Low-altitude unmanned aerial vehicle flight safety guarantee system based on intelligent weather and airspace monitoring

Abstract

The invention relates to the technical field of flight safety guarantee systems, in particular to a low-altitude unmanned aerial vehicle flight safety guarantee system based on intelligent weather and airspace monitoring, which comprises a weather guarantee system, an airspace safety management system, a take-off and landing system, an intelligent power supply system and a comprehensive quality control system, the meteorological guarantee system is used for collecting, processing, transmitting and storing real-time meteorological data and guaranteeing takeoff, flight path planning and safe landing of the unmanned aerial vehicle; and the airspace safety management system is used for integrating the meteorological support system and evaluating the safety of the airspace by using meteorological data to monitor the surrounding airspace. The intelligent meteorological support system is introduced, and key meteorological information can be collected in real time and preprocessed through the airborne laser radar, the ground laser radar sensor and the mobile meteorological station. The integrated meteorological data processing not only improves the accuracy of data acquisition, but also enhances the adaptability of the unmanned aerial vehicle to complex meteorological conditions, and ensures the safety of flight.

Description

Technical Field

[0001] The present invention relates to the technical field of flight safety assurance systems, and in particular to a low-altitude unmanned aerial vehicle flight safety assurance system based on intelligent meteorological and airspace monitoring. Background Art

[0002] With the rapid development of low-altitude UAV technology, low-altitude UAVs are increasingly used in military, commercial and civilian fields. Low-altitude UAV flight safety assurance refers to the use of various technical means to ensure the safety of low-altitude UAVs during flight, prevent low-altitude UAVs from colliding with other aircraft, and ensure the stable flight of UAVs under various weather conditions. Low-altitude UAV flight safety assurance is of great significance to the development of low-altitude economy and the organization of human activities. It can not only improve the safety of UAV flight and reduce the probability of accidents, but also promote the application of low-altitude UAVs in various fields and promote the development of low-altitude economy.

[0003] The traditional low-altitude UAV flight safety assurance platform has some shortcomings in practical applications. First, the traditional low-altitude UAV flight safety assurance platform lacks an intelligent meteorological assurance system, and is unable to collect key meteorological information in real time and perform pre-processing, resulting in the inability to effectively ensure the flight safety of low-altitude UAVs under complex meteorological conditions. Secondly, the traditional low-altitude UAV flight safety assurance platform lacks a comprehensive airspace safety management system, and is unable to monitor and evaluate other aircraft in the surrounding airspace in real time, predict potential collision risks, and automatically perform obstacle avoidance actions, thereby failing to effectively reduce the possibility of flight accidents. In addition, the traditional low-altitude UAV flight safety assurance platform lacks an intelligent take-off and landing system and power supply system, and is unable to ensure the stable take-off and landing and continuous operation of low-altitude UAVs in various environments, nor can it provide low-altitude UAVs with efficient data processing and decision support capabilities. Summary of the invention

[0004] The purpose of the present invention is to provide a low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring to solve the problems raised in the above background technology. To achieve the above purpose, the present invention provides the following technical solutions: a low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring, including a meteorological assurance system, an airspace safety management system, a take-off and landing system, an intelligent power supply system and a comprehensive quality control system;

[0005] The meteorological support system is used to collect, process, transmit and store real-time meteorological data to ensure the take-off, flight path planning and safe landing of the UAV;

[0006] The airspace safety management system is used to integrate the meteorological support system, use meteorological data to evaluate the safety of the airspace and monitor the surrounding airspace to ensure that the UAV will not collide with other aircraft during flight;

[0007] The take-off and landing system works closely with the meteorological support system and the airspace safety management system to ensure the safety of the take-off and landing process;

[0008] The intelligent power supply system is used to intelligently switch power sources, automatically switching to an external power source when the battery power is low, or switching to a backup battery when the power is interrupted, so as to ensure that the UAV can obtain a stable power supply in different situations, thereby ensuring the continuous operation of the UAV;

[0009] The comprehensive quality control system is used to integrate and analyze data from various modules and make corresponding decisions.

[0010] Preferably, the meteorological support system includes a data acquisition module, a data processing module, a communication module and a storage device;

[0011] The data acquisition module includes synthetic aperture radar, laser cloud height meter, wind radar, aerosol wind radar and temperature and humidity profile radar. The synthetic aperture radar is used to detect surrounding obstacles and terrain information. The laser cloud height meter, wind radar, aerosol wind radar and temperature and humidity profile radar are used to collect key meteorological information such as cloud height, wind speed, wind direction, visibility, temperature and humidity.

[0012] The data processing module is used to control the real-time data processing software to pre-process the collected flight data information, including denoising, filtering, registration and data fusion. The data processing module uses a random forest algorithm for calculation and analysis;

[0013] The communication module is used to control the radio transceiver, signal processor, data encryption device and data transmission protocol processor to transmit the meteorological data to the integrated quality control system and other related systems in real time;

[0014] The storage device includes a built-in solid state hard disk or an external storage device, which is used to store the collected meteorological data.

[0015] Preferably, the airspace safety management system includes an airspace monitoring subsystem, a data fusion and analysis subsystem, and a communication and coordination subsystem;

[0016] The airspace monitoring subsystem includes an airborne laser radar, an ADS-B receiver (Automatic Dependent Surveillance-Broadcast Receiver), a GPS receiver and an optical sensor, which is responsible for real-time monitoring of the airspace around the drone, including the position, speed and flight path of other aircraft;

[0017] The data fusion and analysis subsystem is used to control the central workstation, is responsible for receiving data from the airspace monitoring subsystem and the above-mentioned meteorological support system, and realizes the integration and analysis of multi-source data based on the multi-target tracking algorithm, collision risk assessment algorithm and dynamic obstacle avoidance algorithm;

[0018] The multi-target tracking algorithm includes a Kalman filter, a particle filter and a multi-hypothesis tracking algorithm;

[0019] The collision risk assessment algorithm includes collision probability calculation, collision time estimation and distance threshold judgment algorithm, which evaluates the possibility of collision between the UAV and other aircraft based on the current state and predicted trajectory of the aircraft;

[0020] The dynamic obstacle avoidance algorithm includes optimal control theory and potential field method;

[0021] The communication and coordination subsystem includes VHF radio and satellite communication equipment, which is responsible for data exchange and real-time communication with the meteorological support system, and is also used to receive and send flight plans, real-time position data and other key information.

[0022] Preferably, the take-off and landing system includes an intelligent vertical take-off and landing platform, an autonomous take-off control system, and an autonomous landing control system;

[0023] The intelligent vertical take-off and landing platform is manifested as a ground automatic stabilization platform and a communication module;

[0024] The ground automatic stabilization platform is used to ensure the stability of the drone's take-off and landing and its safety in standby mode;

[0025] The communication module includes a radio transceiver, a signal processor, a data encryption device and a communication transmission protocol processor, which is used to control the data transmission and command reception between the UAV and the ground automatic stabilization platform or other UAVs;

[0026] The autonomous take-off control system is used to automatically control the gyroscope, GPS receiver and barometer to monitor the attitude, geographical location and altitude of the UAV, and drive the power system controller. The autonomous take-off control system uses an attitude stabilization algorithm, a safety monitoring algorithm, and a take-off path planning algorithm for calculation, and is linked with the intelligent vertical take-off and landing platform;

[0027] The autonomous landing control system is used to automatically control the camera, laser rangefinder, and GPS-assisted landing system to evaluate the descent rate and speed of the drone approaching the ground.

[0028] Preferably, the intelligent power supply system includes a power monitoring unit, a power management system, an energy storage unit and an external energy interface;

[0029] The power monitoring unit is used to monitor the current power status of the drone in real time, including a battery power sensor, an external power sensor, and a power usage rate sensor;

[0030] The battery power sensor is used to monitor the battery power; the battery power sensor includes a voltage sensor, a current sensor and a temperature sensor;

[0031] The external power sensor is used to detect the availability and stability of the external power supply; the external power sensor includes a voltage sensor and a current sensor;

[0032] The power usage rate sensor is used to monitor the real-time power consumption of the drone and help predict the remaining usage time of the power supply;

[0033] The power management system is used to automatically manage the power controller, realize the conversion between different power sources, optimize the use efficiency of different power sources based on the adaptive algorithm and the power distribution algorithm, ensure the stable power supply during the conversion process, and avoid interference with the operation of the drone;

[0034] The energy storage unit is used to store electrical energy, including a main battery, a backup battery and a supercapacitor, and adopts a battery health management algorithm to predict the battery life by monitoring the charge and discharge cycle and temperature parameters of the battery, and replace or maintain the battery when necessary;

[0035] The external power supply interface is used for automatically controlling identification and adapting to different types of external power supplies, and the external voltage includes a mains power supply and an emergency power supply.

[0036] Preferably, the comprehensive quality control system includes a data integration unit, a data analysis unit, a decision-making unit, a safety monitoring unit and a user interface;

[0037] The data integration unit is used to control the data fusion algorithm to convert data in different formats from the meteorological support system, airspace safety management system, take-off and landing system, and intelligent power supply system into a unified format;

[0038] The data analysis unit is used to control the cluster analysis and anomaly detection algorithms to analyze the patterns and trends of the integrated data to identify potential risks and optimization opportunities during the UAV flight process;

[0039] The decision-making unit is used to control the decision tree algorithm to realize intelligent decision-making based on data, and to make corresponding flight decisions based on the output of the data analysis unit;

[0040] The safety monitoring unit is used to control the prediction model and risk assessment algorithm to monitor the flight status of the UAV in real time and automatically trigger safety measures when potential safety risks are detected;

[0041] The user interface is used to control the graphical interface and interactive design to provide an interactive interface with the operator, displaying the system status, warnings and decision-making suggestions.

[0042] Preferably, the process of the random forest algorithm includes:

[0043] S1. Use the Bootstrap sampling method to randomly select multiple subsets from the original training set with replacement, and each subset is used to train a decision tree.

[0044] S2. When constructing each decision tree, only some features are randomly selected for split selection for each node split;

[0045] S3. For each subset obtained by Bootstrap sampling, build a decision tree.

[0046] S4. Use the majority voting method to vote on the prediction results of all decision trees. The category with the most votes is taken as the final prediction result, and the average of the prediction values ​​of all decision trees is taken as the final prediction result.

[0047] S5. Use cross-validation and out-of-bag error (OOB Error) methods to evaluate the performance of the model, and adjust the parameters of the random forest based on the evaluation results, including the number of trees (n_estimators), maximum depth (max_depth), and maximum number of features.

[0048] Preferably, the expression for calculating the collision probability is:

[0049]

[0050] in, represents the collision probability, is the number of sampling times, and Respectively represent the state space of the drone and the obstacle at a certain moment, and the function is an indicator function, when and If there is an intersection (i.e. a collision occurs), the function value is 1; otherwise it is 0.

[0051] Preferably, the process of estimating the collision time includes:

[0052] S1, clearly identify the objects involved in the collision, which are two solid objects or an object and a surface;

[0053] S2, obtains the current position, speed, and acceleration motion information of the object, and measures and collects it through sensor radar, camera, and accelerometer;

[0054] S3, predicting the object's motion trajectory in the future based on the object's current motion information and possible motion patterns using physical laws or mathematical models;

[0055] S4, setting collision judgment conditions: the distance between the two objects is less than a certain threshold and the movement trajectories of the two objects intersect;

[0056] S5, calculating the estimated time of the collision based on the future trajectory of the object and the collision condition.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (1) The platform introduces an intelligent meteorological support system, which can collect and pre-process key meteorological information in real time through airborne lidar, ground lidar sensors and mobile weather stations. This integrated meteorological data processing not only improves the accuracy of data collection, but also enhances the adaptability of drones to complex meteorological conditions, ensuring flight safety;

[0059] (2) The platform applies a comprehensive airspace safety management system, combining multi-target tracking algorithms, collision risk assessment algorithms and dynamic obstacle avoidance algorithms, which greatly improves the flight safety of drones in complex airspace. The system can monitor and evaluate other aircraft in the surrounding airspace in real time, predict potential collision risks, and automatically perform obstacle avoidance actions, effectively reducing the possibility of flight accidents;

[0060] (3) The design of the take-off and landing system and the intelligent power supply system ensures the stable take-off and landing and continuous operation of the UAV in various environments. The application of the autonomous take-off control system and the autonomous landing control system improves the automation level of the UAV take-off and landing process and reduces the need for operator intervention. At the same time, the intelligent power supply system ensures the stable power supply of the UAV under different power supply conditions through power supply monitoring and switching control;

[0061] (4) The integrated quality control system uses data integration units, data analysis units, and decision-making units to provide the UAV with efficient data processing and decision support capabilities. By analyzing the data from each module, it can identify potential risks during the flight process and formulate corresponding flight strategies based on the decision tree algorithm, ensuring efficient execution of the mission and accurate acquisition of data;

[0062] (5) The user interface design of the integrated quality control system provides an intuitive and easy-to-operate interactive interface, allowing operators to easily monitor the flight status of the drone and receive system warnings and decision-making suggestions. This humanized design not only improves the convenience of operation, but also reduces the training cost of operators and improves overall work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flowchart diagram of a low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to the present invention;

[0064] Figure 2 It is a schematic diagram of the meteorological support system of the low-altitude UAV flight safety support system based on intelligent meteorological and airspace monitoring of the present invention;

[0065] Figure 3 It is a schematic diagram of the airspace safety management system of the low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring of the present invention;

[0066] Figure 4 This is a schematic diagram of the take-off and landing system of the low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring of the present invention;

[0067] Figure 5 This is a schematic diagram of the intelligent power supply system of the low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring of the present invention;

[0068] Figure 6 It is a schematic diagram of the comprehensive quality control system of the low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring of the present invention. DETAILED DESCRIPTION

[0069] 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 technical personnel in this field without creative work are within the scope of protection of the present invention.

[0070] See also Figures 1 to 6 ,The present invention provides a technical solution: a low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring, including a meteorological assurance system, an airspace safety management system, a take-off and landing system, an intelligent power supply system and a comprehensive quality control system;

[0071] The meteorological support system is used to collect, process, transmit and store real-time meteorological data to ensure the take-off, flight path planning and safe landing of drones;

[0072] The airspace safety management system is used to integrate the meteorological support system, use meteorological data to assess the safety of the airspace and monitor the surrounding airspace to ensure that the drone will not collide with other aircraft during flight;

[0073] The take-off and landing system works closely with the meteorological support system and the airspace safety management system to ensure the safety of the take-off and landing process;

[0074] The intelligent power supply system is used to intelligently switch power sources, automatically switching to external power when the battery is low, or switching to backup batteries when the power is interrupted, ensuring that the drone can obtain a stable power supply in different situations, thereby ensuring the continuous operation of the drone;

[0075] The comprehensive quality control system is used to integrate and analyze data from various modules and make corresponding decisions.

[0076] In this embodiment, the meteorological support system includes a data acquisition module, a data processing module, a communication module and a storage device;

[0077] The data acquisition module includes synthetic aperture radar, laser cloud height meter, wind radar, aerosol wind radar and temperature and humidity profile radar. The synthetic aperture radar is used to detect surrounding obstacles and terrain information, while the laser cloud height meter, wind radar, aerosol wind radar and temperature and humidity profile radar are used to collect key meteorological information such as cloud height, wind speed, wind direction, visibility, temperature and humidity.

[0078] The data processing module is used to control the real-time data processing software and pre-process the collected flight data information, including denoising, filtering, registration and data fusion. The data processing module uses the random forest algorithm for calculation and analysis;

[0079] The communication module is used to control the radio transceiver, signal processor, data encryption device and data transmission protocol processor to transmit meteorological data to the integrated quality control system and other related systems in real time;

[0080] The storage device includes a built-in solid-state hard drive or an external storage device for storing the collected meteorological data.

[0081] In this embodiment, the airspace safety management system includes an airspace monitoring subsystem, a data fusion and analysis subsystem, and a communication and coordination subsystem;

[0082] The airspace monitoring subsystem includes an airborne lidar, an ADS-B receiver (automatic dependent surveillance-broadcast receiver), a GPS receiver, and optical sensors, which are responsible for real-time monitoring of the airspace around the drone, including the position, speed, and flight path of other aircraft;

[0083] The data fusion and analysis subsystem is used to control the central workstation, responsible for receiving data from the airspace monitoring subsystem and the above-mentioned meteorological support system, and integrating and analyzing multi-source data based on multi-target tracking algorithm, collision risk assessment algorithm and dynamic obstacle avoidance algorithm;

[0084] Multi-target tracking algorithms include Kalman filter, particle filter and multi-hypothesis tracking algorithm;

[0085] The collision risk assessment algorithm includes collision probability calculation, collision time estimation and distance threshold judgment algorithm, which evaluates the possibility of collision between the UAV and other aircraft based on the current state and predicted trajectory of the aircraft;

[0086] Dynamic obstacle avoidance algorithms include optimal control theory and potential field methods;

[0087] The communication and coordination subsystem includes VHF radio and satellite communication equipment, which is responsible for data exchange and real-time communication with the meteorological support system, and is also used to receive and send flight plans, real-time position data and other critical information.

[0088] In this embodiment, the take-off and landing system includes an intelligent vertical take-off and landing platform, an autonomous take-off control system, and an autonomous landing control system;

[0089] The intelligent vertical take-off and landing platform is manifested as a ground-based automatic stabilization platform and a communication module;

[0090] The ground automatic stabilization platform is used to ensure the stability of the drone's take-off and landing, as well as its safety in standby mode;

[0091] The communication module includes a radio transceiver, a signal processor, a data encryption device and a communication transmission protocol processor, which is used to control the data transmission and command reception between the UAV and the ground automatic stabilization platform or other UAVs;

[0092] The autonomous take-off control system is used to automatically control the gyroscope, GPS receiver and barometer to monitor the attitude, geographic location and altitude of the drone, and drive the power system controller. The autonomous take-off control system uses attitude stabilization algorithm, safety monitoring algorithm and take-off path planning algorithm for calculation, and is linked with the intelligent vertical take-off and landing platform.

[0093] The autonomous landing control system is used to automatically control the camera, laser rangefinder, and GPS-assisted landing system to assess the drone's descent rate and speed approaching the ground.

[0094] In this embodiment, the intelligent power supply system includes a power monitoring unit, a power management system, an energy storage unit and an external energy interface;

[0095] The power monitoring unit is used to monitor the current power status of the drone in real time, including a battery charge sensor, an external power sensor, and a power usage rate sensor;

[0096] The battery power sensor is used to monitor the battery power; the battery power sensor includes a voltage sensor, a current sensor and a temperature sensor;

[0097] The external power sensor is used to detect the availability and stability of the external power supply; the external power sensor includes a voltage sensor and a current sensor;

[0098] The power usage sensor is used to monitor the real-time power consumption of the drone and help predict the remaining power usage time;

[0099] The power management system is used to automatically manage the power controller, realize the conversion between different power sources, optimize the efficiency of different power sources based on adaptive algorithms and power allocation algorithms, ensure stable power supply during the conversion process, and avoid interference with the operation of the drone;

[0100] The energy storage unit is used to store electrical energy, including main batteries, backup batteries, and supercapacitors. It uses a battery health management algorithm to monitor the battery's charge and discharge cycles and temperature parameters to predict battery life and perform battery replacement or maintenance when necessary.

[0101] The external power supply interface is used for automatic control, identification and adaptation of different types of external power supplies, including mains power supply and emergency power supply.

[0102] In this embodiment, the comprehensive quality control system includes a data integration unit, a data analysis unit, a decision-making unit, a security monitoring unit, and a user interface;

[0103] The data integration unit is used to control the data fusion algorithm to convert data in different formats from the meteorological support system, airspace safety management system, take-off and landing system, and intelligent power supply system into a unified format;

[0104] The data analysis unit is used to control cluster analysis and anomaly detection algorithms to analyze patterns and trends in the integrated data to identify potential risks and optimization opportunities during the UAV flight process;

[0105] The decision-making unit is used to control the decision tree algorithm to achieve intelligent decision-making based on data, and to make corresponding flight decisions based on the output of the data analysis unit;

[0106] The safety monitoring unit is used to control the prediction model and risk assessment algorithm to monitor the flight status of the drone in real time and automatically trigger safety measures when potential safety risks are detected;

[0107] The user interface is used to control the graphical interface and interactive design to provide an interactive interface with the operator, displaying the system status, warnings and decision suggestions.

[0108] In this embodiment, the process of the random forest algorithm includes:

[0109] S1. Use the Bootstrap sampling method to randomly select multiple subsets from the original training set with replacement, and each subset is used to train a decision tree.

[0110] S2. When constructing each decision tree, only some features are randomly selected for split selection for each node split;

[0111] S3. For each subset obtained by Bootstrap sampling, build a decision tree.

[0112] S4. Use the majority voting method to vote on the prediction results of all decision trees. The category with the most votes is taken as the final prediction result, and the average of the prediction values ​​of all decision trees is taken as the final prediction result.

[0113] S5. Use cross-validation and out-of-bag error (OOB Error) methods to evaluate the performance of the model, and adjust the parameters of the random forest based on the evaluation results, including the number of trees (n_estimators), maximum depth (max_depth), and maximum number of features.

[0114] In this embodiment, the expression for calculating the collision probability is:

[0115]

[0116] in, represents the collision probability, is the number of sampling times, and Respectively represent the state space of the drone and the obstacle at a certain moment, and the function is an indicator function, when and If there is an intersection (i.e. a collision occurs), the function value is 1; otherwise it is 0.

[0117] In this embodiment, the process of estimating the collision time includes:

[0118] S1, clearly identify the objects involved in the collision, which are two solid objects or an object and a surface;

[0119] S2, obtains the current position, speed, and acceleration motion information of the object, and measures and collects it through sensor radar, camera, and accelerometer;

[0120] S3, predicting the object's motion trajectory in the future based on the object's current motion information and possible motion patterns using physical laws or mathematical models;

[0121] S4, setting collision judgment conditions: the distance between the two objects is less than a certain threshold and the movement trajectories of the two objects intersect;

[0122] S5, calculating the estimated time of the collision based on the future trajectory of the object and the collision condition.

[0123] The above shows and describes the basic principles, main features and advantages of the present invention. Technical personnel in this industry should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring, characterized by: Including meteorological support system, airspace safety management system, take-off and landing system, intelligent power supply system and comprehensive quality control system; The meteorological support system is used to collect, process, transmit and store real-time meteorological data to ensure the take-off, flight path planning and safe landing of the UAV; The airspace safety management system is used to integrate the meteorological support system, use meteorological data to evaluate the safety of the airspace and monitor the surrounding airspace to ensure that the UAV will not collide with other aircraft during flight; The take-off and landing system works closely with the meteorological support system and the airspace safety management system to ensure the safety of the take-off and landing process; The intelligent power supply system is used to intelligently switch power sources, automatically switching to an external power source when the battery power is low, or switching to a backup battery when the power is interrupted, so as to ensure that the UAV can obtain a stable power supply in different situations, thereby ensuring the continuous operation of the UAV; The comprehensive quality control system is used to integrate and analyze data from various modules and make corresponding decisions.

2. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 1 is characterized by: The meteorological support system includes a data acquisition module, a data processing module, a communication module and a storage device; The data acquisition module includes synthetic aperture radar, laser cloud height meter, wind radar, aerosol wind radar and temperature and humidity profile radar. The synthetic aperture radar is used to detect surrounding obstacles and terrain information. The laser cloud height meter, wind radar, aerosol wind radar and temperature and humidity profile radar are used to collect key meteorological information such as cloud height, wind speed, wind direction, visibility, temperature and humidity. The data processing module is used to control the real-time data processing software to pre-process the collected flight data information, including denoising, filtering, registration and data fusion. The data processing module uses a random forest algorithm for calculation and analysis; The communication module is used to control the radio transceiver, signal processor, data encryption device and data transmission protocol processor to transmit the meteorological data to the integrated quality control system and other related systems in real time; The storage device includes a built-in solid state hard disk or an external storage device, which is used to store the collected meteorological data.

3. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 1 is characterized by: The airspace safety management system includes an airspace monitoring subsystem, a data fusion and analysis subsystem, and a communication and coordination subsystem; The airspace monitoring subsystem includes an airborne lidar, an ADS-B receiver, a GPS receiver, and an optical sensor, which is responsible for real-time monitoring of the airspace around the drone, including the position, speed, and flight path of other aircraft; The data fusion and analysis subsystem is used to control the central workstation, is responsible for receiving data from the airspace monitoring subsystem and the above-mentioned meteorological support system, and realizes the integration and analysis of multi-source data based on the multi-target tracking algorithm, collision risk assessment algorithm and dynamic obstacle avoidance algorithm; The multi-target tracking algorithm includes a Kalman filter, a particle filter and a multi-hypothesis tracking algorithm; The collision risk assessment algorithm includes collision probability calculation, collision time estimation and distance threshold judgment algorithm, which evaluates the possibility of collision between the UAV and other aircraft based on the current state and predicted trajectory of the aircraft; The dynamic obstacle avoidance algorithm includes optimal control theory and potential field method; The communication and coordination subsystem includes VHF radio and satellite communication equipment, which is responsible for data exchange and real-time communication with the meteorological support system, and is also used to receive and send flight plans, real-time position data and other key information.

4. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 1 is characterized by: The take-off and landing system includes an intelligent vertical take-off and landing platform, an autonomous take-off control system, and an autonomous landing control system; The intelligent vertical take-off and landing platform is manifested as a ground automatic stabilization platform and a communication module; The ground automatic stabilization platform is used to ensure the stability of the drone's take-off and landing and its safety in standby mode; The communication module includes a radio transceiver, a signal processor, a data encryption device and a communication transmission protocol processor, which is used to control the data transmission and command reception between the UAV and the ground automatic stabilization platform or other UAVs; The autonomous take-off control system is used to automatically control the gyroscope, GPS receiver and barometer to monitor the attitude, geographical location and altitude of the UAV, and drive the power system controller. The autonomous take-off control system uses an attitude stabilization algorithm, a safety monitoring algorithm, and a take-off path planning algorithm for calculation, and is linked with the intelligent vertical take-off and landing platform; The autonomous landing control system is used to automatically control the camera, laser rangefinder, and GPS-assisted landing system to evaluate the descent rate and speed of the drone approaching the ground.

5. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 1 is characterized by: The intelligent power supply system includes a power monitoring unit, a power management system, an energy storage unit and an external energy interface; The power monitoring unit is used to monitor the current power status of the drone in real time, including a battery power sensor, an external power sensor, and a power usage rate sensor; The battery power sensor is used to monitor the battery power; The external power sensor is used to detect the availability and stability of the external power supply; The power usage rate sensor is used to monitor the real-time power consumption of the drone and help predict the remaining usage time of the power supply; The power management system is used to automatically manage the power controller, realize the conversion between different power sources, optimize the use efficiency of different power sources based on the adaptive algorithm and the power distribution algorithm, ensure the stable power supply during the conversion process, and avoid interference with the operation of the drone; The energy storage unit is used to store electrical energy, including a main battery, a backup battery and a supercapacitor, and adopts a battery health management algorithm to predict the battery life by monitoring the charge and discharge cycle and temperature parameters of the battery, and replace or maintain the battery when necessary; The external power supply interface is used for automatically controlling identification and adapting to different types of external power supplies.

6. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 1 is characterized by: The comprehensive quality control system includes a data integration unit, a data analysis unit, a decision-making unit, a safety monitoring unit and a user interface; The data integration unit is used to control the data fusion algorithm to convert data in different formats from the meteorological support system, airspace safety management system, take-off and landing system, and intelligent power supply system into a unified format; The data analysis unit is used to control the cluster analysis and anomaly detection algorithms to analyze the patterns and trends of the integrated data to identify potential risks and optimization opportunities during the UAV flight process; The decision-making unit is used to control the decision tree algorithm to realize intelligent decision-making based on data, and to make corresponding flight decisions based on the output of the data analysis unit; The safety monitoring unit is used to control the prediction model and risk assessment algorithm to monitor the flight status of the UAV in real time and automatically trigger safety measures when potential safety risks are detected; The user interface is used to control the graphical interface and interactive design to provide an interactive interface with the operator, displaying the system status, warnings and decision-making suggestions.

7. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 2 is characterized by: The process of the random forest algorithm includes: S1. Use the Bootstrap sampling method to randomly select multiple subsets from the original training set with replacement, and each subset is used to train a decision tree. S2. When constructing each decision tree, only some features are randomly selected for split selection for each node split; S3. For each subset obtained by Bootstrap sampling, build a decision tree. S4. Use majority voting to vote on the prediction results of all decision trees. The category with the most votes is taken as the final prediction result. The average of all decision tree prediction values ​​is taken as the final prediction result. S5. Use cross-validation and out-of-bag error methods to evaluate the performance of the model, and adjust the parameters of the random forest based on the evaluation results, including the number of trees, maximum depth, and maximum number of features.

8. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 3 is characterized by: The expression for calculating the collision probability is: ； in, represents the collision probability, is the number of sampling times, and Respectively represent the state space of the drone and the obstacle at a certain moment, and the function is an indicator function, when and If there is an intersection, the function value is 1; otherwise it is 0.

9. The low-altitude UAV flight safety assurance system based on intelligent meteorological and airspace monitoring according to claim 1 is characterized by: The process of collision time estimation includes: S1, clearly identify the objects involved in the collision, which are two solid objects or an object and a surface; S2, obtains the current position, speed, and acceleration motion information of the object, and measures and collects it through sensor radar, camera, and accelerometer; S3, predicting the object's motion trajectory in the future based on the object's current motion information and possible motion patterns using physical laws or mathematical models; S4, setting collision judgment conditions: the distance between the two objects is less than a certain threshold and the movement trajectories of the two objects intersect; S5, calculating the estimated time of the collision based on the future trajectory of the object and the collision condition.

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