CN-117456780-B - Forced landing area assessment method and device, aircraft and readable storage medium

CN117456780BCN 117456780 BCN117456780 BCN 117456780BCN-117456780-B

Abstract

The application discloses a forced landing area assessment method, a device, an aircraft and a readable storage medium, wherein the method comprises the steps of obtaining a track neighborhood semantic set corresponding to a forced landing area of the aircraft, wherein the track neighborhood semantic set comprises forced landing information and semantic information, the semantic information comprises obstacle information of a preset area corresponding to the forced landing area, determining track concentration degree, track redundancy and obstacle area distribution index of a reducible area corresponding to the aircraft based on the track neighborhood semantic set, and determining score information of the forced landing area based on the track concentration degree, the track redundancy and the obstacle area distribution index of the reducible area. The application can accurately obtain the score of the forced landing area according to the forced landing information and the semantic information, accurately quantize the reasonable selection degree of the forced landing area through the semantic information, further accurately evaluate the rationality of the forced landing area according to the score information, and improve the safety and rationality of the forced landing of the aircraft.

Inventors

FU ZHIGANG
TAO YONGKANG
PENG DENG
DONG BO

Assignees

广东汇天航空航天科技有限公司

Dates

Publication Date: 20260512
Application Date: 20221226

Claims (10)

1.A forced landing area assessment method, comprising: Acquiring a track neighborhood semantic set corresponding to a forced landing area of an aircraft, wherein the track neighborhood semantic set comprises forced landing information and semantic information, and the semantic information comprises obstacle information of each preset area corresponding to the forced landing area; determining the trace aggregation degree, trace redundancy and obstacle region distribution index of the reducible region corresponding to the aircraft based on the trace neighborhood semantic set; carrying out weighted summation based on the trace aggregation degree, trace redundancy and obstacle region distribution indexes of the degradable region, and determining scoring information of the forced landing region; the step of determining the trace aggregation degree, trace redundancy and obstacle region distribution index of the reducible region corresponding to the aircraft based on the trace neighborhood semantic set comprises the following steps: Determining the track aggregation degree of the degradable region based on each degradable candidate region in the forced landing information, wherein the distance between two adjacent degradable candidate regions is acquired based on the track neighborhood semantic set, the region area corresponding to each degradable candidate region is acquired, the maximum distance in the distance is acquired, the neighborhood area and the track length of the track neighborhood corresponding to the track neighborhood semantic set are acquired, and the track aggregation degree of the degradable region is calculated by adopting a formula of the track aggregation degree of the degradable region based on the maximum distance, the region area, the neighborhood area and the track length; Determining the track redundancy based on each touchdown candidate area in the forced landing information and semantic information, wherein a non-touchdown area in a track neighborhood corresponding to the track neighborhood semantic set is determined based on each touchdown candidate area, barrier information in the non-touchdown area is determined according to the semantic information, track areas without barriers in each non-touchdown area are determined according to the barrier information in the non-touchdown area, a minimum width is determined according to the width of each track area, and the track redundancy is determined based on the minimum width and a preset width; The obstacle region distribution index is determined based on the semantic information, wherein Euclidean distance between obstacles corresponding to the obstacle information and potential of a semantic set corresponding to the semantic information are obtained, average distance corresponding to the obstacle information is determined based on the Euclidean distance and the quantity of the obstacle information, and the obstacle region distribution index is calculated by adopting a formula of the obstacle region distribution index based on the potential and the average distance.
2. The forced landing area assessment method according to claim 1, wherein the step of determining score information of the forced landing area based on weighted summation of the degradable area trajectory aggregation level, trajectory redundancy, and obstacle area distribution index comprises: Acquiring a first weight corresponding to the trace aggregation degree of the degradable region, a second weight corresponding to the trace redundancy and a third weight corresponding to the obstacle region distribution index; and determining scoring information of the forced landing area based on the track aggregation degree, track redundancy, obstacle area distribution index, the first weight, the second weight and the third weight of the degradable area.
3. The forced landing zone assessment method according to any one of claims 1 to 2, wherein a data acquisition device is arranged below the aircraft, and the step of acquiring a trajectory neighborhood semantic set corresponding to the forced landing zone of the aircraft comprises: The forced landing information corresponding to the aircraft is obtained from a cloud server, and the semantic information is obtained based on the data acquisition device; and determining the track neighborhood semantic set based on the forced landing information and the semantic information.
4. The forced landing area assessment method according to claim 3, wherein the data acquisition device comprises a lidar and a camera, and wherein the step of acquiring the semantic information based on the data acquisition device comprises: Determining a target data acquisition device in the lidar and camera based on a current altitude of the aircraft; acquiring acquisition data of the target data acquisition device, and determining the semantic information based on the acquisition data.
5. An aircraft, characterized in that, the aircraft comprises: The acquisition module is used for acquiring a track neighborhood semantic set corresponding to a forced landing area of the aircraft, wherein the track neighborhood semantic set comprises forced landing information and semantic information, and the semantic information comprises barrier information of each preset area corresponding to the forced landing area; the determining module is used for determining the trace aggregation degree, the trace redundancy and the obstacle region distribution index of the reducible region corresponding to the aircraft based on the trace neighborhood semantic set; the scoring module is used for carrying out weighted summation based on the trace aggregation degree, trace redundancy and obstacle region distribution indexes of the degradable region to determine scoring information of the forced landing region; The determining module is further configured to: Determining the track aggregation degree of the degradable region based on each degradable candidate region in the forced landing information, wherein the distance between two adjacent degradable candidate regions is acquired based on the track neighborhood semantic set, the region area corresponding to each degradable candidate region is acquired, the maximum distance in the distance is acquired, the neighborhood area and the track length of the track neighborhood corresponding to the track neighborhood semantic set are acquired, and the track aggregation degree of the degradable region is calculated by adopting a formula of the track aggregation degree of the degradable region based on the maximum distance, the region area, the neighborhood area and the track length; Determining the track redundancy based on each touchdown candidate area in the forced landing information and semantic information, wherein a non-touchdown area in a track neighborhood corresponding to the track neighborhood semantic set is determined based on each touchdown candidate area, barrier information in the non-touchdown area is determined according to the semantic information, track areas without barriers in each non-touchdown area are determined according to the barrier information in the non-touchdown area, a minimum width is determined according to the width of each track area, and the track redundancy is determined based on the minimum width and a preset width; The obstacle region distribution index is determined based on the semantic information, wherein Euclidean distance between obstacles corresponding to the obstacle information and potential of a semantic set corresponding to the semantic information are obtained, average distance corresponding to the obstacle information is determined based on the Euclidean distance and the quantity of the obstacle information, and the obstacle region distribution index is calculated by adopting a formula of the obstacle region distribution index based on the potential and the average distance.
6. The aircraft of claim 5, wherein the scoring module is further to: Acquiring a first weight corresponding to the trace aggregation degree of the degradable region, a second weight corresponding to the trace redundancy and a third weight corresponding to the obstacle region distribution index; and determining scoring information of the forced landing area based on the track aggregation degree, track redundancy, obstacle area distribution index, the first weight, the second weight and the third weight of the degradable area.
7. The aircraft according to any one of claims 5 to 6, wherein a data acquisition device is provided under the aircraft, the acquisition module being further configured to: The forced landing information corresponding to the aircraft is obtained from a cloud server, and the semantic information is obtained based on the data acquisition device; and determining the track neighborhood semantic set based on the forced landing information and the semantic information.
8. The aircraft of claim 7, wherein the data acquisition device comprises a lidar and a camera, the acquisition module further to: Determining a target data acquisition device in the lidar and camera based on a current altitude of the aircraft; acquiring acquisition data of the target data acquisition device, and determining the semantic information based on the acquisition data.
9. A forced landing area assessment arrangement, characterized in that it comprises a memory, a processor and a forced landing area assessment program stored on the memory and executable on the processor, which forced landing area assessment program, when executed by the processor, implements the steps of the forced landing area assessment method according to any one of claims 1 to 4.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a forced landing area evaluation program, which when executed by a processor, implements the steps of the forced landing area evaluation method according to any one of claims 1 to 4.

Description

Forced landing area assessment method and device, aircraft and readable storage medium Technical Field The application relates to the technical field of aircrafts, in particular to a forced landing area assessment method and device, an aircraft and a readable storage medium. Background With the development of technology, an aircraft has become one of main transportation and transportation means, and with the increase of service time, various faults of the aircraft inevitably occur, and various degrees of damage can be caused to the aircraft when the aircraft encounters severe weather conditions, so that the aircraft cannot safely reach a destination. When an aircraft cannot continue flying in the presence of an accident, an emergency drop needs to be made on the ground or on the water to reduce the speed of the aircraft's descent, a process known as forced landing. At present, when an aircraft selects a forced landing area, the forced landing area is often selected according to the landform and the topographic information monitored in real time, and when the forced landing area is a relatively complex ground environment, for example, a large people flow, a traffic flow and the like exist in the urban environment of the forced landing area, the forced landing area is selected only according to the landform and the topographic information, the rationality of the forced landing area cannot be effectively evaluated, and the forced landing area is unreasonable to select. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The application mainly aims to provide a forced landing area assessment method, a forced landing area assessment device, an aircraft and a readable storage medium, and aims to solve the technical problem that the existing aircraft is difficult to assess the rationality of a forced landing area. In order to achieve the above object, the present application provides a forced landing area evaluation method, including: Acquiring a track neighborhood semantic set corresponding to a forced landing area of an aircraft, wherein the track neighborhood semantic set comprises forced landing information and semantic information, and the semantic information comprises obstacle information of each preset area corresponding to the forced landing area; determining the trace aggregation degree, trace redundancy and obstacle region distribution index of the reducible region corresponding to the aircraft based on the trace neighborhood semantic set; And determining scoring information of the forced landing area based on the track aggregation degree, track redundancy and obstacle area distribution index of the degradable area. Further, the step of determining the trace aggregation degree, the trace redundancy and the obstacle region distribution index of the reducible region corresponding to the aircraft based on the trace neighborhood semantic set includes: Determining the track aggregation degree of the touchdown regions based on each touchdown candidate region in the forced landing information; Determining the track redundancy based on each touchdown candidate area in the forced landing information and semantic information; And determining the obstacle region distribution index based on the semantic information. Further, the step of determining the trace aggregation degree of the touchdown region based on each touchdown candidate region in the forced landing information includes: acquiring the distance between two adjacent touchable candidate areas based on the trajectory neighborhood semantic set; Acquiring the area of each region corresponding to the touchable candidate region; And determining the concentration degree of the degradable region track based on the distance and the region area. Further, the step of determining the concentration of the reducible region trajectories based on the distance and the region area further comprises: Obtaining the maximum distance in the distances, and obtaining the neighborhood area of the track neighborhood corresponding to the track neighborhood semantic set and the track length; The degradable region track concentration is determined based on the maximum distance, the region area, the neighborhood area, and the track length. Further, the step of determining the trace redundancy based on the semantic information and each touchdown candidate region in the forced landing information includes: determining non-touchdown regions in the track neighborhood corresponding to the track neighborhood semantic set based on each touchdown candidate region; determining barrier information in the non-falling area according to the semantic information; And determining the track redundancy according to the obstacle information in the non-falling area. Further, the step of determining the obstacle informatio