Flight management system based on cloud-edge cooperation and route planning method thereof

Abstract

The invention provides a flight management system based on cloud-edge collaboration and an air route planning method thereof. The flight management system comprises an airborne end, a cloud platform and an edge computing node, the route planning method of the flight management system comprises the following steps: step 1, calculating an optimal flight path by a cloud platform, and predicting a meteorological condition on the flight route and corresponding real-time airspace information; 2, the cloud platform completes the route optimization of the current flight according to the data in the step 1, and sends the data to an edge computing node and an airborne end; step 3, when an emergency occurs, the edge computing node computes a corresponding release path and issues the corresponding release path to the airborne end and the air traffic control center, and the air traffic control center determines whether the aircraft executes the release path or not; according to the method, the defects of a traditional FMS in the aspects of dynamic planning capability, computing power limitation and the like can be overcome, and an extensible, high-reliability and low-cost solution is provided for a next-generation flight management system.

Description

Technical Field

[0001] This invention relates to the field of civil aircraft avionics systems technology, and in particular to a cloud-edge collaborative flight management system and its route planning method. Background Technology

[0002] The traditional Flight Management System (FMS), as the core of modern avionics systems, has become a core avionics device for modern commercial aircraft since its development began in the 1970s. Its core functions include flight planning, trajectory prediction, flight guidance, navigation calculation, performance calculation, and radio tuning. Its main role is to reduce the pilot's workload, improve flight comfort and economy through route optimization, and ensure flight safety while making flight smoother, more comfortable, and fuel-efficient.

[0003] Traditional FMS typically has two technical implementations: (1) The integrated modular avionics (IMA) system architecture is adopted, and the FMS software resides in the avionics core processing system (ACPS) platform.

[0004] (2) Relies on dedicated embedded computers (such as PowerPC architecture processors) and customized hardware modules (ARINC429 / AFDX bus interface).

[0005] The data management portion of existing flight management systems (FMS) stores navigation databases locally and updates them periodically via physical media. With increasingly congested airspace, the rising frequency of extreme weather events, and the International Civil Aviation Organization's (ICAO) Carbon Offset and Reduction in International Aviation (CORSIA) mandates net-zero carbon emissions by 2050, airlines also need to reduce fuel costs. These factors place higher demands on the route planning functions of FMS. While traditional FMS technology is mature, its inherent limitations are becoming increasingly apparent in the context of the aviation industry's digital transformation.

[0006] The main problems with existing flight management systems and route planning methods include the following aspects: (1) The route planning is static and cannot dynamically respond to real-time environmental changes. Traditional Flight Management System (FMS) route planning functions plan the entire route before takeoff based on aircraft performance and predicted weather data. However, it cannot integrate dynamic information such as weather and airspace traffic in real time, resulting in low flight efficiency. Especially when flying across oceans or in complex airspace, sudden weather events require manual intervention.

[0007] (2) Insufficient real-time data processing capability Civil aircraft have strict requirements on the weight and power of onboard equipment, which limits the local computing power of traditional FMS (typical FMS computing power <1 TOPS), making it difficult to support real-time route optimization and large-scale data processing. At the same time, traditional FMS relies on ACARS to transmit weather update data, with a response delay of 5-10 minutes, which cannot meet the needs of high-density airspace.

[0008] (3) Limited functional expandability Existing flight management systems typically employ strong coupling between software and hardware, requiring recertification for new functions, which takes too long.

[0009] (4) Lack of ecological coordination capabilities Traditional FMS cannot interact with air traffic control systems and airline operations centers (AOCs) in real time, resulting in low airspace resource utilization, inability to share data across flights and airlines, and unrealized potential for global optimization.

[0010] Therefore, there is a need to provide a cloud-edge collaborative flight management system and its route planning method to address the fundamental shortcomings of traditional FMS in terms of dynamic planning capabilities and computing power limitations. This would provide a scalable, highly reliable, and low-cost solution for the next generation of flight management systems, which is particularly suitable for high-density airspace, emerging electric VTOL (eVTOL) and drone logistics scenarios. Summary of the Invention

[0011] To address the problems in existing technologies, this invention provides a cloud-edge collaborative flight management system and its route planning method, which can solve the fundamental defects of traditional FMS in terms of dynamic planning capabilities and computing power limitations. It provides a scalable, highly reliable, and low-cost solution for the next generation of flight management systems, and is particularly suitable for high-density airspace, emerging electric VTOL and drone logistics scenarios.

[0012] To achieve the above objectives, the present invention provides the following technical solution: a cloud-edge collaborative flight management system, comprising: Airborne terminal, deployed on the aircraft, is used for data security verification and flight management of the aircraft; The cloud platform, deployed on the ground, is used to optimize routes based on historical route data, meteorological data, airspace data, and aircraft performance data, and sends the optimized route information to the airborne terminal and edge computing nodes. Edge computing nodes are deployed near airports, air traffic control centers, navigation stations, or 5G base stations to predict meteorological phenomena and airspace conditions within the coverage area that may have a significant impact on aircraft flight. Based on the prediction results, the coordinates of dangerous areas and suggested detour routes are sent to the airborne terminal. At the same time, a real-time airspace situation map is constructed, and escape routes are calculated. The corresponding escape routes are then sent to the airborne terminal and the air traffic control center, which decides whether the aircraft should follow the escape route.

[0013] In the preferred technical solution, the cloud platform includes a route historical data analysis module, a meteorological data analysis module, an airspace data analysis module, an aircraft performance database, and a route optimization model; The route history data analysis module uses big data technology to analyze the flight data of all flights on the corresponding route to obtain the optimal flight path of the aircraft, and sends it to the route optimization model. The meteorological data analysis module is used to receive current meteorological data and historical meteorological data for the same period from the meteorological center, predict the meteorological conditions along the flight route, and send them to the route optimization model. The airspace data analysis module is used to calculate the corresponding real-time airspace information and send it to the route optimization model when encountering emergencies such as thunderstorms or airspace control. The aircraft performance database is used to store complete aircraft performance parameters for the corresponding aircraft. The route optimization model is used to optimize the current flight route based on data provided by the route history data analysis module, meteorological data analysis module, airspace data analysis module, and aircraft performance database, and sends the optimized route information to the edge computing node and the airborne terminal.

[0014] In a preferred embodiment, the edge computing node includes a node meteorological data analysis module, a node airspace analysis module, and a detour path calculation module. The node meteorological data analysis module is used to receive real-time meteorological data from the meteorological center, airport meteorological radar, and aircraft meteorological radar data, predict extreme meteorological phenomena such as wind shear and thunderstorms that could have a significant impact on aircraft flight in the next fifteen minutes, and send the coordinates of the danger zone and the suggested detour route to the airborne terminal via ACARS or CPDLC. The node airspace analysis module receives the position and speed information of surrounding aircraft via ADS-B, constructs a real-time airspace situation map, and sends the current airspace situation to the escape path calculation module. The escape path calculation module receives data from the node meteorological data analysis module, the node airspace analysis module, and the cloud platform route optimization model, calculates the corresponding escape path, and sends the corresponding escape path to the airborne terminal and the air traffic control center. The air traffic control center then decides whether the aircraft should execute the escape path.

[0015] In a preferred embodiment, the airborne terminal includes a data security verification module and an airborne FMS; the data security verification module is used to perform security verification on all data sent from the cloud platform and edge computing nodes to the airborne terminal, and only after the data is confirmed to be secure through security verification is it sent to the airborne FMS; the airborne FMS is used for flight management of the aircraft.

[0016] The preferred technical solution, the cloud-edge collaborative flight management system, also includes a federated learning algorithm module, which is used to encrypt the corresponding data of each airline before uploading it to the cloud platform.

[0017] In a preferred embodiment, the federated learning algorithm module includes a local training module and a federated learning core module. The local training module is used by each airline to train its model using local data, generating a local model without leaving the local data, and then transmitting the trained model parameters to the federated learning core module. The federated learning core module is used to encrypt and aggregate the parameters of the local training models of each airline to generate a global model, and then push the encrypted global model parameters to the local models of each airline, thereby optimizing the global model while ensuring the data privacy of each airline.

[0018] Another object of the present invention is to provide a route planning method for a cloud-edge collaborative flight management system, which includes the following steps: Step 1: The route history data analysis module uses big data technology to analyze all flight data uploaded by all airlines along the corresponding route, derives the optimal flight path for the corresponding aircraft, and sends it to the route optimization model; the meteorological data analysis module receives current meteorological data and historical meteorological data for the same period from the meteorological center, predicts the meteorological conditions along the current flight route, and sends it to the route optimization model; when encountering emergencies such as thunderstorms or airspace control, the airspace data analysis module combines the current number of aircraft in the airspace and flight trajectory information to calculate the corresponding real-time airspace information and sends it to the route optimization model. Step 2: The route optimization model optimizes the current flight route based on data provided by the route history data analysis module, meteorological data analysis module, airspace data analysis module, and aircraft performance database, and sends the optimized route information to the edge computing node and the airborne terminal. Step 3: When encountering sudden situations such as wind shear, thunderstorms, or airspace control issues, the node meteorological data analysis module receives real-time meteorological data from the meteorological center, airport weather radar, and aircraft weather radar data. It predicts the meteorological conditions, such as wind shear or thunderstorms, that could significantly impact aircraft flight in the next 15 minutes and sends the coordinates of the hazardous area and suggested detour routes to the airborne terminal via ACARS or CPDLC. The node airspace analysis module receives the position and speed information of surrounding aircraft via ADS-B, constructs a real-time airspace situation map, and sends the current airspace situation to the escape path calculation module. The escape path calculation module receives data from the node meteorological data analysis module, the node airspace analysis module, and the cloud platform's route optimization model, calculates the corresponding escape path, and sends the corresponding escape path to the airborne terminal and the air traffic control center. The air traffic control center then decides whether the aircraft should execute the escape path.

[0019] In the preferred technical solution, before the airlines upload the flight data for all flights in step one, the corresponding data of each airline is encrypted using a federated learning algorithm module.

[0020] In the preferred technical solution, when the optimized route information is sent to the airborne terminal in step two, the data sent to the airborne terminal will be subject to security verification.

[0021] In the preferred technical solution, when the release command corresponding to the release path is sent to the airborne terminal in step three, the data sent to the airborne terminal will be subject to security verification.

[0022] Compared with existing technologies, the beneficial effects of the cloud-edge collaborative flight management system and its route planning method of this invention are: 1. This invention can solve the fundamental defects of traditional FMS in terms of dynamic planning capability and computing power limitations, and provides a scalable, highly reliable and low-cost solution for the next generation of flight management systems, especially suitable for high-density airspace, emerging electric aviation (eVTOL) and drone logistics scenarios.

[0023] 2. This invention adopts an architecture of cloud, edge nodes, and airborne FMS, which solves the problem of insufficient computing power of airborne FMS while ensuring security. On the basis of overcoming computing power limitations, it introduces a federated learning-driven route model, with each airline training the model locally and uploading encrypted parameters to the cloud for aggregation, protecting data privacy while improving global fuel efficiency. The introduction of AI algorithms reduces the rejection rate of FMS generated paths. It breaks the closed nature of traditional avionics hardware, allowing third parties such as weather providers, airlines, and air traffic control to participate in the operation and calculation of FMS, thus solving the problem of data silos in traditional FMS.

[0024] 3. This invention can improve fuel efficiency of route planning through global optimization by cloud AI and real-time correction at the edge; the federated learning model introduces airspace data to improve airspace utilization and reduce flight delays; the three-level computing power collaboration of cloud, edge and airborne improves the computing power of traditional FMS, and compared with the pure cloud solution, the overall computing power requirement is reduced and the efficiency is improved.

[0025] 4. This invention enhances real-time performance and reliability. Edge computing nodes handle aircraft approach or departure tasks more accurately than traditional FMS and with lower latency than pure cloud-based models.

[0026] 5. This invention provides good data privacy protection by employing federated learning to ensure that airline data does not leave the local area, thus meeting GDPR / CCAR-121-R7 requirements. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the software architecture of the cloud-edge collaborative flight management system in Example 1.

[0028] Figure 2 This is a schematic diagram of the route planning process of the present invention.

[0029] Figure 3 This is a schematic diagram of the route planning process when encountering wind shear scenarios.

[0030] Figure 4 This is a schematic diagram of the cloud-edge collaborative flight management system architecture in Example 2.

[0031] Figure 5 This is a schematic diagram of the architecture of the federated learning algorithm module. Detailed Implementation

[0032] The relevant terms used in this invention are explained as follows: FMS: Flight Management System; ACPS: Avionics Core Processing System; IMA: Integrated Modular Avionics; ICAO: International Civil Aviation Organization; CORSIA: International Aviation Carbon Offset and Reduction Program; TOPS: Tera Operations Per Second, is a unit of processor computing power; 1 TOPS means that the processor can perform one trillion operations per second. ACARS: Aircraft Communications Addressing and Reporting System; AOC: Airline Operations Center; CPDLC: Controller-Pilot Data Link Communication; ADS-B: Automatic Dependent Monitoring Broadcast.

[0033] Reference Figures 1 to 5 The invention provides a further explanation of the cloud-edge collaborative flight management system architecture and its scanning method.

[0034] Example 1

[0035] like Figure 1 As shown: The cloud-edge collaborative flight management system includes a cloud platform, edge computing nodes, and an airborne terminal; the cloud platform includes a route history data analysis module, a meteorological data analysis module, an airspace data analysis module, an aircraft performance database, and a route optimization model; the edge computing node includes a node meteorological data analysis module, a node airspace analysis module, and an escape route calculation module; the airborne terminal includes an airborne FMS and a data security verification module.

[0036] Edge computing module nodes are deployed near airports, air traffic control centers, navigation stations, or 5G base stations to ensure low-latency coverage of terminal areas, approach corridors, and key areas. The node meteorological data analysis module receives real-time meteorological data from the meteorological center, airport weather radar, and aircraft weather radar. It primarily predicts wind shear, thunderstorms, and other weather conditions that could significantly impact takeoff and landing within the next fifteen minutes. The module then transmits the coordinates of hazardous areas and suggested detour routes to the data security verification module via ACARS or CPDLC (Controller-Pilot Data Link Communication). Once data security is confirmed, the data is sent to the onboard FMS. The node airspace analysis module receives the position and speed information of surrounding aircraft via ADS-B (Automatic Dependent Surveillance-Broadcast), constructs a real-time airspace situation map, and sends the airspace situation to the escape path calculation module. The escape path calculation module receives the outputs from the node meteorological data analysis module, the node airspace analysis module, and the route optimization model on the cloud platform, and calculates the escape path according to the provisions of ICAO DOC 4444 "Air Traffic Management", such as the minimum fuel consumption path, the fastest escape path, and the optimal detour path. The escape command is then sent to the data security verification module. After confirming data security, it is sent to the airborne FMS and the air traffic control center, which decides whether the aircraft should execute the escape path.

[0037] The cloud platform is deployed on the ground, leveraging powerful computing capabilities to support a collaborative mechanism of "historical data learning + weather data analysis" for global route optimization. The route historical data analysis module uses big data technology to analyze flight data from all flights along the route to determine the optimal flight path. The meteorological data analysis module receives meteorological data from the meteorological center and historical data for the same period to predict weather conditions along the current flight route. The airspace data analysis module provides airspace information to the route optimization model in the event of emergencies such as thunderstorms or airspace control, combining information such as the number of aircraft in the airspace and their flight trajectories. The aircraft performance database is a complete database; however, the onboard FMS typically scales it down by 10% due to limitations in computing power and storage space. Based on the data provided by the route historical data analysis module, meteorological data analysis module, airspace data analysis module, and aircraft performance database, the route optimization model completes the route optimization for the current flight and sends the route information to the data security verification module. After confirming data security, the information is then sent to the onboard FMS.

[0038] like Figure 2 The flowchart shown illustrates the route planning process. First, the route historical data analysis module, meteorological data analysis module, airspace data analysis module, and flight performance database integrate multi-source real-time data, including airline strategy, meteorological, airspace, and aircraft performance data. This data is then processed to generate a candidate route set. Second, the processed data is sent to the route optimization model. This model generates constraints based on air traffic control, airline, and crew requirements, further optimizing the candidate route set. If a suitable optimal route cannot be automatically generated, manual intervention can be used to reduce the constraints, ultimately generating the optimal route. Third, the optimal route is sent to the edge computing nodes and the airborne FMS. The meteorological data from the edge computing nodes is distributed... After analyzing the meteorological data and airspace traffic in the node area, the analysis module and the node airspace analysis module send the analyzed data to the escape path calculation module to complete the real-time fine-tuning of the optimal route to generate the final route, and then send it to the airborne FMS. Finally, after receiving the cloud-based route data and the edge stage route data, the data security verification module of the airborne FMS performs security verification on the data respectively. If the security verification conditions are met, the route data sent by the edge node is used first. Only when the data sent by the edge node fails (security verification fails, or it is not sent to the airborne terminal, etc.) is the route data sent by the cloud used. If all the sent data fails, the route data of the airborne FMS is used.

[0039] like Figure 3The diagram shows the flight path planning process in a wind shear scenario. First, combining integrated weather radar, aircraft status, and airspace data, the wind shear model prediction is completed by the edge computing node weather data analysis module. Second, based on the wind shear prediction results, it is determined whether to continue monitoring or adjust the path. Third, the airspace analysis module analyzes the airspace conditions, and the escape path calculation module integrates all data and calculates the path with the shortest departure distance and minimum fuel consumption according to ICAO DOC 4444 "Air Traffic Management". Finally, with air traffic control approval, the data is sent to the airborne FMS for execution while ensuring data security.

[0040] Example 2

[0041] like Figure 4 As shown, Example 2 adds a federated learning algorithm module to Example 1. This module ensures data security for all airlines while allowing all participants to share the model through a process of local training → parameter encryption → secure aggregation → noise addition → model distribution. This module addresses the problems of limited data coverage by individual airlines and the inability of airlines to share core data (such as historical flight trajectories, fuel consumption, and engine performance) due to competition and compliance requirements, leading to insufficient data samples and poor generalization in traditional route optimization models. Through the federated learning algorithm module, each airline trains the model locally, uploading only encrypted model parameters; the original data remains local. The cloud aggregates the parameters to generate a global model, which can utilize industry-wide experience without direct data sharing. Finally, the data generated by the model is distributed to all participants.

[0042] like Figure 5 The diagram shows the structure of the federated learning algorithm module. This module is divided into a local training module and a federated learning core module. Through the local training module, each airline trains its model using local data (flight data / meteorological data, etc.), ensuring the data remains locally. The model parameters are then transmitted to the federated learning core module. The core module encrypts and aggregates the parameters of each airline's locally trained model, generating a global model. It then encrypts the global model parameters and pushes them back to each airline's local model, ensuring global model optimization while protecting airline data privacy.

[0043] The route planning method of the cloud-edge collaborative flight management system in this embodiment 2 is as follows: Step 1: The route history data analysis module uses big data technology to analyze all flight data uploaded by all airlines along the corresponding route, derives the optimal flight path for the corresponding aircraft, and sends it to the route optimization model. The meteorological data analysis module receives current meteorological data and historical meteorological data for the same period from the meteorological center, predicts the meteorological conditions along the current flight route, and sends it to the route optimization model. When encountering emergencies such as thunderstorms or airspace control issues, the airspace data analysis module combines the current number of aircraft in the airspace and flight trajectory information to calculate the corresponding real-time airspace information and sends it to the route optimization model. Before airlines upload all flight data, the corresponding data from each airline is encrypted using a federated learning algorithm module.

[0044] Step 2: The route optimization model optimizes the current flight route based on data provided by the route history data analysis module, meteorological data analysis module, airspace data analysis module, and aircraft performance database, and sends the optimized route information to the edge computing node and the airborne terminal. When sending the optimized route information to the airborne terminal, the data security verification module performs security verification on the data sent to the airborne terminal.

[0045] Step 3: When encountering sudden situations such as wind shear, thunderstorms, or airspace control issues, the node meteorological data analysis module receives real-time meteorological data from the meteorological center, airport weather radar, and aircraft weather radar data. It predicts the meteorological conditions that could significantly impact aircraft flight, such as wind shear or thunderstorms, within the next 15 minutes and sends the coordinates of the hazardous area and suggested detour routes to the airborne terminal via ACARS or CPDLC. The node airspace analysis module receives the position and speed information of surrounding aircraft via ADS-B, constructs a real-time airspace situation map, and sends the current airspace situation to the escape path calculation module. The escape path calculation module receives data from the node meteorological data analysis module, the node airspace analysis module, and the route optimization model on the cloud platform, calculates the corresponding escape path, and issues the escape command for the corresponding escape path to the airborne terminal and the air traffic control center. The air traffic control center then decides whether the aircraft should execute the escape path. During the issuance of the escape command for the corresponding escape path to the airborne terminal, the data security verification module performs security verification on the data sent to the airborne terminal.

[0046] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cloud-edge collaborative flight management system, characterized in that: It includes: Airborne terminal, deployed on the aircraft, is used for data security verification and flight management of the aircraft; The cloud platform, deployed on the ground, is used to optimize routes based on historical route data, meteorological data, airspace data, and aircraft performance data, and sends the optimized route information to the airborne terminal and edge computing nodes. Edge computing nodes are deployed near airports, air traffic control centers, navigation stations, or 5G base stations to predict meteorological phenomena and airspace conditions within the coverage area that may have a significant impact on aircraft flight. Based on the prediction results, the coordinates of dangerous areas and suggested detour routes are sent to the airborne terminal. At the same time, a real-time airspace situation map is constructed, and escape routes are calculated. The corresponding escape routes are then sent to the airborne terminal and the air traffic control center, which decides whether the aircraft should follow the escape route.

2. The cloud-edge collaborative flight management system according to claim 1, characterized in that: The cloud platform includes a route historical data analysis module, a meteorological data analysis module, an airspace data analysis module, an aircraft performance database, and a route optimization model; The route history data analysis module uses big data technology to analyze the flight data of all flights on the corresponding route to obtain the optimal flight path of the aircraft, and sends it to the route optimization model. The meteorological data analysis module is used to receive current meteorological data and historical meteorological data for the same period from the meteorological center, predict the meteorological conditions along the flight route, and send them to the route optimization model. The airspace data analysis module is used to calculate the corresponding real-time airspace information in the event of thunderstorms, airspace control, or other emergencies, by combining the current number of aircraft in the airspace and their flight trajectory information, and then send it to the route optimization model. The aircraft performance database is used to store complete aircraft performance parameters for the corresponding aircraft. The route optimization model is used to optimize the current flight route based on data provided by the route history data analysis module, meteorological data analysis module, airspace data analysis module, and aircraft performance database, and sends the optimized route information to the edge computing node and the airborne terminal.

3. The cloud-edge collaborative flight management system according to claim 2, characterized in that: The edge computing node includes a node meteorological data analysis module, a node airspace analysis module, and a detour path calculation module. The node meteorological data analysis module is used to receive real-time meteorological data from the meteorological center, airport meteorological radar, and aircraft meteorological radar data, predict wind shear, thunderstorms, and other meteorological phenomena that may have a significant impact on aircraft flight in the next fifteen minutes, and send the coordinates of the danger zone and the suggested detour route to the airborne terminal via ACARS or CPDLC. The node airspace analysis module receives the position and speed information of surrounding aircraft via ADS-B, constructs a real-time airspace situation map, and sends the current airspace situation to the escape path calculation module. The escape path calculation module receives data from the node meteorological data analysis module, the node airspace analysis module, and the cloud platform route optimization model, calculates the corresponding escape path, and sends the escape command for the corresponding escape path to the airborne terminal and the air traffic control center. The air traffic control center then decides whether the aircraft should execute the escape path.

4. The cloud-edge collaborative flight management system according to claim 3, characterized in that: The airborne terminal includes a data security verification module and an airborne FMS; the data security verification module is used to perform security verification on all data sent from the cloud platform and edge computing nodes to the airborne terminal, and only after the data is confirmed to be secure through security verification is it sent to the airborne FMS; the airborne FMS is used for the flight management of the aircraft.

5. The cloud-edge collaborative flight management system according to claim 4, characterized in that: It also includes a federated learning algorithm module, which is used to encrypt the data that each airline uploads to the cloud platform.

6. The cloud-edge collaborative flight management system according to claim 5, characterized in that: The federated learning algorithm module includes a local training module and a federated learning core module. The local training module is used by each airline to train its model using local data, generating a local model without leaving the local data, and then transmitting the trained model parameters to the federated learning core module. The federated learning core module is used to encrypt and aggregate the parameters of the local training models of each airline to generate a global model, and then push the encrypted global model parameters to the local models of each airline, thereby optimizing the global model while ensuring the data privacy of each airline.

7. A route planning method for a cloud-edge collaborative flight management system, characterized in that: It includes the following steps: Step 1: The route history data analysis module uses big data technology to analyze all flight data uploaded by all airlines along the corresponding route, derives the optimal flight path for the corresponding aircraft, and sends it to the route optimization model; the meteorological data analysis module receives current meteorological data and historical meteorological data for the same period from the meteorological center, predicts the meteorological conditions along the current flight route, and sends it to the route optimization model; when encountering thunderstorms, airspace control, or other emergencies, the airspace data analysis module combines the current number of aircraft in the airspace and flight trajectory information to calculate the corresponding real-time airspace information and sends it to the route optimization model. Step 2: The route optimization model optimizes the current flight route based on data provided by the route history data analysis module, meteorological data analysis module, airspace data analysis module, and aircraft performance database, and sends the optimized route information to the edge computing node and the airborne terminal. Step 3: When encountering wind shear, thunderstorms, airspace control, or other emergencies, the node meteorological data analysis module receives real-time meteorological data from the meteorological center, airport weather radar, and aircraft weather radar data, predicts wind shear, thunderstorms, and other meteorological phenomena that may significantly affect aircraft flight in the next fifteen minutes, and sends the coordinates of the danger zone and suggested detour routes to the airborne terminal via ACARS or CPDLC; the node airspace analysis module receives the position and speed information of surrounding aircraft via ADS-B, constructs a real-time airspace situation map, and sends the current airspace situation to the escape path calculation module; The escape path calculation module receives data from the node meteorological data analysis module, the node airspace analysis module, and the cloud platform route optimization model, calculates the corresponding escape path, and sends the corresponding escape path to the airborne terminal and the air traffic control center. The air traffic control center then decides whether the aircraft should execute the escape path.

8. The route planning method for a cloud-edge collaborative flight management system according to claim 7, characterized in that: In step one, before the airlines upload flight data for all flights, they encrypt the corresponding data of each airline through the federated learning algorithm module.

9. The route planning method for a cloud-edge collaborative flight management system according to claim 8, characterized in that: In step two, when the optimized route information is sent to the airborne terminal, the data sent to the airborne terminal will be subject to security verification.

10. The route planning method for a cloud-edge collaborative flight management system according to claim 9, characterized in that: In step three, when the corresponding decoupling path is sent to the airborne terminal, the data sent to the airborne terminal will be subject to security verification.

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