Energy complementing path planning method and system for low-altitude unmanned aerial vehicle

Abstract

The invention relates to the technical field of flight path planning, and provides an energy complementing path planning method and system for a low-altitude unmanned aerial vehicle. The method comprises the following steps: acquiring real-time monitoring data of a low-altitude flight area; and according to the real-time monitoring data, determining an energy complementing point candidate set, determining a plurality of energy complementing paths by combining the position point cloud and the residual electric quantity of the low-altitude unmanned aerial vehicle, carrying out adaptive preferential selection, determining an adaptive energy complementing path, and carrying out energy complementing navigation planning. According to the method and the device, the technical problems that weather sudden change, barrier dynamic change and airspace temporary control are difficult to respond in real time and the aircraft is forced to interrupt a task or is faced with the risk of insufficient endurance due to the use of a static map and a preset rule in the existing energy complementing path planning are solved, and the aim of planning the energy complementing path through weather information, geographical barrier distribution information and airspace control information is achieved. And the environmental adaptation degree of the energy complementing point is dynamically evaluated, a plurality of energy complementing paths are generated in combination with the aircraft position point cloud and the remaining electric quantity, and the endurance demand is accurately matched.

Description

Technical Field

[0001] This application relates to the field of flight path planning technology, specifically to a method and system for planning the refueling path of a low-altitude unmanned aerial vehicle. Background Technology

[0002] Unmanned aerial vehicles (UAVs) have limited endurance, and the rational layout of refueling points and efficient path planning are key to ensuring their long-term stable operation. However, the low-altitude flight environment is highly dynamic, and changes in weather conditions, the distribution of geographical obstacles, and real-time adjustments to airspace control policies place higher demands on the refueling path planning of UAVs.

[0003] Current methods for planning refueling paths for low-altitude unmanned aerial vehicles (UAVs) largely rely on static maps and preset rules, making it difficult to adapt to dynamic scenarios such as sudden weather changes (e.g., strong winds, heavy rain) and temporary airspace restrictions. This leads to a disconnect between the planned path and the actual environment, forcing the UAV to interrupt its mission or face the risk of insufficient range. Furthermore, most path planning methods only focus on the shortest path or the lowest energy consumption as a single objective, ignoring multi-dimensional constraints such as airspace congestion and flight safety. In high-density flight areas, this can easily cause path conflicts and safety hazards, making it difficult to meet the needs of efficient and safe operation of low-altitude UAVs. Summary of the Invention

[0004] This application provides a method and system for planning refueling paths for low-altitude unmanned aerial vehicles (UAVs), aiming to solve the technical problem that existing refueling path planning methods, which use static maps and preset rules, are difficult to respond in real time to sudden weather changes, dynamic changes in obstacles, and temporary airspace control, and may lead to the risk of the UAV being forced to interrupt its mission or facing insufficient endurance.

[0005] The first aspect disclosed in this application provides a method for planning a refueling path for a low-altitude unmanned aerial vehicle (UAV). The method includes: acquiring real-time monitoring data of a low-altitude flight area, the real-time monitoring data including meteorological information, geographical obstacle distribution information, and airspace control information; determining a candidate set of refueling points based on the real-time monitoring data; introducing the location point cloud and remaining battery power of the low-altitude UAV, and combining it with the candidate set of refueling points to determine multiple refueling paths; performing adaptation and optimization on the multiple refueling paths to determine a suitable refueling path; and using the suitable refueling path to plan refueling navigation for the low-altitude UAV.

[0006] Preferably, the environmental adaptability of each power replenishment point is evaluated; power replenishment points with environmental adaptability lower than a preset adaptability threshold are deleted, and the candidate set of power replenishment points is determined.

[0007] Preferably, the refueling queuing time of each refueling point is predicted; refueling points whose refueling queuing time exceeds the preset waiting time are deleted, and each refueling path in the candidate set of refueling points contains at least two refueling points that meet the preset waiting time.

[0008] Preferably, the path energy consumption information corresponding to the multiple refueling paths is obtained based on the energy consumption parameters of the low-altitude unmanned aerial vehicle; and the first adaptation and selection constraint condition is configured based on the path energy consumption information corresponding to the multiple refueling paths.

[0009] Preferably, the service capability information of each charging point, including the charging power of the charging equipment and the number of available charging interfaces, is uploaded; and the second adaptation and selection constraint is configured based on the service capability information of each charging point.

[0010] Preferably, based on the first and second adaptation selection constraints, an energy consumption weight coefficient and a service capability weight coefficient are set; the adaptation degree of multiple energy replenishment paths is obtained by weighting the combination of the energy consumption weight coefficient and the service capability weight coefficient; the multiple energy replenishment paths are sorted from high to low according to their adaptation degree to determine the suitable energy replenishment path.

[0011] Preferably, historical flight data is acquired, and combined with the airspace control information, the predicted congestion probability of the adapted refueling path is obtained; based on the predicted congestion probability, it is determined whether to trigger the alternative path generation mechanism.

[0012] Another aspect of this application discloses a refueling path planning system for a low-altitude unmanned aerial vehicle (UAV). The system includes: a data acquisition module for acquiring real-time monitoring data of a low-altitude flight area, including meteorological information, geographical obstacle distribution information, and airspace control information; a refueling point determination module for determining a candidate set of refueling points based on the real-time monitoring data; a path determination module for determining multiple refueling paths by incorporating the UAV's location point cloud and remaining battery power, combined with the candidate refueling point set; and a navigation planning module for adapting and optimizing the multiple refueling paths to determine a suitable refueling path, and then using the suitable refueling path to plan the refueling navigation for the low-altitude UAV.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0014] The above-mentioned method for planning refueling paths for low-altitude unmanned aerial vehicles achieves the technical effect of dynamically assessing the environmental adaptability of refueling points by using meteorological information, geographical obstacle distribution information, and airspace control information, and generating multiple refueling paths by combining the aircraft's location point cloud and remaining power, thus accurately matching the range requirements.

[0015] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a refueling path planning method for a low-altitude unmanned aerial vehicle in one embodiment.

[0018] Figure 2 This is a diagram of a refueling path planning system architecture for a low-altitude unmanned aerial vehicle in one embodiment.

[0019] Explanation of reference numerals in the attached diagram: Data acquisition module 11, Recharge point determination module 12, Path determination module 13, Navigation planning module 14. Detailed Implementation

[0020] This application provides a method and system for planning the refueling path of a low-altitude unmanned aerial vehicle, which solves the technical problem that existing refueling path planning uses static maps and preset rules, making it difficult to respond in real time to sudden weather changes, dynamic changes in obstacles, and temporary airspace control, and thus the risk of the aircraft being forced to interrupt its mission or facing insufficient endurance.

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0023] Example 1, as Figure 1 As shown, this application provides a method for planning a refueling path for a low-altitude unmanned aerial vehicle, the method comprising:

[0024] Acquire real-time monitoring data of the low-altitude flight area, including meteorological information, geographical obstacle distribution information, and airspace control information; determine a candidate set of refueling points based on the real-time monitoring data.

[0025] Specifically, the low-altitude flight area refers to the airspace at a certain altitude above the ground (generally below 1000 meters), where the flight environment is complex and variable. Real-time monitoring data is a collection of information reflecting the current status of the low-altitude flight area, obtained through various sensors, monitoring equipment, and communication systems. Meteorological information includes parameters such as wind speed, wind direction, temperature, air pressure, rainfall, and visibility, which affect the flight safety and energy consumption of unmanned aerial vehicles (UAVs) in real time. Geographic obstacle distribution information includes data on the location, height, and shape of fixed or temporary obstacles such as buildings, mountains, communication towers, and bridges within the low-altitude area, crucial for planning flight paths to avoid collisions. Airspace control information covers the openness of airspace, temporary flight restrictions, no-fly zone designations, and air traffic control instructions, guiding UAVs to fly legally and orderly. The candidate set of refueling points is a collection of refueling points selected from numerous possibilities based on real-time monitoring data that meet certain preliminary conditions; these refueling points may provide energy replenishment services for UAVs.

[0026] Acquiring real-time monitoring data of low-altitude flight areas is a fundamental step in the entire refueling path planning process. To accurately plan a refueling path, a comprehensive and precise understanding of the flight environment is essential. This is achieved by deploying meteorological monitoring stations within the low-altitude flight area, utilizing satellite remote sensing technology to acquire geographical obstacle information, and engaging in real-time data exchange with air traffic control information systems. This allows for the acquisition of massive amounts of real-time monitoring data every minute or even shorter intervals. For example, meteorological monitoring stations can update meteorological information every 1-5 minutes, including detailed data such as wind speed accurate to 0.1 m / s and temperature accurate to 0.1 degrees Celsius. Geographic obstacle distribution information can be updated through regular topographic mapping and real-time construction reporting systems, ensuring that the aircraft is aware of the latest obstacle locations and altitude changes. Airspace control information provides real-time updates on airspace openness and restrictions, offering accurate environmental data for subsequently determining the candidate set of refueling points.

[0027] The purpose of determining the candidate set of refueling points based on these real-time monitoring data is to initially screen out locations suitable for UAV refueling under the current environmental conditions. Only these refueling points that initially meet the requirements in terms of environmental adaptability can be included in the final refueling path planning, thereby ensuring the feasibility and safety of the refueling path planning. For example, in a 10-square-kilometer low-altitude flight area, there were originally 20 refueling points, but after considering factors such as meteorological conditions (such as excessive wind speed causing severe turbulence around some refueling points), geographical obstacles (new super-tall buildings appearing near some refueling points), and airspace control (the airspace where some refueling points are located is controlled due to temporary activities), 8 refueling points were selected to enter the candidate set, providing key basic data support for the next stage of path generation and selection.

[0028] The location point cloud and remaining battery power of the low-altitude unmanned aerial vehicle are introduced, and multiple recharge paths are determined by combining them with the candidate set of recharge points. The multiple recharge paths are then adapted and optimized to determine the optimal recharge path, and the recharge navigation planning of the low-altitude unmanned aerial vehicle is performed using the optimal recharge path.

[0029] Specifically, the point cloud of a low-altitude unmanned aerial vehicle (UAV) refers to the set of three-dimensional position data points of the UAV in a low-altitude environment, obtained by multiple sensors (such as lidar, GPS, etc.). These data points can accurately describe the UAV's position status in space. The remaining battery power is the amount of power that the UAV currently has left in its battery, which is directly related to whether the UAV can safely reach the refueling point. The refueling path refers to a complete route from the UAV's current position to the refueling point, including the starting point, the ending point, and information such as waypoints that may be involved in the flight. Each refueling path has its unique characteristics such as flight distance, time, and energy consumption.

[0030] Adaptation and optimization is a decision-making process that aims to select the most suitable refueling path for the aircraft's current condition based on the aircraft's status (such as remaining battery power and flight speed) and the characteristics of the refueling path (such as path length, estimated flight time, and energy consumption estimates), combined with the service capabilities of the refueling point (such as charging power and the number of available interfaces). Refueling navigation planning, on the other hand, generates detailed navigation instructions for the aircraft based on the determined adaptive refueling path, guiding it to the refueling point safely and efficiently along the planned path. This involves the generation of instructions for multiple aspects, including path guidance, flight attitude adjustment, and speed control.

[0031] After introducing the location point cloud and remaining battery power of the low-altitude unmanned aerial vehicle (UAV), the process involves determining multiple refueling paths by combining them with a candidate set of refueling points. Specifically, the location point cloud provides precise three-dimensional position information of the UAV, while the remaining battery power clarifies the UAV's urgent endurance requirements. Starting from the location point cloud and ending at each refueling point in the candidate set, multiple feasible refueling paths are generated using path planning algorithms (such as A* algorithm, Dijkstra's algorithm, etc.) while considering various constraints in the low-altitude flight environment (such as obstacles, airspace restrictions, etc.).

[0032] The process of adapting and selecting the optimal refueling path from multiple options and then planning the refueling navigation is the key decision-making step in the entire refueling path planning methodology. This process requires comprehensive consideration of various factors. For example, from the perspective of remaining battery power, if the aircraft has low remaining battery power, a shorter refueling path with lower expected energy consumption may be prioritized. From a flight safety perspective, refueling paths that avoid airspace control areas and areas with dense obstacles are more advantageous. From a refueling efficiency perspective, paths with high charging power and a large number of available interfaces are preferred. Through structural optimization... An adaptive selection model is established to quantitatively evaluate each refueling path, such as assigning different weight coefficients (e.g., distance weight 0.4, safety weight 0.3, and refueling efficiency weight 0.3). The comprehensive adaptability score of each path is evaluated, and the paths are selected according to their scores from high to low. Based on the determined adaptive refueling paths, a refueling navigation plan is generated, including instructions such as flight direction, speed, and altitude, to guide the aircraft to accurately reach the refueling point, ensuring that it can refuel in time and continue to complete its flight mission. This effectively improves the operational efficiency and safety of the aircraft in complex and dynamic low-altitude environments.

[0033] Furthermore, this application provides a method for determining a candidate set of energy replenishment points based on the real-time monitoring data, the method comprising:

[0034] Evaluate the environmental adaptability of each power replenishment point; delete power replenishment points whose environmental adaptability is lower than a preset adaptability threshold, and determine the candidate set of power replenishment points.

[0035] Specifically, environmental adaptability is used to measure the suitability of a refueling point in the current low-altitude flight environment. It takes into account various environmental factors, such as the impact of meteorological conditions (wind speed, rainfall, etc.) on the safety of refueling operations, the distance and relative position of geographical obstacles (such as buildings, hills, etc.) to the refueling point, and the degree of airspace control restrictions on the area where the refueling point is located. The preset adaptability threshold is a benchmark value pre-set based on the unmanned aerial vehicle's operational safety standards and refueling efficiency requirements. It selects refueling points that are sufficiently suitable for refueling operations in the current environment and excludes those refueling points with excessively harsh environmental conditions or significant safety hazards.

[0036] Assessing the environmental suitability of each refueling point is a crucial screening step in refueling route planning. In low-altitude flight environments, changes in weather conditions can significantly impact flight safety and refueling operations. For example, when wind speeds exceed a certain threshold, aircraft may experience flight instability as they approach a refueling point, increasing the risk of collision. Rainfall may interfere with communication equipment or affect the normal operation of refueling equipment. The presence of geographical obstacles can also affect whether an aircraft can safely reach a refueling point. If there are tall buildings or mountains near the refueling point, the aircraft needs to detour or adjust its flight altitude, increasing energy consumption and flight time. Airspace control directly determines whether an aircraft can legally enter the area where the refueling point is located. By comprehensively considering these factors and conducting an environmental suitability assessment for each refueling point, the applicability of each refueling point can be quantified.

[0037] The process of removing refueling points with environmental adaptability below a preset adaptability threshold and determining the candidate set of refueling points effectively narrows the selection range, improving the efficiency and safety of subsequent path planning. After excluding refueling points with environmental adaptability below the preset adaptability threshold, the remaining refueling points form the candidate set. These refueling points are relatively more suitable for refueling operations in terms of environmental conditions, providing better basic data for determining multiple refueling paths based on the aircraft's position and remaining battery power. This improves the feasibility and effectiveness of the entire refueling path planning scheme, ensuring the safety of refueling and the continuity of missions for the unmanned aerial vehicle in complex low-altitude environments.

[0038] Furthermore, the method described in this application includes:

[0039] Predict the queuing time for each refueling point; delete refueling points whose queuing time exceeds a preset waiting time, wherein each refueling path in the candidate set of refueling points contains at least two refueling points that meet the preset waiting time.

[0040] Specifically, the recharge queuing time refers to the time that a recharge point can serve a limited number of aircraft at the same time, and other aircraft need to wait for recharge to be completed. For example, if a recharge point has 3 charging ports, and 5 aircraft need recharge, the last 2 aircraft need to queue and wait for the aircraft in front to finish recharging. This time is affected by factors such as the service efficiency of the recharge point (e.g., charging power, number of ports), the number of aircraft currently in the queue, and the recharge demand of each aircraft. The preset waiting time is a maximum acceptable waiting time preset based on the urgency of the unmanned aerial vehicle's mission and its endurance. Its purpose is to select recharge points that can meet the mission time requirements of the aircraft and avoid the aircraft waiting at the recharge point for a long time, which would delay the execution of the mission.

[0041] Predicting the recharge queuing time at each recharge point is estimated by analyzing its service capacity and current usage. The service capacity of a recharge point includes its charging power and the number of available charging ports. If a recharge point has a charging power of 10kW, 5 ports, and each port can charge one aircraft simultaneously, and there are currently 6 aircraft in the queue, with each aircraft requiring an average of 20kWh of recharge, then the recharge time for each aircraft is approximately 2 hours (20kWh / 10kW). The recharge queuing time is then accumulated according to the queuing order. For example, aircraft 1 to 5 do not need to wait in the queue, aircraft 6 needs to wait for about 2 hours, and so on. In addition, combining historical data and real-time flight plans can more accurately predict the recharge queuing time, such as by analyzing the historical queuing situation of the recharge point in the same time period and the currently known aircraft recharge plans.

[0042] After deleting refueling points whose queuing time exceeds the preset waiting time, each refueling path in the candidate set of refueling points contains at least two refueling points that meet the preset waiting time. This process ensures that the aircraft has multiple reliable options during the refueling process, avoiding delays in missions due to excessively long queuing times. Preferably, after excluding these refueling points, each refueling path in the remaining candidate set of refueling points contains at least two refueling points that meet the waiting time requirement. For example, one refueling path contains refueling point A (queueing time 0.5 hours) and refueling point B (queueing time 0.3 hours), and another refueling path contains refueling point C (queueing time 0.4 hours) and refueling point D (queueing time 0.2 hours). In this way, in subsequent path planning, the aircraft can select appropriate refueling points for refueling according to the actual situation, which can meet the endurance requirements and ensure the efficient execution of the mission, improving the practicality and reliability of the entire refueling path planning.

[0043] Furthermore, the method described in this application also includes:

[0044] Based on the energy consumption parameters of the low-altitude unmanned aerial vehicle, obtain the path energy consumption information corresponding to the multiple refueling paths; and configure the first adaptation and selection constraint conditions through the path energy consumption information corresponding to the multiple refueling paths.

[0045] Specifically, energy consumption parameters refer to parameters related to energy consumption during the flight of a low-altitude unmanned aerial vehicle (UAV), including the vehicle's weight, flight speed, power system efficiency, and energy consumption coefficients under different flight attitudes (such as climb, level flight, and hovering), reflecting the energy consumption characteristics of the vehicle under different flight states. Path energy consumption information refers to the expected energy consumption data calculated for each refueling path, combining energy consumption parameters and path characteristics (such as distance, changes in flight altitude, and estimated flight time), which is an important basis for evaluating whether a refueling path is suitable for the current endurance status of the vehicle. Adaptive selection constraints refer to the limiting criteria set to select the most suitable refueling path for the vehicle. The first adaptive selection constraint, based on path energy consumption information, aims to ensure that when selecting a refueling path, the energy consumption of the selected path can meet the vehicle's remaining power and mission endurance requirements.

[0046] The process of obtaining path energy consumption information for multiple refueling paths requires comprehensive consideration of the aircraft's energy consumption parameters and the characteristics of the refueling paths. Taking the aircraft's weight as an example, assuming the aircraft weighs 5 kg, its energy consumption is 0.1 kWh per kilometer in level flight and an additional 0.05 kWh per 100 meters of altitude increase during climb. For a refueling path containing a 10 km level flight segment and a 200 m climb, its path energy consumption can be calculated as follows: level flight energy consumption is 10 km × 0.1 kWh / km = 1 kWh, climb energy consumption is 200 m / 100 m × 0.05 kWh = 0.1 kWh, and the total path energy consumption is 1.1 kWh. In this way, the energy consumption information of each refueling path can be evaluated.

[0047] Using path energy consumption information to configure the first adaptation and selection constraint can ensure that the energy consumption of the selected refueling path is feasible. The first adaptation and selection constraint can be set as: path energy consumption ≤ remaining power - reserve power. When screening multiple refueling paths, only refueling paths that meet this condition will be retained and enter the subsequent selection stage. This filters out refueling paths whose energy consumption exceeds the aircraft's tolerance range in advance, preventing the aircraft from being unable to complete the refueling process or returning due to insufficient energy, and improving the rationality and safety of refueling path planning.

[0048] Furthermore, the method described in this application also includes:

[0049] Upload service capability information for each charging point, including the charging power of the charging equipment and the number of available charging interfaces; configure the second adaptation and selection constraint based on the service capability information of each charging point.

[0050] Specifically, uploading refers to sending the service capability information of each refueling point to the path planning system via a communication network (such as a wireless communication network) so that the system can obtain and use this information for subsequent analysis and decision-making. The service capability information of the refueling point includes the charging power of the refueling equipment and the number of available charging interfaces. The charging power represents the speed at which the refueling equipment charges the aircraft. The number of available charging interfaces reflects how many aircraft the refueling point can provide refueling services to at the same time. For example, if a refueling point has 5 available charging interfaces, it can serve 5 aircraft at the same time. The second adaptation and selection constraint is formulated based on the service capability information of the refueling point. Its purpose is to ensure that the refueling point can efficiently provide refueling services to the aircraft.

[0051] Uploading service capacity information for each charging station is a crucial step in charging route planning. Through the communication network, information such as the charging power and the number of available charging interfaces at each charging station is uploaded to the route planning system in real time. For example, charging station A reports a charging power of 10kW and 3 available charging interfaces; charging station B reports a charging power of 8kW and 5 available charging interfaces. After collecting this information, the system can understand the charging capacity and service capacity of each charging station.

[0052] By configuring the second adaptation and selection constraints based on this service capability information, it can be ensured that the refueling points in the refueling path selected by the aircraft can meet its refueling needs. For example, assuming that the aircraft needs to replenish 8kWh of energy within 1 hour, the second adaptation and selection constraints can be set as follows: the charging power of the refueling point is ≥8kW (8kWh / 1h), and the number of available charging interfaces is ≥1 (ensuring that there are available interfaces). In this way, when screening multiple refueling paths, only those refueling paths that meet the second adaptation and selection constraints will be retained. This avoids delays caused by insufficient charging interfaces or slow charging speed after the aircraft arrives at the refueling point, effectively improving the reliability and efficiency of refueling path planning, and ensuring that the aircraft can complete refueling in a timely and efficient manner and continue to perform its mission.

[0053] Furthermore, this application provides a method for adapting and selecting the optimal power replenishment path from the multiple power replenishment paths, the method comprising:

[0054] Based on the first and second adaptation selection constraints, energy consumption weight coefficients and service capability weight coefficients are set; the adaptation degree of multiple energy replenishment paths is obtained by weighting the combination of the energy consumption weight coefficients and service capability weight coefficients; the adaptation degree of the multiple energy replenishment paths is sorted from high to low according to the adaptation degree of the multiple energy replenishment paths to determine the suitable energy replenishment path.

[0055] Specifically, the energy consumption weight coefficient is a proportional coefficient used to measure the importance of energy consumption factors in the refueling path suitability assessment, reflecting the aircraft mission's focus on energy consumption; the service capability weight coefficient measures the importance of the refueling point's service capability factors in the suitability assessment, reflecting the mission's requirements for refueling efficiency and reliability; the suitability integrates both energy consumption and service capability factors to quantify the applicability of each refueling path, with a higher suitability indicating that the path better meets the aircraft's refueling needs; combined weighting refers to multiplying each assessment factor (here referring to energy consumption and service capability) by its corresponding weight coefficient and then adding them together to obtain a comprehensive assessment result, namely the refueling path suitability.

[0056] The process of setting energy consumption weight coefficients and service capability weight coefficients based on the first and second adaptation selection constraints (energy consumption constraints and service capability constraints) is similar to assigning importance weights to two key factors. For example, in emergency missions, rapid refueling is crucial. In this case, the service capability weight coefficient can be set to 0.6 and the energy consumption weight coefficient to 0.4, because rapid refueling can provide energy support to the aircraft more promptly and ensure mission continuity. If a refueling path scores 80 points (out of 100) in energy consumption assessment and 90 points in service capability assessment, then its adaptation degree is calculated as: Adaptation degree = (0.4 × 80) + (0.6 × 90) = 86 points. In this way, by comprehensively considering both energy consumption and service capability, a score reflecting the overall applicability of each refueling path is calculated.

[0057] The process of sorting multiple refueling paths based on their suitability and determining the most suitable refueling path can select the most appropriate refueling path for the aircraft. After sorting them from high to low suitability, the path with the highest score is likely to have lower energy consumption and stronger service capabilities at the refueling point. Such a path can enable the aircraft to complete refueling efficiently and quickly, better ensuring mission execution efficiency and flight safety.

[0058] Furthermore, the method described in this application includes:

[0059] Historical flight data is acquired, and combined with the airspace control information, the predicted congestion probability of the adapted refueling path is obtained; based on the predicted congestion probability, it is determined whether to trigger the alternative path generation mechanism.

[0060] Specifically, historical flight data refers to various information recorded by aircraft during past flights in low-altitude areas, including flight time, flight trajectory, refueling points encountered, airspace control conditions, flight speed, and energy consumption. This data reflects the flight status and patterns of aircraft in different airspaces and environments. Predicting congestion probability refers to estimating the probability of congestion on suitable refueling paths based on historical flight data and airspace control information. Congestion is generally caused by various factors, such as aircraft clustering due to airspace control and a surge in refueling demand during specific periods. The alternative path generation mechanism is a strategy or method that generates alternative refueling paths in a timely manner when problems may occur on the main path (such as a high probability of congestion), aiming to ensure that aircraft can complete refueling tasks smoothly and efficiently.

[0061] The process of obtaining the predicted congestion probability of suitable refueling routes requires a comprehensive analysis of historical flight data and airspace control information. Mining historical flight data reveals the frequency and severity of congestion on refueling routes under different time periods, airspace regions, and airspace control policies. For example, historical data shows that between 9:00 AM and 11:00 AM on weekdays, the probability of congestion on refueling routes in a certain low-altitude area increases due to concentrated refueling by surrounding logistics aircraft. Airspace control information reflects real-time airspace restrictions, such as temporary no-fly zones and flow restrictions. These control measures may alter aircraft flight paths, leading to increased congestion probabilities on certain refueling routes. Combining this information with statistical models or machine learning algorithms, the congestion probability of suitable refueling routes under current conditions can be predicted relatively accurately.

[0062] Determining whether to trigger the alternative path generation mechanism based on the predicted congestion probability can effectively improve the reliability and flexibility of refueling path planning. For example, if a congestion probability threshold of 30% is set, the alternative path generation mechanism will be automatically triggered when the predicted congestion probability exceeds this threshold. When there are multiple refueling paths, the system will quickly generate new alternative paths based on real-time data and algorithms. These alternative paths may bypass congested areas or utilize other airspace resources. In this way, the aircraft can adjust its refueling plan in a timely manner, choose a smoother path to the refueling point, avoid refueling delays caused by congestion, and ensure the continuity and efficiency of flight missions.

[0063] In summary, the embodiments of this application have at least the following technical effects:

[0064] This application achieves the technical effect of dynamically evaluating the environmental adaptability of refueling points by acquiring real-time monitoring data of low-altitude flight areas, including meteorological information, geographical obstacle distribution information, and airspace control information; determining a candidate set of refueling points based on the real-time monitoring data; incorporating the location point cloud and remaining battery power of the low-altitude UAV, and combining it with the candidate refueling point set to determine multiple refueling paths; and optimizing the multiple refueling paths to determine the optimal refueling path, which is then used for refueling navigation planning of the low-altitude UAV. This results in accurately matching the range requirements by using meteorological information, geographical obstacle distribution information, and airspace control information to generate multiple refueling paths based on the UAV's location point cloud and remaining battery power.

[0065] Example 2, based on the same inventive concept as the refueling path planning method for a low-altitude unmanned aerial vehicle in the foregoing examples, such as... Figure 2 As shown, this application provides a refueling path planning system for low-altitude unmanned aerial vehicles, the system comprising:

[0066] Data acquisition module 11: Acquires real-time monitoring data of low-altitude flight areas, including meteorological information, geographical obstacle distribution information, and airspace control information.

[0067] Energy replenishment point determination module 12: Determines a candidate set of energy replenishment points based on the real-time monitoring data.

[0068] Path determination module 13: Introduces the location point cloud and remaining power of the low-altitude unmanned aerial vehicle, and combines it with the candidate set of recharge points to determine multiple recharge paths.

[0069] Navigation planning module 14: Adapts and selects the best among the multiple refueling paths, determines the best refueling path, and uses the best refueling path to plan the refueling navigation for the low-altitude unmanned aerial vehicle.

[0070] Furthermore, the energy replenishment point determination module 12 is used to perform the following method:

[0071] Evaluate the environmental adaptability of each power replenishment point; delete power replenishment points whose environmental adaptability is lower than a preset adaptability threshold, and determine the candidate set of power replenishment points.

[0072] Furthermore, the energy replenishment point determination module 12 is also used to perform the following method:

[0073] Predict the queuing time for each refueling point; delete refueling points whose queuing time exceeds a preset waiting time, wherein each refueling path in the candidate set of refueling points contains at least two refueling points that meet the preset waiting time.

[0074] Furthermore, the energy replenishment point determination module 12 is also used to perform the following method:

[0075] Based on the energy consumption parameters of the low-altitude unmanned aerial vehicle, obtain the path energy consumption information corresponding to the multiple refueling paths; and configure the first adaptation and selection constraint conditions through the path energy consumption information corresponding to the multiple refueling paths.

[0076] Furthermore, the energy replenishment point determination module 12 is also used to perform the following method:

[0077] Upload service capability information for each charging point, including the charging power of the charging equipment and the number of available charging interfaces; configure the second adaptation and selection constraint based on the service capability information of each charging point.

[0078] Furthermore, the navigation planning module 14 is used to perform the following method:

[0079] Based on the first and second adaptation selection constraints, energy consumption weight coefficients and service capability weight coefficients are set; the adaptation degree of multiple energy replenishment paths is obtained by weighting the combination of the energy consumption weight coefficients and service capability weight coefficients; the adaptation degree of the multiple energy replenishment paths is sorted from high to low according to the adaptation degree of the multiple energy replenishment paths to determine the suitable energy replenishment path.

[0080] Furthermore, the navigation planning module 14 is also used to perform the following methods:

[0081] Historical flight data is acquired, and combined with the airspace control information, the predicted congestion probability of the adapted refueling path is obtained; based on the predicted congestion probability, it is determined whether to trigger the alternative path generation mechanism.

[0082] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0083] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0084] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for planning a refueling path for a low-altitude unmanned aerial vehicle, characterized in that, The method comprises: Acquire real-time monitoring data of low-altitude flight areas, including meteorological information, geographical obstacle distribution information, and airspace control information; Based on the real-time monitoring data, a candidate set of refueling points is determined; By incorporating the location point cloud and remaining battery power of the low-altitude unmanned aerial vehicle, and combining it with the candidate set of recharge points, multiple recharge paths are determined. The multiple refueling paths are adapted and optimized to determine the optimal refueling path, and the low-altitude unmanned aerial vehicle is refueling navigation planned using the optimal refueling path.

2. The method as described in claim 1, characterized in that, Based on the real-time monitoring data, a candidate set of refueling points is determined, the method comprising: Assess the environmental adaptability of each refueling point; Delete the energy replenishment points whose environmental adaptability is lower than the preset adaptability threshold, and determine the candidate set of energy replenishment points.

3. The method as described in claim 2, characterized in that, The method comprises: Predict the refueling queue time at each refueling point; Delete the replenishment points whose replenishment queuing time exceeds the preset waiting time. Each replenishment path in the candidate replenishment point set contains at least two replenishment points that meet the preset waiting time.

4. The method as described in claim 1, characterized in that, The method further includes: Based on the energy consumption parameters of the low-altitude unmanned aerial vehicle, obtain the path energy consumption information corresponding to the multiple refueling paths; Configure the first adaptation and selection constraint condition based on the path energy consumption information corresponding to the multiple energy replenishment paths.

5. The method as described in claim 4, characterized in that, The method further includes: Upload service capacity information for each charging point, including the charging power of the charging equipment and the number of available charging ports; Configure the second adaptation and selection constraints based on the service capability information of each replenishment point.

6. The method as described in claim 5, characterized in that, The method involves performing an optimal selection process among the multiple energy replenishment paths to determine the optimal energy replenishment path. Based on the first and second adaptation selection constraints, energy consumption weight coefficients and service capability weight coefficients are set. The suitability of multiple energy replenishment paths is obtained by weighting the energy consumption weight coefficient and the service capability weight coefficient. The suitable power replenishment paths are determined by sorting the multiple power replenishment paths from high to low based on their adaptability.

7. The method as described in claim 6, characterized in that, The method comprises: By acquiring historical flight data and combining it with the airspace control information, the predicted congestion probability of the adaptive refueling path is obtained. Based on the predicted congestion probability, determine whether to trigger the alternative path generation mechanism.

8. A refueling path planning system for a low-altitude unmanned aerial vehicle, characterized in that, The system is used to execute the refueling path planning method for a low-altitude unmanned aerial vehicle according to any one of claims 1-7, including: Data acquisition module: Acquires real-time monitoring data of low-altitude flight areas, including meteorological information, geographical obstacle distribution information, and airspace control information; Energy replenishment point determination module: Determines a candidate set of energy replenishment points based on the real-time monitoring data; Path determination module: Incorporating the location point cloud and remaining battery power of the low-altitude unmanned aerial vehicle, and combining it with the candidate set of recharge points, multiple recharge paths are determined; Navigation planning module: Adapts and selects the best among the multiple refueling paths, determines the best refueling path, and performs refueling navigation planning for the low-altitude unmanned aerial vehicle using the best refueling path.

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