Low-altitude airspace slice edge cloud collaborative scheduling system

Abstract

The invention discloses a low-altitude airspace slice edge cloud collaborative scheduling system, and relates to the technical field of airspace control systems, and the system comprises the steps that a central cloud management and control platform constructs a global virtual mapping model, and generates a global airspace operation view; the low-altitude dynamic airspace slice manager processes the slice request and completes SLA negotiation and resource mapping; the edge node cluster executes real-time scheduling and conflict detection; the service request processing module receives and verifies the request; the resource mapping and instantiation module allocates resources and issues an instantiation instruction; the edge cloud collaborative operation module realizes center and edge collaboration; the slice life cycle management module adjusts or cancels slices; and the security isolation and conflict processing module ensures the security of the slices and resolves conflicts. According to the method, the resource utilization rate is improved through dynamic airspace slicing, the real-time performance and reliability are enhanced through an edge cloud collaborative architecture, the service safety is guaranteed through logic isolation, elastic telescoping adapts to various requirements, the cost is reduced through automatic management, and refined and intelligent upgrading of low-altitude management is promoted.

Description

Technical Field

[0001] This invention relates to the field of airspace control system technology, and in particular to a low-altitude airspace slicing edge-cloud collaborative scheduling system. Background Technology

[0002] With the booming development of low-altitude economic sectors such as drone logistics, air taxis, and emergency rescue, low-altitude airspace below 120 meters, as a core operational space, is facing unprecedented congestion pressure. The high-density, multi-type, and high-frequency operation of various aircraft places stringent demands on the dynamic adaptation, resource allocation, and safety management of airspace. Traditional airspace management models are based on static delineation, allocating airspace resources through fixed areas such as permanent no-fly zones and reporting areas. This model lacks flexibility and cannot dynamically adjust the airspace structure according to real-time business needs. For example, during peak logistics periods like holidays or large-scale temporary events, existing fixed airspace delineation is insufficient to meet sudden high-density flight demands, leading to insufficient airspace resource supply; while during off-peak periods, a large amount of airspace resources remain idle, resulting in resource waste and low overall airspace utilization.

[0003] Some existing technologies attempt to use a centralized cloud platform for unified scheduling, which can achieve a certain degree of global optimization, but suffers from significant centralized bottlenecks. The centralized cloud platform needs to handle scheduling commands, status monitoring, and path planning for all aircraft. As the number of aircraft increases, the computing load on the central server increases dramatically, leading to response delays and making it difficult to meet the low-latency, high-reliability control requirements of large-scale UAV swarms. Furthermore, the centralized architecture carries the risk of single points of failure. If the central cloud platform fails, the entire airspace scheduling system will be paralyzed, causing flight disorder and severely impacting the continuity and safety of low-altitude operations. In addition, long-distance data transmission may encounter network fluctuations and signal attenuation, further exacerbating scheduling delays and reliability risks.

[0004] Different low-altitude operations have significantly different requirements for airspace security, communication quality, and priority. Logistics drones require high bandwidth to ensure data transmission, manned flights have extremely high safety requirements, and emergency rescue operations need to prioritize airspace resources. However, existing systems lack the logical capability to divide "virtual airspace," making it impossible to achieve safe, efficient, and collaborative operation of different types of operations in physically overlapping airspace. When aircraft for different operations fly in the same airspace, path conflicts and communication interference are likely to occur, which not only affect operational efficiency but may also lead to safety accidents. Summary of the Invention

[0005] This invention proposes a low-altitude airspace slice edge cloud collaborative scheduling system to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a low-altitude airspace slicing edge-cloud collaborative scheduling system, comprising 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 (Service Level Agreement) 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, 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.

[0007] Furthermore, 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.

[0008] Furthermore, 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 within the slice is updated synchronously.

[0009] Furthermore, 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 predictions, and historical operating patterns to automatically trigger slice elastic adjustments. 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.

[0010] Furthermore, 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, It provides a normalized value for airspace congestion; supports time window prediction, calculating conflict hotspots and peak periods 5 to 10 minutes in advance, with the central cloud sending early warning information to relevant edge nodes; and establishes a tiered 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 takeoff 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.

[0011] Furthermore, it includes an SLA dynamic adaptation module that monitors the SLA performance of slices in real time, collecting data every 5 seconds and comparing it with preset thresholds; when the performance deviates, 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, automatically pushing degradation plans and compensation suggestions to business parties when resources are scarce and the initial SLA requirements cannot be met, and adjusting performance indicators and resource configurations after confirmation.

[0012] Furthermore, it also includes a multi-dimensional environmental perception module, integrating meteorological sensors, airspace monitoring radar, electronic fence detection equipment, and geographic information systems 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 have a dynamic electronic fence function to adjust the electronic fence range in real time according to environmental changes and update the airspace boundary synchronously.

[0013] Furthermore, 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; establishes an aircraft health assessment system, calculates a health score based on multi-dimensional data, and triggers anomaly handling procedures when the score is lower than a preset threshold, triggers return-to-base or nearby emergency landing instructions and pushes emergency landing point information when the battery is low, activates emergency communication channels and isolates the flight area when there is a power anomaly, and replans the route when there is a navigation deviation; supports fault self-healing suggestion function, automatically generates troubleshooting steps and emergency plans according to the anomaly type and model and pushes them to the business side, while reporting alarm information, anomaly data and processing progress to the central cloud.

[0014] Furthermore, it also includes a slice operation visualization management unit, which constructs 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.

[0015] Furthermore, 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. Adjacent nodes 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. The scheduling authority is transferred one minute before the aircraft enters the next edge node, synchronizing the trajectory, SLA requirements, and safety policies. Subsequent nodes reserve resources and plan paths in advance. It supports unified lifecycle management of cross-regional slices, with the central cloud coordinating the creation, adjustment, and cancellation processes.

[0016] Compared with existing technologies, the beneficial effects of this invention are: In terms of airspace resource utilization, the system innovatively introduces the concept of dynamic airspace slicing, transforming physical airspace into virtual resources that can be allocated on demand, completely changing the rigid model of traditional static partitioning. Through the elastic scaling function of slicing, the airspace range, altitude layer, and resource configuration can be adjusted in real time according to changes in business traffic. Airspace capacity is expanded during peak periods, and idle resources are released during off-peak periods, significantly improving airspace resource utilization. Simultaneously, the resource optimization allocation mechanism is based on multi-dimensional evaluation of business priority and urgency, achieving orderly allocation of limited resources, ensuring resource supply for high-priority businesses, avoiding inefficiencies caused by resource competition, and realizing efficient flow and maximum utilization of airspace resources.

[0017] In terms of system reliability and real-time performance, the edge-cloud collaborative architecture constructs a two-layer management and control model of "central cloud strategic layer + edge node tactical layer," effectively overcoming the inherent bottlenecks of centralized architectures. The central cloud is responsible for global situational awareness, strategy formulation, and resource arbitration, while edge nodes undertake high-frequency tasks such as real-time scheduling of aircraft within their jurisdiction and millisecond-level conflict resolution. By decentralizing high-real-time tasks to the edge, data transmission distance and central load are reduced, significantly lowering scheduling latency. This distributed architecture avoids the impact of single-point failures on the entire system. Even if some edge nodes or the central cloud malfunction, the collaborative mechanism can ensure the normal operation of core functions, greatly improving the reliability and stability of system operation and meeting the operational needs of large-scale UAV swarms.

[0018] In terms of security isolation and collaboration, the system uses logical isolation technology to build independent "virtual airspace private networks" for different services. Different airspace slices are isolated from each other in terms of communication frequency bands, airway networks, and security policies, effectively avoiding interference and conflicts between different types of services in physically overlapping airspaces. For example, manned flight slices and logistics flight slices can operate in parallel in the same physical airspace without affecting their respective security levels and communication quality. This ensures the operational safety of services with high security requirements while achieving efficient collaboration between different services. The dual conflict detection mechanism and tiered early warning strategy can predict potential conflict risks in advance and resolve them in a timely manner, further enhancing airspace operational safety.

[0019] In terms of intelligent management and adaptability, the system, based on an SLA-based automated negotiation, deployment, and operation and maintenance mechanism, enables rapid business deployment and flexible adjustments. Business stakeholders can submit requirements through standardized interfaces, and the system automatically completes the evaluation, negotiation, and slice instantiation without manual intervention, significantly reducing management costs. The system supports slice merging, splitting, and rapid creation of temporary slices, flexibly adapting to diverse scenarios such as periodic peaks and sudden emergency rescue. The cross-regional collaborative management function solves the scheduling and coordination problems of long-distance flights, enabling seamless switching of aircraft across regions and adapting to cross-regional businesses such as cross-city logistics and intercity inspections, further expanding the system's application scope.

[0020] Overall, through its core innovations of dynamic slicing and edge-cloud collaboration, the system comprehensively solves the problems of static rigidity, poor real-time performance, insufficient security isolation, and low resource utilization in traditional airspace management. It promotes the transformation of low-altitude management from "static allocation" to "dynamic flexibility," from "centralized control" to "edge-cloud collaboration," and from "hybrid operation" to "precise isolation," providing strong support for the large-scale, safe, and efficient development of the low-altitude economy. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of the low-altitude airspace slicing edge cloud collaborative scheduling system proposed in this invention; Figure 2 A bar chart comparing the overall system performance; Figure 3 A line graph showing the change in airspace resource utilization over time; Figure 4 A bar chart comparing the elastic stretching capacity of slices. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0025] Reference Figures 1 to 4 A low-altitude airspace slicing edge-cloud collaborative scheduling system 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. It collects real-time data on the position, speed, mission type, and environmental meteorological data of aircraft within the airspace. Through data fusion and situational analysis, it generates a global airspace operation view, providing data support for slice strategy formulation and resource arbitration. The Low Altitude Dynamic Airspace Slicing Manager, as the core control unit, includes a slice template library, an SLA negotiation and mapping unit, and a virtual resource orchestration unit. It receives airspace slicing requests from service providers, evaluates the feasibility of the requests by combining historical data and digital twin simulations, completes SLA negotiation, generates slice templates, and maps SLA requirements into combinations of virtual resources such as space, spectrum, computing, and network. The edge node cluster is deployed in different airspace areas. Each edge node is equipped with a slice controller instance runtime environment, a local real-time scheduling engine and a conflict resolution algorithm module. It 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 of aircraft within its jurisdiction and millisecond-level conflict detection. The business request processing module provides a standardized API interface, which supports business parties to submit slice request parameters such as geographical range, time window, business type, and SLA requirements. It 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 concrete and executable resource configuration scheme, allocates a three-dimensional 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 competition between slices, and triggering elastic scaling strategies. The edge nodes are responsible for aircraft path planning within the slice, communication quality assurance, and local resource scheduling, thereby achieving collaboration between the strategic and tactical layers. The slice lifecycle management module tracks the entire slice operation process in real time. Based on the end of business, SLA changes, or emergency situations, it triggers the slice dynamic adjustment or safe revocation process, guides edge nodes to release resources such as airspace, spectrum, and computing, and updates the resource occupancy status for other slice applications. The security isolation and conflict handling module uses logical isolation technology to achieve service isolation between different slices. It ensures slice security through airspace boundary division, communication frequency band isolation, and encrypted data transmission. It establishes a dual conflict detection mechanism within and between slices, and resolves flight conflicts by combining global arbitration and local resolution algorithms.

[0026] This invention 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 usage requirements. The evaluation formula is as follows: ,in Assign priority coefficients to resources. This is a priority weighting coefficient, which is positively correlated with business importance. Preset the priority of the slices, with levels from 1 to 5. This is a time-weighted coefficient, positively correlated with the urgency of business time. This is a normalized value for the remaining available time of the service. This is a resource weighting coefficient, which is positively correlated with resource demand intensity. This is the normalized value of total resource demand. This is a cost weighting coefficient, which is positively correlated with resource usage costs. It normalizes resource usage costs and allocates limited resources in an orderly manner based on quantitative evaluation results, ensuring resource supply for high-priority services; it supports dynamic adjustment of weighting coefficients, automatically optimizing based on real-time spatial load, service type proportion, and resource scarcity. Values, such as those increased during peak logistics periods like holidays. Values ​​are increased when resources are scarce. Value selection; Establish a resource pre-allocation mechanism to reserve some elastic resources in advance for periodic peak business, so as to avoid insufficient resources when applying temporarily. If the pre-allocated resources are not used after the timeout, they will be automatically released to the public resource pool.

[0027] This invention 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 the optimal collision avoidance path. The collision avoidance path adopts a segmented design. First, it avoids the conflict area by adjusting the lateral offset, and then returns to the original planned flight path. At the same time, the flight speed is dynamically adjusted to make the collision avoidance process smooth. Considering the differences in the maneuverability of different aircraft, differentiated collision avoidance strategies are customized for different types of aircraft such as fixed-wing and multi-rotor 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 collision avoidance 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 operation trajectory data in the slice are updated synchronously to ensure the flight order and operation efficiency in the slice.

[0028] This invention also includes a slice elastic scaling control unit. The global arbitration module of the central cloud, combined with real-time airspace traffic data, peak service prediction, and historical operating patterns, automatically triggers slice elastic adjustment. When the number of aircraft in a slice exceeds a preset threshold by more than 30%, the width of the three-dimensional airway network of that slice is widened or a vertical height layer is added. When the traffic drops below the threshold, resource allocation is contracted to release excess airspace and spectrum resources. Slice merging and splitting functions are supported. During low-traffic periods, adjacent slices with compatible service types are merged for management to reduce resource waste. During high-traffic periods, large slices are split into multiple smaller slices to improve the fineness of scheduling. For temporary and sudden service needs, such as emergency rescue and temporary event support, it supports the rapid creation of temporary slices and priority allocation of idle resources. Resources are automatically reclaimed within 10 minutes after the service ends, realizing dynamic adaptation and efficient utilization of airspace resources. At the same time, historical elastic scaling data is recorded to provide a basis for subsequent strategy optimization.

[0029] This invention 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 The conflict risk level is categorized from 0 to 10. This is the distance weighting coefficient, which is negatively correlated with conflict distance. This is the normalized value of the shortest expected distance between aircraft. This is the speed weighting coefficient, which is positively correlated with relative speed. This is the normalized value of the relative velocity of the aircraft. This is the spatial weighting coefficient, which is positively correlated with spatial complexity. The system normalizes airspace congestion levels and triggers warnings when the risk level reaches a preset threshold by predicting potential conflict risks in advance. It supports time window prediction, calculating conflict hotspots and peak periods 5 to 10 minutes in advance. The central cloud sends warning information to relevant edge nodes, which then adjust the takeoff time, flight path, or speed parameters of aircraft within their slices. A tiered warning mechanism is established: for risk levels 3 to 5, only edge nodes are alerted; for levels 6 to 8, local path optimization is initiated; and for levels 9 to 10, takeoff of relevant aircraft is suspended and their trajectories are forcibly adjusted, reducing the probability of conflict from the source.

[0030] This invention also includes an SLA dynamic adaptation module, which monitors in real time the SLA performance of communication bandwidth, transmission latency, security protection, and other indicators during the slice operation, collecting indicator data every 5 seconds and comparing it with preset thresholds. When an indicator deviates from the preset requirements, the module automatically initiates an adaptation and adjustment process. When communication bandwidth is insufficient, it coordinates with the spectrum resource management unit to allocate additional frequency bands or optimizes bandwidth allocation strategies to prioritize high-priority service flows. When transmission latency exceeds limits, it optimizes routing strategies, moving some computing tasks from the central cloud to edge nodes to reduce data transmission distance or utilizing redundant computing power from edge nodes to improve processing speed. When the security level is not up to standard, it strengthens data encryption, tightens access control permissions, and increases the frequency of security audits. It supports an SLA degradation negotiation mechanism, automatically pushing degradation solutions and compensation suggestions to service providers when resources are insufficient to meet the initial SLA requirements. After confirmation, it adjusts the SLA indicators and resource configurations to ensure that the slice operation complies with the negotiated agreement throughout.

[0031] This invention also includes a multi-dimensional environmental perception module, integrating meteorological sensors, airspace monitoring radar, electronic fence detection equipment, and a geographic information system. It collects real-time meteorological data such as wind speed, visibility, precipitation, and lightning; location and height data of obstacles such as buildings, bridges, and power lines within the airspace; and control information such as temporary no-fly zones and activity control zones. It establishes an environmental data fusion analysis model, removes abnormal data and supplements missing values, generating an accurate airspace environmental situation map, which is synchronized to the central cloud and edge nodes in real time. It supports environmental trend prediction, forecasting environmental change trends over the next 1 to 3 hours based on historical meteorological data and real-time monitoring results. The central cloud avoids high-risk environmental areas during slice planning, and edge nodes dynamically adjust flight parameters during scheduling, such as reducing aircraft speed in strong winds, shortening the safe distance between aircraft and activating obstacle avoidance sensors in low visibility conditions. It also features a dynamic electronic fence function, adjusting the electronic fence range in real time according to environmental changes such as sudden fires or road construction, and synchronously updating the airspace boundaries of relevant slices to ensure flight safety.

[0032] This invention also includes an aircraft status monitoring and anomaly handling unit, which collects real-time operational data such as battery level, power system status, navigation accuracy, and attitude stability of the aircraft through airborne sensors and communication links. Edge nodes perform real-time data analysis and anomaly identification. An aircraft health assessment system is established, which calculates a health score based on multi-dimensional data. When the score is lower than a preset threshold, an anomaly handling process is triggered. When the battery level is low, a return-to-base or nearby emergency landing instruction is triggered, and the location and resource status of surrounding emergency landing points are pushed. When the power system is abnormal, an emergency communication channel is activated to isolate the relevant flight area and notify surrounding aircraft to avoid it. When the navigation deviation is abnormal, the path is replanned and the trajectory is corrected. The invention also supports a fault self-healing suggestion function, which automatically generates fault investigation steps and emergency handling plans based on the anomaly type and aircraft model, and pushes them to the business side and aircraft operators. At the same time, alarm information, abnormal data, and processing progress are reported to the central cloud to ensure flight safety and business continuity.

[0033] This invention also includes a slice operation visualization management unit, which constructs a three-dimensional visualization monitoring interface. Based on a digital twin model, it displays in real time the distribution of slices across the entire domain, the geographical range of each slice, the number and location of aircraft, resource occupancy status, SLA compliance status, and conflict handling records. It supports multi-dimensional data query and filtering, allowing managers to query slice details and view historical operation data and trend analysis charts by business type, priority, region, and other conditions. It provides an intelligent early warning function, automatically identifying potential problems such as low resource utilization, declining SLA compliance rate, and frequent conflicts based on data analysis, and alerting managers through pop-ups, color-coding, and other methods. It supports manual intervention, allowing adjustment of slice priority, modification of resource configuration, forced cancellation of slices, or issuance of temporary flight control orders in emergency situations. All manual operations are logged and synchronized to the audit module. It has an automatic report generation function, supporting the generation of daily, weekly, and monthly operation reports, including key indicators such as the number of slices created, resource utilization, conflict handling efficiency, and SLA compliance rate. It supports custom report dimensions and export functions, providing a basis for management decisions and system optimization.

[0034] This invention also includes a cross-regional slice collaborative management unit. For long-distance slices spanning multiple edge node jurisdictions, such as intercity logistics and intercity inspection, a collaborative communication mechanism is established between edge nodes. Adjacent edge nodes share the aircraft's operating status, scheduling plan, and resource occupancy within the slice in real time. The central cloud establishes a cross-regional arbitration submodule to uniformly coordinate resource allocation and conflict resolution for cross-regional slices, avoiding scheduling conflicts caused by poor coordination among multiple edge nodes. It ensures cross-regional business continuity. When an aircraft is about to enter the jurisdiction of the next edge node, the scheduling authority is transferred one minute in advance. The preceding edge node synchronizes the aircraft trajectory, SLA requirements, and safety policies with the subsequent edge nodes. The subsequent edge nodes complete resource reservation and path planning in advance, ensuring uninterrupted and smooth cross-regional flight of the aircraft. It supports unified lifecycle management of cross-regional slices. The central cloud coordinates the creation, adjustment, and cancellation processes of slices to ensure consistent actions of all edge nodes and synchronized resource release, adapting to the cross-regional business needs of drone logistics, intercity inspection, and other applications.

[0035] The following two examples further illustrate specific embodiments of the present invention: Example 1: Application of Urban Drone Instant Logistics Scenarios This embodiment is applied to drone-based real-time logistics delivery in the core business districts of first-tier cities, covering three business districts, 12 office buildings, and surrounding residential areas. It involves two core business areas: logistics delivery and park inspection. Peak delivery times are 11:30-13:30 on weekdays and 18:00-21:00 in the evening, requiring support for hundreds of drones operating simultaneously. The logistics slice requires stable communication bandwidth and low scheduling latency, while the park inspection slice has a lower priority and can dynamically adjust airspace resources. The system deployment includes one central cloud management platform, three edge nodes, two types of slice template libraries (logistics and inspection), and a standardized API interface for food delivery platforms to submit slice requests.

[0036] I. Core Implementation Details Slicing Request and Negotiation Process: The food delivery platform submits an instant logistics slice request via API 24 hours before the peak delivery period, specifying the geographical scope as covering core business districts, the time window as 18:00-21:00, the business type as food delivery, and the SLA requirements as high priority, low scheduling latency, and high communication reliability. The business request processing module performs format validation and compliance review on the request before forwarding it to the Low-Altitude Dynamic Airspace Slicing Manager. The Slicing Manager, combining historical delivery data, real-time airspace situation, and digital twin simulations, assesses the feasibility of the request, completes SLA negotiation with the food delivery platform, and generates a slice template including a 3D airway network, dedicated communication frequency bands, and edge node computing power configuration.

[0037] Resource Mapping and Instantiation: The resource mapping and instantiation module maps SLA requirements to specific resource combinations. Spatial resources are allocated for three multi-layered ring-shaped aerial corridors, with three vertical layers to avoid conflicts. Spectrum resources are allocated for dedicated communication frequency bands to ensure stable data transmission. Computing resources are reserved at edge nodes around the business district for real-time path planning and conflict resolution. Network resources are allocated for dedicated virtual network bandwidth. At 17:50, the central cloud issues a slice instantiation command to the target edge nodes. The edge nodes create and initialize the logistics slice controller, load resource policies and scheduling algorithms, and complete resource reservation and device adaptation.

[0038] Edge-cloud collaborative operation: At 18:00, the logistics slice was officially launched, with hundreds of delivery drones connected to the slice. The local real-time scheduling engine of the edge nodes allocated precise corridor layers and flight times to each drone based on real-time order data, achieving high-density aerial cross-shaped operation. The central cloud monitored the slice's operational status in real time, visually displaying drone locations, link bandwidth, and resource usage through a global airspace digital twin module. At 19:30, the central cloud detected a sudden light rain in the area through a multi-dimensional environmental perception module and immediately issued weather degradation strategies to the edge nodes. The conflict resolution algorithm module of the edge nodes increased the drone flight intervals, initiated alternate landing point planning, and simultaneously ensured uninterrupted data transmission through communication frequency band isolation.

[0039] Elastic scaling and conflict handling: When the number of drones in a certain air corridor exceeds a preset threshold of 30%, the slice elastic scaling control unit automatically widens the corridor and adds one vertical height layer to alleviate congestion. Edge nodes monitor the drone operation status within the slice in real time. When a risk of path intersection is detected between two drones through millisecond-level conflict detection, a lateral offset collision avoidance path is immediately planned. Fixed-wing drones adjust their heading, and multi-rotor drones hover briefly. After the conflict is resolved, the trajectory data is synchronously updated to the central cloud. The safety isolation module uses logical isolation technology to ensure that the logistics slice and the park inspection slice operate independently in physically overlapping airspace, without communication interference or path conflicts.

[0040] Slice Cancellation and Resource Release: Delivery operations conclude at 21:00. The slice lifecycle management module triggers the cancellation process, and edge nodes guide all drones to safely land or fly away from the slice area, reporting their status to the central cloud. Upon confirmation by the central cloud, a resource release command is issued. Edge nodes release airspace, spectrum, computing power, and network resources, and the resource status is updated to the public resource pool for use in expanding the slice for park inspections. Management personnel can view the airspace utilization rate, conflict resolution records, and SLA compliance status of this delivery through the slice operation visualization management unit, generating operational reports to provide a basis for subsequent peak-hour scheduling optimization.

[0041] Table 1: Performance Comparison Table for Urban Logistics Scenarios Table 1 clearly demonstrates the advantages of this invention in urban logistics scenarios. Traditional airspace management systems use a static partitioning model, resulting in an airspace resource utilization rate of only 45%. Congestion is common during peak hours, with a flight conflict rate of 12%, severely impacting delivery efficiency. This invention, through dynamic slicing and elastic scaling, increases airspace resource utilization to 92% and reduces the conflict rate to 1.5%. The average latency of scheduling commands is reduced from 280ms to 35ms, meeting the low-latency control requirements of drones; resource release time is reduced from 15 minutes to 2 minutes, improving resource flow efficiency; and the SLA compliance rate reaches 99%, ensuring the stability and reliability of logistics delivery, perfectly adapting to the operational needs of high-density, real-time logistics.

[0042] Example 2: Cross-regional emergency rescue scenario application This embodiment is applied to cross-regional emergency rescue operations in mountainous areas, covering three counties and involving the jurisdiction of five edge nodes. It needs to support collaborative operations of multiple types of aircraft, including rescue drones, command aircraft, and medical transport aircraft. Emergency rescue operations require high priority access to airspace resources, strong anti-interference capabilities of communication links, low scheduling latency, and seamless switching across edge nodes to ensure real-time transmission of rescue commands and coordinated aircraft operation. The system deployment includes a central cloud management platform, five edge nodes, a dedicated emergency rescue slice template library, and multi-dimensional environmental sensing equipment to monitor meteorological and topographical data in mountainous areas.

[0043] I. Core Implementation Details Rapid Emergency Slice Creation and Resource Allocation: Following a sudden missing persons incident in a mountainous area, the rescue command center urgently submits an emergency rescue slice request via a standardized API. This request specifies the geographical scope covering the search and rescue area, the time window from the start of the rescue operation to its completion, the business type as emergency search and rescue, and the SLA requirements for highest priority, low communication latency, and interference-resistant transmission. The business request processing module quickly passes the verification, and the low-altitude dynamic airspace slice manager, based on the emergency slice template library, skips the conventional negotiation process and directly generates high-priority slice templates. The resource mapping and instantiation module rapidly allocates 3D search and rescue airways and interference-resistant communication frequency bands, reserving high-performance computing power at five edge nodes to ensure real-time path planning and multi-machine collaborative scheduling.

[0044] Cross-regional slice collaborative operation: The central cloud issues slice instantiation instructions to five edge nodes. Within three minutes, the edge nodes complete the creation and initialization of the slice controller, loading emergency rescue-specific scheduling algorithms and safety policies. The cross-regional slice collaborative management unit establishes a collaborative communication mechanism between edge nodes, allowing adjacent edge nodes to share drone operating status, scheduling plans, and resource usage in real time. The central cloud establishes a cross-regional arbitration submodule to coordinate resource allocation among the five edge nodes, avoiding resource competition between slices. When a rescue drone enters a second region from the jurisdiction of the first edge node, the preceding edge node synchronizes the aircraft trajectory, SLA requirements, and safety policies with subsequent nodes one minute in advance. Subsequent nodes then complete resource reservation and path planning in advance, achieving seamless cross-regional switching.

[0045] Environmental Awareness and Conflict Resolution: The multi-dimensional environmental awareness module integrates meteorological sensors, airspace monitoring radar, and a geographic information system to collect real-time data on mountainous wind speed, visibility, topography, and obstacles such as trees and cliffs. This generates a precise airspace situation map, which is synchronized to the central cloud and edge nodes. Based on historical meteorological data predicting increased wind speed within the next two hours, the central cloud instructs edge nodes to adjust flight parameters, reducing drone speed and increasing safety distances. When the edge node's conflict resolution algorithm detects a path conflict between a rescue drone and a medical transport aircraft, it plans a differentiated collision avoidance path based on the differences in their maneuverability. The rescue drone adjusts its altitude, while the medical transport aircraft maintains its course, completing conflict resolution within milliseconds and ensuring the continuity of rescue operations.

[0046] Aircraft Status Monitoring and SLA Dynamic Adaptation: The aircraft status monitoring and anomaly handling unit collects real-time data on the battery level, power system status, and navigation accuracy of the rescue drone through onboard sensors. Edge nodes perform real-time data analysis. When a drone's battery level is detected to be below a preset threshold, a return-to-home command is immediately triggered, pushing the location and resource status of nearby alternate landing points, and generating fault self-healing suggestions for the operator's reference. The SLA dynamic adaptation module monitors indicators such as communication bandwidth and transmission latency in real time. When signal attenuation in mountainous areas leads to insufficient bandwidth, it coordinates with the spectrum resource management unit to allocate additional anti-interference frequency bands to prioritize the transmission of rescue commands. When transmission latency exceeds limits, some computational tasks are offloaded to edge nodes to reduce data transmission distance and ensure that SLA indicators meet rescue requirements.

[0047] Slicing Adjustment and Resource Recovery: After the rescue mission is completed, the command center submits a slice cancellation request through the visual management unit. The slice lifecycle management module triggers the safe cancellation process, and edge nodes guide all aircraft to land safely and report the mission completion status to the central cloud. After confirmation by the central cloud, a resource release command is issued, and edge nodes release airspace, spectrum, computing power, and other resources within 5 minutes, updating them to the public resource pool. Simultaneously, the data storage and management module categorizes and stores the dispatch logs, safety event records, and resource usage data for this rescue operation, supporting subsequent traceability and analysis, and providing data support for optimizing slicing strategies for similar emergency rescue missions.

[0048] Table 2 Performance Comparison Table for Cross-Regional Emergency Rescue Scenarios Table 2 data highlights the application value of this invention in cross-regional emergency rescue scenarios. Traditional airspace management systems suffer from delayed emergency response, with slice creation taking 40 minutes, failing to meet rescue timeliness requirements and achieving a cross-regional switchover success rate of only 72%, easily leading to interruptions in rescue commands. This invention, through its rapid emergency slice creation mechanism, can complete instantiation in just 3 minutes, gaining valuable time for rescue; the cross-regional switchover success rate is increased to 99.5%, ensuring continuous aircraft operation; the rescue command transmission latency is reduced from 350ms to 40ms, ensuring real-time command issuance; resource release efficiency is improved from 25 minutes to 5 minutes, increasing resource reuse rate; multi-dimensional environmental perception and dynamic adaptation capabilities enable the system to operate stably even in harsh mountainous environments, providing reliable airspace scheduling support for emergency rescue and significantly improving the rescue success rate.

[0049] Reference Figure 2 This diagram comprehensively showcases the integrated advantages of the system of this invention. Traditional systems achieve only 42% airspace resource utilization due to a rigid, static partitioning model. This invention, through dynamic slicing and elastic scaling, increases the utilization rate to 93%, maximizing resource utilization. The flight conflict rate is reduced from 13% to 1.2%, thanks to millisecond-level conflict detection and differentiated collision avoidance strategies, significantly reducing safety risks. Scheduling command latency is reduced from 260ms to 32ms, demonstrating the technical advantages of the edge-cloud collaborative architecture in pushing real-time tasks to the edge. Slice creation time is shortened from 38 minutes to 2.5 minutes, meeting the rapid response requirements of scenarios such as emergency rescue. The SLA compliance rate is 99.2%, verifying the system's precise adaptability to business needs and comprehensively supporting low-altitude economical and efficient operation.

[0050] Reference Figure 3This figure illustrates the resource adaptability of the system of this invention at different times. Traditional systems, limited by static partitioning, have utilization rates of only 35% and 28% during off-peak and nighttime periods, respectively, resulting in severe resource idleness. While peak-hour utilization reaches 68%-72%, it is close to the capacity limit and prone to congestion. This invention, through the elastic scaling of dynamic airspace slicing, maintains a high utilization rate of 85% during off-peak periods, avoiding resource waste; during peak periods, the utilization rate remains stable at 92%-94%, expanding capacity by widening the airspace and adding altitude layers to meet high-density demands; and at night, it maintains an 80% utilization rate, adapting to nighttime logistics, emergency response, and other operations. The line trend shows that this invention achieves dynamic adaptation of airspace resources over time, completely changing the traditional system's predicament of "insufficient resources during busy periods and wasted resources during idle periods."

[0051] Reference Figure 4 This diagram illustrates the core advantage of the flexible scaling of airspace slicing in this invention. Traditional systems use static airspace partitioning with a fixed available capacity of 150 drones. When the number of drones exceeds 150, access can only be restricted or congestion may occur. This invention, through the flexible scaling of dynamic airspace slicing, adjusts the airspace capacity in real time according to traffic volume: 180 drones are available with redundancy at 100 drones; the capacity expands to 320 drones at 200, 450 at 300, and reaches 700 at 500, with capacity increasing linearly with traffic volume. This flexible adaptability allows the system to efficiently utilize resources during off-peak periods with low traffic volume, while also handling the access demands of high-density scenarios such as holiday logistics peaks and large-scale events. It can automatically expand capacity without manual intervention, significantly improving airspace carrying capacity and supporting the large-scale development of the low-altitude economy.

[0052] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

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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