Aircraft route planning system and method based on big data analysis

Abstract

The invention relates to the technical field of aircraft route planning, in particular to an aircraft route planning system and method based on big data analysis, and the system comprises a data collection preprocessing module, an environment modeling module, a route planning module, a dynamic obstacle avoidance module, a performance evaluation module, a collaborative planning module, an interaction module and a privacy module. The data acquisition preprocessing module is used for data acquisition and data cleaning integration; the environment modeling module is used for environment modeling and weather prediction. Compared with a traditional air route planning method, through automatic data collection, integration and processing and an efficient algorithm model, a plurality of feasible initial air routes can be rapidly generated and optimized and selected, the air route planning efficiency is greatly improved, the big data analysis and machine learning technologies are utilized, and the air route planning efficiency is greatly improved. The meteorological condition can be predicted more accurately, and the performance of the aircraft can be evaluated, so that a safer and more economical flight path can be planned.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft route planning, and in particular to an aircraft route planning system and method based on big data analysis. Background Art

[0002] Aircraft route planning technology refers to the technology of planning an optimal or suboptimal flight path for an aircraft based on the starting point, end point, and various constraints during the flight (such as terrain, weather, no-fly zones, etc.). This path needs to meet the requirements of safety, economy, feasibility, etc. Route planning is an important part of aircraft mission planning and is of great significance for improving flight efficiency, reducing flight costs, and ensuring flight safety.

[0003] Traditional aircraft route planning methods mainly rely on human experience and simple algorithm models. These methods usually include the steps of manual information collection, manual planning, evaluation and adjustment. However, traditional route planning methods are usually time-consuming and labor-intensive, and are difficult to adapt to the rapidly changing flight environment. Due to reliance on human experience and simple algorithms, the planned path may not be optimal, and may even pose a safety hazard. It is impossible to make dynamic adjustments in real time according to changes during the flight (such as sudden weather changes, changes in no-fly zones, etc.).

[0004] In summary, it is necessary to propose an aircraft path planning system and method based on big data analysis to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an aircraft route planning system and method based on big data analysis to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Aircraft route planning system based on big data analysis, including data acquisition preprocessing module, environment modeling module, route planning module, dynamic obstacle avoidance module, performance evaluation module, collaborative planning module, interaction module and privacy module;

[0008] The data acquisition preprocessing module is used for data acquisition and data cleaning and integration;

[0009] The environmental modeling module is used for environmental modeling and weather forecasting;

[0010] The route planning module is used for initial route generation and route optimization;

[0011] The dynamic obstacle avoidance module is used for real-time obstacle avoidance and online replanning;

[0012] The performance evaluation module is used for aircraft performance simulation and performance index analysis;

[0013] The collaborative planning module is used for collaborative route planning and conflict detection and resolution;

[0014] The interactive module is used for route planning visualization and user interaction;

[0015] The privacy module is used for data encryption and privacy protection.

[0016] Preferably, the data acquisition preprocessing module further includes a data acquisition unit and a data cleaning and integration unit;

[0017] The data acquisition unit collects the position, speed, altitude and weather condition data of the aircraft in real time through GPS, radar, weather station and other sensors, and uses the Internet of Things (IoT) technology to connect various sensors to the system and transmit the data to the data center in real time through the MQTT protocol;

[0018] The data cleaning and integration unit cleans the collected raw data, removes noise and outliers, and integrates data from different sources to form a data set in a unified format. Python's Pandas library is used for data cleaning, and Hadoop or Spark big data processing framework is used for data integration.

[0019] Preferably, the environmental modeling module further includes an environmental modeling unit and a meteorological prediction unit;

[0020] The environmental modeling unit builds a three-dimensional model of the flight environment based on historical data and real-time data, including terrain, weather, and no-fly zone information, using GIS (Geographic Information System) technology in combination with dsMax three-dimensional modeling software for environmental modeling;

[0021] The weather forecast unit uses historical weather data and machine learning algorithms to predict weather conditions in the future to provide weather references for route planning. Weather forecasts are performed using an LSTM (Long Short-Term Memory Network) time series prediction model combined with historical weather data.

[0022] Preferably, the route planning module further includes an initial route generation unit and a route optimization unit;

[0023] The initial route generation unit is used to generate multiple feasible initial routes according to the starting point, the end point and the environmental model of the aircraft, and uses the Dijkstra algorithm or the A* algorithm to find the shortest path in the environmental model;

[0024] The route optimization unit optimizes the initial route, taking into account weather conditions and aircraft performance factors, to select the optimal route, and iteratively optimizes the route using a genetic algorithm or a particle swarm optimization algorithm, with the goal of minimizing flight time and fuel consumption.

[0025] Preferably, the dynamic obstacle avoidance module further includes a real-time obstacle avoidance unit and an online replanning unit;

[0026] The real-time obstacle avoidance unit is used to monitor obstacles around the aircraft in real time during flight and calculate the obstacle avoidance path, using SLAM (simultaneous localization and mapping) technology based on laser radar or visual sensors combined with dynamic window method (DWA) to perform real-time obstacle avoidance;

[0027] The online replanning unit is used to replan the route online when encountering sudden weather changes or changes in no-fly zones, and uses a rolling horizon optimization (RHC) method in combination with an environmental model and real-time data to quickly replan the route.

[0028] Preferably, the performance evaluation module further includes an aircraft performance simulation unit and a performance index analysis unit;

[0029] The aircraft performance simulation unit is used to evaluate the performance of the aircraft on different routes by simulating the flight process, using a physics-based flight simulator combined with the aircraft's dynamics model to simulate the flight;

[0030] The performance indicator analysis unit is used to analyze the simulated flight results, extract flight time, fuel consumption, and safety key performance indicators, and use Python's Pandas and Matloplib libraries to perform visual analysis on the simulated data and extract key performance indicators.

[0031] Preferably, the collaborative planning module further comprises a collaborative route planning unit and a conflict detection and resolution unit;

[0032] The collaborative route planning unit is used to plan collaborative routes for multiple aircraft to avoid collision and interference between each other, and adopts the distributed multi-agent system (MAS) theory and combines the consensus algorithm to perform collaborative route planning;

[0033] The conflict detection and resolution unit is used to monitor potential conflicts between multiple aircraft in real time during flight and take measures to resolve conflicts. It uses graph theory and conflict detection algorithms in combination with real-time communication technology to detect and resolve conflicts between aircraft.

[0034] Preferably, the interaction module further comprises a route planning visualization unit and a user interaction unit;

[0035] The route planning visualization unit is used to provide an intuitive route planning visualization interface, which is convenient for users to view and modify routes. The visualization interface is constructed by using WebGL or Threejs three-dimensional graphics library combined with Vuejs front-end framework;

[0036] The user interaction unit allows the user to interact through the interface system, including setting the starting point, the end point, and adjusting the route parameters, and adopts the RESTful API or WebSocket protocol to realize the data interaction between the front and back ends.

[0037] Preferably, the privacy module further includes a data encryption unit and a privacy protection unit;

[0038] The data encryption unit is used to encrypt the transmitted and stored data to ensure the security of the data, and uses the AES (Advanced Encryption Standard) encryption algorithm to encrypt sensitive data;

[0039] The privacy protection unit is used to take measures to protect the user's privacy information when processing and analyzing data, and adopts Differential Privacy technology to anonymize the data to prevent privacy leakage.

[0040] Based on the above system, the present invention also proposes an aircraft route planning method based on big data analysis, comprising the following steps:

[0041] S1. Collect multi-source data on the aircraft's position, speed, altitude, and weather conditions in real time, and use IoT technology to transmit various sensor data to the data center through the MQTT protocol. Clean the collected raw data, remove noise and outliers, and integrate them into a unified format data set to provide a basis for subsequent analysis.

[0042] S2. Combine historical data with real-time data to build a three-dimensional model of the flight environment, including terrain, weather, and no-fly zone information. Use GIS technology and three-dimensional modeling software to visualize the environment. Use machine learning algorithms combined with historical meteorological data to predict meteorological conditions in the future and provide meteorological references for route planning.

[0043] S3. Generate multiple feasible initial routes using a path planning algorithm based on the starting point, end point and environmental model of the aircraft, and iteratively optimize the initial routes using an optimization algorithm taking into account weather conditions and aircraft performance factors to minimize flight time and fuel consumption;

[0044] S4. During the flight, the obstacles around the aircraft are monitored in real time, and SLAM technology and dynamic window method are used for real-time obstacle avoidance calculation to ensure flight safety. When encountering sudden weather changes or changes in no-fly zones, the route is quickly replanned online using the rolling time domain optimization method combined with real-time data to adapt to environmental changes;

[0045] S5. Use a physics-based flight simulator combined with an aircraft dynamics model to simulate the flight process, evaluate the performance of the aircraft on different routes, analyze the simulated flight results, extract key performance indicators, and provide data support for route optimization;

[0046] S6. When multiple aircraft are flying at the same time, use distributed multi-agent system theory and consensus algorithm to conduct collaborative route planning to avoid collision and interference between each other, monitor potential conflicts between multiple aircraft in real time, and use graph theory and conflict detection algorithm combined with real-time communication technology to timely discover and resolve conflicts to ensure flight safety;

[0047] S7. Provides an intuitive route planning visualization interface, built with a 3D graphics library and front-end framework, which is convenient for users to view and modify routes, realizes user interaction functions, allows users to set the starting point and end point, and adjust route parameters through the interface, and uses a standard data interaction protocol to realize data exchange between the front and back ends;

[0048] S8. Encrypt the transmitted and stored data, adopt advanced encryption standards to ensure data security, and use privacy protection technology to anonymize the data when processing and analyzing data to prevent the leakage of user privacy information.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Compared with the traditional route planning method, the present invention can quickly generate multiple feasible initial routes and optimize the selection through automated data collection, integration and processing, as well as efficient algorithm models, which greatly improves the efficiency of route planning. By using big data analysis and machine learning technology, it can more accurately predict meteorological conditions and evaluate aircraft performance, thereby planning a safer and more economical flight path. At the same time, through real-time obstacle avoidance and online re-planning functions, it can ensure that the aircraft always maintains the optimal route in a complex and changeable flight environment, can monitor the flight environment and aircraft status in real time, and can quickly re-plan online when encountering emergencies, ensuring that the aircraft can respond to environmental changes in a timely manner and ensure flight safety. Through collaborative route planning and conflict detection and resolution functions, it can ensure that multiple aircraft avoid collisions and interference with each other when flying at the same time, thereby improving the utilization efficiency of airspace resources.

[0050] 2. The present invention also provides an intuitive route planning visualization interface and user interaction function, which makes it convenient for users to view and modify routes, improves the system's usability and user experience, and ensures the security of transmitted and stored data through data encryption and privacy protection technology, prevents the leakage of user privacy information, and ensures the security and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The topology diagram of the aircraft route planning system based on big data analysis of the present invention is shown;

[0052] Figure 2 A flow chart of an aircraft route planning method based on big data analysis of the present invention is shown. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] See also Figure 1 ,The present invention proposes an aircraft route planning system based on big data analysis, which includes a data acquisition preprocessing module, an environment modeling module, a route planning module, a dynamic obstacle avoidance module, a performance evaluation module, a collaborative planning module, an interaction module and a privacy module;

[0056] Among them, it should also be noted that the data acquisition preprocessing module of this system is used for data acquisition and data cleaning and integration; the environmental modeling module of this system is used for environmental modeling and weather forecasting; the route planning module of this system is used for initial route generation and route optimization; the dynamic obstacle avoidance module of this system is used for real-time obstacle avoidance and online replanning; the performance evaluation module of this system is used for aircraft performance simulation and performance index analysis; the collaborative planning module of this system is used for collaborative route planning and conflict detection and resolution; the interactive module of this system is used for route planning visualization and user interaction; the privacy module of this system is used for data encryption and privacy protection.

[0057] In this embodiment, it should also be noted that the data acquisition preprocessing module of the present invention also includes a data acquisition unit and a data cleaning and integration unit;

[0058] The data acquisition unit collects the position, speed, altitude, and weather condition data of the aircraft in real time through GPS, radar, and weather station sensors. It uses the Internet of Things (IoT) technology to connect various sensors to the system and transmit the data to the data center in real time through the MQTT protocol.

[0059] Among them, the data cleaning and integration unit cleans the collected raw data, removes noise and outliers, and integrates data from different sources to form a data set in a unified format. It uses Python's Pandas library for data cleaning and uses Hadoop or Spark big data processing framework for data integration.

[0060] In this embodiment, it should also be noted that the environment modeling module of the present invention also includes an environment modeling unit and a weather forecasting unit;

[0061] Among them, the environmental modeling unit builds a three-dimensional model of the flight environment based on historical data and real-time data, including terrain, weather, and no-fly zone information, using GIS (Geographic Information System) technology combined with three-dimensional modeling software (including dsMax) for environmental modeling;

[0062] Among them, the weather forecast unit uses historical meteorological data and machine learning algorithms to predict weather conditions in the future, which is used to provide meteorological reference for route planning. It uses the LSTM (long short-term memory network) time series prediction model and combines historical meteorological data to perform weather forecasts.

[0063] In this embodiment, it should also be noted that the route planning module of the present invention further includes an initial route generation unit and a route optimization unit;

[0064] The initial route generation unit is used to generate multiple feasible initial routes according to the starting point, end point and environment model of the aircraft, and uses the Dijkstra algorithm or A* algorithm to find the shortest path in the environment model;

[0065] Among them, the route optimization unit optimizes the initial route, considers meteorological conditions and aircraft performance factors, and is used to select the optimal route. It uses genetic algorithms or particle swarm optimization algorithms to iteratively optimize the route with the goal of minimizing flight time and fuel consumption.

[0066] In this embodiment, it should also be noted that the dynamic obstacle avoidance module of the present invention further includes a real-time obstacle avoidance unit and an online replanning unit;

[0067] The real-time obstacle avoidance unit is used to monitor obstacles around the aircraft in real time during flight and calculate the obstacle avoidance path. It uses SLAM (simultaneous localization and mapping) technology based on laser radar or visual sensors and combines it with the dynamic window method (DWA) to perform real-time obstacle avoidance.

[0068] Among them, the online replanning unit is used to replan the route online when encountering sudden weather changes or changes in no-fly zones. It uses the rolling horizon optimization (RHC) method, combined with environmental models and real-time data, to quickly replan the route.

[0069] In this embodiment, it should also be noted that the performance evaluation module of the present invention also includes an aircraft performance simulation unit and a performance index analysis unit;

[0070] The aircraft performance simulation unit simulates the flight process to evaluate the performance of the aircraft on different routes. It uses a physics-based flight simulator combined with the aircraft's dynamics model to simulate flight.

[0071] Among them, the performance indicator analysis unit is used to analyze the simulated flight results, extract key performance indicators such as flight time, fuel consumption, and safety, and use Python's Pandas and Matloplib libraries to visualize the simulation data and extract key performance indicators.

[0072] In this embodiment, it should also be noted that the collaborative planning module of the present invention also includes a collaborative route planning unit and a conflict detection and resolution unit;

[0073] Among them, the collaborative route planning unit is used to plan collaborative routes for multiple aircraft to avoid collision and interference between each other. It adopts the distributed multi-agent system (MAS) theory and combines the consensus algorithm for collaborative route planning;

[0074] Among them, the conflict detection and resolution unit is used to monitor potential conflicts between multiple aircraft in real time during the flight and take measures to resolve the conflicts. It uses graph theory and conflict detection algorithms, combined with real-time communication technology, to detect and resolve conflicts between aircraft.

[0075] In this embodiment, it should also be noted that the interaction module of the present invention also includes a route planning visualization unit and a user interaction unit;

[0076] Among them, the route planning visualization unit is used to provide an intuitive route planning visualization interface, which is convenient for users to view and modify routes. It uses WebGL or Threejs three-dimensional graphics library and combines Vuejs front-end framework to build a visualization interface;

[0077] Among them, the user interaction unit allows users to interact through the interface system, including setting the starting point, end point, and adjusting route parameters. It uses RESTful API or WebSocket protocol to realize data interaction between the front and back ends.

[0078] In this embodiment, it should also be noted that the privacy module of the present invention also includes a data encryption unit and a privacy protection unit;

[0079] Among them, the data encryption unit is used to encrypt the transmitted and stored data to ensure the security of the data, and uses the AES (Advanced Encryption Standard) encryption algorithm to encrypt sensitive data;

[0080] Among them, the privacy protection unit is used to take measures to protect the user's privacy information when processing and analyzing data, and uses differential privacy technology to anonymize the data to prevent privacy leakage.

[0081] Example 2

[0082] See also Figure 2 In actual application, the aircraft path planning method based on the above system specifically includes the following steps:

[0083] S1. Data collection and integration:

[0084] S1.1. Configure GPS, radar, weather station and other sensors to ensure that they can capture the aircraft's position, speed, altitude and weather conditions in real time and accurately:

[0085] S1.2. Use the Internet of Things (IoT) technology to connect these sensors to the system to ensure the stability and reliability of data transmission:

[0086] S1.3. The real-time collected data is transmitted to the data center through the MQTT protocol to provide raw data support for subsequent data processing and analysis:

[0087] S1.4. Conduct a preliminary check on the collected raw data to identify and remove obvious noise and outliers to ensure data accuracy:

[0088] S1.5. Use Python's Pandas library to further clean the data, including missing value processing and data type conversion:

[0089] S1.6. Integrate the cleaned data into a unified data set to facilitate subsequent data processing and analysis:

[0090] S1.7. Use Hadoop or Spark to integrate data from different sources to form a comprehensive data set:

[0091] S1.8. Normalize or standardize data to ensure comparability and consistency of data from different sources:

[0092] S1.9. Store the integrated data set in the database to provide data support for subsequent route planning:

[0093] S2. Environmental Modeling and Weather Forecasting:

[0094] S2.1. Collect historical terrain, weather, no-fly zone and other information as basic data for environmental modeling:

[0095] S2.2. Use GIS technology and dsMax 3D modeling software, combined with real-time data, to build a 3D model of the flight environment:

[0096] S2.3. Verify and optimize the model to ensure its accuracy and reliability and provide accurate environmental information for route planning:

[0097] S2.4. Collect historical meteorological data, including temperature, humidity, wind speed, wind direction, etc., as the basis for weather forecasts:

[0098] S2.5. Use the LSTM time series prediction model combined with historical meteorological data to predict meteorological conditions in the future:

[0099] S2.6. Integrate weather forecast results into environmental models to provide weather references for route planning and ensure the safety and feasibility of routes:

[0100] S3. Initial route generation and optimization:

[0101] S3.1. Based on the starting point, end point and environment model of the aircraft, use the Dijkstra algorithm or A* path planning algorithm to generate multiple feasible initial routes:

[0102] S3.2. Conduct a preliminary evaluation on the generated initial routes and select routes that meet the basic requirements as candidate routes:

[0103] S3.3. Store candidate routes in the database to provide a basis for subsequent optimization and selection:

[0104] S3.4. Considering factors such as weather conditions and aircraft performance, construct the route optimization objective function:

[0105] S3.5. Use particle swarm optimization to iteratively optimize candidate routes to minimize flight time and fuel consumption:

[0106] S3.6. Verify and evaluate the optimized route to ensure that it meets safety and feasibility requirements:

[0107] S4. Real-time obstacle avoidance and online re-planning:

[0108] S4.1. During flight, use lidar or visual sensors to monitor obstacles around the aircraft in real time:

[0109] S4.2. Use SLAM technology and dynamic window method to calculate obstacle avoidance path to ensure that the aircraft can safely avoid obstacles:

[0110] S4.3. Feedback the obstacle avoidance results to the aircraft control system in real time to ensure the safe flight of the aircraft:

[0111] S4.4. When encountering sudden weather changes, no-fly zone changes, etc., immediately start the online re-planning process:

[0112] S4.5. Use the rolling horizon optimization method to combine real-time data and environmental models to quickly replan the route online:

[0113] S4.6. Send the re-planned route to the aircraft control system to ensure that the aircraft can fly safely along the new route:

[0114] S5. Aircraft performance simulation and evaluation:

[0115] S5.1. Use a physics-based flight simulator combined with an aircraft dynamics model to simulate the flight process:

[0116] S5.2. Real-time monitoring and recording of key parameters during simulated flight, including flight time, fuel consumption, speed, altitude, etc.:

[0117] S5.3. Store the simulated flight results in the database to provide data support for subsequent performance evaluation:

[0118] S5.4. Analyze the simulated flight results and extract the key performance indicators of flight time, fuel consumption and safety:

[0119] S5.5. Use Python’s Pandas and Matplotlib library data analysis tools to visualize key performance indicators:

[0120] S5.6. Based on the performance evaluation results, optimize and adjust the route planning scheme to improve the performance and efficiency of the aircraft:

[0121] S6. Collaborative route planning and conflict detection:

[0122] S6.1. When multiple aircraft are flying simultaneously, the distributed multi-agent system theory and consensus algorithm are used for collaborative route planning:

[0123] S6.2. Consider the relative positions and velocities between the aircraft and the constraints in the environmental model to generate a collaborative route planning solution:

[0124] S6.3. Send the collaborative route planning scheme to each aircraft control system to ensure that the aircraft can fly collaboratively according to the planning scheme:

[0125] S6.4. Real-time monitoring of potential conflicts between multiple aircraft, using graph theory and conflict detection algorithms for conflict detection:

[0126] S6.5. When a conflict is detected, the conflict resolution process is immediately initiated, using real-time communication technology to coordinate and resolve:

[0127] S6.6. Feedback the conflict resolution results to each aircraft control system to ensure that the aircraft can fly safely and orderly:

[0128] S7. Route planning visualization and user interaction:

[0129] S7.1. Use WebGL or Threejs and Vuejs front-end frameworks to build an intuitive route planning visualization interface:

[0130] S7.2. Display the route planning results in a three-dimensional graphic form on a visual interface to facilitate user viewing and understanding:

[0131] S7.3. Provide interactive functions to allow users to adjust route parameters and viewing angles through the interface to meet different viewing requirements:

[0132] S7.4. Implement user interaction functions, allowing users to set the starting point, end point, and adjust route parameters through the interface:

[0133] S7.5. Use standard data interaction protocol to realize data exchange between front-end and back-end:

[0134] S7.6. Verify and validate user input to ensure the accuracy and legality of input data:

[0135] S8.Data encryption and privacy protection:

[0136] S8.1. Encrypt the transmitted and stored data and use the Advanced Encryption Standard (AES) to ensure data security:

[0137] S8.2. Update encryption keys regularly to prevent key leakage and cracking:

[0138] S8.3. Store and transmit encrypted data to ensure data security during transmission and storage:

[0139] S8.4. When processing and analyzing data, use differential privacy protection methods to anonymize the data:

[0140] S8.5. Perform access control on private data to ensure that only authorized personnel can access and process the data:

[0141] S8.6. Review and update the privacy protection policy regularly to adapt to changing privacy protection needs.

[0142] Through the above steps, the aircraft route planning system and method based on big data analysis of the present invention has significant advantages over traditional methods, can improve the efficiency, accuracy and real-time performance of route planning, and ensure the safe, economical and efficient flight of the aircraft.

[0143] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Aircraft route planning system based on big data analysis, characterized in that: It includes data acquisition preprocessing module, environment modeling module, route planning module, dynamic obstacle avoidance module, performance evaluation module, collaborative planning module, interaction module and privacy module; The data acquisition preprocessing module is used for data acquisition and data cleaning and integration; The environmental modeling module is used for environmental modeling and weather forecasting; The route planning module is used for initial route generation and route optimization; The dynamic obstacle avoidance module is used for real-time obstacle avoidance and online replanning; The performance evaluation module is used for aircraft performance simulation and performance index analysis; The collaborative planning module is used for collaborative route planning and conflict detection and resolution; The interactive module is used for route planning visualization and user interaction; The privacy module is used for data encryption and privacy protection.

2. The aircraft route planning system based on big data analysis according to claim 1, characterized in that: The data acquisition preprocessing module also includes a data acquisition unit and a data cleaning and integration unit; The data acquisition unit is used to connect various sensors to the system and transmit data to the data center in real time through the MQTT protocol; The data cleaning and integration unit is used to clean the collected raw data, remove noise and outliers, and integrate data from different sources.

3. The aircraft route planning system based on big data analysis according to claim 2, characterized in that: The environmental modeling module also includes an environmental modeling unit and a weather forecasting unit; The environment modeling unit is used to establish a three-dimensional model of the flight environment based on historical data and real-time data; The weather forecasting unit uses historical weather data and machine learning algorithms to predict weather conditions in the future to provide weather references for route planning.

4. The aircraft route planning system based on big data analysis according to claim 3, characterized in that: The route planning module also includes an initial route generation unit and a route optimization unit; The initial route generation unit is used to generate a plurality of feasible initial routes according to the starting point, the end point and the environment model of the aircraft; The route optimization unit is used to optimize the initial route.

5. The aircraft route planning system based on big data analysis according to claim 4, characterized in that: The dynamic obstacle avoidance module also includes a real-time obstacle avoidance unit and an online replanning unit; The real-time obstacle avoidance unit is used to monitor obstacles around the aircraft in real time during flight and calculate an obstacle avoidance path; The online re-planning unit is used to perform online re-planning of the route when encountering sudden weather changes or changes in the no-fly zone.

6. The aircraft route planning system based on big data analysis according to claim 5, characterized in that: The performance evaluation module also includes an aircraft performance simulation unit and a performance index analysis unit; The aircraft performance simulation unit is used to evaluate the performance of the aircraft on different routes by simulating the flight process; The performance index analysis unit is used to analyze the simulated flight results.

7. The aircraft route planning system based on big data analysis according to claim 6, characterized in that: The collaborative planning module also includes a collaborative route planning unit and a conflict detection and resolution unit; The collaborative route planning unit is used to plan collaborative routes for multiple aircraft to avoid collision and interference between them; The conflict detection and resolution unit is used to monitor potential conflicts between multiple aircraft in real time during flight and take measures to resolve the conflicts.

8. The aircraft route planning system based on big data analysis according to claim 7, characterized in that: The interaction module also includes a route planning visualization unit and a user interaction unit; The route planning visualization unit is used to provide an intuitive route planning visualization interface; The user interaction unit allows the user to interact through the interface system, including setting the starting point, the end point, and adjusting the route parameters, so as to realize the data interaction between the front and back ends.

9. The aircraft route planning system based on big data analysis according to claim 8, characterized in that: The privacy module also includes a data encryption unit and a privacy protection unit; The data encryption unit is used to encrypt the transmitted and stored data; The privacy protection unit is used to take measures to protect the user's privacy information when processing and analyzing data.

10. An aircraft route planning method based on big data analysis, according to the aircraft route planning system based on big data analysis according to claims 1-9, characterized in that: The following steps are involved: S1. Collect multi-source data on the aircraft's position, speed, altitude, and weather conditions in real time, use IoT technology to transmit various sensor data to the data center through the MQTT protocol, clean the collected raw data, remove noise and outliers, and integrate them into a unified format data set; S2. Combine historical data with real-time data to build a three-dimensional model of the flight environment, including terrain, weather, and no-fly zone information. Use GIS technology and three-dimensional modeling software to visualize the environment. Use machine learning algorithms combined with historical weather data to predict weather conditions in the future. S3. Based on the starting point, end point and environmental model of the aircraft, a path planning algorithm is used to generate multiple feasible initial routes, and the initial routes are iteratively optimized using an optimization algorithm taking into account meteorological conditions and aircraft performance factors; S4. During the flight, the obstacles around the aircraft are monitored in real time, and the SLAM technology and dynamic window method are used for real-time obstacle avoidance calculation. When encountering sudden changes in weather and no-fly zones, the route is quickly replanned online using the rolling time domain optimization method combined with real-time data; S5. Use a physics-based flight simulator combined with an aircraft dynamics model to simulate the flight process, evaluate the performance of the aircraft on different routes, analyze the simulated flight results, and extract key performance indicators; S6. When multiple aircraft are flying simultaneously, use the distributed multi-agent system theory and consensus algorithm to conduct collaborative route planning, monitor potential conflicts between multiple aircraft in real time, and use graph theory and conflict detection algorithms combined with real-time communication technology to promptly discover and resolve conflicts; S7. Provides an intuitive route planning visualization interface, built with a 3D graphics library and front-end framework, which is convenient for users to view and modify routes, realizes user interaction functions, allows users to set the starting point and end point, and adjust route parameters through the interface, and uses a standard data interaction protocol to realize data exchange between the front and back ends; S8. Encrypt the transmitted and stored data, adopt advanced encryption standards to ensure data security, and use privacy protection technology to anonymize the data when processing and analyzing data to prevent the leakage of user privacy information.

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