Aircraft flight route planning method and device and storage medium

Abstract

The invention discloses an aircraft flight route planning method, an aircraft flight route planning device and a non-transitory computer readable storage medium. A plurality of flight routes can be generated according to a starting position, an ending position, a total mileage upper limit and a network coverage rate lower limit. A feature score is given to the total mileage and the network coverage rate in each flight route, a total score is calculated after the total mileage feature score and the network coverage rate feature score are calculated through a mathematical relational expression, and the highest score is the optimal flight route.

Description

Technical Field

[0001] The present invention relates to a route planning technology, and more particularly to an aircraft flight route planning method, device and non-temporary computer-readable storage medium which are applicable to aircraft and can select a flight route according to network communication quality conditions. Background Art

[0002] The flight routes of existing aircraft such as drones are usually planned through software, and the flight routes are planned based on ideal conditions such as good weather, good GPS signals, and good network communication conditions, and the shortest route is planned. However, such planning is often too optimistic because the shortest route may not necessarily have good network coverage. If the drone encounters poor network communication during flight, it is easy to cause data or command transmission interruption, or even cause the drone to deviate from the course.

[0003] If a drone deviates from its course and there is no stable network signal to transmit real-time images and flight information, the operator or control console will not be able to respond immediately, which may easily lead to an accident. Summary of the invention

[0004] In view of this, embodiments of the present invention provide a method, device, and non-transitory computer-readable storage medium for planning an aircraft flight route, which can select a flight route according to network communication quality conditions.

[0005] An aircraft flight route planning method according to an embodiment of the present invention includes (but is not limited to) the following steps: receiving a request including a starting location and an end location; using flight geographic data to generate multiple flight routes; calculating a total mileage and a network coverage rate for each flight route, and assigning a characteristic score to each flight route, and using the total mileage characteristic score and the network coverage characteristic score to calculate a mathematical relationship to obtain a total score for each flight route; and outputting an optimal flight route as a preset flight route for the aircraft, which is selected from the highest score among the total scores of all flight routes.

[0006] An aircraft flight route planning device according to an embodiment of the present invention can provide an optimal flight route for a drone, and includes (but is not limited to) a wireless communication module, a storage module, and a processor. The wireless communication module is configured to communicate with the drone; the storage module is configured to store flight geographic data; the processor is electrically coupled to the wireless communication module and the storage module, and is configured to execute instructions to perform the operations in the aforementioned method.

[0007] A non-transitory computer-readable storage medium according to an embodiment of the present invention stores instructions, which, when executed by a processor, cause the processor to execute the aforementioned method.

[0008] In summary, according to some embodiments of the drone flight route planning method, device and non-temporary computer-readable storage medium, an optimal flight route can be automatically generated based on different considerations, such as flight distance limitation, network coverage limitation, avoiding network congested areas, avoiding prohibited flight areas, or avoiding areas with actual poor network quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A network diagram of an embodiment of the present invention;

[0010] Figure 2 A system architecture diagram of an aircraft flight route planning device according to an embodiment of the present invention;

[0011] Figure 3 A system architecture diagram of an aircraft in one embodiment of the present invention;

[0012] Figure 4 A flowchart of a method for providing an optimal flight route according to an embodiment of the present invention;

[0013] Figure 5 A flight route planning diagram according to an embodiment of the present invention;

[0014] Figure 6 This is a flight route planning diagram according to another embodiment of the present invention.

[0015] Explanation of symbols

[0016] 2: UAV flight route planning device

[0017] 20: Processor

[0018] 21: Wireless communication module

[0019] 22: Storage Module

[0020] 23:Memory storage module

[0021] 24: Output device

[0022] 25: Input Device

[0023] 26: Bus

[0024] 211: Wireless communication antenna

[0025] BS: Base Station

[0026] D: Drone

[0027] D0: Processing Unit

[0028] D1: Wireless communication unit

[0029] D2: Sensor

[0030] D3: Memory

[0031] D4: Output unit

[0032] D5: Input unit

[0033] D6: Bus unit

[0034] D11: Wireless communication antenna

[0035] Pi: Starting position

[0036] Pd: end position

[0037] R1, R2, R3: Candidate routes

[0038] Rs: Shortest route

[0039] Za1: Network congestion area

[0040] Za2: No-fly zone

[0041] Za3: Area with poor actual network quality

[0042] S100~S130: Steps DETAILED DESCRIPTION

[0043] The aircraft of the present invention is described by taking a drone as an example. Figure 1 , which is a network diagram of an embodiment of the present invention, which shows the situation that the drone D communicates with the drone flight route planning device 2 through the base station BS during flight. The base station BS can be a mobile communication network base station, and the mobile communication network can be at least one of the third generation (3G), fourth generation (4G), long term evolution (LTE), 5G NR (New Radio), time division multiple access (TDMA), code division multiple access (CDMA), CDMA 2000, wideband CDMA (WCDMA), global system for mobile communications (GSM), single carrier radio transmission technology (1xRTT) and universal mobile telecommunications system (UMTS).

[0044] Please also read Figure 2, which is a system architecture diagram of a drone flight route planning device in one embodiment of the present invention; as shown in the figure, the drone flight route planning device 2 includes a processor 20, a wireless communication module 21, a storage module 22, a memory storage module 23, an output device 24, and an input device 25, and the above devices are electrically coupled to each other through a bus 26. In one embodiment, the drone flight route planning device 2 can be a dedicated machine for drone control, a desktop computer, a laptop computer, a notebook computer, a tablet phone, a handheld computing device, a mainframe computer, a supercomputer, a workstation or a server.

[0045] The processor 20 may include, but is not limited to, one or more general-purpose processors, one or more special-purpose processors (such as a digital signal processing chip, a graphics acceleration processor, an application-specific integrated circuit (ASIC) or other equivalent chips or components), or other processing structures or components that can be configured to perform one or more of the methods described herein.

[0046] The wireless communication module 21 can form a communication connection with the drone D directly, or can form a communication connection with the drone D through the base station BS. The wireless communication module 21 may include (but not limited to) a modem, a network card, an infrared communication device, a wireless communication device and / or a chipset (such as a Bluetooth device, an IEEE 802.11 device, an IEEE 802.15.4 device, a WiFi device, a WiMax device, a cellular communication facility, etc.) or other equivalent components; moreover, the wireless communication module 21 may also communicate via one or more wireless communication antennas 211 that send and receive wireless signals.

[0047] The storage module 22 is configured to store flight geographic data, program codes, instructions, operating systems, and related software, the aforementioned software may include methods provided by the embodiments described herein, or configure systems provided by other embodiments; the flight geographic data includes latitude and longitude coordinates, terrain map data, and network coverage map data. Among them, the terrain map data includes information on natural terrain, artificial terrain, buildings, etc. combined with coordinates and altitudes; the network coverage map data is network quality distribution information of connection quality conditions of mobile broadband systems (such as 5G NR or 4G LTE) provided by various telecommunications operators in different geographical areas. The storage module 22 can be any available medium that can be accessed by a computer, such as (but not limited to) a compact disc read-only memory (CD-ROM) or other optical disk storage, magnetic disk storage, semiconductor storage or other storage device, or other other media that can be used to store program codes in the form of instructions or data structures and can be accessed by a computer.

[0048] The memory storage module 23 may include (but not limited to) random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), read-only memory (ROM), electronically erasable programmable design ROM (EEPROM), or other types of non-transitory computer-readable storage media. The output device 24 may include (but not limited to) a display, a printer, an LED, a speaker, or the like; the input device 25 may include (but not limited to) a camera, a touch screen, a touch pad, a microphone, a keyboard, a mouse, a button, a dial, a switch, or the like.

[0049] Please see again Figure 3 , which is a system architecture diagram of a drone in one embodiment of the present invention; the drone D includes a processing unit D0, a wireless communication unit D1, a sensor D2, a memory D3, an output unit D4, and an input unit D5, and the above devices are electrically coupled to each other through a bus unit D6. The processing unit D0 is configured to monitor and control various functions, subsystems and other components of the drone D, such as any combination of modules, software, instructions, circuits, hardware, etc. related to propulsion, navigation, power management, sensor management, and stability management.

[0050] The wireless communication unit D1 can directly form a communication connection with the drone flight route planning device 2, or can form a communication connection with the drone flight route planning device 2 through the base station BS. The wireless communication unit D1 may include (but not limited to) a wireless communication device or chipset (such as a Bluetooth device, an IEEE 802.11 device, an IEEE 802.15.4 device, a WiFi device, a WiMax device, a cellular communication facility, etc.) or other equivalent components. Moreover, the wireless communication unit D1 can also communicate via one or more wireless communication antennas D11 that send and receive wireless signals.

[0051] The sensor D2 may include (but is not limited to) an image sensor, an altimeter, an anemometer, a gyroscope, a barometer, an accelerometer, a proximity sensor, a light sensor, an electronic compass, a satellite positioning system receiver, and a network quality detector.

[0052] Memory D3 may include, but is not limited to, local and network accessible memory, disk drives, drive arrays, optical storage devices, solid-state storage devices such as random access memory (RAM) and / or read-only memory (ROM), or the like. Such storage devices may be configured to implement any suitable data storage, including, but not limited to, various file systems, database structures, or the like.

[0053] The output unit D4 may include (but not limited to) a display, a light emitting diode (LED), a speaker or other equivalent components. The input unit D5 may be used to receive control instructions (e.g., flight route change, start camera, etc.) or data from the drone flight route planning device 2, and collect data on various conditions related to the drone D, such as collecting various environmental data through the sensor D2; and may also include a screen, a touch panel, a microphone, a switch or other similar devices.

[0054] Please also see Figure 2 , Figure 4 and Figure 5 , Figure 4 is a flow chart of a method for providing an optimal flight route according to an embodiment of the present invention. Figure 5 FIG. 1 is a flight route planning diagram according to an embodiment of the present invention. Figure 4 The method shown can be executed by the drone D before the flight by the drone flight route planning device 2 or can be executed in real time during the flight.

[0055] In step S100, the drone flight route planning device 2 receives a request including a starting position Pi, an end position Pd, and flight conditions through the input device 25. The starting position Pi is the latitude and longitude coordinates of the scheduled departure or instantaneous time of the drone D, and the end position Pd is the latitude and longitude coordinates of the scheduled arrival time of the drone D. In one embodiment, the flight conditions include the upper limit of the flight mileage and the lower limit of the network coverage rate. The upper limit of the mileage is usually related to the battery power and flight time of the drone D, because the shorter the flight mileage, the shorter the flight time and the earlier the arrival time. The network coverage map data obtained from the telecommunications company can be used to obtain network connection quality data at different geographical locations. In this embodiment, the network coverage rate can refer to the total proportion of the total length of a flight route that has experienced network connection quality exceeding a certain threshold value. For example, if the network connection quality is divided into 0 to 5 points (0 is no signal and 5 is the strongest signal), and the threshold value is assumed to be 3 points, then the proportion of one or more sections of a flight route that have experienced 3 points accounts for 40% of the total length, the proportion of one or more sections of a flight route that have experienced 4 points accounts for 30% of the total length, and the proportion of one or more sections of a flight route that have experienced 5 points accounts for 20% of the total length. We can then obtain the effective network coverage rate of this flight route as 40%+30%+20%=90% (indicating that the proportion of areas where this flight route passes through with a network connection quality of more than 3 points is as high as 90%). However, the network coverage of a flight route is not limited to this algorithm. It is also possible to not set a threshold value and calculate the total ratio of each length ratio of 1 to 5 minutes (at least there is a signal) except 0 (no signal) as the network coverage. Other suitable algorithms can also be used to define and calculate, but basically the calculation will be based on the distribution of network connection quality on the flight route.

[0056] In step S110, the drone flight route planning device 2 inserts the starting position Pi, the end position Pd, and the flight conditions into the flight geographic data in the storage module 22 to generate a plurality of flight routes. Figure 5 As shown, in one embodiment, four flight routes are generated, including a shortest route Rs and three candidate routes R1, R2, and R3, wherein the three candidate routes R1, R2, and R3 all meet the aforementioned flight conditions, such as a mileage limit of 20 kilometers and a network coverage rate of more than 50%.

[0057] In step S120 , the drone flight route planning device 2 calculates a total mileage and a network coverage rate of each flight route, and assigns a feature score to each of the flight routes, and applies different weights to the feature scores to obtain a total score for each flight route.

[0058] To further explain in detail, the processor 20 uses the flight geographic data to calculate the total mileage and network coverage of each candidate route R1, R2, and R3, and assigns a characteristic score to each. Furthermore, the characteristic score is calculated in this embodiment, for example, by setting the characteristic score of the total mileage of the shortest route Rs to a full score, such as 100 points; and the characteristic score of the total mileage of the three candidate routes R1, R2, and R3 can be obtained according to the equation described below, which is Sr=Sc×((Dr-Ds) / Ds); wherein Sr is the characteristic score of the total mileage of the candidate routes R1, R2, and R3, Sc is the aforementioned full score, Dr is the total mileage of the candidate routes R1, R2, and R3, and Ds is the total mileage of the shortest route Rs. On the other hand, the characteristic score of the network coverage is calculated in this embodiment, for example, by multiplying the network coverage of the shortest route Rs and the three candidate routes R1, R2, and R3 by the full score (the full score in this embodiment is 100).

[0059] Next, a route weight is applied to the feature scores of the total mileage of the shortest route Rs and the three candidate routes R1, R2, and R3, and each flight route obtains a route weighted feature score; in one embodiment, the route weight is set to 0.2. Similarly, a network weight is applied to the feature scores of the network coverage of the shortest route Rs and the three candidate routes R1, R2, and R3, and each flight route can obtain a network weighted feature score; in one embodiment, the network weight is set to 0.8. Finally, after summing up the route weighted feature score and the network weighted feature score of each flight route, a total score of each flight route can be obtained. In this embodiment, the route weight and the network weight are preset to 2:8 (i.e., 0.2 and 0.8), but in other embodiments of the present invention, it can also be 3:7 or even 5:5, depending on whether the user pays more attention to the route length or the network quality stability during actual operation. However, the total score of a flight route in the present invention is not limited to the sum of the above-mentioned weights. In other embodiments, a specific mathematical relationship between the characteristic score of the total mileage and the characteristic score of the network coverage can also be used to calculate the total score (for example, adding the influencing factors of other flight conditions or weather factors).

[0060] Table 1 below lists the total mileage feature scores, route weighted feature scores, network coverage feature scores, and network weighted feature scores of the shortest route Rs and three candidate routes R1, R2, and R3. In the embodiment of Table 1 below, the total mileage of the shortest route Rs is 10 kilometers, but the network coverage is only 30%; the total mileage of the candidate route R1 is 13 kilometers, and the network coverage is 75%; the total mileage of the candidate route R2 is 15 kilometers, and the network coverage is 90%; the total mileage of the candidate route R3 is 20 kilometers, and the network coverage is 100%.

[0061] Table 1

[0062]

[0063] Finally, in step S130, the drone flight route planning device 2 outputs the best flight route as the preset flight route, which is selected from the flight routes with the highest total score. In the embodiment of Table 1, after comparing the total scores of all flight routes, the processor 20 selects the candidate route R2 with the highest total score as the best flight route, and outputs it to the output device 24, such as displaying it on a display, or transmitting it to the drone D through the wireless communication module 21. However, the preset flight route can also be just a preferred suggestion rather than a final decision. For example, in other embodiments of the present invention, other flight conditions (such as a no-fly zone) or other on-site factors (such as weather conditions) can be added or referred to to select other higher-scoring flight routes.

[0064] in addition, Figure 5 There are areas showing different network signal qualities. In other embodiments, in addition to setting the network coverage, the flight conditions can also further set network signal quality conditions. For example, the coverage of medium or above network signal quality must be at least 60%, which can be presented as candidate route R3.

[0065] See also Figure 6 , which is a flight route planning diagram of another embodiment of the present invention; the main difference between this embodiment and the previous embodiment is the addition of flight conditions. Further explanation: network congestion may still occur in areas covered by the network, so it is necessary to take into account hot spots where network congestion often occurs. In addition, it is also possible that due to emergencies or accidents, the network quality in some areas is temporarily poor or even interrupted, which is inconsistent with the network coverage information published by the original telecommunications operator. This situation also needs to be taken into consideration.

[0066] Accordingly, in one embodiment, the flight avoidance area can be included in the flight conditions. The flight avoidance area means the area that should be avoided when planning the flight route, such as the network congestion area Za1, the no-fly area Za2, and the actual poor network quality area Za3. Among them, the network congestion area Za1 is an area where the network quality is reduced due to a large number of network users. It can be achieved by receiving and analyzing historical network quality information through a data-mining engine, such as receiving real-time network quality information published by telecommunications system operators, and crowd or traffic information provided by mobile phone software operators (such as Google). In some areas, network congestion may only occur during certain time periods (for example, 8AM to 10AM), so the relevant conditions of the network congestion area Za1 may also include information of the corresponding time.

[0067] The no-fly zone Za2 may be, for example, a military base, an airport, a government building, a medical or public engineering facility (e.g., a power plant), a landmark, and other government or private property; it may be described by altitude restrictions, bounding boxes, geographic fences, and special markings defined using various coordinate systems. In some areas, the no-fly restrictions may only apply during certain time periods (e.g., 7PM to 7AM), and therefore the relevant conditions of the no-fly zone Za2 may also include information about the corresponding restricted time of the restricted area.

[0068] The actual poor network quality area Za3 can be realized by analyzing or counting historical flight records. For example, according to the recent flight records of the drone, the network quality of a specific area is not as good as the coverage network information published by the telecommunications operator. It may be caused by natural disasters or other unexpected events that cause local base station failures. In one embodiment, the drone can obtain the network quality monitoring data of the entire flight route through sensors (network quality detectors). When the network quality of a specific area is found to be poor in the recent flight process, and it is inconsistent with the coverage network information published by the telecommunications operator, it can be identified as the actual poor network quality area Za3. Therefore, in one embodiment, the actual poor network quality area Za3 can be included in the flight avoidance area by using the historical flight route signal quality statistics.

[0069] In addition, in other embodiments, the flight avoidance area may also include other environmental or human factors, such as extreme weather factors such as strong winds or storms, bird strikes, human attacks, and heavy flight traffic, etc. However, these factors or events can be achieved by analyzing or counting historical flight records or other related flight information.

[0070] Please also refer to Figure 4 and Figure 6 In step S100, when setting the flight conditions, flight avoidance areas including network congestion area Za1, no-fly area Za2, and actual network poor quality area Za3 can be included; in step S110, the multiple flight routes generated by the system will avoid these areas or select flight routes that do not pass through these areas.

[0071] In summary, in some embodiments, the user only needs to input the starting location and the end location, and set the flight conditions according to actual needs, such as the upper limit of mileage, the lower limit of network coverage, and the flight avoidance zone, etc.; and the device or method of the present invention will automatically plan multiple flight routes, and automatically evaluate them according to the set calculation rules and weights, and output an optimal flight route to the drone. However, this optimal flight route can not only achieve an excellent balance between network coverage and mileage, but also avoid flight avoidance zones including network congestion areas, poor signal areas, and no-fly areas, thereby providing a safe, reliable and fast drone flight route.

[0072] For implementations involving firmware and / or software, the methods may be implemented by executing the functions or steps described herein. Any machine-readable medium that is capable of implementing the instructions of the methods described herein may be used to implement the methods herein. For example, software program codes may be stored in a memory and executed by a processor. If implemented in firmware or software, the functions may be stored as one or more instructions or program codes on a computer-readable storage medium, which may include RAM, ROM, electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disk storage, magnetic disk storage, semiconductor memory or other storage device, or any other medium that can be used to store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0073] Although the present invention is disclosed in conjunction with the above embodiments, they are not intended to limit the present invention. Any ordinary technician in the technical field may make slight changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the definition of the attached claims.

Claims

1. A method for aircraft flight route planning, comprising the following steps: receiving a request including a starting position and an end position; Use flight geographic data to generate multiple flight routes; Calculate the total mileage and network coverage of each flight route, assign characteristic scores respectively, and use the total mileage characteristic score and the network coverage characteristic score to calculate the mathematical relationship to obtain the total score of each flight route; as well as The best flight route is output as a default flight route of the aircraft, which is selected from the flight routes with the highest total score.

2. The method for aircraft flight route planning according to claim 1, wherein: The request further includes at least one flight condition, which includes at least one of a mileage upper limit and a network coverage lower limit.

3. The method for aircraft flight route planning according to claim 2, wherein: The at least one flight condition also includes a flight avoidance area, which includes at least one of a network congestion area, a no-fly area, and an area with actual poor network quality; the actual area with actual poor network quality is obtained from at least one historical flight record.

4. The method for aircraft flight route planning according to claim 3, wherein: The flight geographic data includes latitude and longitude coordinates, terrain map data, network coverage map data and the flight avoidance area.

5. The method for aircraft flight route planning according to claim 1, wherein: The calculation steps of the mathematical relationship are: respectively applying route weights to the feature scores of the total mileage of the flight routes to obtain multiple route weighted feature scores; and respectively applying network weights to the feature scores of the network coverage of the flight routes to obtain multiple network weighted feature scores; and respectively adding up the route weighted feature scores and the network weighted feature scores of each of the flight routes to respectively obtain the total score of each of the flight routes.

6. The method for aircraft flight route planning according to claim 5, wherein: The network weight is greater than the route weight.

7. The method for aircraft flight route planning according to claim 5, wherein: The flight routes include the shortest route and at least one candidate route; the characteristic score of the total mileage of the shortest route is the full score, and the characteristic score of the total mileage of the at least one candidate route is obtained according to the equation, which is Sr=Sc×((Dr-Ds) / Ds); wherein Sr is the characteristic score of the total mileage of the at least one candidate route, Sc is the full score, Dr is the total mileage of the at least one candidate route, and Ds is the total mileage of the shortest route.

8. The method for aircraft flight route planning according to claim 7, wherein: The characteristic scores of the network coverage rates of the shortest route and the at least one candidate route are the network coverage rates multiplied by the full score value.

9. An aircraft flight route planning device, for providing an optimal flight route to an aircraft, comprising: A wireless communication module, configured to be communicatively coupled to the drone; a storage module configured to store flight geographic data; as well as A processor is electrically coupled to the wireless communication module and the storage module, and is configured to execute instructions to perform the following operations: receiving a request including a starting position and an end position; Using the flight geographic data to generate a plurality of flight routes; Calculating the total mileage and network coverage of each flight route, assigning feature scores respectively, and using the total mileage feature score and the network coverage feature score to calculate a mathematical relationship to obtain a total score for each flight route; and The best flight route is output as a default flight route for the aircraft, which is selected from the flight routes with the highest total scores.

10. The aircraft flight route planning device according to claim 9, wherein: The request further includes at least one flight condition, which includes at least one of a mileage upper limit and a network coverage lower limit.

11. The aircraft flight route planning device according to claim 10, wherein: The at least one flight condition also includes a flight avoidance area, which includes at least one of a network congestion area, a no-fly area, and an area with actual poor network quality; the actual area with actual poor network quality is obtained from at least one historical flight record.

12. The aircraft flight path planning device according to claim 11, wherein: The flight geographic data includes latitude and longitude coordinates, terrain map data, network coverage map data, and the flight avoidance area.

13. The aircraft flight path planning device according to claim 9, wherein: The calculation steps of the mathematical relationship are: respectively applying route weights to the feature scores of the total mileage of the flight routes to obtain multiple route weighted feature scores; respectively applying network weights to the feature scores of the network coverage of the flight routes to obtain multiple network weighted feature scores; and respectively adding up the route weighted feature scores and the network weighted feature scores of each of the flight routes to obtain the total score of each of the flight routes.

14. The aircraft flight path planning device according to claim 13, wherein: The network weight is greater than the route weight.

15. The aircraft flight path planning device according to claim 13, wherein: The flight routes include the shortest route and at least one candidate route; the characteristic score of the total mileage of the shortest route is the full score, and the characteristic score of the total mileage of the at least one candidate route is obtained according to the equation, which is Sr=Sc×((Dr-Ds) / Ds); wherein Sr is the characteristic score of the total mileage of the at least one candidate route, Sc is the full score, Dr is the total mileage of the at least one candidate route, and Ds is the total mileage of the shortest route.

16. The aircraft flight path planning device according to claim 15, wherein: In the step (B), the characteristic scores of the network coverage rates of the shortest route and the at least one candidate route are the network coverage rates multiplied by the full score value.

17. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform operations for aircraft flight path planning, the operations comprising: receiving a request including a starting position and an end position; Use flight geographic data to generate multiple flight routes; Calculating the total mileage and network coverage of each flight route, assigning feature scores respectively, and using the total mileage feature score and the network coverage feature score to calculate a mathematical relationship to obtain a total score for each flight route; and The best flight route is output as a default flight route of the aircraft, which is selected from the flight routes with the highest total score.

18. The non-transitory computer-readable storage medium of claim 17, wherein: The request further includes at least one flight condition, which includes at least one of a mileage upper limit, a network coverage lower limit and a flight avoidance area; the flight avoidance area includes at least one of a network congestion area, a no-fly area and an actual network poor quality area; the actual network poor quality area is obtained from at least one historical flight record.

19. The non-transitory computer-readable storage medium of claim 17, wherein: The calculation steps of the mathematical relationship are: applying route weights to the feature scores of the total mileage of the flight routes respectively to obtain multiple route weighted feature scores; applying network weights to the feature scores of the network coverage of the flight routes respectively to obtain multiple network weighted feature scores; and summing up the route weighted feature scores and the network weighted feature scores of each of the flight routes respectively to obtain the total score of each of the flight routes; wherein the network weight is greater than the route weight.

20. The non-transitory computer-readable storage medium of claim 19, wherein: The flight routes include the shortest route and at least one candidate route; the characteristic score of the total mileage of the shortest route is the full score, and the characteristic score of the total mileage of the at least one candidate route is obtained according to the equation, which is Sr=Sc×((Dr-Ds) / Ds); wherein Sr is the characteristic score of the total mileage of the at least one candidate route, Sc is the full score, Dr is the total mileage of the at least one candidate route, and is the total mileage of the shortest route; the characteristic scores of the network coverage rates of the shortest route and the at least one candidate route are the network coverage rates multiplied by the full score values ​​respectively.

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