Monitoring platform for same-field operation of unmanned aerial vehicle and manned vehicle

Abstract

The invention discloses a platform for monitoring the same-field operation of an unmanned aerial vehicle and a manned vehicle, and the platform comprises a sensor network which is composed of a plurality of sensors and is used for collecting the related information of the unmanned aerial vehicle and the manned vehicle; the data processing center is used for processing the related information, collected by the sensor network, of the unmanned aerial vehicle and the manned vehicle to generate warning information; the monitoring interface is used for displaying the flight states of the unmanned aerial vehicle and the manned vehicle; and the alarm system is used for giving an alarm for the unmanned aerial vehicle and the manned vehicle after receiving the warning information. Through multi-dimensional monitoring capability, efficient data processing and comprehensive safety monitoring, more intelligent same-field operation management of the unmanned aerial vehicle and the manned vehicle can be realized, and the take-off and landing safety of the manned vehicle and the flight safety of the unmanned aerial vehicle are ensured.

Description

Technical Field

[0001] The present invention relates to the field of monitoring platforms, and in particular to a monitoring platform for co-operation of unmanned aerial vehicles and manned aircraft. Background Art

[0002] Co-operation monitoring of drones and manned aircraft refers to the process of real-time monitoring and management of drones (unmanned aerial vehicles) and manned aircraft (manned aircraft) operating simultaneously in the same airspace or the same airport area. This monitoring is intended to ensure that the two types of aircraft can share airspace resources safely and orderly to avoid mid-air collisions or other safety accidents; When unmanned aerial vehicles and manned aircraft are operating and monitoring at the same time, the monitoring platform will be used to ensure aviation safety and improve the efficiency of airspace utilization.

[0003] The existing monitoring platform has poor monitoring and protection effects and cannot coordinate UAVs well to ensure the stable take-off and landing of manned aircraft, which has a certain impact on the use of the monitoring platform. Therefore, it is proposed to operate the monitoring platform on the same field with UAVs and manned aircraft. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem that the existing monitoring platform has poor monitoring and protection effects and cannot coordinate well with drones to ensure the stable take-off and landing of manned aircraft, which has brought certain impacts on the use of the monitoring platform. A monitoring platform for the co-operation of drones and manned aircraft is provided.

[0005] The present invention solves the above technical problems through the following technical solutions, which include: Sensor network, which consists of a variety of sensors and is used to collect information related to UAVs and manned aircraft; Data processing center, the data processing center is used to process the information about UAVs and manned aircraft collected by the sensor network to generate warning information; Monitoring interface: The monitoring interface is used to display the flight status of UAVs and manned aircraft; Alarm system: The alarm system is used to alarm UAVs and manned aircraft after receiving warning information.

[0006] Furthermore, the sensor network includes radar, optical monitoring equipment and sound detection equipment; Among them, radar is used to detect and track flying objects in the airspace to obtain radar detection data. Flying objects include drones and manned aircraft. Radar detection data includes drone detection data and manned aircraft detection data. Both drone detection data and manned aircraft detection data include the location, speed and altitude information of the flying objects. Optical monitoring equipment includes high-definition cameras and infrared cameras, which are used to capture images and video information of flying objects, and process the images and video information to obtain flying object information. The flying object information includes UAV information and manned aircraft information. The UAV information and manned aircraft information both include the type, shape and color characteristics of the flying object; The sound detection equipment includes the equipment for capturing the sound information generated by flying objects, including engine noise and propeller rotation sound. The sound information of UAVs is the propeller rotation sound, and the sound information of manned aircraft is engine noise.

[0007] Furthermore, the data processing center includes: A data receiving module, used for receiving data collected by the sensor network; The data processing module processes the received data and generates warning information; The data storage module is used to store the acquired warning information.

[0008] Furthermore, the data processing module processes the received data to generate the warning information in the following specific process: Process radar detection data and generate warning information; Process the flying object information and generate warning information; Process the sound information and generate warning information.

[0009] Furthermore, the specific process of processing the radar detection data is as follows: extracting the UAV detection data and the manned aircraft detection data from the radar detection data; The position of the flying object in the UAV detection data is marked as A1, the speed is marked as A2, and the altitude information is marked as A3; The position of the flying object in the manned aircraft detection data is marked as B1, the speed is marked as B2, and the altitude information is marked as B3; The distance between A1 and B1 is collected, and the straight-line distance between A1 and B1 is collected. When the distance between A1 and B1 is less than the preset value, a warning message is generated; A2 is processed, and when A2 is greater than a preset value, a warning message is generated; Process B2. When the manned aircraft is in the take-off or landing state, if B2 is greater than the preset value, a warning message is generated; A3 is processed, and when A3 is greater than a preset value, a warning message is generated; B3 is processed, and when the manned aircraft is in the take-off or landing state and B3 exceeds the preset range, a warning message is generated.

[0010] Furthermore, the process of acquiring the type, shape and color characteristics of the flying object is as follows: performing denoising and enhancement preprocessing operations on the collected image and video information of the flying object; Then extract features from the image, including shape and color; Use image processing algorithms and machine learning models to locate the position of flying objects in images; Then, based on the extracted features, the classification algorithm is used to identify the flying object and obtain the type of the flying object; The process of processing the flying object information and generating warning information is as follows: The drone information is matched with the corresponding drone warning conditions. When the drone information triggers the drone warning condition, a warning message is generated. The drone information triggers the drone warning condition when any one of the drone's type, shape and color characteristics is different from the preset drone conditions for allowing passage.

[0011] Matching the manned aircraft information with the corresponding manned aircraft warning condition, and generating warning information when the manned aircraft information triggers the manned aircraft warning condition; The manned aircraft information triggers the drone warning condition when any one of the manned aircraft's type, shape, and color characteristics is different from the preset manned aircraft conditions for allowing passage.

[0012] Furthermore, the specific process of processing the sound information to generate the warning information is as follows: extracting the sound information of the drone, performing voiceprint processing on the propeller, obtaining the real-time drone voiceprint information, and performing similarity comparison between the real-time drone voiceprint information and the preset standard drone voiceprint. When the similarity between the real-time drone voiceprint information and the preset standard drone voiceprint is less than a preset value, the warning information is generated; The sound information of the manned aircraft is extracted, the engine noise is processed into voiceprint, the real-time manned aircraft voiceprint information is obtained, and the real-time manned aircraft voiceprint information is compared with the preset standard manned aircraft voiceprint for similarity. When the similarity between the real-time manned aircraft voiceprint information and the preset standard manned aircraft voiceprint is less than the preset value, a warning message is generated.

[0013] Furthermore, the monitoring platform also includes a real-time control system, and the real-time control system is used to perform real-time drone control; The specific process of the real-time control system for real-time drone control is as follows: real-time collection of airport information, including the airport's current take-off and landing manned aircraft information and airport drone information. The airport's current take-off and landing manned aircraft information includes the presence of take-off and landing manned aircraft and the absence of take-off and landing manned aircraft; When the current take-off and landing information of manned aircraft at the airport indicates that there are take-off and landing manned aircraft, the airport UAV information and the take-off and landing time points of the manned aircraft are extracted; Drone information includes the current location of the drone and the flight speed of the drone; Warning zones are set up for takeoff and landing of manned aircraft, and drones are not allowed to fly in the warning zones; The current position and flight speed of the drone are extracted. When the current position of the drone is within the warning area, the distance between the current position of the drone and the edge of the warning area is extracted to obtain the evaluation distance. Calculate the ratio of the evaluation distance to the flight speed of the drone to obtain the departure time; Then extract the current time point, calculate the difference between the manned aircraft take-off and landing time point and the current time point, and obtain the time difference; When the time difference is less than the departure time, the first control information is generated, and the UAV is controlled to land at the farthest position away from the center of the warning area that it can fly to before the manned aircraft takes off and lands, and then leaves the warning area after the manned aircraft takes off and lands. When the time difference is greater than the departure time and the evaluation distance is less than the preset value, the second control information is generated to control the UAV to fly outside the warning area and land before the manned aircraft takes off and lands.

[0014] Compared with the prior art, the present invention has the following advantages: the unmanned aerial vehicle and the manned aircraft operate on the same monitoring platform, which can monitor and warn in real time, and collect the flight data of the unmanned aerial vehicle and the manned aircraft in real time through the sensor network.

[0015] The data is analyzed, and once a potential safety risk is detected, a warning message is immediately generated, and the flight plan of the drone is dynamically adjusted based on the airport information and drone information.

[0016] During the take-off and landing of manned aircraft, by setting warning areas and controlling drones to land at designated locations, conflicts between drones and manned aircraft can be effectively avoided, improving the safety of airport operations.

[0017] The data processing center can automatically process the collected data, generate warning information and control instructions, and reduce the need for human intervention.

[0018] The real-time control system can dynamically adjust the flight plan of the drone according to the current airport operation conditions and the flight capabilities of the drone, which helps to optimize the flight path and take-off and landing times of the drone, improve flight efficiency, and reduce unnecessary waiting and delays.

[0019] In addition, the multi-source data fusion method improves the accuracy and reliability of the data, helps to more accurately judge the status and intention of flying objects, intelligently predict potential safety risks and optimize flight plans, making the platform more worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural block diagram of the present invention. DETAILED DESCRIPTION

[0021] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0022] like Figure 1 As shown, this embodiment provides a technical solution: a monitoring platform for co-operation of a UAV and a manned aircraft, including: Sensor network, which consists of a variety of sensors and is used to collect information related to UAVs and manned aircraft; Data processing center, the data processing center is used to process the information about UAVs and manned aircraft collected by the sensor network to generate warning information; Monitoring interface: The monitoring interface is used to display the flight status of UAVs and manned aircraft; Alarm system: The alarm system is used to alarm UAVs and manned aircraft after receiving warning information.

[0023] The sensor network includes radar, optical monitoring equipment and sound detection equipment; Among them, radar is used to detect and track flying objects in the airspace to obtain radar detection data. Flying objects include drones and manned aircraft. Radar detection data includes drone detection data and manned aircraft detection data. Both drone detection data and manned aircraft detection data include the location, speed and altitude information of the flying objects. Optical monitoring equipment includes high-definition cameras and infrared cameras, which are used to capture images and video information of flying objects, and process the images and video information to obtain flying object information. The flying object information includes UAV information and manned aircraft information. The UAV information and manned aircraft information both include the type, shape and color characteristics of the flying object; The sound detection equipment includes the equipment for capturing the sound information generated by flying objects, including engine noise and propeller rotation sound. The sound information of UAVs is the propeller rotation sound, and the sound information of manned aircraft is engine noise.

[0024] The data processing center includes: A data receiving module, used for receiving data collected by the sensor network; The data processing module processes the received data and generates warning information; A data storage module, used to store the acquired warning information; The above process realizes multi-dimensional monitoring. The radar can detect and track flying objects in the airspace and provide key information such as position, speed and altitude, which is essential for monitoring and warning the activities of drones and manned aircraft.

[0025] Optical monitoring equipment captures image and video information through high-definition cameras and infrared cameras, which can further refine the characteristics of flying objects, such as type, shape and color, which helps to identify flying objects more accurately.

[0026] Sound detection equipment can capture specific sound information produced by flying objects, such as the sound of drone propellers and the engine noise of manned aircraft, providing an additional dimension for identifying flying objects.

[0027] The data receiving module of the data processing center can receive a large amount of data from the sensor network to ensure the real-time and integrity of the data.

[0028] The data processing module can efficiently process the received data and generate warning information, which is crucial for timely detection and response to potential threats or risks.

[0029] The data storage module can store the acquired warning information to facilitate subsequent analysis and tracing.

[0030] Comprehensive safety monitoring: By combining data from radar, optical monitoring equipment and sound detection equipment, comprehensive monitoring of flying objects can be achieved, reducing monitoring blind spots and improving safety.

[0031] The sensor network can update the location and status information of flying objects in real time, providing timely and accurate data support for decision-making.

[0032] The design of the sensor network allows devices to be added or removed as needed to adapt to different monitoring needs and scenarios.

[0033] The data processing center is also scalable and can increase computing resources or storage resources according to data processing needs.

[0034] Easy integration and deployment: Sensor networks and data processing centers are usually designed with ease of use and compatibility in mind, making it easy to integrate with other security systems or platforms.

[0035] The deployment process is relatively simple and can be quickly deployed to areas that need to be monitored, improving response speed.

[0036] The advantages of the above process are mainly reflected in multi-dimensional monitoring capabilities, efficient data processing, comprehensive security monitoring, flexibility and scalability, and easy integration and deployment. These advantages make the sensor network and its data processing center have broad application prospects in the fields of drone and manned aircraft monitoring, air traffic management, safety warning, etc.

[0037] The data processing module processes the received data to generate warning information. The specific process is as follows: Process radar detection data and generate warning information; Process the flying object information and generate warning information; Process the sound information and generate warning information.

[0038] The specific process of processing the radar detection data is as follows: extracting the UAV detection data and the manned aircraft detection data from the radar detection data; The position of the flying object in the UAV detection data is marked as A1, the speed is marked as A2, and the altitude information is marked as A3; The position of the flying object in the manned aircraft detection data is marked as B1, the speed is marked as B2, and the altitude information is marked as B3; The distance between A1 and B1 is collected, and the straight-line distance between A1 and B1 is collected. When the distance between A1 and B1 is less than the preset value, a warning message is generated; A2 is processed, and when A2 is greater than a preset value, a warning message is generated; Process B2. When the manned aircraft is in the take-off or landing state, if B2 is greater than the preset value, a warning message is generated; A3 is processed, and when A3 is greater than a preset value, a warning message is generated; Processing B3, when the manned aircraft is in take-off or landing state, if B3 exceeds the preset range, a warning message is generated; The above process realizes refined data classification and processing. By subdividing radar detection data into UAV detection data and manned aircraft detection data, more accurate analysis and processing can be performed on different types of flying objects.

[0039] Mark each type of data (such as A1, A2, A3, etc.) to facilitate subsequent processing and judgment.

[0040] Distance monitoring and conflict warning collect the distance information of the UAV and manned aircraft (A1 and B1), and monitor the relative position relationship between the two in real time.

[0041] When the distance between the two is less than a preset value within the same time period, a warning message is generated, which helps prevent potential air conflicts or collisions.

[0042] Speed ​​monitoring and anomaly detection processes the speed information (A2 and B2) of UAVs and manned aircraft to monitor the speed changes of flying objects.

[0043] When the speed exceeds the preset value, a warning message is generated, which helps to promptly detect and respond to abnormal flight situations such as speeding.

[0044] Altitude monitoring and safety assessment: by processing the altitude information (A3 and B3) of UAVs and manned aircraft, it is possible to assess whether the flight altitude of the flying object is safe.

[0045] Special monitoring of the altitude of manned aircraft, especially during take-off and landing, helps ensure flight safety.

[0046] Comprehensive judgment and warning generation combines multi-dimensional information such as position, speed and altitude to make a comprehensive judgment and generate warning information when specific conditions are met.

[0047] This comprehensive judgment method can improve the accuracy and reliability of warning information and help take timely response measures.

[0048] The process of acquiring the type, shape and color features of the flying object is as follows: performing denoising and enhancement preprocessing operations on the collected image and video information of the flying object; Then extract features from the image, including shape and color; Use image processing algorithms and machine learning models to locate the position of flying objects in images; Then, based on the extracted features, the classification algorithm is used to identify the flying object and obtain the type of the flying object; The process of processing the flying object information and generating warning information is as follows: The drone information is matched with the corresponding drone warning conditions. When the drone information triggers the drone warning conditions, a warning message is generated.

[0049] The manned aircraft information is matched with the corresponding manned aircraft warning condition, and when the manned aircraft information triggers the manned aircraft warning condition, the warning information is generated.

[0050] The specific process of processing the sound information to generate the warning information is as follows: extracting the sound information of the drone, performing voiceprint processing on the propeller, obtaining the real-time drone voiceprint information, and comparing the real-time drone voiceprint information with the preset standard drone voiceprint. When the similarity between the real-time drone voiceprint information and the preset standard drone voiceprint is less than a preset value, the warning information is generated; Extract the sound information of the manned aircraft, process the engine noise into voiceprint, obtain the real-time manned aircraft voiceprint information, compare the real-time manned aircraft voiceprint information with the preset standard manned aircraft voiceprint, and generate a warning message when the similarity between the real-time manned aircraft voiceprint information and the preset standard manned aircraft voiceprint is less than a preset value; Through denoising and enhancement preprocessing operations, the quality of image and video information can be significantly improved, noise interference can be reduced, and key features can be extracted more accurately.

[0051] Using image processing algorithms and machine learning models for positioning and identification can more accurately identify the type, shape and color characteristics of flying objects and reduce the misidentification rate.

[0052] The process of extracting key features and performing classification and identification enables the system to cope with image and video information under different lighting conditions, shooting angles and occlusion conditions, enhancing the robustness and adaptability of the system.

[0053] It provides a basis for the generation of warning information, accurately obtains the type, shape and color characteristics of flying objects, and provides reliable basic data for the subsequent generation of warning information, making the warning information more accurate, timely and useful.

[0054] Matching the flying object information with the corresponding warning conditions can accurately determine whether the flying object has triggered the warning conditions, thereby generating accurate warning information.

[0055] Different warning conditions are set for UAVs and manned aircraft, so that the system can provide targeted warnings based on the characteristics of different types of flying objects.

[0056] Through the automated and intelligent warning information generation process, real-time monitoring and early warning of flying objects can be achieved, improving response speed and reducing delays in human intervention.

[0057] The warning information generated in real time helps to promptly detect and respond to potential air threats or risks, providing strong protection for flight safety.

[0058] For abnormal flight behavior or potential conflicts, the system can quickly issue warnings and remind relevant personnel to take countermeasures.

[0059] Improve the accuracy of voice recognition: Through voiceprint processing, the unique sound features of drones and manned aircraft can be extracted, making voice recognition more accurate.

[0060] The similarity comparison process can determine the degree of match between real-time sound information and preset standard sound information, further improving the accuracy of sound recognition.

[0061] The preset standard sound information provides a reliable reference for sound recognition, enabling the system to more accurately determine the type of flying objects.

[0062] The setting of the similarity threshold can be adjusted according to actual needs to adapt to the sound recognition needs in different scenarios.

[0063] Providing possibility for sound warning: through the processing of sound information, the system can realize real-time monitoring and early warning of the sound of flying objects.

[0064] When the real-time sound information does not match the preset standard sound information, the system can quickly issue an alarm to alert relevant personnel to possible abnormal situations.

[0065] The above process brings many benefits in terms of acquiring flying object characteristics, generating warning information, and processing sound information, which helps to achieve comprehensive, efficient, and accurate monitoring and early warning of flying objects.

[0066] The monitoring platform also includes a real-time control system, which is used to perform real-time drone control; The specific process of the real-time control system for real-time drone control is as follows: real-time collection of airport information, including the current take-off and landing manned aircraft information at the airport and the airport drone information. The current take-off and landing manned aircraft information at the airport includes the presence of manned aircraft taking off and landing and the absence of manned aircraft taking off and landing; When the current take-off and landing information of manned aircraft at the airport indicates that there are take-off and landing manned aircraft, the airport UAV information and the take-off and landing time points of the manned aircraft are extracted; Drone information includes the current location of the drone and the flight speed of the drone; Warning zones are set up for takeoff and landing of manned aircraft, and drones are not allowed to fly in the warning zones; The current position and flight speed of the drone are extracted. When the current position of the drone is within the warning area, the distance between the current position of the drone and the edge of the warning area is extracted to obtain the evaluation distance. Calculate the ratio of the evaluation distance to the flight speed of the drone to obtain the departure time; Then extract the current time point, calculate the difference between the manned aircraft take-off and landing time point and the current time point, and obtain the time difference; When the time difference is less than the departure time, the first control information is generated, and the UAV is controlled to land at the farthest position away from the center of the warning area that it can fly to before the manned aircraft takes off and lands, and then leaves the warning area after the manned aircraft takes off and lands. When the time difference is greater than the departure time and the evaluation distance is less than the preset value, the second control information is generated to control the UAV to fly outside the warning area and land before the manned aircraft takes off and lands; To prevent aerial conflicts, warning areas are set up and drones are restricted from flying within the area during takeoff and landing of manned aircraft, effectively preventing aerial conflicts between drones and manned aircraft and ensuring flight safety.

[0067] Reduce accident risks: By collecting airport information in real time, including the take-off and landing status of manned aircraft and the status of unmanned aircraft, the system can promptly detect and handle potential flight conflicts, reducing the risk of flight accidents.

[0068] The system can dynamically calculate the departure duration and time difference based on the take-off and landing time points of the manned aircraft and the current position and flight speed of the drone, thereby generating corresponding control information and realizing refined management of drone flights.

[0069] Optimize the flight path. When generating the first control information, the system guides the drone to fly to the farthest position away from the center of the warning area and then land. This not only meets the safety requirements, but also optimizes the flight path of the drone as much as possible and improves flight efficiency.

[0070] Reduce user intervention. The entire process is completed automatically by the system, which reduces the user's manual intervention in drone flight and improves user convenience.

[0071] Provide clear feedback. By generating control information, the system can clearly convey the flight restrictions and control requirements of drones to users, helping users understand and comply with relevant regulations.

[0072] Adaptable to different scenarios, this process is not only applicable to airport scenarios, but can also be adjusted and optimized as needed to adapt to other scenarios that require managing drone flights.

[0073] In summary, the specific process of real-time drone control by the real-time control system brings many benefits such as security, efficient management, improved user experience and system flexibility, which helps to achieve safe, orderly and efficient management of drone flights.

[0074] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0075] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0076] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. The monitoring platform for UAVs and manned aircrafts operating at the same time is characterized by: include: Sensor network, which consists of a variety of sensors and is used to collect information related to UAVs and manned aircraft; Data processing center, the data processing center is used to process the information about UAVs and manned aircraft collected by the sensor network to generate warning information; Monitoring interface: The monitoring interface is used to display the flight status of UAVs and manned aircraft; Alarm system: The alarm system is used to alarm UAVs and manned aircraft after receiving warning information.

2. The unmanned aerial vehicle and manned aircraft co-operation monitoring platform according to claim 1 is characterized by: The sensor network includes radar, optical monitoring equipment and sound detection equipment; Among them, radar is used to detect and track flying objects in the airspace to obtain radar detection data. Flying objects include drones and manned aircraft. Radar detection data includes drone detection data and manned aircraft detection data. Both drone detection data and manned aircraft detection data include the location, speed and altitude information of the flying objects. Optical monitoring equipment includes high-definition cameras and infrared cameras, which are used to capture images and video information of flying objects, and process the images and video information to obtain flying object information. The flying object information includes UAV information and manned aircraft information. The UAV information and manned aircraft information both include the type, shape and color characteristics of the flying object; The sound detection equipment includes the equipment for capturing the sound information generated by flying objects, including engine noise and propeller rotation sound. The sound information of UAVs is the propeller rotation sound, and the sound information of manned aircraft is engine noise.

3. The unmanned aerial vehicle and manned aircraft co-operation monitoring platform according to claim 2 is characterized by: The data processing center includes: A data receiving module, used for receiving data collected by the sensor network; The data processing module processes the received data and generates warning information; The data storage module is used to store the acquired warning information.

4. The unmanned aerial vehicle and manned aircraft co-operation monitoring platform according to claim 3 is characterized by: The data processing module processes the received data to generate warning information in the following specific process: Process radar detection data and generate warning information; Process the flying object information and generate warning information; Process the sound information and generate warning information.

5. The unmanned aerial vehicle and manned aircraft co-operation monitoring platform according to claim 4 is characterized by: The specific process of processing the radar detection data is as follows: extracting the UAV detection data and the manned aircraft detection data from the radar detection data; The position of the flying object in the drone detection data is marked as A1, the speed is marked as A2, and the altitude information is marked as A3; The position of the flying object in the manned aircraft detection data is marked as B1, the speed is marked as B2, and the altitude information is marked as B3; The distance between A1 and B1 is collected, and the straight-line distance between A1 and B1 is collected. When the distance between A1 and B1 is less than the preset value, a warning message is generated; A2 is processed, and when A2 is greater than a preset value, a warning message is generated; Process B2. When the manned aircraft is in the take-off or landing state, if B2 is greater than the preset value, a warning message is generated; A3 is processed, and when A3 is greater than a preset value, a warning message is generated; B3 is processed, and when the manned aircraft is in the take-off or landing state and B3 exceeds the preset range, a warning message is generated.

6. The unmanned aerial vehicle and manned aircraft co-operation monitoring platform according to claim 5 is characterized by: The process of acquiring the type, shape and color features of the flying object is as follows: performing denoising and enhancement preprocessing operations on the collected image and video information of the flying object; Then extract features from the image, including shape and color; Use image processing algorithms and machine learning models to locate the position of flying objects in images; Then, based on the extracted features, the classification algorithm is used to identify the flying object and obtain the type of the flying object; The process of processing the flying object information and generating warning information is as follows: Matching drone information with corresponding drone warning conditions, when drone information triggers drone warning conditions, generating warning information; The manned aircraft information is matched with the corresponding manned aircraft warning condition, and when the manned aircraft information triggers the manned aircraft warning condition, the warning information is generated.

7. The unmanned aerial vehicle and manned aircraft co-operation monitoring platform according to claim 4 is characterized by: The specific process of processing the sound information to generate the warning information is as follows: extracting the sound information of the drone, performing voiceprint processing on the propeller, obtaining the real-time drone voiceprint information, and comparing the real-time drone voiceprint information with the preset standard drone voiceprint. When the similarity between the real-time drone voiceprint information and the preset standard drone voiceprint is less than a preset value, the warning information is generated; The sound information of the manned aircraft is extracted, the engine noise is processed into voiceprint, the real-time manned aircraft voiceprint information is obtained, and the real-time manned aircraft voiceprint information is compared with the preset standard manned aircraft voiceprint for similarity. When the similarity between the real-time manned aircraft voiceprint information and the preset standard manned aircraft voiceprint is less than the preset value, a warning message is generated.

8. The unmanned aerial vehicle and manned aircraft co-operation monitoring platform according to claim 1 is characterized by: The monitoring platform also includes a real-time control system, which is used to perform real-time drone control; The specific process of the real-time control system for real-time drone control is as follows: real-time collection of airport information, including the airport's current take-off and landing manned aircraft information and airport drone information. The airport's current take-off and landing manned aircraft information includes the presence of take-off and landing manned aircraft and the absence of take-off and landing manned aircraft; When the current take-off and landing information of manned aircraft at the airport indicates that there are take-off and landing manned aircraft, the airport UAV information and the take-off and landing time points of the manned aircraft are extracted; Drone information includes the current location of the drone and the flight speed of the drone; Warning zones are set up for takeoff and landing of manned aircraft, and drones are not allowed to fly in the warning zones; The current position and flight speed of the drone are extracted. When the current position of the drone is within the warning area, the distance between the current position of the drone and the edge of the warning area is extracted to obtain the evaluation distance. Calculate the ratio of the evaluation distance to the flight speed of the drone to obtain the departure time; Then extract the current time point, calculate the difference between the manned aircraft take-off and landing time point and the current time point, and obtain the time difference; When the time difference is less than the departure time, the first control information is generated, and the UAV is controlled to land at the farthest position away from the center of the warning area that it can fly to before the manned aircraft takes off and lands, and then leaves the warning area after the manned aircraft takes off and lands. When the time difference is greater than the departure time and the evaluation distance is less than the preset value, the second control information is generated to control the UAV to fly outside the warning area and land before the manned aircraft takes off and lands.

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