CN-122022443-A - Bird strike risk situation prediction and accurate bird repelling method, control center and system

CN122022443ACN 122022443 ACN122022443 ACN 122022443ACN-122022443-A

Abstract

The embodiment of the invention discloses a bird strike risk situation prediction and accurate bird repelling method and system; the bird activity data processing method comprises the steps of screening bird activity data according to scene classification results, bird species and activity mode classification results, carrying out parameter learning on a bird activity space-time probability prediction model by adopting the data, obtaining current bird activity data, predicting bird strike risk situation based on the bird activity space-time probability prediction model, and adopting a bird strike dispelling strategy by combining bird strike risk situation results and aircraft information. The embodiment of the invention fully utilizes the intelligent level of the expert knowledge lifting system in the aspects of risk judgment and prevention and control strategies, combines the risk judgment and bird repelling operation with the ability of birds to observe and avoid aircrafts from the principle, and forms an innovative risk prevention and control mode. The advantage of the information technology is further utilized, a large amount of bird activity data is used as a basis, so that not only is the expertise of an expert supported the data, but also the effectiveness of the implementation effect can be proved by using the big data.

Inventors

WU HONGGANG
SUI YUNFENG
HE DONGLIN
WEI KAIZHEN
Che Yaoming

Assignees

中国民用航空总局第二研究所

Dates

Publication Date: 20260512
Application Date: 20251211

Claims (10)

1. A bird strike risk situation prediction and accurate bird repelling method is characterized by comprising the following steps: screening historical bird activity data from the historical data according to classification conditions, wherein the classification conditions comprise scene classification results, bird species and activity mode classification results; parameter learning is carried out on the bird activity space-time probability prediction model by adopting the historical bird activity data; acquiring current bird activity data and aircraft activity data, and classifying and predicting bird strike risk situations based on the bird activity space-time probability prediction model after parameter learning; And combining with a bird strike risk situation prediction result, and adopting an optimal bird-repellent strategy for high-risk bird activities according to bird-repellent stress response prediction.
2. The method of claim 1, wherein the historical bird activity data is used to perform parameter learning on a bird activity space-time probability prediction model, in particular: After carrying out smooth pretreatment on each type of screened historical bird activity data, respectively calculating the mean value and the statistical distribution value of motion track characteristics, wherein the motion track characteristics comprise speed, acceleration, direction and duration; generating a simulation motion track by using a simulation platform and taking a calculation result as a parameter; And calculating the space-time distribution prediction of the bird activity by using a maximum likelihood estimation method to obtain a probability distribution function according to the simulation motion trail generated by each classification and a distribution model summarized by expert experience or a best matching model obtained by using data visual analysis.
3. The method according to claim 1, wherein predicting a bird strike risk situation is specifically: extracting bird activity positions from the current bird activity data, and continuously judging whether the current birds are in a safe area according to the bird activity positions, the bird species and activity mode classification results and the scene classification results; aiming at the current birds which are not in the safety area, calculating a bird strike risk situation result by adopting the bird activity space-time probability prediction model.
4. The method of claim 3, wherein the scene classification result comprises a free-running scene, an aircraft stress response scene and a bird repellent stress scene, the safe area is obtained based on a safe distance, and the safe distance is calculated by the following steps: For any bird species, respectively extracting activity data of the bird species in a free activity scene and an aircraft stress response scene as data to be processed; segmenting the data to be processed according to the distance between the bird species and the take-off and landing channel to obtain a plurality of segmented data; Calculating a motion trail feature value for each piece of segment data respectively; And comparing the differences of the characteristic values of the motion trail in the two types of scenes according to each piece of segmented data, and determining the safety distance according to the comparison result.
5. The method of claim 4, further comprising a bird repellent means decision, in particular: Bird data in a stress scene of a bird repelling means are taken, and a bird activity probability distribution function of stress reaction is calculated; Predicting the risk situation of each bird repelling means after implementation according to the bird activity probability distribution function; and taking the bird-repellent means with the lowest risk situation and taking the implementation parameters of the bird-repellent means as the optimal bird-repellent means.
6. A method according to claim 3, characterized in that an optimal bird repellent strategy is adopted, in particular: when the distance between the aircraft and the aircraft is far, the bird activities in the low-risk situation are not interfered, and a driving-away means is adopted for the bird activities in the high-risk situation; Warning means are adopted for birds in a high/low risk situation immediately before the aircraft reaches the perceived distance of the birds; after the aircraft enters the sensing distance of birds, selecting the optimal bird repelling means, if the bird repelling means is predicted to be implemented, the risk can be reduced, otherwise, the bird repelling means is not implemented.
7. The method of claim 6, wherein the calculating of the perceived distance is: Selecting bird activity data that is active outside the safe area; Carrying out smooth pretreatment on each piece of bird activity data, carrying out sliding window average treatment on acceleration at all moments, and taking the moment with the maximum acceleration as activity change moment; taking the distance between the bird and the aircraft at the moment of activity change as a sensing distance, counting the sensing distance distribution, removing the long tail distribution part, if the distribution is more dispersed, the sensing distance is uncertain, and if the distribution is more concentrated, the average value is the sensing distance.
8. The method according to any one of claims 1-7, further comprising periodically learning and adjusting parameters of the bird activity spatiotemporal probability prediction model and bird repellent means decision, in particular: selecting recent bird activity data, and screening according to classification conditions; adjusting the bird activity space-time probability prediction model according to the screened recent bird screening data; And selecting bird activity data after the bird repelling means is implemented, and adjusting the bird repelling means to make a decision according to the bird activity data.
9. A control center comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of claim 8.
10. The system for predicting and accurately driving birds in the situation of risk of bird strike comprises radar detection equipment, optical detection equipment, tracking shooting equipment, a control center and bird driving equipment, and is characterized in that the control center is in accordance with claim 9, receives data acquired by the radar detection equipment, the optical detection equipment and the tracking shooting equipment, and controls the bird driving equipment to execute a bird driving strategy.

Description

Bird strike risk situation prediction and accurate bird repelling method, control center and system Technical Field The invention relates to the technical field of bird strike risk analysis in airport flight areas, in particular to a bird strike risk situation prediction and accurate bird repelling method, a control center and a system. Background In order to prevent and control the risk of bird strike in airports, some systems for detecting bird activities and intelligently preventing and controlling risks have been developed in recent years. These systems have two general disadvantages: firstly, risk judgment is inaccurate, and a prevention and control object is inaccurate. Many systems identify all birds at the airport as high risk activities, meaning that all birds at the airport are repelled. Either not achievable or may cause increased risk of confusion. Some systems predict bird strike risk by detecting bird activity and predicting activity trajectories. Because birds have greater randomness in movement, long-term prediction is not reliable enough and short-term prediction is not timely enough. Secondly, the complex bird strike risk prevention and control work is processed too simply. Some automated systems start the expelling device after detecting bird activity, simply and crudely. In order to protect birds, most of the repelling means are only frightened and harmless. Excessive use of such means accelerates the adaptability of birds to bird repellent means, resulting in ineffective repellent. Some systems have designed targeted expelling functions. However, due to the limitation of civil aviation safety regulations, bird repelling devices are basically installed at a place far from the runway. Improper driving patterns or opportunities may drive birds toward the runway, increasing risk. Disclosure of Invention Aiming at the defects of the prior art in the background technology, the embodiment of the invention aims to provide a bird strike risk situation prediction and accurate bird repelling method, a control center and a system. In order to achieve the above object, in a first aspect, an embodiment of the present invention provides a method for predicting and accurately driving birds with risk situations of bird strikes, including: screening historical bird activity data from the historical data according to classification conditions, wherein the classification conditions comprise scene classification results, bird species and activity mode classification results; parameter learning is carried out on the bird activity space-time probability prediction model by adopting the historical bird activity data; acquiring current bird activity data and aircraft activity data, and classifying and predicting bird strike risk situations based on the bird activity space-time probability prediction model after parameter learning; And combining with a bird strike risk situation prediction result, and adopting an optimal bird-repellent strategy for high-risk bird activities according to bird-repellent stress response prediction. As a specific implementation mode of the application, the historical bird activity data is adopted to carry out parameter learning on the bird activity space-time probability prediction model, and the specific implementation mode is as follows: After carrying out smooth pretreatment on each type of screened historical bird activity data, respectively calculating the mean value and the statistical distribution value of motion track characteristics, wherein the motion track characteristics comprise speed, acceleration, direction and duration; generating a simulation motion track by using a simulation platform and taking a calculation result as a parameter; And calculating the space-time distribution prediction of the bird activity by using a maximum likelihood estimation method to obtain a probability distribution function according to the simulation motion trail generated by each classification and a distribution model summarized by expert experience or a best matching model obtained by using data visual analysis. As a specific implementation mode of the application, the predicted bird strike risk situation is specifically: extracting bird activity positions from the current bird activity data, and continuously judging whether the current birds are in a safe area according to the bird activity positions, the bird species and activity mode classification results and the scene classification results; aiming at the current birds which are not in the safety area, calculating a bird strike risk situation result by adopting the bird activity space-time probability prediction model. As a specific implementation mode of the method, the scene classification result comprises a free activity scene, an aircraft stress response scene and a bird-repellent means stress scene, wherein the safety area is obtained based on a safety distance, and the calculation process of the safety distance is as follows: