FOD protection system and method for detection radar and tool box

Abstract

The invention relates to an FOD protection system and method for a detection radar and a toolbox, and the method comprises the steps: transmitting a test signal to the detection radar according to a preset frequency, and obtaining a test feedback signal; judging whether the detection radar has a fault according to the test feedback signal; acquiring a detection radar work log, and extracting target runway information of a detection task executed by the detection radar; obtaining a task log of aircraft take-off and landing executed by a target runway in a preset time period, extracting take-off and landing aircraft information data and take-off and landing time information data, and forming a risk source data set; acquiring monitoring video data of a target runway in a preset time period, and preprocessing the monitoring video data to form a video analysis data set; analyzing the monitoring video data to obtain a plurality of video frames with foreign matter information, and then analyzing the plurality of video frames to obtain foreign matter movement track information; and through a pre-trained protection early warning model, the foreign matter collision probability is predicted, and countermeasures are taken in advance according to a prediction result.

Description

Technical Field

[0001] The present invention relates to the field of airport FOD protection, and in particular to a FOD protection system, method and tool box for detecting radar. Background Art

[0002] FOD is the abbreviation of Foreign Object Debris, which generally refers to some foreign substances that may damage aircraft or systems, often referred to as runway foreign objects. Foreign objects on the airport runway can be easily sucked into the engine, causing engine failure. Debris can also accumulate in mechanical devices, affecting the normal operation of equipment such as landing gear and flaps. Therefore, FOD detection equipment (FOD detection radar) is now widely deployed in airports. The so-called FOD detection equipment refers to a radio positioning service station that uses the transmission and reception of radio signals to determine the location of foreign objects on the airport runway. Its deployment methods mainly include edge light type, tower type and mobile type. Among them, the edge light type is generally installed on the runway shoulder, adjacent to the runway edge light, and the detection distance is less than 100 meters; the tower type is mostly installed outside the runway takeoff and landing strip, 200 meters away from the runway centerline, with a detection distance of 800 to 1000 meters; the mobile type is mostly installed on a vehicle, can work while moving, and the detection distance is less than 200 meters.

[0003] In reality, some foreign objects on the runway brought by the aircraft will hit the FOD detection equipment around the runway, causing damage to the FOD detection equipment, making it unable to work normally, and thus affecting the detection function of foreign objects on the runway. Therefore, in order to further ensure the normal operation of the airport FOD detection system, it is necessary to incorporate the FOD protection of the FOD detection equipment (FOD detection radar) into the FOD detection system of the entire airport. Summary of the invention

[0004] The present invention integrates radar monitoring, log analysis, video processing and machine learning models into a unified system to achieve seamless flow and processing of data. It can effectively predict the probability of foreign objects colliding with the detection radar, take corresponding countermeasures according to the collision probability, and achieve the effect of active protection.

[0005] The technical solution proposed by the present invention is: a FOD protection method for detecting radar, the method comprising: Send a test signal to the detection radar at a preset frequency to obtain a test feedback signal; According to the test feedback signal, determine whether the detection radar fails. If so, proceed to the next step, otherwise return to the previous step; Obtain the detection radar work log and extract the target runway information for the detection radar to perform the detection mission; Obtain the mission log of aircraft takeoff and landing on the target runway within the preset time period, extract the takeoff and landing aircraft information data and takeoff and landing time information data, and form a risk source data set; Acquire monitoring video data of the target runway within a preset time period, and form a video analysis data set after preprocessing, wherein the preprocessing includes denoising and contrast enhancement; Analyze the monitoring video data to obtain multiple video frames with foreign object information, and then obtain the foreign object movement trajectory information by analyzing the multiple video frames; Through the pre-trained protection warning model, the probability of foreign object impact is predicted, and countermeasures can be taken in advance based on the prediction results.

[0006] Preferably, sending a test signal to the detection radar at a preset frequency to obtain a test feedback signal comprises the following steps: Sending a test signal of a preset frequency to the detection radar, wherein the preset frequency is within the operating frequency range of the detection radar; Record the time and power parameters of sending the test signal to form a benchmark parameter set; Receive the return signal from the detection radar, record the receiving time, frequency and power parameters of the received test feedback signal, and form a comparison parameter set.

[0007] Preferably, judging whether the detection radar fails according to the test feedback signal comprises the following steps: Compare the data in the comparison parameter set and the reference parameter set to determine whether the test feedback signal is delayed, attenuated or missing; Analyze the quality of the test feedback signal, including but not limited to analyzing the signal-to-noise ratio and waveform integrity of the test feedback signal; Setting the power threshold and signal-to-noise ratio threshold ; if It is judged that the test feedback signal attenuation is abnormal, where: and Respectively represent the power of the test signal and the power of the test feedback signal; if , then it is judged that the test feedback signal is distorted; if and , then it is judged that the detection radar is faulty; otherwise, the test feedback signal is acquired again.

[0008] Preferably, the obtaining of the detection radar work log and the extraction of the target runway information for the detection radar to perform the detection mission include: Obtaining a detection radar work log from an airport monitoring system; the detection radar work log includes operating status data and task data of the detection radar; Extracting the working time period, working frequency information, number information and distribution position information of the detection radar in the airport from the operation status data of the detection radar; Acquire a detection task type and detection area data from the task data of the detection radar, wherein the detection task type includes a fixed detection task and a mobile detection task; Extract the specific runway information monitored by the detection radar from the monitored airport area information.

[0009] Preferably, the step of obtaining the mission log of aircraft takeoff and landing on the target runway within a preset time period, extracting the takeoff and landing aircraft information data and the takeoff and landing time information data, and forming a risk source data set includes the following steps: Construct query conditions based on target runway information and preset time period; Using the query conditions to obtain from the airport monitoring system the mission log of the aircraft taking off and landing on the target runway within the preset time period; Extract the model and flight number information of each aircraft from the acquired aircraft takeoff and landing mission logs; The aircraft model, flight number and take-off and landing time of each flight constitute a preset time period Risk source dataset within ;in, Indicates the total number of take-off and landing flights. ; , Respectively represent the starting time point and the ending time point of the preset time period; ;in, Indicates Flight data vector, and Respectively represent Aircraft model, flight number, landing time and take-off time of the flight.

[0010] Preferably, the monitoring video data of the target runway within a preset time period is acquired and preprocessed to form a video analysis data set, wherein the preprocessing includes denoising and contrast enhancement, and comprises the following steps: Obtain the number information of the detection radar and its distribution location information within the airport; Using the number information of the detection radar and the distribution location information in the airport as search conditions, obtain the number information of the video surveillance device that cooperates with the detection radar; According to the serial number information of the video surveillance device, query the database arrive Video data within a time period; The video data is processed for denoising and contrast enhancement to form a video analysis dataset, which is then sent to the cloud for storage.

[0011] Preferably, the step of analyzing the monitoring video data to obtain multiple video frames with foreign object information, and then obtaining the foreign object movement trajectory information by analyzing the multiple video frames includes the following steps: Download the video analysis dataset after noise reduction and contrast enhancement to your local computer; Use the target detection algorithm YOLO to perform target recognition on each video frame in the video data in order to identify foreign objects; Marking the position of the identified foreign object in the video frame, saving the foreign object information and adding the corresponding video frame timestamp, wherein the foreign object information includes the position, size and shape of the foreign object; According to the target recognition result, multiple video frames with foreign object information are screened out to form a key video frame sequence for subsequent analysis of the foreign object's motion trajectory; For each pair of consecutive key video frames, the optical flow information is calculated using the streamer algorithm, specifically including: using the Lucas-Kanade algorithm in the streamer algorithm to calculate the horizontal motion speed of the pixel points between pixel frames, and using the Horn-Schunck algorithm in the streamer algorithm to calculate the vertical motion speed of the pixel points; Constructing the motion trajectory of the foreign object according to the calculated optical flow information specifically includes: obtaining the motion trajectory by analyzing the time stamp of each video frame with foreign object information and the position relationship of the foreign object on the corresponding video frame; Constructing a motion trajectory curve of the foreign body; specifically, by curve fitting or completing the motion trajectory; Analyze the constructed motion trajectory curve, extract key motion features, and construct a key feature set ,in, Respectively The velocity vector and acceleration vector of the foreign body at each moment; in, , ; and Respectively The horizontal velocity, vertical velocity, horizontal acceleration and vertical acceleration at the moment.

[0012] Preferably, predicting the probability of foreign object impact by using a pre-trained protection warning model comprises the following steps: Extract time periods from key feature value sets The average horizontal velocity of the foreign body , vertical average speed , horizontal average acceleration , vertical average acceleration and the average angle ; Will , , and After normalization, the input feature set of the protection warning model is formed ,in, , Indicates the number of video frames with foreign object information; Bring the input feature set into the protection warning model , output the probability of foreign body impact; where, and denote the intercept and error term respectively, and represents the regression coefficient; if , it is judged that the foreign object will hit the detection radar; otherwise, it is judged that the foreign object will hit the detection radar; The method of taking countermeasures in advance according to the prediction results includes the following steps: if , judge that the risk of foreign object impact is medium, send the prediction result to the airport management personnel, remind the airport management personnel to handle it, and obtain the handling status; if , judge that the risk of foreign object impact is high, send the prediction results to the airport management personnel, notify the airport management personnel to adjust the aircraft take-off and landing activities, and obtain the handling status; Store each prediction result and the corresponding processing status for later tracing.

[0013] The present invention also provides a FOD protection system for detecting radar, wherein the system is used to execute the FOD protection method for detecting radar.

[0014] The present invention also provides a toolbox, wherein a computer-readable storage medium is provided in the toolbox, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the FOD protection method for detecting radar.

[0015] Beneficial effects of the present invention: 1. The method of the present invention realizes real-time monitoring and automatic judgment of the radar status by sending a test signal to the detection radar at a preset frequency and obtaining a test feedback signal. This is more efficient than the traditional manual inspection method, can detect radar failures in the first place, and reduce the detection blind spots caused by equipment failures.

[0016] 2. The present invention uses the radar work log and the aircraft take-off and landing mission log, and combines the monitoring video data of the target runway. The fusion of multi-source data makes the risk assessment more comprehensive and accurate. For example, by extracting the take-off and landing aircraft information data and the take-off and landing time information data, it can accurately locate the high-risk time period and area.

[0017] 3. The present invention obtains multiple video frames with foreign object information through analysis of monitoring video data, and further analyzes these video frames to obtain the movement trajectory information of foreign objects. This is more dynamic and continuous than simple static image analysis, and helps to predict the future location of foreign objects and possible impact risks. The use of pre-trained protection warning models to predict the probability of foreign object impact can make accurate predictions based on historical data and pattern recognition. Take countermeasures in advance: Take countermeasures in advance according to the prediction results, such as notifying airport management personnel, suspending aircraft takeoffs and landings, or clearing runways, which can effectively prevent potential impact incidents. More forward-looking and preventive than traditional passive response mechanisms, taking measures in advance can also reduce confusion and delays caused by emergencies and improve the efficiency and safety of airport operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a FOD protection method for detecting radar. DETAILED DESCRIPTION

[0019] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0020] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0021] refer to Figure 1 The technical solution provided by the present invention is: a FOD protection method for detecting radar, comprising the following steps: 1. Send a test signal to the detection radar at a preset frequency to obtain a test feedback signal, specifically: A test signal of a preset frequency is sent to the detection radar, where the preset frequency is within the operating frequency range of the detection radar; the time and power parameters of sending the test signal are recorded to form a benchmark parameter set; the return signal of the detection radar is received, and the receiving time, frequency and power parameters of the received test feedback signal are recorded to form a comparison parameter set.

[0022] 2. According to the test feedback signal, determine whether the detection radar is faulty. If so, proceed to the next step, otherwise return to the previous step. The specific steps for determining whether the detection radar is faulty are as follows: Compare the data in the comparison parameter set and the reference parameter set to determine whether the test feedback signal is delayed, attenuated, or missing; analyze the quality of the test feedback signal, including but not limited to analyzing the signal-to-noise ratio and waveform integrity of the test feedback signal; set the power threshold and signal-to-noise ratio threshold ;if It is judged that the test feedback signal attenuation is abnormal, where: and Represent the power of the test signal and the power of the test feedback signal respectively; if , then the test feedback signal is judged to be distorted; if and , then it is judged that the detection radar is faulty; otherwise, the test feedback signal is acquired again.

[0023] 3. Obtain the detection radar work log and extract the target runway information of the detection radar performing the detection task, which specifically includes the following steps: connect to the radar system or airport database, use query statements or API interfaces to obtain the radar work log, analyze the records in the work log, and find information related to the target runway. Extract the identifier and related information of the target runway. Obtain the detection radar work log from the airport monitoring system, including the operation status data and task data of the detection radar; extract the working time period, working frequency information, number information and distribution location information of the detection radar in the airport from the operation status data of the detection radar; obtain the detection task type and detection area data from the task data of the detection radar, and the detection task type includes fixed detection tasks and mobile detection tasks. Because there are fixed detection radars (tower type or edge light type) and vehicle-mounted detection radars in real airports, the type, working frequency and detection area of ​​the detection radar are distinguished by identifying the detection task type. Each detection radar has different processing measures, detection methods and detection areas; which affects the subsequent data acquisition method and risk level judgment and disposal. The edge-light-type detection radar is closer to the runway and is more susceptible to being hit by foreign objects that do not fly very high. The tower-type is farther away from the runway and is more susceptible to being hit by foreign objects that fly high in the air. The detection vehicle is in a moving state, and foreign objects will not only collide with the detection radar, but also with the vehicle, causing damage to the vehicle or personnel.

[0024] 4. Obtain the mission log of aircraft takeoff and landing on the target runway within the preset time period, extract the takeoff and landing aircraft information data and takeoff and landing time information data, and form a risk source data set, which specifically includes the following steps: The step of obtaining the mission log of aircraft takeoff and landing on the target runway within a preset time period, extracting the takeoff and landing aircraft information data and the takeoff and landing time information data, and forming a risk source data set includes the following steps: Construct query conditions based on target runway information and preset time period; Using the query conditions to obtain from the airport monitoring system the mission log of the aircraft taking off and landing on the target runway within the preset time period; Extract the model and flight number information of each aircraft from the acquired aircraft takeoff and landing mission logs; The aircraft model, flight number and take-off and landing time of each flight constitute a preset time period Risk source dataset within ;in, Indicates the total number of take-off and landing flights. ; , Respectively represent the starting time point and the ending time point of the preset time period; ;in, Indicates Flight data vector, and Respectively represent The aircraft model, flight number, landing time and take-off time of the flight; Obtain risk source data sets to facilitate the inspection of aircraft when foreign objects are detected to confirm whether the aircraft fell during that time period.

[0025] 5. Since the detection radar is judged to be faulty, the speed of the foreign object cannot be measured by the detection radar. Therefore, it is necessary to obtain the monitoring video data of the target runway within a preset time period, and form a video analysis data set after preprocessing. The preprocessing includes denoising and contrast enhancement, which specifically includes the following steps: Obtain the number information and the distribution location information of the detection radar in the airport from the detection radar management system by accessing a database or API; Using the detection radar's number information and its distribution location information in the airport as search conditions, obtain the number information of the video surveillance device that cooperates with the detection radar. Before this, it is necessary to build a table to store the association between the detection radar's number, location information and the video surveillance device.

[0026] According to the serial number information of the video surveillance device, query the database arrive The video data within a certain period of time is processed by denoising and contrast enhancement to form a video analysis dataset, which is then sent to the cloud for storage.

[0027] 6. Analyze the monitoring video data to obtain multiple video frames with foreign object information, and then obtain the foreign object movement trajectory information by analyzing the multiple video frames, which specifically includes the following steps: download the video analysis data set after noise reduction and contrast enhancement processing to the local computer; Use the target detection algorithm YOLO to perform target recognition on each video frame in the video data in order to identify foreign objects; Marking the position of the identified foreign object in the video frame, saving the foreign object information and adding the corresponding video frame timestamp, wherein the foreign object information includes the position, size and shape of the foreign object; According to the target recognition result, multiple video frames with foreign object information are screened out to form a key video frame sequence for subsequent analysis of the foreign object's motion trajectory; For each pair of consecutive key video frames, the optical flow information is calculated using the streamer algorithm, specifically including: using the Lucas-Kanade algorithm in the streamer algorithm to calculate the horizontal motion speed of the pixel points between pixel frames, and using the Horn-Schunck algorithm in the streamer algorithm to calculate the vertical motion speed of the pixel points; The motion trajectory of the foreign object is constructed based on the calculated optical flow information, including: Obtain the timestamp of each video frame with foreign object information and the location of the foreign object on the corresponding video frame; Constructing a motion trajectory curve of the foreign body; specifically, by curve fitting or completing the motion trajectory; Analyze the constructed motion trajectory curve, extract key motion features, and construct a key feature set ,in, Respectively The velocity vector and acceleration vector of the foreign body at each moment; in, , ; and Respectively The horizontal velocity, vertical velocity, horizontal acceleration and vertical acceleration at the moment.

[0028] 7. Predict the probability of foreign object impact through the pre-trained protection warning model, and take countermeasures in advance according to the prediction results, including the following steps: Extract time periods from key feature value sets The average horizontal velocity of the foreign body , vertical average speed , horizontal average acceleration , vertical average acceleration and the average angle ; Will , , and After normalization, the input feature set of the protection warning model is formed ,in, , Indicates the number of video frames with foreign object information; Bring the input feature set into the protection warning model , output the probability of foreign body impact; where, and denote the intercept and error term respectively, and represents the regression coefficient; if , it is judged that the foreign object will hit the detection radar; otherwise, it is judged that the foreign object will hit the detection radar; The method of taking countermeasures in advance according to the prediction results includes the following steps: if , judge that the risk of foreign object impact is medium, send the prediction result to the airport management personnel, remind the airport management personnel to deal with it, take measures according to the mission type and the type of detection radar, such as clearing the foreign object, reducing the speed of the detection vehicle, or adjusting the detection route of the detection vehicle, and obtain feedback on the handling situation; if , judge the risk of foreign object impact as high, send the prediction results to the airport management personnel, notify the airport management personnel to adjust the aircraft take-off and landing activities, and obtain feedback on the handling situation; Finally, each prediction result and the corresponding processing feedback are stored for later tracing.

[0029] The present invention also provides a FOD protection system for detecting radar, wherein the system is used to execute the FOD protection method for detecting radar.

[0030] The present invention also provides a toolbox, wherein a computer-readable storage medium is provided in the toolbox, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the FOD protection method for detecting radar.

[0031] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0032] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0033] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

Claims

1. A FOD protection method for detecting radar, characterized in that: The method comprises: Send a test signal to the detection radar at a preset frequency to obtain a test feedback signal; According to the test feedback signal, determine whether the detection radar fails. If so, proceed to the next step, otherwise return to the previous step; Obtain the detection radar work log and extract the target runway information for the detection radar to perform the detection mission; Obtain the mission log of aircraft takeoff and landing on the target runway within the preset time period, extract the takeoff and landing aircraft information data and takeoff and landing time information data, and form a risk source data set; Acquire monitoring video data of the target runway within a preset time period, and form a video analysis data set after preprocessing, wherein the preprocessing includes denoising and contrast enhancement; Analyze the monitoring video data to obtain multiple video frames with foreign object information, and then obtain the foreign object movement trajectory information by analyzing the multiple video frames; Through the pre-trained protection warning model, the probability of foreign object impact is predicted, and countermeasures can be taken in advance based on the prediction results.

2. A FOD protection method for detecting radar according to claim 1, characterized in that: The step of sending a test signal to the detection radar at a preset frequency and obtaining a test feedback signal comprises the following steps: Sending a test signal of a preset frequency to the detection radar, wherein the preset frequency is within the operating frequency range of the detection radar; Record the time and power parameters of sending the test signal to form a benchmark parameter set; Receive the return signal from the detection radar, record the receiving time, frequency and power parameters of the received test feedback signal, and form a comparison parameter set.

3. A FOD protection method for detecting radar according to claim 2, characterized in that: The method of judging whether the detection radar fails according to the test feedback signal comprises the following steps: Compare the data in the comparison parameter set and the reference parameter set to determine whether the test feedback signal is delayed, attenuated or missing; Analyze the quality of the test feedback signal, including but not limited to analyzing the signal-to-noise ratio and waveform integrity of the test feedback signal; Setting the power threshold and signal-to-noise ratio threshold ; if It is judged that the test feedback signal attenuation is abnormal, where: and Respectively represent the power of the test signal and the power of the test feedback signal; if , then it is judged that the test feedback signal is distorted; if and , then it is judged that the detection radar is faulty; otherwise, the test feedback signal is acquired again.

4. A FOD protection method for detecting radar according to claim 3, characterized in that: The step of obtaining the detection radar work log and extracting the target runway information of the detection radar performing the detection task includes: Obtaining a detection radar work log from an airport monitoring system; the detection radar work log includes operating status data and task data of the detection radar; Extracting the working time period, working frequency information, number information and distribution position information of the detection radar in the airport from the operation status data of the detection radar; Acquire a detection task type and detection area data from the task data of the detection radar, wherein the detection task type includes a fixed detection task and a mobile detection task; Extract the specific runway information monitored by the detection radar from the monitored airport area information.

5. A FOD protection method for detecting radar according to claim 4, characterized in that: The step of obtaining the mission log of aircraft takeoff and landing on the target runway within a preset time period, extracting the takeoff and landing aircraft information data and the takeoff and landing time information data, and forming a risk source data set includes the following steps: Construct query conditions based on target runway information and preset time period; Using the query conditions to obtain from the airport monitoring system the mission log of the aircraft taking off and landing on the target runway within the preset time period; Extract the model and flight number information of each aircraft from the acquired aircraft takeoff and landing mission logs; The aircraft model, flight number and take-off and landing time of each flight constitute a preset time period Risk source dataset within ;in, Indicates the total number of take-off and landing flights. ; , Respectively represent the starting time point and the ending time point of the preset time period; ;in, Indicates Flight data vector, and Respectively represent Aircraft model, flight number, landing time and take-off time of the flight.

6. A FOD protection method for detecting radar according to claim 5, characterized in that: The monitoring video data of the target runway within the preset time period is obtained and preprocessed to form a video analysis data set, wherein the preprocessing includes denoising and contrast enhancement, and includes the following steps: Obtain the number information of the detection radar and its distribution location information within the airport; Using the number information of the detection radar and the distribution location information in the airport as search conditions, obtain the number information of the video surveillance device that cooperates with the detection radar; According to the serial number information of the video surveillance device, query the database arrive Video data within a time period; The video data is processed for denoising and contrast enhancement to form a video analysis dataset, which is then sent to the cloud for storage.

7. A FOD protection method for detecting radar according to claim 6, characterized in that: The method of analyzing the monitoring video data to obtain multiple video frames with foreign object information, and then obtaining the foreign object movement trajectory information by analyzing the multiple video frames, includes the following steps: Download the video analysis dataset after noise reduction and contrast enhancement to your local computer; Use the target detection algorithm YOLO to perform target recognition on each video frame in the video data in order to identify foreign objects; Marking the position of the identified foreign object in the video frame, saving the foreign object information and adding the corresponding video frame timestamp, wherein the foreign object information includes the position, size and shape of the foreign object; According to the target recognition result, multiple video frames with foreign object information are screened out to form a key video frame sequence for subsequent analysis of the foreign object's motion trajectory; For each pair of consecutive key video frames, the optical flow information is calculated using the streamer algorithm, specifically including: using the Lucas-Kanade algorithm in the streamer algorithm to calculate the horizontal motion speed of the pixel points between pixel frames, and using the Horn-Schunck algorithm in the streamer algorithm to calculate the vertical motion speed of the pixel points; Constructing the motion trajectory of the foreign object according to the calculated optical flow information specifically includes: obtaining the motion trajectory by analyzing the timestamp of each video frame with foreign object information and the position relationship of the foreign object on the corresponding video frame; Constructing a motion trajectory curve of the foreign body; specifically, by curve fitting or completing the motion trajectory; Analyze the constructed motion trajectory curve, extract key motion features, and construct a key feature set ,in, Respectively The velocity vector and acceleration vector of the foreign body at each moment; in, , ; and Respectively The horizontal velocity, vertical velocity, horizontal acceleration and vertical acceleration at the moment.

8. A FOD protection method for detecting radar according to claim 7, characterized in that: The method of predicting the probability of foreign body impact by using the pre-trained protection warning model includes the following steps: Extract time periods from key feature value sets The average horizontal velocity of the foreign body , vertical average speed , horizontal average acceleration , vertical average acceleration and the average angle ; Will , , and After normalization, the input feature set of the protection warning model is formed ,in, , Indicates the number of video frames with foreign object information; Bring the input feature set into the protection warning model , output the probability of foreign body impact; where, and denote the intercept and error term respectively, and represents the regression coefficient; if , it is judged that the foreign object will hit the detection radar; otherwise, it is judged that the foreign object will hit the detection radar; The method of taking countermeasures in advance according to the prediction results includes the following steps: if , judge that the risk of foreign object impact is medium, send the prediction result to the airport management personnel, remind the airport management personnel to handle it, and obtain the handling status; if , judge that the risk of foreign object impact is high, send the prediction results to the airport management personnel, notify the airport management personnel to adjust the aircraft take-off and landing activities, and obtain the handling status; Store each prediction result and the corresponding processing status for later tracing.

9. A FOD protection system for detecting radar, characterized in that: The system is used to execute a FOD protection method for detecting radar as described in any one of claims 1-8.

10. A tool box, characterized in that: A computer-readable storage medium is provided in the toolbox, and the computer-readable storage medium stores a computer program. The computer program is executed by a processor to implement a FOD protection method for detecting radar as described in any one of claims 1 to 8.

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