business resource unit, and the first resource is used by the business resource unit to process the business application according to the business data.

2. The scheduling method according to claim 1, characterized in that: Generating the optimized route information about the target UAV by the digital twin system according to the business data and the business intent includes: In response to the target UAV performing the spraying operation, the digital twin system determines whether the target UAV needs to replenish the agent according to the remaining agent and the completion status of the spraying operation; and In response to the target UAV needing to replenish medicine, the digital twin system generates optimized route information based on the remaining power of the target UAV and the location of a target medicine replenishment point within a preset distance range from the target UAV, wherein the target medicine replenishment point is the medicine replenishment point closest to the target UAV when the inventory can meet the replenishment demand and the remaining power can reach it.

3. The scheduling method according to claim 1, characterized in that: Generating the optimized route information about the target UAV by the digital twin system according to the business data and the business intent includes: In response to the target UAV performing logistics operations, the digital twin system assigns a first cargo mission to the target UAV based on cargo demand, cargo capacity, remaining power and current location of the target UAV, and generates optimized route information based on the first cargo mission.

4. The scheduling method according to claim 3, characterized in that: The first cargo mission is a cargo mission of the target UAV in the process of flying from the UAV hangar located in the first area to the UAV hangar in the second area. Generating the optimized route information according to the first cargo mission includes: When the target UAV completes the first cargo mission, in response to the UAV hangar where the target UAV is currently located being unable to charge the target UAV, the digital twin system assigns the target UAV a second cargo mission that matches the cargo capacity and remaining power of the target UAV, and generates optimized route information based on the second cargo mission.

5. The scheduling method according to claim 3, characterized in that: The digital twin system allocates the first cargo mission to the target drone based on cargo demand, cargo capacity, remaining power, and current location of the target drone, including: In a case where at least one of the starting point and the end point of the freight task to be assigned is not in the drone hangar, in response to the logistics range of the freight task to be assigned being less than or equal to the current maximum range of the target drone, the digital twin system determines that the freight task to be assigned is a freight task that can be assigned to the target drone; The digital twin system determines a first freight task assigned to the target drone from freight tasks that can be assigned to the target drone based on freight demand, the freight capacity of the target drone, the remaining power and the current location.

6. The scheduling method according to claim 1, characterized in that: Generating the optimized route information about the target UAV by the digital twin system according to the business data and the business intent includes: In response to the difference between the remaining power of the target UAV and the preset warning power being less than or equal to the preset warning threshold, the digital twin system generates optimized route information based on the current position of the target UAV and the position of the target supply warehouse, wherein the preset warning power is the power required for the target UAV to fly to the rechargeable target supply warehouse closest to the current position; In response to the difference between the remaining power of the target UAV and the preset warning power being greater than the preset warning threshold, the digital twin system generates optimized route information according to the remaining power and the execution status of the current business.

7. The scheduling method according to claim 6, characterized in that: The digital twin system generates optimized route information according to the remaining power and the execution status of the current business, including: In response to the target UAV performing the spraying service, the digital twin system determines a first remaining endurance time T0 according to the flight speed of the target UAV, the power consumption of performing the spraying service, and the remaining power; When the remaining medicine amount is less than or equal to the preset medicine remaining threshold, the digital twin system determines a first supply warehouse that can replenish medicine within the flight range of the target UAV based on the first remaining flight time T0, and determines the time T2 required for the target UAV to reach the first supply warehouse; When T0≥T2, the digital twin system generates optimized route information based on the current position of the target UAV and the position of the first supply warehouse.

8. The scheduling method according to claim 1, characterized in that: Generating the optimized route information about the target UAV by the digital twin system according to the business data and the business intent includes: The digital twin system generates optimized route information based on the energy consumption of the target UAV and the signal coverage distribution of the base station, wherein the optimized route information satisfies the requirement that the number of base stations passed through is minimal when the energy consumption of the target UAV can meet the business being performed.

9. The scheduling method according to claim 1, characterized in that: Determining, by the digital twin system, whether there is a first target resource unit on the first base station for scheduling the first resource to the service resource unit includes: The digital twin system determines, from the resource units on the first base station, resource units whose utilization rate of the first resource is less than a first utilization threshold, to generate a first set, wherein the first utilization threshold is less than or equal to the target utilization threshold; The digital twin system determines whether the first target resource unit exists on the first base station according to whether the first set is an empty set.

10. The scheduling method according to claim 9, characterized in that: Determining, by the digital twin system, whether the first target resource unit exists on the first base station according to whether the first set is an empty set includes: In response to the first set being a non-empty set, the digital twin system determines a resource unit with the lowest service priority in the first set as a first target resource unit; In response to the first set being an empty set: The digital twin system determines that the first target resource unit does not exist on the first base station; or In a case where the first utilization threshold is less than the target utilization threshold, the digital twin system determines, from the resource units on the first base station, resource units whose utilization of the first resource is less than a second utilization threshold, to generate a second set, wherein the second utilization threshold is greater than the first utilization threshold, and the second utilization threshold is less than or equal to the target utilization threshold; The digital twin system determines whether the first target resource unit exists on the first base station according to whether the second set is an empty set.

11. The scheduling method according to claim 10, characterized in that: Determining, by the digital twin system, whether the first target resource unit exists on the first base station according to whether the second set is an empty set includes: In response to the second set being a non-empty set, the digital twin system determines a resource unit with the lowest service priority in the second set as a first target resource unit; In response to the second set being an empty set: The digital twin system determines that the first target resource unit does not exist on the first base station; or In a case where the second utilization threshold is less than the target utilization threshold, the digital twin system determines, from the resource units on the first base station, resource units whose utilization rate of the first resource is less than a third utilization threshold, to generate a third set, wherein the third utilization threshold is greater than the second utilization threshold, and the third utilization threshold is less than or equal to the target utilization threshold; In response to the third set being a non-empty set, the digital twin system determines that a resource unit with the lowest service priority in the third set is a first target resource unit; In response to the third set being an empty set, the digital twin system determines that the first target resource unit does not exist on the first base station.

12. The scheduling method according to any one of claims 1 to 11, characterized in that: The scheduling method further includes: In response to the absence of the first target resource unit on the first base station, determining by the digital twin system whether a second target resource unit exists from a target base station that meets a preset delay requirement and is different from the first base station, wherein the second target resource unit is of the same category as the service resource unit and a utilization rate of the first resource in the second target resource unit is less than a fourth utilization threshold, and the fourth utilization threshold is less than or equal to the target utilization threshold; In response to the existence of a second target resource unit, the digital twin system sends a forwarding command to the first base station to forward the service data to the target base station where the second target resource unit is located.

13. The scheduling method according to claim 12, characterized in that: The scheduling method further includes: In response to the business application not being deployed on the second target resource unit, the digital twin system sends a deployment command to the target base station to deploy the business application on the second target resource unit.

14. The scheduling method according to claim 12, characterized in that: Determining, by the digital twin system, whether there is a second target resource unit from a target base station that meets a preset delay requirement and is different from the first base station includes: The digital twin system determines, from the target base station, resource units of the same category as the service resource unit, resource units whose utilization rate of the first resource is less than a fourth utilization threshold, so as to generate a fourth set; In response to the fourth set being a non-empty set, the digital twin system determines that the resource unit with the largest margin of the first resource among the resource units in the fourth set is the second target resource unit.

15. The scheduling method according to claim 1, characterized in that: The first resource includes a wireless resource for accessing a network and / or a computing resource for executing a computing task.

16. The scheduling method according to claim 1, characterized in that: The scheduling method comprises: The digital twin system determines whether the target UAV meets the preset flight conditions according to at least one of the preset route, flight speed and data storage space margin of the target UAV; In response to the target UAV not satisfying the preset flight condition, a hovering command is sent to the target UAV to hover at the current location.

17. The scheduling method according to claim 16, characterized in that: The target UAV includes a first UAV and a second UAV, and the digital twin system determines whether the target UAV meets the preset flight condition according to at least one of the preset route, flight speed and data storage space margin of the target UAV, including: In response to the existence of an intersection between a first preset route of the first drone and a second preset route of the second drone and the time difference between the time when the first drone flies to the intersection based on the first flight speed of the first drone and the time when the second drone flies to the intersection based on the second flight speed of the second drone is less than a preset time threshold, the digital twin system determines that the drone with a lower priority in the business executed by the first drone and the second drone does not meet the preset flight condition.

18. The scheduling method according to claim 16, characterized in that: The digital twin system determines whether the target drone meets the preset flight conditions according to at least one of the preset route, flight speed and data storage space margin of the target drone, including: In response to the data storage space margin of the target UAV being less than a preset storage margin threshold, determining that the target UAV does not meet a preset flight condition; The scheduling method further includes: In response to the target UAV not meeting the preset flight condition, the digital twin system sends a return command to the target UAV to transmit all data in the data storage space back to the first base station; In response to the target drone having transmitted all data of the data storage space of the target drone back to the first base station, the digital twin system sends a first release command to release the data storage space to the target drone.

19. The scheduling method according to claim 1, characterized in that: The scheduling method further includes: When the target UAV switches from the communication connection with the first base station to the communication connection with the second base station: The digital twin system sends a second allocation command to the second base station for allocating wireless resources to the target drone; In response to the second base station completing the allocation of wireless resources for the target drone, the digital twin system sends a second release command to the first base station to release the wireless resources used for the target drone.

20. The scheduling method according to claim 19, characterized in that: The scheduling method further includes: The digital twin system sends a reservation command to the first base station to reserve wireless resources for the target drone during the process of the second base station allocating wireless resources to the target drone.

21. The scheduling method according to claim 19, characterized in that: The scheduling method further includes: In response to a first delay in which the data generated on the first base station and related to the business data is forwarded via the first base station to the second base station and then forwarded by the second base station to the target drone is less than or equal to a preset delay threshold, the digital twin system sends a retention command to the first base station to retain the stored business data and the generated data related to the business data; and / or In response to the first delay being greater than the preset delay threshold, the digital twin system sends a migration command to the first base station to forward the stored business data and the generated data related to the business data to the second base station.

22. The scheduling method according to claim 1, characterized in that: The scheduling method further includes: In response to the target UAV going to a target area that cannot be covered by the signal of the first base station, the digital twin system determines a first UAV hangar within a first preset distance from the target area, and sends a dispatch command to the first UAV hangar to dispatch a relay UAV for the target UAV to transmit back to the first base station; and / or In response to detecting that the data transmission rate of the signal received by the target UAV from the first base station is lower than the preset transmission rate requirement of the corresponding service, the digital twin system determines a second UAV hangar within a second preset distance range from the target UAV, and sends a dispatch command to the second UAV hangar to dispatch a relay UAV for transmitting the signal from the target UAV back to the first base station.

23. The scheduling method according to claim 1, characterized in that: The scheduling method further includes: In response to the number of service drones for executing the target business in the first target drone hangar being less than a preset number, the digital twin system sends a drone dispatching command to the second target drone hangar that can dispatch the service drone to the first target drone hangar, The preset number is determined according to a preset ratio of the number of business drones that perform the target business in the first target drone hangar within a target period.

24. A scheduling method, characterized in that: The scheduling method comprises: The base station receives the service request of the target UAV; The base station identifies the service intention of the target UAV according to the service request, and receives the service data sent by the target UAV; and The base station sends the business intention and business data of the target UAV to the digital twin system so that the digital twin system can generate corresponding scheduling commands; In response to receiving a scheduling command sent by the digital twin system to the base station, the base station executes scheduling corresponding to the scheduling command.

25. The scheduling method according to claim 24, characterized in that: The scheduling command includes a second release command, wherein, when the target UAV switches from a communication connection with the base station to a communication connection with another base station different from the base station, and the other base station completes the allocation of wireless resources to the target UAV, the base station performs scheduling corresponding to the scheduling command, including: The base station releases the wireless resources used for the target drone.

26. The scheduling method according to claim 25, characterized in that: The scheduling command further includes a reservation command, wherein the base station performing scheduling corresponding to the scheduling command includes: The base station reserves the wireless resources for the target drone during the process of the other base stations allocating wireless resources to the target drone.

27. The scheduling method according to claim 25, characterized in that: The scheduling command further includes a retention command, wherein the base station performing scheduling corresponding to the scheduling command includes: The base station retains the stored business data and the generated data related to the business data.

28. The scheduling method according to claim 25, characterized in that: The scheduling command further includes a migration command, wherein the base station performing scheduling corresponding to the scheduling command includes: The base station forwards the stored service data and the generated data related to the service data to the other base stations.

29. The scheduling method according to claim 24, characterized in that: The dispatch command includes a second allocation command, wherein, in a case where the target UAV switches from a communication connection with another base station different from the base station to a communication connection with the base station, the base station performs the dispatch corresponding to the dispatch command, including: The base station allocates wireless resources to the target UAV.

30. The scheduling method according to claim 24, characterized in that: The scheduling command includes a first allocation command, wherein, when a utilization rate of a first resource in a service resource unit corresponding to the service intention of the target drone on the base station is greater than a target utilization threshold, and there is a first target resource unit on the base station for scheduling the first resource to the service resource unit, the base station performs scheduling corresponding to the scheduling command, including: The base station schedules the first resource in the first target resource unit to the service resource unit according to a preset reduction ratio of the first resource margin.

31. The scheduling method according to claim 30, characterized in that: The scheduling command includes a forwarding command, and when the first target resource unit does not exist on the base station, and a second target resource unit exists in a target base station different from the base station that meets a preset delay requirement, wherein the second target resource unit is of the same category as the service resource unit and a utilization rate of the first resource in the second target resource unit is less than a fourth utilization threshold, and the fourth utilization threshold is less than or equal to the target utilization threshold, the base station executes scheduling corresponding to the scheduling command, including: The service data is forwarded to the target base station where the second target resource unit is located.

32. The scheduling method according to any one of claims 24 to 31, characterized in that: The business intention of the target UAV is identified by the base station based on the intent network.

33. A scheduling method, characterized in that: The scheduling method comprises: The drone sends a first service request and first service data to the first base station, so that the first base station identifies the first service intention according to the first service request; and In response to receiving a scheduling command sent by the digital twin system to the drone, the drone executes scheduling corresponding to the scheduling command, wherein the scheduling command is generated by the digital twin system based on at least the first business intent and the first business data.

34. The scheduling method according to claim 33, characterized in that: The scheduling method further includes: In response to the drone receiving a second service request and second service data from a user-end device, the drone sends the second service request and second service data to the first base station, so that the first base station can identify the second service intention of the user-end device based on the second service request, wherein the scheduling command is generated by the digital twin system based on the first service intention, the second service intention, the first service data and the second service data.

35. The scheduling method according to claim 34, characterized in that: The dispatch command includes a flight command, and the dispatch corresponding to the dispatch command executed by the UAV includes: The drone flies based on the optimized route information.

36. The scheduling method according to claim 34, characterized in that: The dispatch command includes a hover command, and the dispatch corresponding to the dispatch command executed by the drone includes: The drone hovers at the current location.

37. The scheduling method according to claim 34, characterized in that: The dispatch command includes a first release command, and the dispatch corresponding to the dispatch command executed by the UAV includes: The drone frees up space for data storage.

38. A digital twin system, characterized in that: The digital twin system includes: A first receiving module, wherein the first receiving module is configured to receive the service data and service intention of the target UAV sent by the first base station; A generating module, wherein the generating module is configured to generate optimized route information about the target UAV according to the business data and the business intent; a determination module, the determination module being configured to determine whether there is a first target resource unit on the first base station for scheduling the first resource to the service resource unit in response to a utilization rate of the first resource in the service resource unit corresponding to the service intent on the first base station being greater than a target utilization threshold, wherein a service application related to the service intent is deployed on the service resource unit, and the first resource is used by the service resource unit to process the service application according to the service data; and A first sending module, wherein the first sending module is configured to: In response to the first target resource unit existing on the first base station, sending a first allocation command to the first base station to schedule the first resource in the first target resource unit to the service resource unit according to a preset reduction ratio of the first resource margin; and A flight command based on the optimized route information is sent to the target UAV.

39. A base station, characterized in that: The base station comprises: a second receiving module, wherein the second receiving module is configured to receive a service request of a target UAV and service data sent by the target UAV; an identification module, the identification module being configured to identify the service intention of the target drone according to the service request; and A second sending module, wherein the second sending module is configured to send the business intention and business data of the target UAV to the digital twin system so that the digital twin system generates a corresponding scheduling command; A first execution module, wherein the first execution module is configured to execute scheduling corresponding to the scheduling command in response to receiving a scheduling command sent by the digital twin system to the base station.

40. A drone, characterized in that: The drone comprises: a third sending module, the third sending module being configured to send a first service request and first service data to the first base station, so that the first base station identifies a first service intention according to the first service request; and A second execution module is configured to execute scheduling corresponding to the scheduling command in response to receiving a scheduling command sent by the digital twin system to the drone, wherein the scheduling command is generated by the digital twin system at least based on the first business intent and the first business data.

41. A scheduling system, characterized in that: include: The digital twin system according to claim 38; The base station according to claim 39; and A drone according to claim 40.

42. A scheduling device, characterized in that: include: processor; as well as A memory having instructions stored thereon, which, when executed by the processor, implement the operation of the scheduling method according to any one of claims 1 to 37.

43. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores instructions, which, when executed by a processor, implement the operation of the scheduling method according to any one of claims 1 to 37.

44. A computer program product, characterized in that The computer program product comprises instructions which, when executed by a processor, implement the operation of the scheduling method according to any one of claims 1 to 37.

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