Intelligent route generation method for flight management equipment

Abstract

The invention discloses a flight management equipment route intelligent generation method, and belongs to the technical field of flight control, and the method comprises the following steps: 1, a basic route is generated through flight management equipment, and the basis conditions for generating the basic route at least comprise a takeoff airport, a landing airport, a flight direction, a civil aviation route and a navigation database; step 2, optimizing the basic route by using a deep learning network model: obtaining meteorological information and air traffic control information corresponding to the basic route according to historical route generation experience and correlation learning rules in combination with a network, optimizing and adjusting the route, and generating a flight route meeting safety requirements; and 3, completing three-dimensional route three-dimensional obstacle avoidance by using global high-precision terrain elevation data. According to the invention, the efficiency and accuracy of route input and the efficiency and safety of flight management are improved.

Description

Technical Field

[0001] The present application relates to a method for intelligently generating routes for flight management equipment, and belongs to the field of flight control technology. Background Art

[0002] With the rapid development of air traffic, the efficiency and safety of flight management have received increasing attention. In flight management, flight routes play a key role, including route input, new route opening and creation. However, there is an urgent problem that needs to be solved, that is, the lack of a method that can quickly generate and analyze routes, as well as a solution that can be evaluated through big data.

[0003] The current route input method has the problem of cumbersome steps and easy to produce errors. Therefore, improving the efficiency and accuracy of route input has become an urgent problem to be solved. To solve this challenge, it is necessary to design a method that can quickly generate routes and evaluate them through big data. Summary of the invention

[0004] According to one aspect of the present application, a method for intelligently generating routes for flight management equipment is provided, which improves the efficiency and accuracy of route input. Compared with the original route input method, the present method relies entirely on algorithms and software for automatic generation, thereby improving the efficiency and safety of flight plan management.

[0005] A method for intelligently generating a flight management device route, characterized in that it comprises the following steps:

[0006] Step 1: Generate a basic route through the flight management device, wherein the basis conditions for generating the basic route include at least the departure airport, the landing airport, the flight direction, the civil aviation route and the navigation database;

[0007] Step 2: Optimize the basic route using a deep learning network model, and optimize the route based on historical route generation experience, associated learning rules, and weather information and air traffic control information corresponding to the basic route obtained through the network to generate a flight route that meets safety requirements;

[0008] Step 3: Use global high-precision terrain elevation data to complete three-dimensional route obstacle avoidance.

[0009] Furthermore, the step 2 includes:

[0010] Dividing the basic route into flight segments to obtain a plurality of first flight segments, and requesting weather information and aviation control information for each first flight segment;

[0011] Conduct quantitative analysis on the danger level of the meteorological information and aviation control information for each first flight segment to determine whether it will affect flight safety;

[0012] If there is a safety impact, the flight segment will be replanned and the route will be adjusted to bypass the meteorological danger area and avoid the waypoints in the danger area. At the same time, new waypoints will be defined and combined to generate a new flight segment.

[0013] Generate flight paths that meet safety requirements.

[0014] Furthermore, the step three includes:

[0015] The two-dimensional plane data in the flight route obtained in step 2 is converted into three-dimensional data through an obstacle avoidance algorithm, wherein the basis for data conversion includes at least: airport altitude, cruising altitude and global high-precision terrain elevation data.

[0016] Furthermore, the method further comprises:

[0017] Generate optimal flight routes:

[0018] Generate an initial database based on departure and arrival airports,

[0019] According to the pilot route system evaluation table, weighted calculation is performed on the option scores in the pilot route system evaluation table to generate a score for the route segment;

[0020] Each time a route is generated in the future, the routes retrieved from the departure and landing airports will be ranked by score, with routes with high scores being used first.

[0021] Furthermore, the scoring options of the pilot route system evaluation form include at least:

[0022] Route comfort, route safety and route integrity.

[0023] Furthermore, the use of global high-precision terrain elevation data to complete three-dimensional route obstacle avoidance includes:

[0024] The pilot enters the cruising altitude of the flight;

[0025] According to the cruising altitude, the global high-precision terrain elevation data is used for data comparison;

[0026] Dividing the flight route into flight segments to obtain a plurality of second flight segments;

[0027] Each second flight segment is tested. If the cruising altitude of the current second flight segment minus the terrain altitude is less than 5000m, the current second flight segment is replanned;

[0028] When there is a waypoint within the set range of the second flight segment and the obstacle can be successfully avoided, the waypoint is used for detour; when there is no waypoint within the set range, the optimal route of the current second flight segment is calculated, a number of custom waypoints are generated, and a complete route is generated by combining the flight cruising speed and cruising altitude with the custom waypoints, thereby completing the route planning with three-dimensional data.

[0029] Furthermore, the global high-precision terrain elevation data has a tile data accuracy of 30m, which is used for stereo analysis of three-dimensional routes.

[0030] The beneficial effects of this application include:

[0031] The present application provides a method for intelligently generating routes for flight management equipment, which has simple steps and does not require complicated operations. It only requires inputting the take-off airport, landing airport and cruising altitude to generate a basic route. The basic route is optimized through a deep network model to generate a flight route that meets safety requirements. At the same time, the perception and obstacle avoidance functions of dangerous terrain are realized through an obstacle avoidance algorithm. Thus, optimal flight route generation is achieved. The implementation of the present invention improves the efficiency and accuracy of route input, thereby improving the efficiency and safety of flight management. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The overall flow chart of the flight management equipment route intelligent generation method provided by the present invention;

[0033] Figure 2 A basic route schematic diagram is generated by drawing a line on a map of a flight management device route intelligent generation method provided by the present invention;

[0034] Figure 3 A basic route generation diagram for the intelligent route generation method for flight management equipment provided by the present invention;

[0035] Figure 4 A flow chart of flight route generation for the flight management equipment route intelligent generation method provided by the present invention;

[0036] Figure 5 A schematic diagram of obstacle avoidance for the intelligent route generation method for flight management equipment provided by the present invention;

[0037] Figure 6 An evaluation table of an intelligent route system in the flight management equipment route intelligent generation method provided by the present invention;

[0038] Figure 7 A schematic diagram of a three-dimensional route obtained by using a visual graph method in the intelligent generation method of a flight management device route provided by the present invention Figure 1 ;

[0039] Figure 8Schematic diagram of obtaining a three - dimensional route using the visibility graph method in the intelligent route generation method for a flight management device provided by the present invention Figure 2 ;

[0040] Fig. 9 Effect diagram of obtaining a three - dimensional route using the visibility graph method in the intelligent route generation method for a flight management device provided by the present invention Detailed implementation manners

[0041] The present application will be described in detail below in conjunction with embodiments, but the present application is not limited to these embodiments.

[0042] Refer to Figure 1-9 , as Figure 1 shown, an intelligent route generation method for a flight management device, characterized by including the following steps:

[0043] Step 1: Generate a basic route through the flight management device. As Figure 2-3 shown, among them, the basis conditions for generating the basic route at least include the departure airport, the arrival airport, the flight direction, the civil aviation route, and the navigation database;

[0044] Specifically, by inputting the departure airport and the arrival airport, roughly draw the flight route and cruise altitude on the map, and the flight management device generates a basic route.

[0045] It should be noted that there are不外乎 two possibilities from the departure airport (S) to the arrival airport (E). Method 1 is to go directly from the departure airport to the arrival airport, and Method 2 is to pass through several nodes (P). Step 1 of the present application is applicable to Method 2. According to the flight direction, first divide out several nodes (P), obtain the longitude and latitude information of several nodes (P). DIS(S, E) is the total distance between multiple nodes (P). For each node (P), obtain multiple waypoints (N) near the node (P) from the civil aviation navigation database. DIS(P, N) is the total distance between the node (P) and multiple waypoints (N). Calculate DIS(P, N) and calculate the previous DTK (Desired Track Angle) angle value. Select the waypoint (N) that meets the minimum value among multiple waypoints (N) and satisfies that the angle at the P angle subtracted by the two - end DTK is less than 90° to replace the current node (P) in the route. Then traverse the subsequent nodes (P) one by one to find the waypoint (N) that meets the conditions. Finally, it should satisfy that (DIS(S, E) / π) < DIS(S, N0) + DIS(N0, N...) + DIS(N..., E) to obtain a complete basic flight route that meets the departure airport (S), the arrival airport (E), and multiple waypoints (N) that meet the flight direction.

[0046] Step 2: Optimize the basic route using a deep learning network model: Based on historical route generation experience, associated learning rules, and the weather information and air traffic control information corresponding to the basic route obtained by the network, optimize the route and generate a flight route that meets safety requirements;

[0047] Step 3: Use global high-precision terrain elevation data to complete three-dimensional route obstacle avoidance.

[0048] Specifically, the pilot of the present application only needs to input the take-off airport, landing airport and cruising altitude to generate a basic route, optimize the basic route through a deep network model, generate a flight route that meets safety requirements, and at the same time realize the perception and obstacle avoidance functions of dangerous terrain through an obstacle avoidance algorithm; thereby achieving the optimal flight route generation. The implementation of the present invention improves the efficiency and accuracy of route input, thereby improving the efficiency and safety of flight management.

[0049] The second step comprises:

[0050] Dividing the basic route into flight segments to obtain a plurality of first flight segments, and requesting weather information and aviation control information for each first flight segment;

[0051] Conduct quantitative analysis on the danger level of the meteorological information and aviation control information for each first flight segment to determine whether it will affect flight safety;

[0052] If there is a safety impact, the flight segment will be replanned and the route will be adjusted to bypass the meteorological danger area and avoid the waypoints in the danger area. At the same time, new waypoints will be defined and combined to generate a new flight segment.

[0053] Generate flight paths that meet safety requirements.

[0054] Specifically, Figure 4 As shown in the figure, the entire route is segmented into 10m segments, and the meteorological information and aviation control information of the segment are requested in segments. For example, if there are various severe weather phenomena that endanger the safety of aircraft in this segment, mainly cumulonimbus clouds, active thunderstorms, hail, tropical storms, strong squall lines, tornadoes, strong sand (dust) storms, strong winds, heavy snow, heavy rain, freezing rain, low clouds, low visibility, cloud-covered mountains, downdrafts, strong wind shear, strong turbulence, severe ice accumulation, etc. According to the quantitative standard of the degree of danger of each phenomenon, it is analyzed to determine whether it has an impact on flight safety. If it has a safety impact, the corresponding segment of the segment is re-planned, the route of the segment is adjusted, the meteorological danger area is bypassed, the nearest waypoint to avoid the danger area is automatically found, or some custom waypoints are generated according to the algorithm, and these new waypoints are combined to generate a new segment.

[0055] The step three comprises:

[0056] The two-dimensional plane data in the flight route obtained in step 2 is converted into three-dimensional data through an obstacle avoidance algorithm, wherein the basis for data conversion includes at least: airport altitude, cruising altitude and global high-precision terrain elevation data.

[0057] Specifically, Figure 5 As shown in the figure, the route obstacle avoidance algorithm based on the safe altitude and high-precision elevation data of the global terrain can only reflect the flight guidance of the horizontal route because the generated route is two-dimensional plane data. At this time, the obstacle avoidance algorithm extends the two-dimensional route into three-dimensional data, which can provide the maximum safety guidance for vertical flight. At this time, the pilot needs to input the cruising altitude. The built-in algorithm of the equipment is based on the airport altitude, cruising altitude, and global high-precision terrain elevation data for unsafe terrain in the route. The route is optimized again through the obstacle avoidance algorithm, so that the route can accurately avoid dangerous terrain such as mountains, canyons, obstacles, etc., further improving flight safety.

[0058] Specifically, the aircraft, target point and each vertex of the polygonal obstacle are combined and connected through the visual graph method, and the connecting straight lines are regarded as arcs. It is required that the lines between the aircraft and each vertex of the obstacle, between the target point and each vertex of the obstacle, and between the vertices of the obstacle cannot pass through the obstacle, that is, the three-dimensional route data is obtained and the route is available.

[0059] The visual graph method assumes the route and obstacle as a particle and an approximate polygon respectively, and uses visible line segments to connect the particle, target point and each vertex of the polygon in an orderly combination, that is, the connection line cannot pass through the obstacle polygon, and the route path planning problem is converted into a search for the shortest line segment set between the starting point and the target point. An improved algorithm is often used to search for the shortest path. Since the searched path is a line segment connecting the vertices of the obstacle, the aircraft is prone to collision with the obstacle. When there are many environmental obstacles and feature information, the search time is long; at the same time, the visual graph method has poor flexibility, poor adaptability to the environment and poor real-time performance, and cannot guarantee that the searched path is the global optimal.

[0060] Therefore, the path planning algorithm based on the visibility graph method mainly includes the following two steps:

[0061] ① Construction of visual graph;

[0062] ② Use some optimization method to search for the optimal path on the constructed visibility graph;

[0063] In the visibility graph algorithm, obstacles are described by polygons, and the starting point S, the target point G, and the vertices V of the polygonal obstacle are o, as the vertices V of the visible graph, connect these vertices to each other, and keep the lines that do not cross obstacles as the edges E of the visible graph, and then assign weights to these edges according to some criteria, such as using the length of these edges as their weights. Then use some optimization method to search for the required optimal path on the constructed visible graph. According to the above process, it is easy to know that the final result is a set of vertices including S and G. These vertices are connected in order to form the obtained path, such as Figure 7 shown.

[0064] From the above process, we can see that the key to constructing a visible graph is to determine whether the line between any two vertices passes through an obstacle. We can simply and crudely directly detect whether the line between any two vertices passes through an obstacle, or we can use the following method to simplify the judgment:

[0065] ① The lines between adjacent vertices of the same obstacle will definitely not pass through the obstacle (i.e., a certain boundary of the obstacle), and the lines between non-adjacent vertices of the same obstacle are generally considered to pass through the obstacle (but for concave polygon obstacles, this may sometimes lead to misjudgment, because the lines between some non-adjacent nodes of concave polygons also do not pass through the obstacle);

[0066] ② The judgment of whether the vertices between different obstacles pass through the obstacle can be transformed into judging whether the line connecting the vertices will intersect with the line connecting the two vertices of the edge that constitutes the obstacle. For example, in the figure below, the line between V1 and V7 passes through the line between V2 and V3, so the line between V1 and V7 passes through the obstacle. Figure 8 shown.

[0067] The path found by the visibility graph method is close to the edge of the obstacle. You can use the method of extending the vertices of the obstacle outward by a certain distance according to the set radius. The effect is as follows: Fig. 9 As shown:

[0068] The method further comprises:

[0069] Generate optimal flight routes:

[0070] Generate an initial database based on departure and arrival airports,

[0071] Among them, the device stores the civil aviation navigation database, which is the initial database, with built-in civil aviation routes and can match the routes.

[0072] According to the pilot route system evaluation table, the option scores in the pilot route system evaluation table are weighted and calculated to generate the score of the route segment. The evaluation table is as follows: Figure 6 As shown;

[0073] Each time a route is generated in the future, the routes retrieved from the departure and landing airports will be ranked by score, with routes with higher scores being used first.

[0074] Specifically, an initial database is generated through the take-off airport and the landing airport. According to the pilot route system evaluation table, weighted calculation is performed according to the scores of each option. The score weights are shown in Table 1. The score of this route is generated. The next time a route is generated, the routes retrieved by the take-off airport and the landing airport are prioritized for ranking, and routes with higher scores are used first.

[0075] As shown in Table 1:

[0076] project Weight Route comfort 15％ Route safety 30％ Route integrity 25％ Does the route meet demand? 20％ Overall Rating 10％

[0077] Table 1

[0078] Specifically, AI big data algorithms are used to self-learn and generate optimal flight routes for continuous optimization;

[0079] Its main contents include:

[0080] Support: The support of an itemset is defined as the proportion of records containing the itemset in the data set to the total records.

[0081] Credibility or confidence: It is defined for a route association rule. The confidence of this rule is defined as "support (route {01,02}) / support ({01})". A confidence of 0.75 means that for all records containing "01", the association rule is applicable to 75% of them.

[0082] In generating optimal flight routes, the goal is to find a set of key waypoints that are frequently found at takeoff and landing airports. The support of a set is used here to measure its frequency of occurrence. The support of a set refers to the proportion of routes that contain the set.

[0083] 1) First, a list of itemsets for all single waypoints is generated;

[0084] 2) Check which item sets meet the minimum support requirements through past route records, and those that do not meet the minimum support will be removed;

[0085] 3) Combine the remaining sets to generate an itemset containing two elements;

[0086] 4) Next, re-query the previous route records, remove the item sets that do not meet the minimum support, and repeat until all item sets are removed.

[0087] To find association rules, we start with a frequent set. We want to know whether we can obtain other content for the elements in the frequent itemset, that is, whether a certain element or a certain set may deduce another element. If there is a frequent itemset {waypoint 1, waypoint 2}, then there may be an association rule "waypoint 1-->waypoint 2", which means that if waypoint 1 is selected, then the probability of connecting waypoint 2 is statistically higher, but the reverse is not necessarily true.

[0088] Based on the frequent item set, a possible rule list is generated, and then the credibility of each rule is tested. If the credibility does not meet the minimum requirement, the rule is removed. Similar to the generation of frequent item sets mentioned above, a frequent item set can generate many possible association rules. If the number of rules can be reduced before calculating the credibility of the rules, the calculation efficiency will be greatly improved.

[0089] One of the rules is: if a rule does not meet the minimum credibility requirement, then all subsets of the rule will not meet the minimum credibility requirement;

[0090] Once the confidence threshold is lowered, more rules can be obtained, thereby improving the accuracy and processing speed of the self-learning algorithm.

[0091] The scoring options of the pilot route system evaluation form include at least:

[0092] Route comfort, route safety and route integrity.

[0093] The use of global high-precision terrain elevation data to complete three-dimensional route obstacle avoidance includes:

[0094] The pilot enters the cruising altitude of the flight;

[0095] According to the cruising altitude, the global high-precision terrain elevation data is used for data comparison;

[0096] Dividing the flight route into flight segments to obtain a plurality of second flight segments;

[0097] Each second flight segment is tested. If the cruising altitude of the current second flight segment minus the terrain altitude is less than 500m, the current second flight segment is replanned;

[0098] The cruising altitude is consistent in each section and does not involve departure, approach and other procedures.

[0099] When there is a waypoint within the set range of the second flight segment and the obstacle can be successfully avoided, the waypoint is used for detour; when there is no waypoint within the set range, the optimal route of the current second flight segment is calculated, a number of custom waypoints are generated, and a complete route is generated by combining the flight cruising speed and cruising altitude with the custom waypoints, thereby completing the route planning with three-dimensional data.

[0100] Specifically, Figure 5 As shown, the pilot inputs the flight cruising altitude, and the global high-precision terrain elevation data is compared according to the cruising altitude, and the terrain is detected every 30 meters. When it is found that the cruising altitude minus the terrain height is less than 500M, the current segment is re-planned. When it is found that there are waypoints nearby and obstacles can be successfully avoided, the flight is detoured through the nearby waypoints, that is, the waypoints within the set range; when there are no waypoints within the set range, the optimal route for the segment is automatically calculated, and several custom points are generated. A complete route can be generated by combining the flight cruising speed and cruising altitude with these custom points, but the route must meet the flight requirements, thereby realizing three-dimensional route planning and completing the planning of the entire flight route.

[0101] The global high-precision terrain elevation data has a tile data accuracy of 30m and is used for stereo analysis of three-dimensional routes.

[0102] Specifically, tile data is a cached picture set formed by preprocessing vector or image data using an efficient cache mechanism (such as a pyramid), which is organized in a "level, row, and column" manner and can be quickly loaded on a web page. Therefore, tile map loading is based on the map range and level requested by the client, and the tiles of the grid at the corresponding level (i.e., the images pre-cropped by the server) are obtained by calculating the row and column numbers, and a map is formed on the client by these tile sets. Tile data with an accuracy of 30m is used in this application for stereo analysis of three-dimensional routes.

[0103] The above are only a few embodiments of the present application and do not constitute any form of limitation to the present application. Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the present application. Any technician familiar with the profession, without departing from the scope of the technical solution of the present application, using the technical content disclosed above to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A method for intelligently generating flight routes for flight management equipment, characterized in that: The steps include: Step 1: Generate a basic route through the flight management device, wherein the basis conditions for generating the basic route include at least the departure airport, the landing airport, the flight direction, the civil aviation route and the navigation database; Step 2: Optimize the basic route using a deep learning network model: Based on historical route generation experience, associated learning rules, and the weather information and air traffic control information corresponding to the basic route obtained by the network, optimize the route and generate a flight route that meets safety requirements; Step 3: Use global high-precision terrain elevation data to complete three-dimensional route obstacle avoidance.

2. A flight management equipment route intelligent generation method according to claim 1, characterized in that: The second step comprises: Dividing the basic route into flight segments to obtain a plurality of first flight segments, and requesting weather information and aviation control information for each first flight segment; Conduct quantitative analysis on the danger level of the meteorological information and aviation control information for each first flight segment to determine whether it will affect flight safety; If there is a safety impact, the flight segment will be replanned and the route will be adjusted to bypass the meteorological danger area and avoid the waypoints in the danger area. At the same time, new waypoints will be defined and combined to generate a new flight segment. Generate flight paths that meet safety requirements.

3. The method for intelligently generating flight routes of a flight management device according to claim 1, characterized in that: The step three comprises: The two-dimensional plane data in the flight route obtained in step 2 is converted into three-dimensional data through an obstacle avoidance algorithm, wherein the basis for data conversion includes at least: airport altitude, cruising altitude and global high-precision terrain elevation data.

4. A method for intelligently generating flight routes for flight management equipment according to claim 1, characterized in that: The method further comprises: Generate optimal flight routes: Generate an initial database based on departure and arrival airports; According to the pilot route system evaluation table, weighted calculation is performed on the option scores in the pilot route system evaluation table to generate a score for the route segment; Each time a route is generated in the future, the routes retrieved from the departure and landing airports will be ranked by score, with routes with higher scores being used first.

5. A flight management equipment route intelligent generation method according to claim 4, characterized in that: The scoring options of the pilot route system evaluation form include at least: Route comfort, route safety and route integrity.

6. A flight management equipment route intelligent generation method according to claim 1 or 3, characterized in that: The use of global high-precision terrain elevation data to complete three-dimensional route obstacle avoidance includes: The pilot enters the cruising altitude for the flight path; According to the cruising altitude, global high-precision terrain elevation data is compared; Dividing the flight route into flight segments to obtain a plurality of second flight segments; Each second flight segment is tested. If the cruising altitude of the current second flight segment minus the terrain altitude is less than 5000m, the current second flight segment is replanned; When there is a waypoint within the set range of the second flight segment and the obstacle can be successfully avoided, the waypoint is used for detour; when there is no waypoint within the set range, the optimal route of the current second flight segment is calculated, a number of custom waypoints are generated, and a complete route is generated by combining the flight cruising speed and cruising altitude with the custom waypoints, thereby completing the route planning with three-dimensional data.

7. A flight management equipment route intelligent generation method according to claim 6, characterized in that: The global high-precision terrain elevation data has a tile data accuracy of 30m and is used for stereo analysis of three-dimensional routes.

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