A weather radar dynamic grid monitoring meteorological avoidance area boundary extraction method

Abstract

The application discloses a weather radar dynamic grid monitoring meteorological avoidance area boundary extraction method, which is characterized in that: three-dimensional space is gridded, weather radar is used to dynamically monitor thunderstorm reflectivity data of each grid, aircraft flight risk of any grid is calculated, whether each grid is in a safe flight state is determined according to a flight safety threshold of a pilot of different types of aircraft, and all grids are combined into a three-dimensional meteorological avoidance area according to their flight-prohibited states, so that different types of aircraft are guided to fly safely.

Description

Technical Field

[0001] This invention relates to the field of weather radar detection technology for flight path planning, and in particular to a method for extracting the boundary of a meteorological avoidance zone in dynamic grid monitoring by weather radar. Background Technology

[0002] Typhoons, torrential rains, and other severe convective weather pose significant threats to civil aviation safety and flight regularity. They are typically accompanied by heavy precipitation, lightning, and strong convection, which can cause aircraft to experience vibrations, pitching, rolling, and low visibility during flight, leading to operational difficulties, inaccurate instrument readings, and other problems that seriously jeopardize flight safety. The timing, location, and extent of any severe convective weather event affect different airports and shared waypoints. Given the spatiotemporal imbalances in air traffic flow, it is crucial to proactively assess the movement trends of hazardous weather areas (referred to as weather avoidance zones) with the support of meteorological products, minimizing the impact of severe convective weather on flight delays at multiple airports. Therefore, the extraction of weather avoidance zone boundaries based on dynamic weather radar monitoring is a prerequisite for this work.

[0003] Currently, there is extensive research both domestically and internationally on flight rerouting and trajectory planning for civil aviation passenger planes during typhoons and heavy rains. Intelligent algorithms, such as ant colony optimization and particle swarm optimization, have been applied to trajectory planning. However, these algorithms generally assume the existence of weather avoidance zones and rarely address methods for extracting their boundaries, especially considering the impact of flight safety thresholds for different types of aircraft pilots (related to aircraft performance, safety standards, and pilot characteristics) on the extent of the weather avoidance zone. Furthermore, weather avoidance zone generation algorithms are highly complex, and current methods, generally employing intelligent algorithms, are unable to efficiently and quickly generate a weather avoidance zone.

[0004] Therefore, this invention presents a method for extracting the boundary of a weather avoidance zone based on dynamic grid monitoring of weather radar. It generates weather avoidance zones suitable for different types of aircraft and pilot preferences, satisfying diverse and personalized areas. This method assists in dynamically planning aircraft routes based on thunderstorm conditions and plays an important role in the rational arrangement of airport operations. Summary of the Invention

[0005] This invention provides a method for extracting the boundary of a weather avoidance zone based on dynamic grid monitoring by weather radar. By combining the thunderstorm reflectivity data of each grid monitored by weather radar, and considering the radar detection distance, safety interval standards, and pilot preferences of different types of aircraft, a three-dimensional weather avoidance zone that prohibits aircraft from flying is generated, thereby guiding different types of aircraft to fly safely.

[0006] This invention provides a method for extracting the boundary of a meteorological avoidance zone from dynamic grid monitoring by weather radar, characterized by comprising the following steps:

[0007] S1. Based on the radar detection range and safety interval standards of different types of aircraft, the airspace is gridded into three dimensions;

[0008] S2. Use weather radar to monitor the thunderstorm reflectivity data of each grid and calculate the flight risk of any grid.

[0009] S3. Determine the safe flight status of each grid based on the flight safety thresholds for pilots of different types of aircraft;

[0010] S4. Merge all grids into a three-dimensional weather avoidance zone based on their no-fly status.

[0011] Furthermore, step S1 proposes a set of airspace grid partitioning methods for different types of aircraft, specifically including:

[0012] S11. Define the three-dimensional airspace range and the types of aircraft involved;

[0013] S12. Determine the length of each three-dimensional grid based on the radar detection range and safety interval standards for different types of aircraft. ,Width and high ;

[0014] S13. Based on the length of each 3D mesh ,Width and high Mesh the spatial domain to determine any grid. spatial coordinates And construct any two grids and Adjacency relationship between .like , grid and Adjacent to each other; if , grid and They are not adjacent to each other.

[0015] Furthermore, step S2 proposes a method for classifying the flight risk level of aircraft using arbitrary grids based on thunderstorm reflectivity data, specifically including:

[0016] S21. Match the thunderstorm reflectivity coordinate data monitored by weather radar with all grids in the entire airspace to obtain the data for each grid. any location within the area Thunderstorm reflectivity data ;

[0017] S22, For each grid The diversity of thunderstorm reflectance data in the region, and the calculation of the regional random statistical distribution of its thunderstorm reflectance in this grid All locations in the region Thunderstorm reflectivity data Thunderstorm reflectivity is categorized into n types based on its frequency of occurrence. And count their occurrence probability. Based on this, a unique expected thunderstorm reflectance value is calculated for each grid region. ;

[0018] in This indicates the grid. Region k-th category thunderstorm reflectivity The probability of occurrence Represents any position Thunderstorm reflectivity data Is it the reflectivity of the k-th category thunderstorm? .

[0019] S23. According to the safety flight standards for different types of aircraft, if any grid Radar reflectivity values The flight risk of aircraft varies depending on the range, and they are classified into four categories: low, medium, high, and extremely high, each corresponding to a critical value for radar reflectivity. 31dBZ 36dBZ, 36dBZ 41dBZ, 41dBZ 46dBZ;

[0020] S24, Based on arbitrary grid Radar reflectivity values Consult the safety flight standards for different types of aircraft to determine their risk levels. .

[0021] Furthermore, step S3 proposes a method for determining the flight state of an airspace grid, specifically including:

[0022] S31. Pilots of different types of aircraft determine flight safety thresholds based on the urgency of the mission. ;

[0023] S32, If each grid Risk level Flight safety threshold greater than or equal to the pilot's It is a no-fly zone, that is Otherwise, it is a state where safe flight is permitted, i.e. .

[0024] Furthermore, step S4 proposes a fast identification algorithm for weather avoidance zone boundaries based on airspace grid flight status, specifically including:

[0025] S41, All grids in a no-fly state ( As an intelligent agent, each agent can detect the grid connected to it. Set the state of all agents to active state. ;

[0026] S42. Find the set of all active grid agents. If the set is not empty Proceed to step S43; otherwise, proceed to step S45.

[0027] S43, Traversing a Set All grid agents in the system, for any grid An intelligent agent only needs to perform the following actions:

[0028] S431, If ​​the current grid The agent detects the six adjacent grids connected to it. There are multiple grids in the middle that indicate no-fly zones. Randomly select a grid. The agent, and the grid Intelligent agents merge to form a new grid intelligent agent and update the set of grid agents. That is: new grid intelligent agent Added to the set, the original two grid agents were set to the dead state. and ;

[0029] S432. If the current grid agent detects 6 adjacent grids connected to it... All are in safe flight condition Set its status to dead. ;

[0030] S44. After all the actions of the grid agents in the set have been executed, return to step S42;

[0031] S45, The algorithm terminates and outputs the boundary of the weather avoidance zone.

[0032] The beneficial effects achieved by this invention are as follows:

[0033] 1. This invention will grid the airspace based on the radar detection range, safety separation standards and pilot preferences of different types of aircraft. By combining the thunderstorm reflectivity data of each grid monitored by weather radar, a proprietary three-dimensional weather avoidance zone for aircraft flight will be generated, which can help aircraft safely avoid or pass through areas affected by thunderstorms.

[0034] 2. This invention is based on the flexible combination of airspace grids by intelligent agents to quickly generate weather avoidance zones. It can realize distributed computing, with fast computing speed and low memory usage. It can quickly find three-dimensional weather avoidance zones for any type of aircraft at different flight safety thresholds in a short time. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the process for extracting the boundary of a weather avoidance zone using dynamic grid monitoring by weather radar according to the present invention.

[0036] Figure 2 This is an implementation process of a weather avoidance zone boundary extraction method for dynamic grid monitoring by weather radar according to the present invention;

[0037] Figure 3 This is a flowchart illustrating a method for classifying the flight risk level of an aircraft based on thunderstorm reflectivity data, according to the present invention.

[0038] Figure 4 This is a schematic diagram of the algorithm for rapid identification of weather avoidance zone boundaries based on airspace grid flight status according to the present invention; Detailed Implementation

[0039] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] like Figure 1 As shown in the figure, this embodiment provides a method for extracting the boundary of a weather avoidance zone based on dynamic grid monitoring by weather radar, which includes the following steps:

[0041] S1. Based on the radar detection range and safety interval standards of different types of aircraft, the airspace is gridded into three dimensions;

[0042] S2. Use weather radar to monitor the thunderstorm reflectivity data of each grid and calculate the flight risk of any grid.

[0043] S3. Determine the safe flight status of each grid based on the flight safety thresholds for pilots of different types of aircraft;

[0044] S4. Merge all grids into a three-dimensional weather avoidance zone based on their no-fly status.

[0045] like Figure 2As shown, the implementation process of the weather avoidance zone boundary extraction method of dynamic grid monitoring by weather radar according to the present invention includes:

[0046] S1. Determine the airspace range and weather radar monitoring data based on the origin and destination points and time of the aircraft mission;

[0047] S2. Based on the type of aircraft safety interval standards and radar detection range, the airspace is gridded in three dimensions, and the flight risk of the aircraft in any grid is calculated.

[0048] S3. Based on the different flight safety thresholds of the aircraft pilot, construct the safe flight status of each grid under different scenarios;

[0049] S4. Based on the different flight safety thresholds of the pilots, all grids are merged according to their no-fly status to generate different three-dimensional weather avoidance zones.

[0050] like Figure 3 As shown, this invention proposes a method for classifying the flight risk level of aircraft based on arbitrary grid data of thunderstorm reflectivity. The specific process includes:

[0051] S20. Data preparation, including airspace gridding and thunderstorm reflectivity coordinate data monitored by weather radar;

[0052] S21. Match the thunderstorm reflectivity coordinate data monitored by the weather radar with all grids in the entire airspace to obtain the thunderstorm reflectivity data for each grid.

[0053] S22. Calculate the regional random statistical distribution of thunderstorm reflectivity for each grid region, taking into account the diversity of thunderstorm reflectivity data.

[0054] S23. Calculate the unique expected thunderstorm reflectivity value for each grid region;

[0055] S24. Based on the radar reflectivity values ​​of any grid, query the safe flight standards for different types of aircraft and determine their risk levels.

[0056] like Figure 4 As shown, this invention proposes a fast identification algorithm for weather avoidance zone boundaries based on airspace grid flight status. The specific process includes:

[0057] S41. Treat all prohibited flying grids as one agent. Each agent can detect grids connected to it. Set the state of all agents to active.

[0058] S42. Find the set of all active grid agents. If the set is not empty, go to step S43; otherwise, go to step S45.

[0059] S43. Traverse all grid agents in the set. For any grid agent, simply perform the following action:

[0060] S431. If a grid agent detects that multiple grids among the six grids connected to it (up, down, front, back, left, right, etc.) are prohibited from flying, randomly select a grid agent, merge it with the grid agent to form a new grid agent, and update the grid agent set, that is: the new grid agent is added to the set, and the original two grid agents are set to the dead state.

[0061] S432. If a grid agent detects that all six grids connected to it (up, down, front, back, left, and right) are in a safe flight state, set its state to dead state.

[0062] S44. After all the actions of the grid agents in the set have been executed, return to step S42;

[0063] S45, The algorithm terminates and outputs the boundary of the weather avoidance zone.

Claims

1. A method for extracting the boundary of a meteorological avoidance zone from dynamic grid monitoring by weather radar, characterized in that, Includes the following steps: S1. Based on the radar detection range and safety interval standards of different types of aircraft, the airspace is gridded into three dimensions; S2. Use weather radar to monitor the thunderstorm reflectivity data of each grid and calculate the flight risk of any grid. S3. Determine the safe flight status of each grid based on the flight safety thresholds for pilots of different types of aircraft; S4. Merge all grids into a three-dimensional weather avoidance zone based on their no-fly status; Specifically, step S1 proposes a method for airspace grid partitioning for different types of aircraft, including: S11. Define the three-dimensional airspace range and the types of aircraft involved; S12. Determine the length of each three-dimensional grid based on the radar detection range and safety interval standards for different types of aircraft. ,Width and high ; S13. Based on the length of each 3D mesh ,Width and high Mesh the spatial domain to determine any grid. spatial coordinates And construct any two grids and Adjacency relationship between ;like , grid and Adjacent to each other; if , grid and They are not adjacent to each other; Step S2 proposes a method for classifying the flight risk level of aircraft using arbitrary grids based on thunderstorm reflectivity data, specifically including: S21. Match the thunderstorm reflectivity coordinate data monitored by weather radar with all grids in the entire airspace to obtain the data for each grid. any location within the area Thunderstorm reflectivity data ; S22, For each grid The diversity of thunderstorm reflectance data in the region, and the calculation of the regional random statistical distribution of its thunderstorm reflectance in this grid All locations in the region Thunderstorm reflectivity data Thunderstorm reflectivity is categorized into n types based on its frequency of occurrence. And count their occurrence probability. Based on this, a unique expected thunderstorm reflectance value is calculated for each grid region. ; in This indicates the grid. Region k-th category thunderstorm reflectivity The probability of occurrence Represents any position Thunderstorm reflectivity data Is it the reflectivity of the k-th category thunderstorm? ; S23. According to the safety flight standards for different types of aircraft, if any grid Radar reflectivity values The flight risk of aircraft varies depending on the range, and they are classified into four categories: low, medium, high, and extremely high, each corresponding to a critical value for radar reflectivity. 31dBZ 36dBZ, 36dBZ 41dBZ, 41dBZ 46dBZ; S24, Based on arbitrary grid Radar reflectivity values Consult the safety flight standards for different types of aircraft to determine their risk levels. ; Step S4 proposes a fast algorithm for identifying the boundary of the weather avoidance zone based on the flight state of the airspace grid, specifically including: S41, All grids in a no-fly state As an intelligent agent, each agent can detect the grid connected to it. Set all agents to the active state; S42. Find the set of all active grid agents. If the set is not empty Proceed to step S43; otherwise, proceed to step S45. S43, Traversing a Set All grid agents in the system, for any grid An intelligent agent only needs to perform the following actions: S431, If ​​the current grid The agent detects the six adjacent grids connected to it. There are multiple grids in the map that prohibit flight; a grid is randomly selected. The agent, and the grid Intelligent agents merge to form a new grid intelligent agent and update the set of grid agents. New Grid Intelligent Agent Add to the set, and set the original two grid agents to the dead state; S432. If the current grid agent detects 6 adjacent grids connected to it... All were in a safe flight state; their status was set to dead. S44. After all the actions of the grid agents in the set have been executed, return to step S42; S45, The algorithm terminates and outputs the boundary of the weather avoidance zone.

2. The method for extracting the boundary of a weather avoidance zone using dynamic grid monitoring by weather radar according to claim 1, wherein step S3 proposes a method for determining the flight state of an airspace grid, specifically including: S31. Pilots of different types of aircraft determine flight safety thresholds based on the urgency of the mission. ; S32, If each grid Risk level Flight safety threshold greater than or equal to the pilot's It is a no-fly zone. ; Otherwise, it is a state where safe flight is permitted. .

Images