Low-altitude airspace slice edge cloud cooperative scheduling system

Abstract

The application discloses a low-altitude airspace slice edge cloud cooperative scheduling system, relates to the technical field of airspace control systems, and comprises the following steps: a center cloud management and control platform constructs a global virtual mapping model to generate a global airspace operation view; a low-altitude dynamic airspace slice manager processes slice requests, completes SLA negotiation and resource mapping; an edge node cluster performs real-time scheduling and conflict detection; a service request processing module receives and checks requests; a resource mapping and instantiation module allocates resources and issues instantiation instructions; an edge cloud cooperative operation module realizes center and edge cooperation; a slice life cycle management module adjusts or cancels slices; and a safety isolation and conflict processing module guarantees slice safety and resolves conflicts. The application improves resource utilization through dynamic airspace slicing, strengthens real-time performance and reliability through edge cloud cooperative architecture, guarantees service safety through logical isolation, adapts to various demands through elastic scaling, reduces costs through automatic management, and promotes fine and intelligent upgrading of low-altitude management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of airspace control systems, and in particular to a low-altitude airspace slice edge cloud collaborative scheduling system. BACKGROUND

[0002] With the vigorous development of low-altitude economic industry forms such as unmanned aerial vehicle logistics, aerial taxi, emergency rescue, etc., the low-altitude airspace below 120 meters as the core operation space is facing unprecedented congestion pressure. The high-density, multi-type, and high-frequency operation of various aircrafts puts forward strict requirements for the dynamic adaptation, resource allocation, and safety control of the airspace. The traditional airspace management mode takes static division as the core, and allocates airspace resources through setting permanent no-fly zones, reporting zones, and other fixed regional division methods. This mode lacks flexibility and cannot dynamically adjust the airspace structure according to real-time business needs. For example, in holiday logistics peak, large-scale temporary activities, and other scenarios, the existing fixed airspace division cannot meet the sudden high-density flight demand, resulting in insufficient supply of airspace resources; while in off-peak periods, a large amount of airspace resources are in idle state, causing resource waste and low overall utilization rate of airspace.

[0003] Some existing technologies attempt to use a central cloud platform for unified scheduling, which can achieve a certain degree of global optimization, but there is a significant centralized bottleneck. The central cloud platform needs to handle scheduling instructions, state monitoring, and path planning of all aircrafts, and as the number of aircrafts increases, the computing load of the central server increases sharply, which is prone to response delay and difficult to meet the control needs of large-scale unmanned aerial vehicle cluster with low latency and high reliability. At the same time, the centralized architecture has the risk of single point failure, and once the central cloud platform fails, it will cause the entire airspace scheduling system to malfunction, causing flight disorder and seriously affecting the continuity and safety of low-altitude operations. In addition, there may be problems such as network fluctuations and signal attenuation in the process of long-distance data transmission, further exacerbating scheduling delay and reliability risks.

[0004] Different low-altitude businesses have significant differences in the safety of airspace, communication quality, and priority requirements. Logistics unmanned aerial vehicles require high-bandwidth data transmission, manned flights have extremely high safety requirements, and emergency rescue operations need to occupy airspace resources in priority. However, the existing system lacks the ability to divide "virtual airspace" at the logical level, and cannot achieve safe, efficient, and collaborative operation of different types of businesses in physically overlapping airspace. When aircrafts of different businesses fly in the same airspace, path conflicts and communication interference may occur, which not only affects operational efficiency, but also may cause safety accidents. SUMMARY

[0005] The low-altitude airspace slice edge cloud collaborative scheduling system proposed by the present application solves the problems mentioned in the above existing technologies.

[0006] In order to achieve the above object, the application adopts the following technical scheme: a low-altitude airspace slice edge cloud cooperative scheduling system comprises the following modules:

[0007] The center cloud management and control platform integrates a global airspace digital twin module and a global situation awareness unit, builds a low-altitude global virtual mapping model, collects aircraft, environment and weather data in real time, and generates a global airspace operation view through data fusion and situation analysis;

[0008] The low-altitude dynamic airspace slice manager includes a slice template library, an SLA (Service Level Agreement) negotiation and mapping unit, and a virtual resource orchestration unit, receives an airspace slice request, evaluates the feasibility in combination with historical data and digital twin deduction, completes SLA negotiation and generates a slice template, and maps SLA requirements to a virtual resource combination;

[0009] The edge node cluster receives the slice instantiation instruction issued by the center cloud, creates and initializes the slice controller, loads the resource strategy and scheduling algorithm, and executes real-time scheduling and conflict detection of aircrafts in the jurisdiction;

[0010] The business request processing module provides a standardized API interface, supports business parties to submit slice request parameters, performs format verification and compliance review on the request, forwards the valid request to the low-altitude dynamic airspace slice manager, and synchronously feeds back the negotiation result and slice state information;

[0011] The resource mapping and instantiation module converts the abstract slice template into a resource configuration scheme, allocates a three-dimensional airway network, a dedicated communication frequency band, an edge node computing power and a virtual network bandwidth to the slice, issues an instantiation instruction to the target edge node, and supervises the slice controller loading and resource reservation process;

[0012] The edge cloud cooperative operation module establishes a bidirectional communication link between the center cloud and the edge node, the center cloud is responsible for monitoring the slice running state, processing resource competition and triggering elastic scaling strategy, and the edge node is responsible for path planning and local resource scheduling;

[0013] The slice life cycle management module tracks the whole process of slice running in real time, guides the edge node to release resources, and updates the resource occupation state for other slice applications;

[0014] The security isolation and conflict processing module uses logical isolation technology to ensure slice safety, establishes a double conflict detection mechanism within and between slices, and processes flight conflicts in combination with global arbitration and local relief algorithms.

[0015] Further, it further comprises a slice resource optimal allocation unit, which establishes a resource allocation priority evaluation model based on slice priority, business emergency degree and resource occupation demand, and the evaluation formula is: wherein is a resource allocation priority coefficient, is a priority weight coefficient, is a preset priority of a slice, is a time weight coefficient, is a service remaining available time normalized value, is a resource weight coefficient, is a total resource demand normalized value, is a cost weight coefficient, is a resource occupation cost normalized value; the weight coefficient is dynamically adjusted, and automatic optimization is performed according to real-time space load, service type proportion, and resource tightness .

[0016] Further, the real-time conflict resolution optimization module is further included, the conflict resolution algorithm module of the edge node combines the real-time position, the speed vector and the airspace boundary constraint of the aircraft, plans a segmented optimal collision avoidance path, the collision avoidance path adopts a segmented design, first offsets horizontally to avoid the conflict area, and then returns to the original planned flight path, and the flight speed is dynamically adjusted synchronously; according to the maneuvering ability of the aircraft, a differentiated collision avoidance strategy is customized, a fixed-wing aircraft preferentially adjusts the heading, and a multi-rotor aircraft preferentially adjusts the height or hovers to wait; the path energy consumption cost and the time loss are calculated in real time during the conflict resolution process, and the scheme with the minimum loss is selected for execution; after the resolution, the processing result, the collision avoidance trajectory and the energy consumption data are reported to the center cloud, and the aircraft running trajectory data in the slice is updated synchronously.

[0017] Further, the slice elastic scaling control unit is further included, the global arbitration module of the center cloud combines real-time airspace flow data, service peak prediction and historical operation law, and automatically triggers slice elastic adjustment; the slice merging and splitting function is supported, adjacent and service type compatible slices are merged and managed during a low flow period, and a large slice is split into multiple small slices during a high flow period; for temporary burst service demand, a temporary slice is quickly created and idle resources are preferentially allocated, and the resources are automatically recovered within 10 minutes after the service ends.

[0018] Further, the flight conflict risk prediction unit is further included, a conflict risk prediction model is established based on the aircraft motion model and the airspace environment parameters, and the prediction formula is: wherein is a conflict risk level, is a distance weight coefficient, is a shortest expected distance normalized value between aircrafts, is a speed weight coefficient, is a relative speed normalized value of the aircraft, is an airspace weight coefficient, The air space congestion degree is normalized; a time window is predicted in advance, and the conflict hotspot area and high incidence period in the future time period are calculated 5 to 10 minutes in advance, and the center cloud sends early warning information to the related edge node; a hierarchical early warning mechanism is established, and only the edge node is sent when the risk level is 3 to 5, the local path optimization is started when the risk level is 6 to 8, and the trajectory is forced to adjust when the risk level is 9 to 10, thereby reducing the probability of conflict from the source.

[0019] Further, the SLA dynamic adaptation module is further included, which monitors the slice SLA index compliance in real time, collects data every 5 seconds and compares it with the preset threshold; when the index deviates, the adaptation adjustment is started, when the communication bandwidth is insufficient, the spectrum resource management unit is coordinated to allocate additional frequency bands or optimize the bandwidth allocation strategy; when the transmission delay exceeds the limit, the routing strategy is optimized, part of the computing task is sunk from the center cloud to the edge node, or the redundant computing power of the edge node is called to improve the processing speed; when the security level is not up to standard, the data encryption strength is strengthened, the access control permission is tightened, and the security audit frequency is increased; the SLA degradation negotiation mechanism is supported, when the resources are insufficient to meet the initial SLA requirements, the degradation scheme and compensation suggestion are automatically pushed to the business party, and after confirmation, the index and resource configuration are adjusted.

[0020] Further, the multi-dimensional environment perception module is further included, which integrates meteorological sensors, air space monitoring radars, electronic fence detection devices and geographic information systems, and collects meteorological data, obstacle data and control information in real time; an environment data fusion analysis model is established to eliminate abnormal data and supplement missing values, generate an air space environment situation map, and synchronize to the center cloud and edge node in real time; based on historical and real-time data, the future 1 to 3 hours of environmental change trend is predicted, the center cloud plans to avoid high-risk areas, and the edge node adjusts the flight parameters dynamically; the dynamic electronic fence function is provided, the electronic fence range is adjusted in real time according to the environmental change, and the air space boundary is updated synchronously.

[0021] Further, the aircraft state monitoring and abnormal processing unit is further included, which collects aircraft operation data in real time through airborne sensors and communication links, and the edge node analyzes and identifies the data in real time; an aircraft health degree evaluation system is established, the health degree score is calculated combined with multi-dimensional data, and when the score is lower than the preset threshold, the abnormal processing process is triggered, when the power is low, the return or nearby emergency landing instruction is triggered and the emergency landing point information is pushed, when the power is abnormal, the emergency communication channel is started and the flight area is isolated, and when the navigation deviates, the path is re-planned; the fault self-healing suggestion function is supported, the troubleshooting steps and emergency scheme are automatically generated according to the abnormal type and model and pushed to the business party, and at the same time, the warning information, abnormal data and processing progress are reported to the center cloud.

[0022] Further, it also includes a slice operation visualization management unit, constructs a three-dimensional visualization monitoring interface, and displays the global slice distribution, geographical range, number and position of aircrafts, resource occupation state, SLA compliance, and conflict processing record in real time based on the digital twin model; supports data query and filtering, provides intelligent early warning prompt function, and prompts potential problems through pop-up window and color marking; all operation records are logged and synchronized to the audit module; has a report automatic generation function, generates operation report containing key indicators, and supports custom dimensions and export.

[0023] Further, it also includes a cross-regional slice collaborative management unit, establishes a collaborative communication mechanism between edge nodes for long-distance slices across multiple edge node jurisdictions, and neighboring nodes share aircraft operation state, scheduling plan, and resource occupation in real time; the center cloud sets up a cross-regional arbitration submodule to uniformly coordinate resource allocation and conflict processing; the scheduling right is transferred 1 minute before the aircraft enters the next edge node, and the trajectory, SLA requirement, and safety policy are synchronized, and the subsequent node reserves resources and plans the path in advance; supports unified life cycle management of cross-regional slices, and the center cloud coordinates the creation, adjustment, and cancellation processes.

[0024] Compared with the existing technology, the beneficial effects of the present application are:

[0025] In terms of airspace resource utilization, the system innovatively introduces the concept of dynamic airspace slicing, converting physical airspace into virtual resources that can be allocated on demand, and completely changing the rigid mode of traditional static division. Through the slicing elastic expansion function, the airspace range, height layer, and resource configuration can be adjusted in real time according to the change of business traffic, expanding the airspace capacity during peak business and releasing idle resources during off-peak, greatly improving the utilization rate of airspace resources. At the same time, the resource optimization allocation mechanism is based on multi-dimensional evaluation of business priority, urgency, etc., realizing the orderly allocation of limited resources, ensuring the resource supply of high-priority businesses, avoiding low efficiency caused by resource competition, and realizing efficient circulation and maximum utilization of airspace resources.

[0026] In terms of system reliability and real-time performance, the edge-cloud collaborative architecture builds a double-layer management and control mode of "center cloud strategic layer + edge node tactical layer", effectively solving the inherent bottleneck of centralized architecture. The center cloud is responsible for global situation awareness, strategy formulation, and resource arbitration, and the edge node undertakes high-frequency tasks such as real-time scheduling of aircrafts within the jurisdiction and millisecond-level conflict resolution, sinking high real-time tasks to the edge, reducing data transmission distance and center load, and significantly reducing scheduling delay. This distributed architecture avoids the impact of single-point failure on the entire system, and even if part of the edge nodes or the center cloud is abnormal, the core functions can still run normally through the collaborative mechanism, greatly improving the reliability and stability of system operation, and meeting the operation needs of large-scale UAV clusters.

[0027] In terms of security isolation and collaboration, the system builds independent "virtual air private network" for different businesses through logical isolation technology. Different airspace slices are isolated from each other in terms of communication frequency band, airway network, security policy, etc., effectively avoiding interference and conflict of different types of businesses in the physically overlapped airspace. For example, manned flight slice and logistics flight slice can run in parallel in the same physical airspace, and the safety level and communication quality of each slice are not affected, which not only ensures the safe operation of high safety demand business, but also realizes the efficient collaboration of different businesses. The double conflict detection mechanism and hierarchical early warning strategy can predict potential conflict risks in advance and resolve them in time, further strengthening the safety of airspace operation.

[0028] In terms of management intelligence and adaptability, the system realizes the rapid online and flexible adjustment of business based on the automatic negotiation, deployment and operation mechanism of SLA. Business parties can submit requirements through standardized interfaces, and the system automatically completes evaluation, negotiation and slice instantiation without human intervention, greatly reducing management costs. The system supports slice merging, splitting and temporary slice rapid creation, and can flexibly adapt to various scene requirements such as periodic peak and emergency rescue. Cross-regional collaborative management function solves the problem of scheduling connection for long-distance flights, realizes seamless switching of aircraft across regions, adapts to cross-regional business such as cross-city logistics and intercity inspection, and further expands the application range of the system.

[0029] Overall, through the core innovation of dynamic slicing and edge cloud collaboration, the system comprehensively solves the problems of traditional airspace management such as static rigidity, poor real-time performance, insufficient security isolation and low resource utilization, promotes the transformation of low-altitude management from "static allocation" to "dynamic flexibility", from "centralized control" to "edge cloud collaboration", and from "mixed operation" to "fine isolation", and provides strong support for the large-scale, safe and efficient development of low-altitude economy. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The schematic block diagram of the low-altitude airspace slicing edge cloud collaborative scheduling system proposed by the present application;

[0031] Figure 2 The column chart for comparison of system comprehensive performance;

[0032] Figure 3 The line chart of airspace resource utilization rate changing with time;

[0033] Figure 4 The column chart for comparison of slice elastic expansion capacity. DETAILED DESCRIPTION

[0034] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0035] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0036] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise explicitly and specifically limited. In addition, the terms "mounting", "connection", "connection" should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication between two elements. For a person of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances, and the present application will be further described in detail with reference to the accompanying drawings.

[0037] Referring to Figures 1 to 4 A low-altitude airspace slicing edge cloud cooperative scheduling system includes the following modules:

[0038] A central cloud management and control platform integrates a global airspace digital twin module and a global situation awareness unit, builds a low-altitude global virtual mapping model, collects real-time aircraft position, speed, task type and environmental meteorological data in the airspace, generates a global airspace operation view through data fusion and situation analysis, and provides data support for slicing strategy formulation and resource arbitration;

[0039] The low-altitude dynamic airspace slice manager, as a core control unit, comprises a slice template library, an SLA negotiation and mapping unit and a virtual resource arrangement unit, receives an airspace slice request of a business party, evaluates feasibility in combination with historical data and digital twin deduction, completes SLA negotiation and generates a slice template, and maps SLA requirements into a combination of virtual resources such as space, spectrum, calculation and network;

[0040] The edge node cluster is deployed in different airspace regions, each edge node is equipped with a slice controller instance running environment, a local real-time scheduling engine and a conflict resolution algorithm module, receives a slice instantiation instruction issued by the center cloud, creates and initializes a slice controller, loads resource strategies and scheduling algorithms, and performs real-time scheduling of aircrafts in the jurisdiction and millisecond-level conflict detection;

[0041] The business request processing module provides a standardized API interface, supports the business party to submit slice request parameters such as geographical range, time window, business type and SLA requirement, performs format verification and compliance review on the request, forwards the valid request to the low-altitude dynamic airspace slice manager, and synchronously feeds back negotiation results and slice state information;

[0042] The resource mapping and instantiation module converts an abstract slice template into a specific executable resource configuration scheme, allocates a three-dimensional airway network, a dedicated communication frequency band, an edge node computing power and a virtual network bandwidth to the slice, issues an instantiation instruction to a target edge node, and supervises the slice controller loading and resource reservation process;

[0043] The edge-cloud collaborative running module establishes a bidirectional communication link between the center cloud and the edge node, the center cloud is responsible for monitoring the slice running state, processing resource competition between slices and triggering an elastic scaling strategy, the edge node is responsible for aircraft path planning in the slice, communication quality guarantee and local resource scheduling, and the strategic layer and the tactical layer are cooperated;

[0044] The slice life cycle management module tracks the whole process of slice running in real time, triggers a slice dynamic adjustment or safety revocation process according to business end, SLA change or emergency, guides the edge node to release airspace, spectrum, calculation and other resources, and updates resource occupation state for use by other slices;

[0045] The safety isolation and conflict processing module realizes business isolation between different slices by using a logical isolation technology, guarantees slice safety by dividing airspace boundaries, isolating communication frequency bands and encrypting data transmission, establishes a double conflict detection mechanism within and between slices, and resolves flight conflicts by combining global arbitration and local resolution algorithms.

[0046] In the application, the slice resource optimization allocation unit is also included, a resource allocation priority evaluation model is established based on slice priority, business emergency degree and resource occupation demand, and the evaluation formula is: wherein is a resource allocation priority coefficient, is a priority weight coefficient, positively related to service importance, is a preset priority of a slice, with a level of 1 to 5, is a time weight coefficient, positively related to service time urgency, is a normalized value of remaining available time of a service, is a resource weight coefficient, positively related to resource demand intensity, is a normalized value of total resource demand, is a cost weight coefficient, positively related to resource occupation cost, is a normalized value of resource occupation cost, the limited resources are sequentially distributed by quantitatively evaluating results, and the resource supply of high-priority services is ensured; the weight coefficient is dynamically adjusted, and the real-time spatial load, the proportion of service types, and the degree of resource shortage are automatically optimized is taken as a value, for example, the value is increased during a holiday logistics peak is taken as a value, the value is increased when the resource is in shortage is taken as a value; a resource pre-allocation mechanism is established, a part of flexible resources is reserved in advance for periodic peak services, resource shortage during temporary application is avoided, and the pre-allocated resources are automatically released to the public resource pool when the pre-allocated resources are not used within a time limit.

[0047] In the application, a real-time conflict resolution optimization module is further included, a conflict resolution algorithm module of the edge node combines the real-time position, the speed vector and the airspace boundary constraint of the aircraft, plans an optimal collision avoidance path, the collision avoidance path is designed in a segmented manner, the conflict area is avoided by adjusting the lateral offset first, then the original planned flight path is returned, and the flight speed is dynamically adjusted to make the collision avoidance process smooth; the differences in the maneuvering capabilities of different aircrafts are considered, and differentiated collision avoidance strategies are customized for different types of aircrafts such as fixed-wing multi-rotor aircrafts, the fixed-wing aircrafts prefer to adjust the heading, and the multi-rotor aircrafts prefer to adjust the height or hover and wait; the energy consumption cost and the time loss of the collision avoidance path are calculated in real time during the conflict resolution process, the scheme with the least loss is selected for execution, the processing result, the collision avoidance trajectory and the energy consumption data are reported to the center cloud after the conflict resolution, and the aircraft running trajectory data in the slice is synchronously updated, so that the flight order and the running efficiency in the slice are ensured.

[0048] In the application, the slice elastic expansion control unit is also included, the global arbitration module of the center cloud combines real-time spatial flow data, service peak prediction and historical operation law, automatically triggers slice elastic adjustment, when detecting that the number of aircrafts in a slice exceeds the preset threshold by more than 30%, the three-dimensional airway network width of the slice is widened or the vertical height layer is increased, when the flow decreases below the threshold, the resource configuration is contracted, and the excess airspace and spectrum resources are released; support slice merging and splitting function, merge and manage adjacent and compatible service type slices in low flow period, reduce resource waste, split large slices into multiple small slices in high flow period, improve scheduling refinement degree; for temporary emergency rescue temporary activity guarantee, support rapid creation of temporary slices and preferential allocation of idle resources, automatically recycle resources within 10 minutes after the service ends, realize dynamic adaptation and efficient utilization of airspace resources, and record elastic expansion historical data to provide basis for subsequent strategy optimization.

[0049] In the application, the flight conflict risk prediction unit is also included, a conflict risk prediction model is established based on the aircraft motion model and the airspace environment parameters, and the prediction formula is: wherein is the conflict risk level, the level range is 0 to 10, is the distance weight coefficient, which is negatively related to the conflict distance, is the normalized value of the shortest expected distance between aircrafts, is the speed weight coefficient, which is positively related to the relative speed, is the normalized value of the relative speed of the aircraft, is the airspace weight coefficient, which is positively related to the airspace complexity, is the airspace congestion degree normalized value, by predicting the potential conflict risk in advance, triggering an early warning when the risk level reaches the preset threshold; support time window prediction, calculate the conflict hot spot area and high incidence period in the future time period in advance by 5 to 10 minutes, the center cloud sends early warning information to the related edge nodes, and the edge nodes adjust the take-off time, flight path or speed parameter of the aircraft in the slice in advance; establish a hierarchical warning mechanism, only send a prompt to the edge node when the risk level is 3 to 5, start local path optimization when the level is 6 to 8, and suspend the take-off of the related aircraft and forcibly adjust the trajectory when the level is 9 to 10, to reduce the probability of conflict from the source.

[0050] In the application, the SLA dynamic adaptation module is also included, which monitors the SLA indicators such as communication bandwidth, transmission delay, security protection, etc. in the process of slice running in real time, and collects index data every 5 seconds and compares it with the preset threshold value; when the index deviates from the preset requirement, the adaptive adjustment process is automatically started, the communication bandwidth is insufficient, the spectrum resource management unit allocates additional frequency bands or optimizes the bandwidth allocation strategy, and the high-priority service flow is preferentially guaranteed; when the transmission delay is out of limit, the routing strategy is optimized, part of the computing task is sunk from the center cloud to the edge node, the data transmission distance is reduced, or the redundant computing power of the edge node is called to improve the processing speed; when the security level is not up to standard, the data encryption strength is strengthened, the access control permission is tightened, and the security audit frequency is increased; the SLA degradation negotiation mechanism is supported, when the resources are insufficient to meet the initial SLA requirements, the degradation scheme and compensation suggestion are automatically pushed to the business side, after confirmation, the SLA indicators and resource configuration are adjusted, so that the slice running throughout the negotiation agreement is met.

[0051] In the application, the multi-dimensional environment perception module is also included, which integrates meteorological sensors, airspace monitoring radars, electronic fence detection equipment and geographic information systems, collects meteorological data such as wind speed, visibility, precipitation, lightning, position and height data of obstacles such as buildings, bridges and power transmission lines in the airspace, and control information such as temporary no-fly areas and active control areas; an environment data fusion analysis model is established to eliminate abnormal data and supplement missing values, generate accurate airspace environment situation maps, and synchronize to the center cloud and edge nodes in real time; support environment trend prediction, based on historical meteorological data and real-time monitoring results, predict the environmental change trend in the next 1 to 3 hours, the center cloud avoids high-risk environmental areas when planning the slice, and the edge node dynamically adjusts the flight parameters when scheduling, such as reducing the flight speed of the aircraft when encountering strong wind, shortening the safety distance between aircrafts and enabling obstacle avoidance sensors when the visibility is low; with dynamic electronic fence function, real-time adjustment of electronic fence range according to environmental changes such as sudden fire and road construction, synchronous update of the airspace boundary of the related slice, and guarantee of flight safety.

[0052] In the application, a flight vehicle state monitoring and abnormality processing unit is also included, which collects the running data of the flight vehicle such as battery capacity, power system state, navigation accuracy, attitude stability and the like in real time through an airborne sensor and a communication link, and the edge node analyzes the data and identifies abnormalities in real time; a flight vehicle health degree evaluation system is established, the health degree score is calculated in combination with multi-dimensional data, and when the score is lower than a preset threshold, an abnormality processing procedure is triggered, a return or nearby emergency landing instruction is triggered when the battery capacity is low, the surrounding emergency landing point position and resource state are pushed at the same time; when the power is abnormal, an emergency communication channel is started, the related flight area is isolated and the surrounding flight vehicles are notified to avoid, when the navigation deviation is abnormal, the path is re-planned and the trajectory is corrected; a fault self-recovery suggestion function is supported, fault troubleshooting steps and emergency processing schemes are automatically generated according to the abnormal type and the flight vehicle model, are pushed to the business party and the flight vehicle control personnel, and at the same time, the warning information, abnormal data and processing progress are reported to the center cloud, so that the flight safety and business continuity are ensured.

[0053] In the application, a slice running visual management unit is also included, a three-dimensional visual monitoring interface is constructed, the global slice distribution, the geographical range of each slice, the number and position of the flight vehicles, the resource occupation state, the SLA compliance condition and the conflict processing record are displayed in real time based on the digital twin model; multi-dimensional data query and screening are supported, the management personnel can query the slice details according to the business type, priority, region and the like, view the historical running data and trend analysis charts; an intelligent early warning prompt function is provided, potential problems such as low resource utilization, decreased SLA compliance rate and frequent conflicts are automatically identified based on data analysis, and the management personnel is prompted through the pop-up window, color marking and the like; a manual intervention function is supported, the slice priority can be adjusted, the resource configuration can be modified, the slice can be forcibly cancelled or the temporary flight control instruction can be issued in an emergency, and all manual operations are logged and synchronized to the audit module; a report automatic generation function is provided, running reports can be generated daily, weekly and monthly, including the number of slice creation, resource utilization, conflict processing efficiency, SLA compliance rate and the like, the self-defined report dimension and export function are supported, and the basis for management decision and system optimization is provided.

[0054] In the present application, a cross-regional slice coordination management unit is also included. For long-distance slices across multiple edge node jurisdictions, such as intercity logistics and intercity inspection, a cooperative communication mechanism between edge nodes is established. Adjacent edge nodes share the aircraft operating status, scheduling plan and resource occupation in real time within the slice. The central cloud sets up a cross-regional arbitration submodule to uniformly coordinate the resource allocation and conflict processing of cross-regional slices, avoiding scheduling conflicts caused by poor coordination of multiple edge nodes. Cross-regional business continuity is ensured. When the aircraft is about to enter the jurisdiction of the next edge node, the scheduling right is handed over 1 minute in advance. The previous edge node synchronizes the aircraft trajectory, SLA requirements and safety policies to the subsequent edge node. The subsequent edge node completes resource reservation and path planning in advance, so that the aircraft can fly across regions without interruption or lag. The central cloud coordinates the creation, adjustment and cancellation processes of the slice to ensure that the actions of each edge node are consistent and the resource release is synchronized, adapting to the cross-regional business needs of unmanned aerial vehicle logistics and intercity inspection.

[0055] The specific embodiments of the present application are further illustrated by two embodiments as follows:

[0056] Embodiment 1: Application of urban unmanned aerial vehicle instant logistics scenario

[0057] This embodiment is applied to unmanned aerial vehicle instant logistics distribution in the core business district of a first-tier city, covering three business circles, 12 office buildings and surrounding residential areas, involving two types of core businesses: logistics distribution and park inspection. The peak time for logistics distribution is from 11:30 to 13:30 in the afternoon and from 18:00 to 21:00 in the evening. Hundreds of unmanned aerial vehicles need to be supported for simultaneous operation. The logistics slice requires stable communication bandwidth and low scheduling delay. The park inspection slice has low priority and can dynamically adjust the airspace resources. The system deployment includes one central cloud management and control platform, three edge nodes, two types of slice template libraries for logistics and inspection, and standardized API interfaces for external delivery platforms to submit slice requests.

[0058] I. Core implementation details

[0059] Slice request and negotiation process: The delivery platform submits an instant logistics slice request through the API 24 hours before the peak delivery time. The request specifies the geographic coverage of the core business district, the time window of 18:00-21:00, the business type of food delivery, and the SLA requirements of high priority, low scheduling delay and high communication reliability. After the business request processing module performs format verification and compliance review, it forwards the request to the low-altitude dynamic airspace slice manager. The slice manager evaluates the feasibility of the request based on historical delivery data, real-time airspace situation and digital twin deduction, and completes SLA negotiation with the delivery platform to generate a slice template that includes a three-dimensional airway network, a dedicated communication frequency band, and edge node computing power configuration.

[0060] Resource mapping and instantiation: The resource mapping and instantiation module maps the SLA requirements to specific resource combinations. Spatial resource allocation allocates 3 multi-layered ring-shaped air corridors, and 3 height layers are set vertically to avoid conflicts. Spectrum resource allocation allocates dedicated communication frequency bands to ensure stable data transmission. Computing resources are reserved at the edge nodes around the business district to be used for real-time path planning and conflict resolution. Network resources are allocated dedicated virtual network bandwidth. At 17:50, the center cloud issues slice instantiation instructions to the target edge node, which creates and initializes the logistics slice controller, loads resource policies and scheduling algorithms, and completes resource reservation and device adaptation.

[0061] Edge-cloud collaborative operation: At 18:00, the logistics slice is officially put into operation, and hundreds of delivery drones are connected to the slice. The local real-time scheduling engine of the edge node allocates precise corridor layers and flight time periods to each drone based on real-time order data, enabling high-density operation in the air. The center cloud monitors the slice operation status in real time and visualizes the drone position, link bandwidth, and resource occupation through the global airspace digital twin module. At 19:30, the center cloud detects a sudden rain in the area through the multi-dimensional environmental perception module and immediately issues weather reduction strategies to the edge node. The conflict resolution algorithm module of the edge node increases the flight interval of the drones, starts the landing point planning, and ensures data transmission through communication frequency band isolation.

[0062] Elastic scaling and conflict handling: When it is detected that the number of drones in a certain air corridor exceeds the preset threshold by 30%, the slice elastic scaling control unit automatically widens the corridor width and adds one vertical height layer to alleviate the congestion. The edge node monitors the operation status of the drones in the slice in real time and plans a lateral offset collision avoidance path when it detects that two drones have a path intersection risk through millisecond-level conflict detection. Fixed-wing drones adjust their heading, and multi-rotor drones temporarily hover, and the trajectory data is updated to the center cloud after conflict resolution. The safety isolation module ensures that the logistics slice and the park inspection slice operate independently in the physically overlapping airspace without communication interference and path conflict through logical isolation technology.

[0063] Slice revocation and resource release: At 21:00, the delivery business ends, and the slice lifecycle management module triggers the revocation process. The edge node guides all drones to safely land or fly away from the slice area and reports the status to the center cloud. After confirmation, the center cloud issues resource release instructions, and the edge node releases the airspace, spectrum, computing power, and network resources. The resource status is updated to the public resource pool for park inspection slice expansion. Management personnel can view the air corridor utilization rate, conflict handling records, and SLA compliance of this delivery through the slice operation visualization management unit, and generate a report to provide a basis for subsequent peak scheduling optimization.

[0064] Table 1: Performance comparison table of urban logistics scenarios

[0065]

[0066] The data in Table 1 clearly shows the application advantages of the present application in the urban logistics scenario. The traditional airspace management system adopts a static division mode, and the airspace resource utilization rate is only 45%, congestion easily occurs during peak hours, and the flight conflict rate reaches 12%, which seriously affects the distribution efficiency. The present application improves the airspace resource utilization rate to 92% and reduces the conflict rate to 1.5% through dynamic slicing and elastic expansion. The average delay of the scheduling instruction is shortened from 280ms to 35ms, meeting the low latency control requirements of unmanned aerial vehicles; the resource release time is compressed from 15 minutes to 2 minutes, improving the resource flow efficiency; the SLA compliance rate reaches 99%, ensuring the stability and reliability of logistics distribution, and perfectly adapting to the operation requirements of high-density instant logistics.

[0067] Embodiment 2: Application in cross-regional emergency rescue scenario

[0068] This embodiment is applied to mountainous cross-regional emergency rescue operations, covering 3 county areas and involving 5 edge node jurisdiction areas, and needs to support collaborative operation of multiple types of aircraft such as rescue unmanned aerial vehicles, command aircraft, and medical transfer aircraft. The emergency rescue business requires high priority occupation of airspace resources, strong anti-interference capability of communication links, low scheduling delay, and seamless switching across edge nodes to ensure real-time transmission of rescue instructions and collaborative operation of aircraft. The system deployment includes a central cloud management and control platform, 5 edge nodes, an emergency rescue dedicated slice template library, and multi-dimensional environment sensing devices to monitor mountainous weather and terrain data.

[0069] I. Core implementation details

[0070] Emergency slice rapid creation and resource allocation: After a sudden missing person event in the mountains, the rescue command center urgently submits an emergency rescue slice request through a standardized API, clearly covering the rescue search area in terms of geography, the time window from rescue start to task end, the business type as emergency search and rescue, and the SLA requirement of highest priority, low communication delay and anti-interference transmission. The business request processing module quickly passes the verification, and the low-altitude dynamic airspace slicing manager generates a high-priority slice template based on the emergency slice template library, skipping the regular negotiation process. The resource mapping and instantiation module quickly allocates three-dimensional search and rescue airways, anti-interference communication frequency bands, and reserves high-performance computing power in 5 edge nodes to ensure real-time path planning and multi-aircraft collaborative scheduling.

[0071] Cross-regional slice collaborative operation: The center cloud issues slice instantiation instructions to the five edge nodes, which complete slice controller creation and initialization within 3 minutes, load emergency rescue-specific scheduling algorithms and security policies. The cross-regional slice collaborative management unit establishes a collaborative communication mechanism between edge nodes, and adjacent edge nodes share real-time UAV operation status, scheduling plans, and resource occupation. The center cloud sets up a cross-regional arbitration submodule to uniformly coordinate resource allocation among the five edge nodes, avoiding resource competition between slices. When the rescue UAV enters the second region from the first edge node jurisdiction area, the previous edge node synchronizes the aircraft trajectory, SLA requirements, and security policies to the subsequent node 1 minute in advance, and the subsequent node completes resource reservation and path planning in advance to achieve seamless cross-regional switching.

[0072] Environmental perception and conflict resolution: The multi-dimensional environmental perception module integrates meteorological sensors, airspace monitoring radars, and geographic information systems to collect real-time data on mountain wind speed, visibility, terrain, and obstacle information such as trees and cliffs, generating accurate airspace environmental situation maps synchronized to the center cloud and edge nodes. Based on historical meteorological data, the center cloud instructs the edge nodes to adjust flight parameters to reduce UAV flight speed and increase safety intervals when predicting that wind will strengthen in the next 2 hours. When the conflict resolution algorithm module of the edge node detects a path conflict between the rescue UAV and the medical transfer machine, it plans differentiated collision avoidance paths based on the difference in maneuvering capabilities of the two, adjusts the height of the rescue UAV, and maintains the heading of the medical transfer machine, completing conflict resolution in milliseconds to ensure the continuity of rescue operations.

[0073] UAV state monitoring and SLA dynamic adaptation: The UAV state monitoring and exception handling unit collects real-time data on battery level, power system status, and navigation accuracy from onboard sensors, and the edge node analyzes the data in real time. When a UAV's battery level is detected to be below a preset threshold, a return instruction is immediately triggered, and the location of nearby emergency landing points and resource status are pushed, along with a fault self-healing suggestion for reference by the operator. The SLA dynamic adaptation module monitors indicators such as communication bandwidth and transmission delay in real time. When signal attenuation in the mountains causes insufficient bandwidth, the spectrum resource management unit allocates additional anti-interference frequency bands to prioritize rescue command transmission; when transmission delay exceeds the limit, some computing tasks are offloaded to the edge node to reduce data transmission distance and ensure that SLA indicators meet rescue requirements.

[0074] Slice adjustment and resource recycling: after the rescue task is completed, the command center submits a slice revocation request through the visual management unit, the slice life cycle management module triggers the safe revocation process, the edge node guides all aircraft to land safely, and reports the task completion to the center cloud. After the center cloud confirms, it issues a resource release instruction, and the edge node releases the airspace, spectrum, computing power and other resources within 5 minutes, and updates to the public resource pool. At the same time, the data storage and management module stores the dispatch log, security event record and resource usage data of this rescue, supports subsequent traceability analysis, and provides data support for slice strategy optimization for similar emergency rescue tasks.

[0075] Table 2 Performance comparison table of cross-regional emergency rescue scene

[0076]

[0077] Table 2 highlights the application value of the application in the cross-regional emergency rescue scene. The traditional airspace management system has a lagging emergency response, and the slice creation takes 40 minutes, which is difficult to meet the rescue time requirement, and the cross-regional switching success rate is only 72%, which is prone to cause interruption of rescue instructions. The application can complete instantiation in 3 minutes through the emergency slice rapid creation mechanism, which can save valuable time for rescue; the cross-regional switching success rate is improved to 99.5%, which ensures continuous operation of the aircraft; the rescue instruction transmission delay is shortened from 350ms to 40ms, which ensures real-time delivery of instructions. The resource release efficiency is improved from 25 minutes to 5 minutes, which improves the resource reuse rate; the multi-dimensional environment perception and dynamic adaptation capability enables the system to still operate stably in a mountainous area under harsh environment, providing reliable airspace scheduling guarantee for emergency rescue, and greatly improving the success rate of rescue.

[0078] Referring to Figure 2 The figure comprehensively presents the comprehensive advantages of the system of the application. The utilization rate of airspace resources of the traditional system is only 42%, which is due to the rigid mode of static division, while the application improves the utilization rate to 93% through dynamic slicing and elastic expansion, realizing maximum utilization of resources. The flight conflict occurrence rate is reduced from 13% to 1.2%, which benefits from millisecond-level conflict detection and differentiated collision avoidance strategy, greatly reducing the safety risk. The scheduling instruction delay is compressed from 260ms to 32ms, which reflects the technical advantage of the edge cloud collaborative architecture that sinks real-time tasks to the edge. The slice creation time is shortened from 38 minutes to 2.5 minutes, which meets the rapid response requirements of scenes such as emergency rescue. The SLA compliance rate is 99.2%, which verifies the precise adaptation capability of the system to business requirements, and provides comprehensive support for low-altitude economic efficient operation.

[0079] Referring to Figure 3The figure exhibits the resource adaptation capability of the system at different time periods. The traditional system is limited by static division, and the utilization rate is only 35% and 28% during flat peak and night, and the resources are seriously idle; the utilization rate reaches 68%-72% during peak period, but has reached the upper limit of capacity, and congestion is easy to occur. The application keeps a high utilization rate of 85% during flat peak period through dynamic airspace slicing and elastic expansion, avoiding resource waste; the utilization rate is stabilized at 92%-94% during peak period, and the capacity is expanded through widening the airspace and increasing the height layer, meeting the high-density demand; the utilization rate is still maintained at 80% during night, adapting to night logistics, emergency standby and other businesses. The broken line trend shows that the application realizes dynamic adaptation of airspace resources with time, and completely changes the dilemma of "not enough during busy time and waste during idle time" of the traditional system.

[0080] Referring to Figure 4 The figure embodies the core advantage of the elastic expansion of the slicing. The traditional system adopts static airspace division, and the available capacity is fixed at 150, when the number of unmanned aerial vehicles exceeds 150, only access can be limited or congestion conflict is faced. The application realizes elastic expansion of dynamic airspace slicing, and adjusts the airspace capacity in real time according to the business volume: 180 when there are 100 unmanned aerial vehicles, 320 when there are 200, 450 when there are 300, and 700 when there are 500, and the capacity increases linearly with the business volume. This elastic adaptation capability makes the system not only meet the efficient utilization of resources during flat peak period with low business volume, but also meet the access demand of high-density scenes such as holiday logistics peak and large-scale activities, and automatically expand without manual intervention, greatly improving the airspace carrying capacity and supporting the scale development of low-altitude economy.

[0081] The above is only the preferred specific embodiment of the application, but the protection scope of the application is not limited thereto, any skilled person in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the application within the technical range disclosed by the application, which should be covered in the protection scope of the application.

Claims

1. A low-altitude airspace slicing edge-cloud collaborative scheduling system, characterized in that, Includes the following modules: The central cloud management platform integrates a global airspace digital twin module and a global situational awareness unit to construct a low-altitude full-domain virtual mapping model, collect aircraft, environmental and meteorological data in real time, and generate a global airspace operation view through data fusion and situational analysis. The low-altitude dynamic airspace slicing manager includes a slicing template library, an SLA negotiation and mapping unit, and a virtual resource orchestration unit. It receives airspace slicing requests, evaluates the feasibility of the requests by combining historical data and digital twin simulations, completes SLA negotiation and generates slicing templates, and maps SLA requirements into virtual resource combinations. The edge node cluster receives slice instantiation instructions from the central cloud, creates and initializes the slice controller, loads resource policies and scheduling algorithms, and performs real-time scheduling and conflict detection of aircraft within its jurisdiction. The business request processing module provides a standardized API interface, supports business parties to submit slice request parameters, performs format validation and compliance review on the requests, forwards valid requests to the low-altitude dynamic airspace slice manager, and synchronously feeds back the negotiation results and slice status information. The resource mapping and instantiation module transforms the abstract slice template into a resource configuration scheme, allocates a 3D waterway network, dedicated communication frequency band, edge node computing power and virtual network bandwidth to the slice, issues instantiation instructions to the target edge node, and supervises the slice controller loading and resource reservation process. The edge-cloud collaborative operation module establishes a two-way communication link between the central cloud and edge nodes. The central cloud is responsible for monitoring the slice operation status, handling resource contention, and triggering elastic scaling strategies, while the edge nodes are responsible for path planning and local resource scheduling. The slice lifecycle management module tracks the entire slice operation process in real time, guides edge nodes to release resources, and updates resource occupancy status for other slices to apply for. The security isolation and conflict handling module uses logical isolation technology to ensure slice security, establishes a dual conflict detection mechanism within and between slices, and combines global arbitration and local resolution algorithms to handle flight conflicts.

2. The low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes a slice resource optimization and allocation unit, which establishes a resource allocation priority evaluation model based on slice priority, business urgency, and resource consumption requirements. The evaluation formula is as follows: ,in Assign priority coefficients to resources. This is the priority weight coefficient. Preset priority for slices, For time weighting coefficients, This is a normalized value for the remaining available time of the service. For resource weighting coefficients, This is the normalized value of total resource demand. This is the cost weighting coefficient. This represents a normalized value for resource usage costs; it supports dynamic adjustment of weighting coefficients, automatically optimizing based on real-time spatial load, business type proportions, and resource scarcity. Values.

3. The low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes a real-time collision avoidance optimization module. The collision avoidance algorithm module of the edge nodes combines the real-time position, velocity vector and airspace boundary constraints of the aircraft to plan a segmented optimal collision avoidance path. The collision avoidance path adopts a segmented design, first offsetting laterally to avoid the conflict area, and then returning to the original planned route, while dynamically adjusting the flight speed in sync. Differentiated collision avoidance strategies are customized according to the maneuverability of the aircraft. Fixed-wing aircraft prioritize adjusting their heading, while multi-rotor aircraft prioritize adjusting their altitude or hovering and waiting. During the collision avoidance process, the energy consumption cost and time loss of the path are calculated in real time, and the scheme with the least loss is selected for execution. After the collision avoidance, the processing results, collision avoidance trajectory and energy consumption data are reported to the central cloud, and the aircraft's running trajectory data in the slice is updated synchronously.

4. The low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes a slice elastic scaling control unit. The global arbitration module of the central cloud combines real-time airspace traffic data, business peak prediction and historical operation patterns to automatically trigger slice elastic adjustment. It supports slice merging and splitting functions. During low traffic periods, adjacent slices with compatible business types are merged and managed. During high traffic periods, large slices are split into multiple smaller slices. For temporary sudden business needs, it supports the rapid creation of temporary slices and priority allocation of idle resources. Resources are automatically reclaimed within 10 minutes after the business ends.

5. A low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes a flight conflict risk prediction unit, which establishes a conflict risk prediction model based on the aircraft motion model and airspace environmental parameters. The prediction formula is as follows: ,in According to the conflict risk level, This is the distance weighting coefficient. This is the normalized value of the shortest expected distance between aircraft. For speed weighting coefficients, This is the normalized value of the relative velocity of the aircraft. Spatial domain weighting coefficients, This is a normalized value for airspace congestion. It supports time window prediction, calculating conflict hotspots and high-incidence periods 5 to 10 minutes in advance, and sending early warning information from the central cloud to relevant edge nodes; it establishes a graded early warning mechanism, sending alerts only to edge nodes when the risk level is 3 to 5, initiating local path optimization when the risk level is 6 to 8, and suspending the take-off of relevant aircraft and forcibly adjusting their trajectories when the risk level is 9 to 10, thereby reducing the probability of conflict from the source.

6. A low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes an SLA dynamic adaptation module, which monitors the SLA performance of slices in real time, collecting data every 5 seconds and comparing it with preset thresholds; when the indicators deviate, it initiates adaptation adjustments; when communication bandwidth is insufficient, it coordinates with the spectrum resource management unit to allocate additional frequency bands or optimize bandwidth allocation strategies; when transmission latency exceeds limits, it optimizes routing strategies, offloading some computing tasks from the central cloud to edge nodes, or calling on redundant computing power of edge nodes to improve processing speed; when the security level does not meet the standards, it strengthens data encryption, tightens access control permissions, and increases the frequency of security audits; it supports an SLA degradation negotiation mechanism, which automatically pushes degradation plans and compensation suggestions to business parties when resources are scarce and the initial SLA requirements cannot be met, and adjusts indicators and resource configurations after confirmation.

7. A low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes a multi-dimensional environmental perception module, integrating meteorological sensors, airspace monitoring radar, electronic fence detection equipment, and geographic information system to collect meteorological data, obstacle data, and control information in real time; establish an environmental data fusion analysis model to remove abnormal data and supplement missing values, generate an airspace environmental situation map, and synchronize it to the central cloud and edge nodes in real time; predict environmental change trends in the next 1 to 3 hours based on historical and real-time data, avoid high-risk areas when planning in the central cloud, and dynamically adjust flight parameters when scheduling at the edge nodes; and has a dynamic electronic fence function to adjust the electronic fence range in real time according to environmental changes and update the airspace boundary synchronously.

8. A low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes an aircraft status monitoring and anomaly handling unit, which collects aircraft operation data in real time through airborne sensors and communication links, and edge nodes perform real-time data analysis and anomaly identification; it establishes an aircraft health assessment system, calculates a health score by combining multi-dimensional data, triggers anomaly handling process when the score is lower than a preset threshold, triggers return to home or nearby emergency landing command and pushes emergency landing point information when the battery is low, activates emergency communication channel and isolates the flight area when the power is abnormal, and replans the route when the navigation deviates. It supports fault self-healing suggestion function, automatically generates troubleshooting steps and emergency plans based on the anomaly type and model, and pushes them to the business side, while reporting alarm information, abnormal data and processing progress to the central cloud.

9. A low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes a slice operation visualization management unit, which builds a 3D visualization monitoring interface. Based on a digital twin model, it displays in real time the distribution of slices across the entire domain, geographical range, number and location of aircraft, resource occupancy status, SLA compliance status, and conflict handling records. It supports data query and filtering, provides intelligent early warning prompts, and alerts potential problems through pop-ups and color-coded indicators. All operation logs are recorded and synchronized to the audit module. It has an automatic report generation function, generating operation reports containing key indicators, and supports custom dimensions and export.

10. A low-altitude airspace slicing edge-cloud collaborative scheduling system according to claim 1, characterized in that, It also includes a cross-regional slice collaborative management unit, which establishes a collaborative communication mechanism between edge nodes for long-distance slices that span the jurisdiction of multiple edge nodes, allowing adjacent nodes to share the aircraft's operating status, scheduling plan, and resource usage in real time; the central cloud sets up a cross-regional arbitration submodule to coordinate resource allocation and conflict resolution. One minute before the aircraft enters the next edge node, the scheduling authority is transferred, the trajectory, SLA requirements and safety policies are synchronized, and subsequent nodes reserve resources and plan paths in advance; it supports unified lifecycle management across regional slices, and the central cloud coordinates the creation, adjustment and cancellation processes.

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