A method and system for dynamically generating a three-dimensional traffic route for a UAV, and an electronic device

Abstract

The application provides a kind of unmanned aerial vehicle three-dimensional traffic airway dynamic generation method and system, electronic equipment, comprising: obtaining the multi-source space-time data of target area, and constructing dynamic three-dimensional airspace model updated with time;Based on dynamic three-dimensional airspace model, the predicted environment information in future period is generated by environment prediction;Energy consumption model representing the energy consumption of unmanned aerial vehicle flight is constructed, and the energy consumption estimate of flight path is output according to flight parameters and predicted environment information;According to the comprehensive optimization target, the path meeting the constraint of predicted environment information is searched in dynamic three-dimensional airspace model, and the main airway and at least one standby airway are generated;The unmanned aerial vehicle is controlled to fly along the main airway, and the changes of flight state parameters and predicted environment information are monitored in real time, and when the preset airway switching condition is met, the unmanned aerial vehicle is dynamically switched from the main airway to a standby airway, the application realizes the risk avoidance and energy consumption optimization of airway, and improves the flight stability, endurance and task continuity.

Description

Technical Field

[0001] This invention relates to the field of dynamic generation technology of three-dimensional traffic routes for unmanned aerial vehicles (UAVs), specifically to a method, system, and electronic equipment for dynamic generation of three-dimensional traffic routes for UAVs. Background Technology

[0002] With the rapid development of smart cities and the low-altitude economy, applications of drones in urban logistics, facility inspection, and emergency response are becoming increasingly widespread. Urban environments are characterized by dense buildings, variable weather, and dynamic adjustments to airspace control, placing stringent demands on the real-time performance, safety, and economy of drone route planning. Currently, most mainstream drone route generation methods employ two-dimensional or three-dimensional path planning algorithms based on static models. These methods generate a predefined route based on a static map acquired before takeoff, with the shortest path or the shortest time as the optimization objective. When onboard sensors detect sudden obstacles, local replanning is triggered for obstacle avoidance.

[0003] However, existing technologies generally have some inherent flaws when dealing with the complex and dynamic urban environment. First, they suffer from a time lag, only initiating obstacle avoidance response after an obstacle appears and is perceived. This passive reaction mechanism cannot anticipate future environmental changes caused by weather variations, temporary construction, airspace control, etc., making it highly susceptible to drones entering risk areas during flight, triggering emergency braking or even flight interruption, severely impacting the safety and smoothness of mission execution. Second, they suffer from a singular optimization approach, neglecting the constraint of energy consumption on endurance. The objective functions of most existing path planning methods focus solely on minimizing spatial distance or flight time, failing to incorporate the energy consumption characteristics of drones into the decision-making process. Real-world measurements show that, between the same origin and destination points, battery consumption differs significantly between windward climb paths and downwind level flight paths. Ignoring the combined impact of factors such as wind field, climb power, and payload weight on energy consumption will result in the route planning results not being the optimal solution for endurance in actual execution, severely limiting the mission radius and effective operating time of a single drone sortie. Furthermore, there are vulnerabilities in the execution dimension, and there is a lack of redundancy guarantees for mission continuity. Typically, only a single route is generated. Once the route becomes unavailable due to sudden environmental changes or communication interruptions, the system must immediately initiate global replanning. Replanning not only consumes a large amount of onboard computing resources, but the safety and energy efficiency of the new route generated cannot be verified in advance. Frequent triggering will significantly reduce the overall efficiency and success rate of flight missions.

[0004] In summary, existing technologies cannot meet the advanced requirements of urban drone traffic in terms of predictive avoidance, comprehensive efficiency optimization, and mission-level reliability assurance. There is an urgent need for a three-dimensional dynamic route generation method and system that can integrate multi-source environmental prediction information, accurately quantify and optimize flight energy consumption, and has rapid redundancy switching capabilities.

[0005] Therefore, the existing technology still needs further development. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a method, system, and electronic equipment for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles (UAVs) to solve the problems existing in the prior art.

[0007] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides a method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles (UAVs), comprising: Acquire multi-source spatiotemporal data of the target area and construct a dynamic three-dimensional spatial domain model that updates over time; Based on the dynamic three-dimensional spatial model, environmental prediction is performed to generate predicted environmental information for future time periods. Construct an energy consumption model to characterize the energy consumption of UAV flight, and output the energy consumption estimate of the flight path based on flight parameters and predicted environmental information; Based on the comprehensive optimization objective, a path that meets the constraints of the predicted environmental information is searched in the dynamic three-dimensional airspace model to generate the main route and at least one backup route. The comprehensive optimization objective is comprehensively evaluated based on flight time, energy consumption estimate, and predicted environmental information. The system controls the UAV to fly along the main route, monitors changes in flight status parameters and predicted environmental information in real time, and dynamically switches the UAV from the main route to an alternative route when the preset route switching conditions are met.

[0008] Specifically, the multi-source spatiotemporal data includes 3D building model data, Geographic Information System (GIS) data, meteorological forecast data, construction plan data, and low-altitude airspace control information. The method for constructing a dynamic 3D airspace model that updates over time includes: The multi-source spatiotemporal data is uniformly transformed to obtain three-dimensional spatial data of coordinate system one. Terrain elevation data, building outline and height data, and airspace control boundary and attribute data are extracted from the transformed data to construct corresponding three-dimensional terrain layers, building layers and airspace control layers. The three-dimensional terrain layer, building layer, and airspace control layer are spatially registered and associated with time attributes to form a timestamped three-dimensional spatial dataset. A spatial index is constructed on the three-dimensional spatial dataset to obtain the dynamic three-dimensional airspace model.

[0009] Specifically, the predicted environmental information includes risk areas, and the method for generating predicted environmental information for future periods based on the dynamic three-dimensional spatial model includes: A time-series prediction model is constructed by inputting historical meteorological data, construction time-series data, and temporary flight restriction zone release records into the model, and outputting wind field parameters, dynamic boundaries of the construction area, and the probability of temporary flight restriction zones taking effect in future periods to determine the risk area.

[0010] Specifically, the energy consumption model includes cruise energy consumption, climb or descent energy consumption, and additional energy consumption due to wind resistance. The cruise energy consumption is determined based on cruise speed, total mass of the UAV, air density, frontal area, and drag coefficient. The climb or descent energy consumption is determined based on the climb or descent speed, climb or descent angle, total mass of the UAV, air density, frontal area, and drag coefficient. The additional energy consumption due to wind resistance is determined by the wind field influence coefficient and the drag coefficient. The wind field influence coefficient is constructed based on the wind speed and the angle between the UAV's flight direction and the wind direction. The drag coefficient is constructed based on the UAV's zero-lift drag coefficient, induced drag coefficient, and flight attitude correction coefficient.

[0011] Specifically, the comprehensive optimization objective is a weighted sum of the flight time, the estimated energy consumption, and the predicted environmental information.

[0012] Specifically, based on the comprehensive optimization objective, a method for searching for paths that satisfy the constraints of the predicted environmental information in a dynamic three-dimensional airspace model, and generating a primary route and at least one backup route, includes: Using the comprehensive optimization objective as the evaluation function, an optimal path that satisfies the predicted environmental information constraints is searched in the dynamic three-dimensional airspace model as the main flight route; In the path space that deviates from the main route, the alternative routes are generated based on different optimization focuses.

[0013] Specifically, the method for generating alternative routes based on different optimization emphases in the path space deviating from the main route includes: With communication signal strength as the optimization focus, a path that meets communication quality constraints is searched in the path space that deviates from the main route to generate a communication-supported backup route; With the optimization focus on avoiding risk areas in the predicted environmental information, a path that meets the risk probability constraint is searched in the path space that deviates from the main route to generate a low-risk alternative route. The optimization focuses on minimizing the energy consumption estimate output by the energy consumption model. Paths that meet energy consumption constraints are searched in the path space that deviates from the main route to generate low-energy backup routes.

[0014] Specifically, the real-time monitoring of changes in flight status parameters and predicted environmental information, and the control of the UAV to dynamically switch from the main route to an alternate route when the monitoring results meet the preset route switching conditions, includes: The flight status parameters include at least one of the following: communication signal strength, deviation distance between the actual flight path and the main route, and deviation between the actual flight energy consumption and the planned energy consumption. When the communication signal strength is lower than a preset signal strength threshold and the duration exceeds a preset duration, or the deviation distance exceeds a preset distance threshold, or the risk probability of the main route in the predicted environmental information in the future period exceeds a preset probability threshold, or the deviation between the actual flight energy consumption and the planned energy consumption exceeds a preset energy consumption deviation threshold, it is determined that the preset route switching conditions are met, and a switch to an alternate route is triggered.

[0015] According to a second aspect of the present invention, a dynamic three-dimensional traffic route generation system for unmanned aerial vehicles (UAVs) is provided, comprising: Multi-source data fusion module: used to acquire multi-source spatiotemporal data of the target area and construct a dynamic three-dimensional spatial domain model that updates over time; Environmental prediction module: used to perform environmental prediction based on the dynamic three-dimensional spatial model and generate predicted environmental information for future time periods; Energy consumption modeling module: used to construct an energy consumption model that characterizes the energy consumption of UAV flight, and output the energy consumption estimate of the flight path based on flight parameters and the predicted environmental information; Path planning module: used to search for paths that meet the constraints of the predicted environmental information in the dynamic three-dimensional airspace model according to the comprehensive optimization objective, and generate the main route and at least one backup route, wherein the comprehensive optimization objective is comprehensively evaluated based on flight time, the energy consumption estimate, and the predicted environmental information; Dynamic adjustment module: used to control the UAV to fly along the main route and monitor the changes in flight status parameters and predicted environmental information in real time. When the preset route switching conditions are met, the module controls the UAV to dynamically switch from the main route to an alternate route.

[0016] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, which, when executed by the processor, implement the above-described method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles.

[0017] Beneficial effects: This invention provides a method and system for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles (UAVs). By constructing a dynamic three-dimensional airspace model that updates over time and combining it with a time-series prediction model to generate risk areas for future periods, route planning shifts from passive obstacle avoidance to active risk mitigation, significantly improving flight stability. By establishing an energy consumption model and incorporating energy consumption estimates, flight time, and environmental risks into a comprehensive optimization objective, time efficiency and energy consumption during path search are optimized, effectively extending the UAV's endurance. By generating differentiated backup routes based on different priorities outside the main route and establishing a dynamic switching mechanism based on multi-dimensional state parameters, it ensures rapid and accurate switching to a suitable route in case of sudden environmental changes or system anomalies, guaranteeing mission continuity and flight safety. This solves the technical problems of poor route stability, lack of energy consumption optimization, and easy mission interruption in existing technologies. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles provided in a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the composition of the UAV three-dimensional traffic route dynamic generation system provided in a specific embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0020] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0021] Example 1 Please see Figure 1This embodiment provides a method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles (UAVs), including: acquiring multi-source spatiotemporal data of a target area and constructing a dynamic three-dimensional airspace model that updates over time; performing environmental prediction based on the dynamic three-dimensional airspace model to generate predicted environmental information for future periods; constructing an energy consumption model characterizing the UAV's flight energy consumption and outputting an estimated energy consumption value for the flight path based on flight parameters and predicted environmental information; searching for paths that satisfy the constraints of predicted environmental information in the dynamic three-dimensional airspace model according to a comprehensive optimization objective, generating a main route and at least one backup route, wherein the comprehensive optimization objective is comprehensively evaluated based on flight time, estimated energy consumption value, and predicted environmental information; controlling the UAV to fly along the main route, monitoring changes in flight status parameters and predicted environmental information in real time, and controlling the UAV to dynamically switch from the main route to a backup route when preset route switching conditions are met.

[0022] It is understood that the above-mentioned technical solution in this embodiment achieves intelligent generation and adaptive execution of UAV routes in complex urban environments through the collaborative work of data fusion, environmental prediction, energy consumption modeling, path search and dynamic adjustment. This ensures that the UAV can quickly and accurately switch to a suitable path in the event of sudden environmental changes or system anomalies, thus guaranteeing mission continuity and flight safety. It also solves the technical problems of poor route stability, lack of energy consumption optimization and easy mission interruption in the prior art.

[0023] See Figure 1 The specific implementation steps of the UAV three-dimensional traffic route dynamic generation method in this embodiment are as follows: S100. Acquire multi-source spatiotemporal data of the target area and construct a dynamic three-dimensional spatial domain model that updates over time.

[0024] Specifically, multi-source spatiotemporal data not only includes static geographic information but also covers dynamic elements with timeliness. For example, data sources can include Building Information Modeling (BIM), Geographic Information System (GIS) data, real-time weather forecast data, municipal construction plans, and low-altitude airspace control notices. The process of constructing a dynamic three-dimensional airspace model is essentially to digitally map and associate the above heterogeneous data under a unified spatiotemporal benchmark. It should be understood that "dynamic" here not only refers to the model being able to be refreshed periodically but also to the fact that the model contains state evolution attributes in the time dimension, so that the same spatial location may correspond to different access attributes at different times. This provides a unified digital foundation for subsequent prediction and planning that reflects physical reality and has the ability to extrapolate time series, solving the problem that traditional static maps cannot represent the time-varying characteristics of the environment.

[0025] In some embodiments, multi-source spatiotemporal data includes 3D building model data, Geographic Information System (GIS) data, meteorological forecast data, construction plan data, and low-altitude airspace control information. These data sources cover the static physical structure and dynamic operating rules of the urban low-altitude environment. 3D building model data typically originates from BIM or oblique photogrammetry, providing high-precision geometric contours. GIS data includes terrain elevation and feature distribution. Meteorological forecast data provides environmental constraints such as wind field and visibility. Construction plan data and low-altitude airspace control information define no-fly or restricted-fly zones that change over time. In practical applications, other auxiliary information such as electromagnetic spectrum monitoring data and mobile communication base station signal coverage data can also be introduced depending on the specific scenario, as long as it can characterize the spatial or temporal attributes of the UAV's flight environment.

[0026] Furthermore, after acquiring the aforementioned multi-source spatiotemporal data, the method for constructing a dynamic three-dimensional airspace model that updates over time includes: performing a unified transformation process on the multi-source spatiotemporal data to obtain three-dimensional spatial data in coordinate system one; extracting terrain elevation data, building outline and height data, and airspace control boundary and attribute data from the transformed data; and constructing corresponding three-dimensional terrain layers, building layers, and airspace control layers.

[0027] It should be noted that since the raw data often comes from different acquisition devices and management platforms, their coordinate systems, elevation datums, and data formats differ significantly. Direct overlay would lead to severe spatial misalignment. Therefore, data standardization preprocessing is performed first. For example, all vector and raster data are uniformly projected to the WGS84 geographic coordinate system, and the elevation datum is uniformly converted to the EGM96 geoid, outputting standardized GeoJSON or vector tile formats. Based on this, hierarchical feature extraction is performed: for terrain data, Gaussian filtering is used to remove measurement noise, and Douglas-Pe... The Bucker algorithm simplifies contour lines, reducing data redundancy while retaining key features such as steep slopes greater than 15 degrees. For building data, it reads geometric information from the BIM model and performs Boolean operations to remove overlapping or invalid temporary building structures, while also labeling overhead obstacles such as tower cranes and communication antennas. For airspace control information, it transforms textual descriptions of no-fly zones and restricted-fly zones into vector polygons with attributes such as control type, effective period, and permitted altitude. Through this layered processing, the heterogeneous original data is decoupled into three independent layers with clear semantics and precise geometry, laying the foundation for subsequent fusion.

[0028] Furthermore, the 3D terrain layer, building layer, and airspace control layer are spatially registered and associated with time attributes to form a timestamped 3D spatial dataset. A spatial index is then constructed on the 3D spatial dataset to obtain a dynamic 3D airspace model.

[0029] Specifically, spatial registration is a crucial step in eliminating residual errors between layers. This embodiment employs the Iterative Closest Point (ICP) algorithm, using high-precision terrain data as a benchmark, to perform fine registration between the building model and the control layer, controlling the horizontal and vertical errors to within 0.5 meters. This ensures accurate calculation of the safety margin when the UAV flies over building edges or control zone boundaries. More importantly, to realize the dynamic characteristics of the model, a time attribute is associated with each spatial entity or layer state during the fusion process. For example, for temporary control zones generated from construction plan data, not only is their spatial extent recorded, but their effective start and end timestamps are also bound; for meteorological data, By associating the forecast timeframe with the forecast timeframe, the dynamic 3D airspace model is not a static 3D grid, but a 4D spatiotemporal database. When the system performs path planning or environmental prediction, it can automatically activate or disable corresponding layer elements by inputting the timestamp of the target time. For example, if the planning time falls within the effective period of a temporary restricted flight zone, the area will be marked as impassable in the model; otherwise, it will be considered free airspace. This mechanism, driven by timestamps, allows the model to truly reflect the dynamic constraints of the urban low-altitude environment over time, avoiding the planning failure problem caused by the inability of traditional static maps to express time-varying rules.

[0030] Furthermore, to support the efficiency of real-time path search and dynamic adjustment, this embodiment constructs an R-tree spatial index for the fused 3D spatial dataset. An R-tree is a hierarchical spatial index structure that can group and nest 3D spatial objects according to their minimum bounding rectangle. Through this index, when performing collision detection, neighborhood queries, or risk area retrieval, the system does not need to traverse the entire dataset; it only needs to access the relevant branches of the index tree to obtain information such as terrain elevation, building height, and control status within a specified spatial range in milliseconds. In other embodiments, other spatial index structures such as quadtrees, octrees, or 3D-Grids can be used according to the data distribution characteristics and query requirements, as long as they can achieve rapid retrieval of multidimensional spatial data. Through the complete process of unified transformation, hierarchical extraction, precise registration, temporal correlation, and index construction described above, a dynamic 3D airspace model with both geometric accuracy and temporal logic, supporting efficient querying, is constructed. This effectively solves the technical problem of inconsistent spatiotemporal benchmarks for multi-source heterogeneous data, providing a reliable data foundation for subsequent environmental prediction and route generation.

[0031] S200: Based on a dynamic three-dimensional spatial model, environmental prediction is performed to generate predicted environmental information for future time periods.

[0032] Specifically, environmental prediction utilizes historical patterns and prior knowledge to predict future states, rather than relying solely on real-time sensor perception. For example, the system can analyze historical meteorological data to predict wind field trends over a future period, or, based on published construction plans and control notices, predict the probability that a specific airspace will become a no-fly zone or high-risk area in the future. The generated predicted environmental information includes, but is not limited to, future wind field parameters, dynamic risk area boundaries, and their confidence levels. This step transforms static model data into dynamic risk and constraint distributions, enabling route planning to be predictable and thus allowing for early avoidance of risks before they actually occur. This is significantly different from the passive response mode of existing technologies that only avoid obstacles when they are encountered.

[0033] In some embodiments, the predicted environmental information includes risk areas. The method for generating predicted environmental information for future periods based on a dynamic three-dimensional airspace model includes: constructing a time-series prediction model, inputting historical meteorological data, construction time-series data, and temporary flight restriction zone release records into the time-series prediction model, and outputting wind field parameters, dynamic boundaries of construction areas, and the probability of temporary flight restriction zones taking effect for future periods to determine risk areas.

[0034] It should be noted that the environmental prediction in this embodiment is not a simple numerical extrapolation, but rather a mining of environmental evolution patterns based on deep learning technology. The preferred time-series prediction model is a Long Short-Term Memory (LSTM) network, because meteorological changes and regulatory activities in urban low-altitude environments have significant temporal correlation and long-range dependence characteristics. LSTM can effectively capture the nonlinear dynamic patterns in such sequence data. Of course, in other embodiments, neural network models with time-series modeling capabilities, such as Gated Recurrent Units (GRU), Temporal Convolutional Networks (TCN), or Transformer architectures, can also be used, as long as they can infer future trends based on historical states. The model's input data covers multi-dimensional historical information. Historical meteorological data includes wind speed, wind direction, air pressure, and temperature sequences over a period of time, used to learn the periodic fluctuations and abrupt changes in local micro-meteorology. Construction time-series data records the progress schedule and equipment scheduling plan of municipal engineering projects, providing prior knowledge for predicting the airspace occupied by construction. The temporary restricted flight zone issuance records include the issuance time, duration, and lifting conditions of past control notices, helping the model infer the likelihood of flight restriction measures taking effect in similar future scenarios. By combining the above-mentioned multi-source heterogeneous time-series data, the model can output a refined environmental state for a specific future period, including wind field parameters such as horizontal wind speed and wind direction angle, the dynamic boundary coordinate set of the construction area over time, and the probability value of a specific airspace becoming a temporary restricted flight zone. These outputs together constitute a digital representation of the risk area, which includes not only deterministic physical obstacle boundaries but also probabilistic potential threat distribution, thus providing dynamic predictive constraint information for route planning that goes beyond static maps.

[0035] Furthermore, to quantify the specific impact of environmental factors on UAV flight, this embodiment introduces a wind field influence coefficient calculation logic based on environmental prediction. The wind field influence coefficient is a dimensionless comprehensive correction factor used to characterize the intensity of the negative effects of predicted wind fields on UAV flight energy consumption and stability. Specifically, the calculation of this coefficient comprehensively considers the predicted horizontal wind speed and the angle between the UAV flight direction and the wind direction. When the angle is in the downwind or crosswind range (e.g., 0 to 90 degrees and 270 to 360 degrees), the airflow mainly plays a role in propelling or lateral support for the UAV. At this time, the increase in the wind field influence coefficient is small, and the weight of the wind speed term in the calculation formula is low. However, when the angle is in the headwind or crosswind range (e.g., 90 to 270 degrees), the airflow directly hinders the UAV's forward movement or increases the difficulty of attitude maintenance. At this time, the increase in the wind field influence coefficient is significantly larger, and the weight of the wind speed term in the calculation formula is correspondingly increased. This piecewise function design reflects the nonlinear characteristics of the vector synthesis of drag and lift in aerodynamics, avoiding the shortcomings of a single scalar wind speed that cannot distinguish the advantages and disadvantages of wind direction. The range of the wind field influence coefficient is usually normalized within a preset range (e.g., 1 to 5). The larger the value, the more severe the comprehensive negative impact of the environment on flight. As a key intermediate variable connecting the environmental prediction module and the subsequent energy consumption modeling module, this coefficient transforms abstract meteorological forecast data into physical parameters that can be directly involved in energy cost calculation. This enables the route planning algorithm to automatically tend to choose downwind or weak wind corridors when searching for paths, thereby achieving true environmental adaptive planning.

[0036] Wind field influence coefficient Taking into account wind speed, wind direction, and the angle between the direction of unmanned flight, the calculation formula is as follows: ; ; in, The angle between the drone's flight direction and the wind direction. For the drone's heading angle, The wind direction angle is used as the basis for the segmented calculation rules according to the above formula: (1) When α∈[0°, 90°]∪[270°,360°], the UAV is in a tailwind or crosswind flight state, the flight wind resistance is small, and the increase in energy consumption due to the wind field is low. The wind field influence coefficient is calculated using the first set of formulas. ; (2) When α∈(90°,270°), the UAV is flying against the wind or crosswind, and the flight drag is large. The wind field has a high increase in energy consumption. The second set of formulas is used to calculate the wind field influence coefficient. ; The range of values ​​for the wind field influence coefficient is: The larger the wind field influence coefficient value is in [1,5], the stronger the negative impact of the wind field on the flight of the UAV and the higher the flight energy consumption.

[0037] Through the aforementioned time-series prediction and coefficient quantification mechanism, this embodiment realizes a complete transformation chain from static data to dynamic probability distribution, and then to calculable cost factors. Compared with the passive mode of existing technologies that only triggers obstacle avoidance after the sensor detects an obstacle, this solution enables the route generation to have active avoidance capabilities by predicting the risk area and environmental resistance distribution in the future. This not only reduces the number of emergency replannings caused by sudden environmental changes during flight, improving the continuity and stability of mission execution, but also reduces the probability of UAVs entering high-risk airspaces by incorporating environmental risks into the planning consideration in advance, thereby enhancing the safety redundancy of the system in complex urban environments.

[0038] S300: Construct an energy consumption model to characterize the energy consumption of UAV flight, and output the energy consumption estimate of the flight path based on flight parameters and predicted environmental information.

[0039] Specifically, the actual energy consumption of a drone is not a constant value, but is affected by the nonlinear coupling of various factors such as payload, flight attitude, wind speed and direction. The energy consumption model in this embodiment uses predicted environmental information as a key input variable. For example, the predicted wind field parameters are substituted into the model to calculate the additional power consumption under tailwind or headwind conditions. Flight parameters include the inherent attributes of the drone itself, such as mass, aerodynamic shape, and cruise speed. Through this coupled calculation, the system can output a refined energy consumption estimate that takes into account the future environmental impact for any given candidate path segment. This ensures that the cost function used in subsequent path planning truly reflects the energy consumption pattern during flight and avoids the deviation in range estimation caused by ignoring environmental factors.

[0040] In some specific embodiments, the energy consumption model includes cruise energy consumption, climb or descent energy consumption, and additional wind resistance energy consumption. Specifically, traditional UAV energy consumption assessment often uses a simplified method of constant power multiplied by time, ignoring the nonlinear energy consumption differences caused by flight state switching and environmental interference. This embodiment decouples the flight process into three independent energy consumption components: cruise, vertical maneuver, and environmental countermeasures, and establishes refined calculation models for each. This allows for accurate characterization of the real energy flow patterns of UAVs performing tasks in complex low-altitude urban environments. This segmented modeling mechanism enables the system to identify which flight segments are high-energy bottlenecks, thereby providing a high-resolution cost map for subsequent path optimization.

[0041] Total energy consumption function of the energy consumption model The calculation is performed using the following formula: ; in, Indicates cruise energy consumption. Indicates the energy consumption during the climb or descent. This indicates the additional energy consumption due to wind resistance.

[0042] Specifically, cruise energy consumption is determined based on cruise speed, total UAV mass, air density, frontal area, and drag coefficient. The cruise phase constitutes the longest portion of the UAV's flight time, and its energy consumption calculation requires comprehensive consideration of aerodynamic efficiency and payload status. In this embodiment, cruise energy consumption... The calculation formula is as follows: ; in, This refers to the total mass of the drone (including payload). It is the acceleration due to gravity. For cruising speed, air density, For the windward area of ​​the drone, This is the drag coefficient. The formula, representing the cruise distance, reflects the energy balance between maintaining lift against gravity and maintaining speed against air resistance during horizontal, uniform flight. It should be understood that air density is not a fixed value, but can be corrected in real time based on meteorological data from a dynamic three-dimensional airspace model, taking into account altitude and temperature, thereby further improving the estimation accuracy.

[0043] Furthermore, the energy consumption during climb or descent is determined based on the climb or descent speed, climb or descent angle, total mass of the UAV, air density, frontal area, and drag coefficient. Specifically, the energy consumption characteristics during the vertical maneuver phase differ significantly from those during the cruise phase, primarily driven by changes in gravitational potential energy. For the climb process (climb angle...),... Energy consumption Represented as: ; For the descent process (descent angle) Energy consumption Represented as: ; in, For the height difference, For the descent speed, For the climb rate, the above formula explicitly incorporates the climb or descent angle. As a key variable, during the climb, The angle determines the power component that does work against gravity. The steeper the angle, the more energy is consumed per unit time to increase potential energy. During descent, the gravity component does positive work, which can theoretically offset some of the aerodynamic drag. In some electric drones, energy can even be recovered by reversing the motor. This embodiment distinguishes between positive and negative angles and uses different sign logic to accurately quantify this physical process, avoiding the error of simply equating all vertical motion with horizontal flight.

[0044] In addition, the additional energy consumption due to wind resistance is determined by the wind field influence coefficient and the drag coefficient. The wind field influence coefficient is constructed based on the wind speed and the angle between the UAV's flight direction and the wind direction, while the drag coefficient is constructed based on the UAV's zero-lift drag coefficient, induced drag coefficient, and flight attitude correction coefficient.

[0045] It should be noted that this is the key difference between the energy consumption model in this embodiment and existing technologies. It solves the problem of dynamic disturbances to energy consumption caused by urban micro-meteorological environment, and addresses the additional energy consumption due to wind resistance. The calculation formula is: ; in, The wind field influence coefficient generated in the aforementioned embodiments. To predict wind speed, For flight time, the above formula transforms the abstract risk parameters output by environmental predictions into specific energy costs. It is particularly important to note that the drag coefficient is used here. It is not a static constant, but a variable that changes dynamically with flight attitude, defined as follows: ; in, The zero-lift drag coefficient is determined by the shape of the drone's fuselage. The induced drag coefficient reflects the increase in drag that accompanies the generation of lift. This is the flight attitude correction factor when the UAV is in level flight. When the ascent or descent angle exceeds a preset threshold (e.g., 10 degrees), the statistic will increase linearly with the angle (e.g., during ascent). +0.02 It should be noted that in high-angle maneuvers or strong crosswinds, the effective frontal area and airflow separation of the UAV will change in order to maintain attitude stability, resulting in actual wind resistance far exceeding the static test value. By multiplying the wind field influence coefficient with the dynamic wind resistance coefficient, a deep coupling between environmental factors and the dynamic characteristics of the airframe is achieved, which can keenly capture extreme high-energy-consuming conditions such as high-angle climbs against the wind.

[0046] In a preferred embodiment, the energy consumption estimate output by the energy consumption model can be calculated in another way. Specifically, the wind field parameters and risk areas in the future time period are fused to generate a four-dimensional (three-dimensional space + time dimension) environmental resistance scalar field. The value of each grid node in this scalar field is obtained by mapping the wind field influence coefficient and the risk probability value together to form a unified environmental resistance coefficient. During path search, the energy consumption model directly indexes the corresponding environmental resistance coefficient in the environmental resistance map based on the candidate path segment and combines it with flight parameters to complete the energy consumption estimate. This mechanism significantly improves the efficiency of online route search.

[0047] Specifically, this mechanism will use the wind field influence coefficients of each spatiotemporal grid within the future time period output by the environmental prediction module. Risk probability value A fusion mapping is performed to pre-generate a four-dimensional (three-dimensional space + time dimension) environmental drag scalar field covering the target airspace. Each grid node in the field stores a uniform environmental drag coefficient. ; Environmental resistance coefficient The construction logic is as follows: based on the wind field influence coefficient Based on this, risk probability is introduced. As a penalized amplification factor, it is fused through a nonlinear mapping function, and its calculation formula is as follows: ; in, ∈[0,1) represents the probability of the risk taking effect at this spatiotemporal node in the future time period, as output by the time series prediction model, and is the risk penalty intensity coefficient. To minimize the positive value and prevent the denominator from being zero, when the risk probability approaches zero, the environmental drag coefficient is approximately equal to the wind field influence coefficient; when As the coefficient increases, the resistance coefficient grows non-linearly. The higher the risk probability, the more drastic the increase in resistance, thus forcing the path search algorithm to actively avoid high-risk areas. During the route planning phase, the energy consumption estimation function is simplified to directly indexing the spatiotemporal coordinates of candidate path segments in the environmental resistance scalar field and substituting them into the simplified energy consumption estimation expression: ; in, The basic energy consumption is calculated based on flight parameters (cruise speed, payload, flight distance, etc.) under risk-free standard conditions. It should be noted that the basic energy consumption can be calculated using the aforementioned cruise energy consumption and climb or descent energy consumption models, and the time integral is determined by combining the geometric length of the path segment and the flight speed.

[0048] In summary, the energy consumption model constructed in this embodiment integrates aerodynamic principles and physical mechanism models based on environmental perception data. It not only considers the inherent properties of the UAV itself, but also internalizes the uncertainty of the external environment into calculable energy consumption fluctuations by introducing wind field influence coefficients and attitude correction coefficients. This modeling approach provides an accurate cost function basis for path search based on comprehensive optimization objectives in subsequent steps, ensuring that the generated route is not only geometrically feasible, but also energy-efficiently achievable. This effectively prevents the risk of forced landings due to insufficient energy consumption estimates, while also avoiding the waste of transport capacity caused by overly conservative estimates.

[0049] S400. Based on the comprehensive optimization objective, search for paths that meet the constraints of the predicted environmental information in the dynamic three-dimensional airspace model, and generate the main route and at least one backup route. The comprehensive optimization objective is based on a comprehensive evaluation of flight time, energy consumption estimates, and predicted environmental information.

[0050] Furthermore, the comprehensive optimization objective is a multi-dimensional evaluation function designed to balance efficiency, economy, and safety. For example, the shortest time, lowest energy consumption prediction, and lowest risk probability can be unified into a scalar cost value through weighted summation. During the search process, the algorithm first seeks the globally optimal solution with the lowest comprehensive cost value as the main route, under the premise of satisfying the predicted environment constraints (such as avoiding predicted high-risk areas). At the same time, to prevent the single optimal solution from failing in the event of sudden environmental changes, at least one backup route is generated. The generation of the backup route is also based on the comprehensive optimization objective, but it can have different weight configurations or constraints, or it can be a suboptimal solution searched within the spatial range deviating from the main route. This redundant design of combining main and backup routes further enhances the robustness of the route system in the face of uncertainty.

[0051] In some embodiments, the overall optimization objective is a weighted sum of flight time, estimated energy consumption, and predicted environmental information.

[0052] It should be noted that, in order to comprehensively consider multi-dimensional heterogeneous indicators in the path search algorithm, this embodiment transforms flight time, energy consumption estimates, and predicted environmental information into a unified cost. For example, this can be achieved through the following weighted evaluation function. To characterize the overall merits of a candidate path: ; in, To estimate flight time, The energy consumption estimate is calculated based on the aforementioned energy consumption model. This is the cumulative risk value obtained based on environmental predictions. , , These are the corresponding weighting coefficients. It should be understood that these weighting coefficients are not fixed, but can be dynamically configured according to the task type or the current status of the drone. For example, when performing an emergency medical supplies delivery mission, the system can automatically adjust the weighting coefficients. To reduce sensitivity to time costs, the speed is increased when performing long-distance inspections and when battery level is low. To ensure endurance and safety, the path search algorithm can efficiently balance efficiency and safety in a large three-dimensional state space through this scalarized comprehensive evaluation mechanism. This avoids the problem of excessive complexity in solving the Pareto front, which is common in multi-objective optimization. It also provides a flexible basis for parameter adjustment for generating alternative routes with different focuses.

[0053] Furthermore, based on the comprehensive optimization objective, a method for searching for paths that satisfy the predicted environmental information constraints in a dynamic three-dimensional airspace model and generating a main route and at least one backup route includes: using the comprehensive optimization objective as the evaluation function, searching for an optimal path that satisfies the predicted environmental information constraints in the dynamic three-dimensional airspace model as the main route; and generating backup routes in the path space that deviates from the main route based on different optimization focuses.

[0054] It should be noted that the main route is the minimum solution of the above evaluation function in the global feasible region, representing the theoretically best choice under the current known environment and mission requirements. However, the urban low-altitude environment is highly uncertain, and a single optimal path often lacks robustness. Therefore, a path space deviating from the main route is introduced to generate backup routes. The path space deviating from the main route refers to the spatial region that maintains a preset safe distance or topological difference from the main route geometrically. When searching for backup routes, the system sets the main route and its neighborhood as an exclusion zone or imposes a high penalty cost, forcing the search algorithm to explore suboptimal solutions in areas outside the main route. The core purpose of this spatial isolation strategy is to avoid the homogenization of the main and backup routes and prevent common-mode failures caused by the same sudden environmental event (such as local severe convective weather or expansion of temporary control areas) that cause both the main and backup routes to fail simultaneously. By constructing this multi-level, spatially separated path redundancy system, the system can quickly switch to physically independent and functionally complementary backup channels when the main route is blocked, significantly improving the success rate of emergency response.

[0055] Furthermore, in the path space deviating from the main route, methods for generating alternative routes based on different optimization emphases include the following three specific implementation forms: (1) Focusing on communication signal strength as the optimization priority, a path that meets the communication quality constraints is searched in the path space that deviates from the main route to generate a communication-guaranteed backup route. Specifically, in urban building clusters, tall buildings often cause communication links to become unstable. The generation of this type of backup route no longer prioritizes time or energy consumption, but instead uses Received Signal Strength Indication (RSSI) as the core optimization variable. A communication coverage layer is superimposed on the dynamic three-dimensional airspace model. The search algorithm tends to select grid nodes with RSSI values ​​higher than a preset threshold (such as -85dBm) and low signal fluctuation. This type of route is specifically designed to deal with communication interruptions or weak signal crises that occur during flight on the main route, ensuring that the UAV is always in a reliable telemetry and control link and preventing loss of connection and control. (2) With the optimization focus on avoiding risk areas in the predicted environmental information, a path that meets the risk probability constraint is searched in the path space that deviates from the main route to generate a low-risk backup route. Specifically, when the environmental prediction output shows that there is a high probability of construction area expansion or temporary restricted flight area in the future, the main route may face the risk of being cut off. At this time, the low-risk backup route will set the area where the predicted risk probability exceeds a certain threshold (such as 30%) as an absolute no-fly zone. Even if this will lead to a significant increase in path length or energy consumption, the route reflects the safety-first strategy and is dedicated to providing a safe detour when there is a high degree of certainty of potential threats in front of the main route. The weight of the risk term in its cost function is set to be much higher than that of the time and energy consumption terms, and even an infinite penalty mechanism is used to ensure the absoluteness of obstacle avoidance. (2) The optimization focuses on minimizing the energy consumption estimate output by the energy consumption model. Paths that meet energy consumption constraints are searched in the path space that deviates from the main route to generate low-energy backup routes. Specifically, this route makes full use of the energy consumption model, focusing on the utilization of the tailwind corridor and the optimization of the climb angle. During the search process, the energy consumption estimate is used as the dominant evaluation factor, allowing for the sacrifice of some flight time in exchange for longer endurance. For example, the algorithm may choose a route that is longer but has a tailwind throughout, or a route with a gentler climb to reduce instantaneous high-power discharge. Such routes are mainly used as solutions when there is abnormal power consumption or insufficient power for return, ensuring that the UAV can still safely reach its destination or emergency landing point under energy-limited conditions.

[0056] Understandably, in practical applications, other types of dedicated backup routes can be generated based on specific mission requirements, in addition to the three types of backup routes mentioned above. These include navigation support routes that emphasize the richness of visual positioning features, or environmental compliance routes that emphasize the avoidance of noise-sensitive areas. Furthermore, these three types of backup routes can be generated and cached in parallel during the mission planning phase, or they can be generated in real time as needed when specific abnormal trends are detected. Through this differentiated and functionally dedicated backup route generation strategy, a three-dimensional route safety assurance system is constructed, enabling UAVs to match the most targeted response path when facing emergencies in different dimensions such as communication, environment, and energy. This solves the technical problems of single-function backup routes and poor switching effects in traditional methods.

[0057] In a better implementation, backup routes are generated in the path space that deviates from the main route. The spatial isolation index between the candidate backup route and the main route can be calculated. This index is determined by the average distance between the two in three-dimensional space, topological similarity, and the number of grids they pass through. Only when the spatial isolation is greater than a preset threshold (e.g., the average distance is greater than a certain safety radius) can the candidate route be selected as a backup route. This prevents the main and backup routes from failing simultaneously due to sudden changes in the local environment (such as the collapse of a building). Based on this, backup routes with complementary functions are generated by combining different optimization focuses. This ensures the independence and effectiveness of redundant routes and solves the defect of high overlap between main and backup routes in traditional planning.

[0058] Specifically, a spatial isolation index is introduced as an admission criterion for candidate routes. This index comprehensively quantifies the degree of spatial difference between candidate alternative routes and the main route through three dimensions: (1) Average distance in three-dimensional space The main route and the candidate backup route are discretized into equally spaced sampling point sequences. A dynamic time warping algorithm is used to spatially align and match the two paths. The three-dimensional Euclidean distance between the matched point pairs is calculated and averaged to obtain the three-dimensional average distance. ; (2) Topological similarity The path direction sequences of the main route and candidate alternative routes are encoded, key turning points are extracted, the ratio of the edit distance between the two path direction sequences to the total length of the sequences is calculated, and topological similarity is defined. ∈[0,1], the closer this value is to 0, the greater the difference in the topological structure of the two paths; (3) Mesh overlap The number of spatial grid cells that candidate alternative routes and the main route pass through in the dynamic three-dimensional airspace model is counted, and the proportion of this number to the total number of grid cells traversed by the main route is calculated to obtain the grid overlap degree. ∈[0,1], the closer this value is to 0, the less overlap in the space occupied by the two paths; Based on the above three dimensions, a spatial isolation index is constructed. The calculation formula is as follows: ; in, To preset the safety radius, , , The weights for each dimension are set to satisfy α+β+γ=1, and can be adjusted according to the security requirements of the task scenario. The system sets an isolation threshold. ,(For example =0.6), only when the candidate path satisfies ≥ Only when the isolation degree is selected can the route be considered as a backup route. On this basis, different optimization focuses such as communication signal strength, risk avoidance degree, and energy consumption estimate are applied to the candidate route set that has passed the isolation degree screening to generate backup routes with complementary functions such as communication guarantee, low risk, and low energy consumption.

[0059] The S500 controls the UAV to fly along the main route, monitors changes in flight status parameters and predicted environmental information in real time, and dynamically switches the UAV from the main route to a backup route when the preset route switching conditions are met.

[0060] Understandably, this is a real-time closed-loop control process of monitoring, decision-making, and execution. Flight status parameters can include communication signal strength, actual trajectory deviation, and remaining battery power. Changes in predicted environmental information refer to the degree of difference between the actual observed environment and the previously predicted values. Preset route switching conditions are logical thresholds that trigger replanning. For example, when the communication signal remains below a certain threshold, or when there is an unexpected temporary air traffic control ahead, the system determines that the main route is no longer applicable and then activates the corresponding pre-planned backup route. This switching is based on a rapid response to a contingency plan, rather than a temporary blind recalculation. Therefore, it can greatly shorten decision delays and ensure the flight safety and mission continuity of UAVs in complex urban environments.

[0061] In some embodiments, changes in flight status parameters and predicted environmental information are monitored in real time. When the monitoring results meet the preset route switching conditions, the UAV is controlled to dynamically switch from the main route to a backup route. The flight status parameters include at least one of the following: communication signal strength, deviation distance between the actual flight path and the main route, and deviation between the actual flight energy consumption and the planned energy consumption.

[0062] It should be noted that the aforementioned monitoring mechanisms constitute a guarantee for UAV flight safety. Communication signal strength (usually represented by RSSI) directly reflects the reliability of the telemetry and control link and data transmission, which is a prerequisite for UAVs to maintain a controlled state in the complex electromagnetic environment of the city. The deviation between the actual flight path and the main flight path characterizes the tracking accuracy and anti-interference capability of the navigation system. Excessive deviation often indicates positioning drift or strong wind disturbance. The deviation between actual flight energy consumption and planned energy consumption is a comprehensive quantitative table of the health of the power system and its environmental compatibility. Abnormal energy consumption may indicate motor failure, load changes, or unforeseen aerodynamic drag. In practical applications, other state parameters such as battery voltage drop rate, IMU vibration amplitude, and number of visual positioning feature points can be introduced according to the type of UAV and mission requirements, as long as they can characterize the flight safety boundary. Through parallel monitoring of multi-dimensional parameters, the system can perceive the UAV's operational status from three independent dimensions: communication, navigation, and power, avoiding monitoring blind spots caused by the failure of a single indicator.

[0063] Furthermore, when the communication signal strength is lower than the preset signal strength threshold and the duration exceeds the preset duration, or the deviation distance exceeds the preset distance threshold, or the risk probability of the main route in the predicted environmental information in the future period exceeds the preset probability threshold, or the deviation between the actual flight energy consumption and the planned energy consumption exceeds the preset energy consumption deviation threshold, it is determined that the preset route switching conditions are met, and the switch to an alternative route is triggered.

[0064] It should be further explained that this multi-condition logic design ensures comprehensive coverage of various anomalies. That is, the system can initiate the switching of the backup route when any dimension is triggered. For communication signal strength, a duration time window parameter is introduced. For example, the preset signal strength threshold is set to -85dBm and the preset duration is 3 seconds. This means that the switching will only be triggered when the RSSI is below -85dBm for 3 consecutive seconds. In urban building clusters, multipath effects and blockages often cause deep signal fading at the millisecond level. If triggered by instantaneous values ​​alone, the UAV is very likely to frequently switch back and forth between the main route and the backup route. This not only fails to restore communication, but also consumes extra power and increases the risk of collision due to violent maneuvers. Through time window filtering, the system can effectively filter out instantaneous interference and only perform the switching when the communication quality deteriorates substantially and continuously, thereby ensuring the stability of flight control.

[0065] Furthermore, regarding the triggering conditions for risk probabilities, for example, when the time-series prediction model outputs a probability exceeding 30% that a temporary restricted flight zone will take effect in the airspace ahead of the main flight path within the next 5 minutes, the system will trigger a switch in advance, even if the UAV has not yet entered the area and everything is normal. This prediction-based proactive switching mechanism allows the UAV to change course before entering a high-risk area, avoiding the passive situation of having to brake or detour only after entering a restricted area, as is the case with existing technologies. Similarly, reasonable thresholds (such as a deviation distance of 5 meters and an energy consumption deviation of 15%) can be set to balance sensitivity and robustness. These thresholds are not fixed but can be adaptively adjusted according to the flight phase (such as takeoff, cruise, and landing), weather conditions, or mission priority. For example, during the landing phase, due to the extremely high accuracy requirements, the deviation distance threshold can be automatically tightened to 1 meter, while during tailwind cruise, the energy consumption deviation threshold can be appropriately relaxed to avoid unnecessary route adjustments.

[0066] Understandably, after determining that the preset route switching conditions are met, the system does not randomly select a backup route, but rather matches a corresponding type of backup route based on the type of triggering condition, forming a closed-loop control of monitoring-decision-execution. Specifically, when the triggering condition is abnormal communication signal strength, the system prioritizes switching to a communication-support type backup route, utilizing the characteristic of this route extending along areas with good base station coverage to quickly restore the link. When the triggering condition is an excessive probability of future risks, the system switches to a low-risk type backup route, utilizing the characteristic of this route avoiding predicted high-risk areas to ensure continued safety. When the triggering condition is excessive energy consumption deviation, the system switches to a low-energy-consumption type backup route, utilizing the optimized aerodynamic efficiency of this route to make up for the energy gap. This fault mode-based directional switching strategy significantly improves the effectiveness of emergency response. In addition, to prevent the switched backup route from also failing to meet safety requirements, this embodiment also sets up secondary stability judgment logic. For example, after switching to a communication-supported backup route, the system continues to monitor RSSI. If the signal does not recover to above the threshold within the preset observation period, the backup route is deemed to have failed, thereby triggering the next level of emergency strategy (such as hovering in place or landing at the nearest emergency point). This hierarchical and progressive fault-tolerant mechanism eliminates the risk of single-point failure to the greatest extent and ensures the safe operation of UAVs in complex urban environments at all times.

[0067] In a preferred embodiment, to improve flight stability during route switching and prevent frequent switching between primary and backup routes by the UAV due to signal fluctuations and other factors, a dual-layer mechanism of delayed triggering and hysteresis control is introduced. The delayed triggering mechanism means that when the preset route switching conditions are met, the system does not immediately execute the switch, but starts a timer. Only after the state continues for a preset delay is the switch command officially triggered. The hysteresis control mechanism compares the comprehensive state parameters of the primary route and the current backup route in real time after switching to the backup route. Only when the comprehensive score of the primary route is better than that of the current backup route for a continuous observation window, and its advantage exceeds the preset hysteresis threshold, does the system allow the switch back to the primary route from the backup route. This design effectively avoids route oscillations caused by instantaneous environmental disturbances and ensures the stability and controllability of the entire flight process.

[0068] Specifically, the hysteresis control mechanism is used to prevent UAVs from switching back to the main route too early or unnecessarily from the backup route. After switching to the backup route, the system continuously calculates and compares the comprehensive status score of the main route and the current backup route. This comprehensive status score can be calculated using the weighted sum of flight time, energy consumption estimates, and predicted environmental information from the aforementioned comprehensive optimization objectives as the evaluation function, and the current monitoring data is input in real time.

[0069] Set hysteresis threshold and observation window During each evaluation cycle within the observation window, the system calculates the comprehensive score for the main route. Overall score with current alternative routes And determine whether the following two conditions are met: The primary route scores better than the alternative route: ; The advantage magnitude exceeds the hysteresis threshold: ; Only when the above two conditions are in the observation window Only when all evaluation cycles within the observation window are met simultaneously will the system determine that the status of the main route has been substantially restored and is better than the current backup route, allowing the execution of the instruction to switch back to the main route from the backup route. If any evaluation cycle within the observation window does not meet the conditions, the timing and observation will restart.

[0070] It should be noted that this embodiment provides a method for dynamically generating three-dimensional traffic routes for UAVs, establishing a complete technical closed loop from data perception, trend prediction, energy quantification to path decision-making and dynamic execution. The dynamic three-dimensional airspace model supports accurate environmental prediction, and the prediction information drives realistic energy consumption modeling. The two work together to search for multi-target paths to generate a primary and backup route system, while the real-time monitoring and switching mechanism ensures the implementation of this system at the execution level. This progressive logical relationship effectively solves the technical problems of lack of predictability in route planning, inaccurate energy consumption assessment, and delayed emergency response in existing technologies.

[0071] Example 2 Please see Figure 2 This embodiment provides a UAV three-dimensional traffic route dynamic generation system. The system adopts the UAV three-dimensional traffic route dynamic generation method described in the aforementioned embodiment one. It realizes intelligent generation and dynamic control of routes through a modular software architecture. The system mainly includes a data fusion module, an environmental prediction module, an energy consumption modeling module, a path planning module, and a dynamic adjustment module.

[0072] The data fusion module is used to acquire multi-source spatiotemporal data of the target area and construct a dynamic three-dimensional spatial model that updates over time. As the data foundation of the entire system, it provides a standardized spatiotemporal data access interface. Specifically, when the upper-layer module needs to know the accessibility attribute of a certain coordinate point at a specific time, it only needs to pass in the spatiotemporal coordinate parameters, and it can automatically associate the layer status of the corresponding timestamp and return the result, without the upper layer needing to care about how the data is fused or stored. This decoupling design of data supply and business logic enables the system to flexibly access new sensor data without affecting the stability of the core algorithm. The environmental prediction module is used to predict the environment based on a dynamic three-dimensional spatial model, generating predicted environmental information for future periods. This module maintains data communication with the multi-source data fusion module, acquires the latest data in real time, and calls the time-series prediction model for extrapolation. At the system collaboration level, the output of the environmental prediction module is not the raw meteorological data, but structured risk information that has undergone semantic processing, such as risk area vector boundaries with confidence levels and wind field influence coefficient distribution maps. These prediction results are published on the system's internal message bus for the energy consumption modeling module and the path planning module to call. It should be understood that although this embodiment treats environmental prediction as an independent module, in some lightweight deployment scenarios, it can also run as a background thread of the multi-source data fusion module, as long as it can realize the function of converting historical data into future risk information.

[0073] The energy consumption modeling module is used to construct an energy consumption model that characterizes the energy consumption of UAV flight. Based on flight parameters and predicted environmental information, it outputs the energy consumption estimate of the flight path. This module is a key bridge connecting environmental perception and path decision-making. It receives environmental variables such as wind field influence coefficient from the environmental prediction module, and obtains real-time flight parameters such as mass and battery status from the UAV's underlying data. It calculates the energy cost of any candidate path segment. In the system architecture, this module is usually encapsulated as a stateless evaluation service that supports high-concurrency calls. This means that the path planning module can frequently request energy consumption estimates during the search process. In addition, the energy consumption modeling module also has a parameter adaptive calibration function, which can correct the drag coefficient or efficiency factor online based on the actual energy consumption deviation fed back by the dynamic adjustment module. This allows the energy consumption model to continuously approach the real physical characteristics as flight missions accumulate.

[0074] The path planning module searches for paths that meet the constraints of predicted environmental information in a dynamic 3D airspace model based on a comprehensive optimization objective, generating a primary route and at least one backup route. The comprehensive optimization objective is evaluated based on flight time, estimated energy consumption, and predicted environmental information. The module integrates a multi-objective search algorithm and a differentiated backup route generation strategy. It performs collision detection by calling the spatial index service of the multi-source data fusion module, obtains cost values ​​from the energy consumption modeling module, and combines risk constraints from the environmental prediction module to ultimately output a set of structured path data containing the primary route and various types of backup routes. In system collaboration, the path planning module not only handles offline planning but also supports online replanning requests. When it receives a replanning instruction from the dynamic adjustment module, it can quickly respond using existing search tree caches instead of calculating from scratch, thus meeting the real-time requirements of emergency scenarios.

[0075] The dynamic adjustment module controls the UAV to fly along the main route and monitors changes in flight status parameters and predicted environmental information in real time. When the preset route switching conditions are met, the module controls the UAV to dynamically switch from the main route to a backup route. The dynamic adjustment module sends navigation commands to the UAV through the communication link and continuously collects telemetry data such as RSSI, trajectory deviation, and actual energy consumption. When the switching conditions are triggered, the module directly selects a matching item from the backup route set pre-generated by the path planning module and issues it, avoiding the delay risk caused by temporary planning. More importantly, the dynamic adjustment module also compares the true environmental values ​​observed during actual flight with the model prediction values. If a significant deviation is found, the parameters of the environmental prediction module or the energy consumption modeling module are updated, forming a system-level self-evolution mechanism of execution-feedback-optimization. Through the close collaboration and data flow between the above modules, this embodiment constructs a traffic route dynamic generation system that has both predictive environmental perception capabilities and energy consumption management and flexible emergency response capabilities, effectively supporting the safe and efficient operation of UAVs in complex low-altitude urban environments.

[0076] Example 3 This embodiment provides an electronic device, including: a memory; and a processor. The memory stores computer-readable instructions, which, when executed by the processor, implement the UAV three-dimensional traffic route dynamic generation method described in the foregoing embodiment. Specifically, this electronic device is the hardware carrier through which the technical solution of this invention is implemented at the physical level. Its form is not limited to a single type, but can be flexibly configured according to actual deployment needs. For example, the electronic device can be an onboard flight computer or mission payload computer integrated inside the UAV fuselage, responsible for real-time route tracking and dynamic switching decisions; it can also be a ground-based monitoring and command terminal or workstation, undertaking the tasks of constructing large-scale dynamic three-dimensional airspace models, long-term environmental prediction, and pre-planning global primary and backup routes; or it can be a server cluster or edge computing node deployed in the cloud, providing computing power offloading and data services to the UAV through a wireless network. It should be understood that regardless of the physical form adopted, as long as it has storage and processing capabilities and can execute the method logic described in this invention, it falls within the protection scope of this embodiment.

[0077] In terms of hardware composition, the processor, as the core of electronic equipment's computation and control center, is responsible for parsing and executing computer-readable instructions in memory to drive the operation of various functional modules such as multi-source data fusion, environmental prediction, energy consumption modeling, path planning, and dynamic adjustment. The specific processor selection can be adapted according to the algorithm complexity and real-time requirements. For example, for environmental prediction modules that need to run LSTM time-series prediction models or perform a large number of matrix operations, a graphics processing unit (GPU) or neural network processor (NPU) is preferred to accelerate the inference process; for path planning modules involving logic-intensive tasks such as R-tree space index lookup, collision detection, and path search, a high-performance central processing unit (CPU) or field-programmable gate array (FPGA) can be used to ensure low-latency response; and in power-sensitive airborne embedded scenarios, a digital signal processor (DSP) or application-specific integrated circuit (ASIC) can be selected to optimize energy efficiency. Similarly, the memory includes not only random access memory (RAM) for temporarily storing runtime data and intermediate variables, but also non-volatile storage media such as flash memory, solid-state drives (SSDs), or read-only memory (ROMs) for persistently storing the operating system, application code, dynamic 3D spatial domain model database, and historical weather records. This heterogeneous computing and hierarchical storage architecture ensures that the electronic device can efficiently handle the massive spatiotemporal data processing and high-concurrency real-time decision-making requirements involved in this invention.

[0078] Furthermore, to achieve efficient collaboration among hardware components and data interaction with external devices, the electronic device typically includes a system bus and a communication interface. The system bus connects the processor, memory, and other peripherals, providing data transmission, addressing, and control signal paths to ensure smooth command and data flow. The communication interface enables the electronic device to access wired or wireless networks, thereby acquiring multi-source spatiotemporal data from weather stations, municipal construction platforms, air traffic control systems, and mobile communication base stations, and transmitting the generated flight path instructions and status information to the UAV flight control system in real time. For example, in airborne electronic devices, the communication interface may include a 4G / 5G module, a Wi-Fi module, or a satellite communication link; in ground or cloud-based devices, it may include an Ethernet port, a fiber optic interface, or a dedicated data line. With the support of this hardware architecture, the UAV 3D traffic route dynamic generation method proposed in this invention can be transformed from an abstract logical process into concrete engineering practice. This not only ensures the stability and timeliness of complex algorithms in actual operating environments but also provides sufficient technical basis and protection for subsequent hardware productization for different application scenarios.

[0079] The following example, using a drone delivery application scenario within an urban building complex, illustrates the working principle of the drone 3D traffic route dynamic generation method in this invention: In this application scenario, a multi-rotor logistics drone is set up to perform a package delivery task from a distribution station to a target community. A main flight route with optimal time, energy consumption, and risk is pre-planned, and three backup flight routes are generated simultaneously: a communication-supported route, a low-risk route, and a low-energy route. To ensure flight safety, the system is configured with the following monitoring thresholds: Received Signal Strength Indicator (RSSI) preset signal strength threshold of -85dBm, preset duration of 3 seconds; preset distance threshold between the actual flight path and the main flight route of 5 meters; preset probability threshold of the main flight route in the predicted environmental information within the future time period of 30%; preset energy consumption deviation threshold between actual flight energy consumption and planned energy consumption of 15%. The above threshold settings take into account both the characteristics of the urban microenvironment and the dynamic constraints of UAVs. For example, setting the RSSI duration to 3 seconds instead of instantaneous triggering aims to effectively suppress the instantaneous deep fading of signals caused by multipath effects due to building reflections using a time window filtering mechanism, and to avoid frequent switching between primary and backup flight paths by UAVs. Setting the risk probability threshold to 30% is a balance between ensuring safety redundancy and maintaining mission efficiency, preventing overreaction to low-risk areas while ensuring early intervention in high-risk situations.

[0080] During the normal cruise of the UAV along the main route, the dynamic adjustment module continuously performs real-time monitoring. Suppose that when flying over a high-density building cluster area, the communication link quality degrades due to building obstruction, and the system detects an RSSI value dropping to -88dBm. At this point, the system does not immediately trigger a switchover; instead, it starts a timer for continuous assessment. If the low signal condition lasts less than 3 seconds, the system determines it as transient interference, controls the UAV to maintain flight along the main route, and attempts to reconnect. If the low signal condition lasts longer than 3 seconds, it is determined that the communication anomaly condition in the preset route switching conditions is met, and the system immediately triggers the switching logic, selecting a communication-supporting backup route from the pre-generated set of backup routes as the target. The backup flight path for communication support was optimized with RSSI field strength distribution as the core objective during the planning stage. Its trajectory deliberately avoids signal blind spots and extends close to streets with good base station coverage, thus avoiding physical sources of obstruction that would cause signal attenuation on the main flight path. After the UAV smoothly transitions to the backup flight path, the system continues to perform a secondary stability assessment. If the RSSI recovers to above -85dBm and remains stable within the subsequent observation window, the switchover is confirmed to be successful, and the UAV continues to perform subsequent tasks along the backup flight path. If the signal still does not improve, an emergency landing strategy is further triggered to guide the UAV to the nearest preset emergency point, thereby constructing a tiered and progressive safety fallback mechanism.

[0081] In another scenario, assuming the drone's communication and trajectory are normal while flying along the main route, but the environmental prediction module, based on the latest municipal construction data, predicts that the probability of the airspace 500 meters ahead of the main route becoming a temporary restricted flight zone within the next 5 minutes has increased to 35%, exceeding the preset probability threshold of 30%. Even though the area is not actually closed at the moment and the drone is in good condition, the system will still determine that the environmental risk conditions in the preset route switching conditions are met and actively trigger a switch to a low-risk backup route. This low-risk backup route has already set the predicted high-risk area as an absolute no-fly zone when it is generated. Although its path increases the flight distance compared to the main route, it avoids potential airspace conflict areas. This prediction-based active switching allows the drone to change lanes before entering the risk area, avoiding the safety hazards and mission interruption risks caused by emergency braking or detouring after entering the restricted area in traditional technologies. This fully demonstrates the synergistic advantages of the environmental prediction-driven and differentiated main and backup route design of this invention.

[0082] Furthermore, if the UAV encounters unforeseen headwinds or load changes during flight, causing the deviation between actual flight energy consumption and planned energy consumption to exceed 15%, the system will trigger a switch to a low-energy backup route. This route fully utilizes the optimization results of the tailwind corridor and climb angle in the energy consumption model, potentially selecting a path that, although slightly longer, is entirely tailwind or has a gentler climb. This sacrifices a small amount of time to restore endurance, ensuring the UAV can safely reach its destination even under energy-constrained conditions. Through the simulation and handling of various abnormal scenarios, this embodiment verifies that the proposed UAV 3D traffic route dynamic generation method can adaptively adjust to different fault modes such as communication interruptions, sudden environmental changes, and abnormal energy consumption, effectively ensuring the safety, continuity, and economy of UAV operations in complex urban low-altitude environments.

[0083] The present invention can also be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the method of the embodiments of the present invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0084] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0085] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0086] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles (UAVs), characterized in that, include: Acquire multi-source spatiotemporal data of the target area and construct a dynamic three-dimensional spatial domain model that updates over time; Based on the dynamic three-dimensional spatial model, environmental prediction is performed to generate predicted environmental information for future time periods. Construct an energy consumption model to characterize the energy consumption of UAV flight, and output the energy consumption estimate of the flight path based on flight parameters and predicted environmental information; Based on the comprehensive optimization objective, a path that meets the constraints of the predicted environmental information is searched in the dynamic three-dimensional airspace model to generate the main route and at least one backup route. The comprehensive optimization objective is comprehensively evaluated based on flight time, energy consumption estimate, and predicted environmental information. The system controls the UAV to fly along the main route, monitors changes in flight status parameters and predicted environmental information in real time, and dynamically switches the UAV from the main route to an alternative route when the preset route switching conditions are met.

2. The method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles according to claim 1, characterized in that, The multi-source spatiotemporal data includes 3D building model data, Geographic Information System (GIS) data, meteorological forecast data, construction plan data, and low-altitude airspace control information. The method for constructing a dynamic 3D airspace model that updates over time includes: The multi-source spatiotemporal data is uniformly transformed to obtain three-dimensional spatial data of coordinate system one. Terrain elevation data, building outline and height data, and airspace control boundary and attribute data are extracted from the transformed data to construct corresponding three-dimensional terrain layers, building layers and airspace control layers. The three-dimensional terrain layer, building layer, and airspace control layer are spatially registered and associated with time attributes to form a timestamped three-dimensional spatial dataset. A spatial index is constructed on the three-dimensional spatial dataset to obtain the dynamic three-dimensional airspace model.

3. The method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles according to claim 1, characterized in that, The predicted environmental information includes risk areas, and the method for generating predicted environmental information for future periods based on the dynamic three-dimensional spatial model includes: A time-series prediction model is constructed by inputting historical meteorological data, construction time-series data, and temporary flight restriction zone release records into the model, and outputting wind field parameters, dynamic boundaries of the construction area, and the probability of temporary flight restriction zones taking effect in future periods to determine the risk area.

4. The method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles according to claim 1, characterized in that, The energy consumption model includes cruise energy consumption, climb or descent energy consumption, and additional energy consumption due to wind resistance. The cruise energy consumption is determined based on cruise speed, total mass of the UAV, air density, frontal area, and drag coefficient. The climb or descent energy consumption is determined based on the climb or descent speed, climb or descent angle, total mass of the UAV, air density, frontal area, and drag coefficient. The additional energy consumption due to wind resistance is determined by the wind field influence coefficient and the drag coefficient. The wind field influence coefficient is constructed based on the wind speed and the angle between the UAV's flight direction and the wind direction. The drag coefficient is constructed based on the UAV's zero-lift drag coefficient, induced drag coefficient, and flight attitude correction coefficient.

5. The method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles according to claim 1, characterized in that, The comprehensive optimization objective is a weighted sum of the flight time, the estimated energy consumption, and the predicted environmental information.

6. The method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles according to claim 1, characterized in that, Based on the comprehensive optimization objective, a method for searching paths that satisfy the constraints of predicted environmental information in a dynamic three-dimensional airspace model and generating a primary route and at least one backup route includes: Using the comprehensive optimization objective as the evaluation function, an optimal path that satisfies the predicted environmental information constraints is searched in the dynamic three-dimensional airspace model as the main flight route; In the path space that deviates from the main route, the alternative routes are generated based on different optimization focuses.

7. The method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles according to claim 6, characterized in that, The method for generating alternative routes based on different optimization emphases in the path space deviating from the main route includes: With communication signal strength as the optimization focus, a path that meets communication quality constraints is searched in the path space that deviates from the main route to generate a communication-supported backup route; With the optimization focus on avoiding risk areas in the predicted environmental information, a path that meets the risk probability constraint is searched in the path space that deviates from the main route to generate a low-risk alternative route. The optimization focuses on minimizing the energy consumption estimate output by the energy consumption model. Paths that meet energy consumption constraints are searched in the path space that deviates from the main route to generate low-energy backup routes.

8. The method for dynamically generating three-dimensional traffic routes for unmanned aerial vehicles according to claim 1, characterized in that, The real-time monitoring of changes in flight status parameters and predicted environmental information, and the control of the UAV to dynamically switch from the main route to an alternate route when the monitoring results meet preset route switching conditions, includes: The flight status parameters include at least one of the following: communication signal strength, deviation distance between the actual flight path and the main route, and deviation between the actual flight energy consumption and the planned energy consumption. When the communication signal strength is lower than a preset signal strength threshold and the duration exceeds a preset duration, or the deviation distance exceeds a preset distance threshold, or the risk probability of the main route in the predicted environmental information in the future period exceeds a preset probability threshold, or the deviation between the actual flight energy consumption and the planned energy consumption exceeds a preset energy consumption deviation threshold, it is determined that the preset route switching conditions are met, and a switch to an alternate route is triggered.

9. A dynamic three-dimensional traffic route generation system for unmanned aerial vehicles (UAVs), characterized in that, The system employing the UAV three-dimensional traffic route dynamic generation method according to any one of claims 1 to 8 includes: Data fusion module: used to acquire multi-source spatiotemporal data of the target area and construct a dynamic three-dimensional spatial domain model that updates over time; Environmental prediction module: used to perform environmental prediction based on the dynamic three-dimensional spatial model and generate predicted environmental information for future time periods; Energy consumption modeling module: used to construct an energy consumption model that characterizes the energy consumption of UAV flight, and output the energy consumption estimate of the flight path based on flight parameters and the predicted environmental information; Path planning module: used to search for paths that meet the constraints of the predicted environmental information in the dynamic three-dimensional airspace model according to the comprehensive optimization objective, and generate the main route and at least one backup route, wherein the comprehensive optimization objective is comprehensively evaluated based on flight time, the energy consumption estimate, and the predicted environmental information; Dynamic adjustment module: used to control the UAV to fly along the main route and monitor the changes in flight status parameters and predicted environmental information in real time. When the preset route switching conditions are met, the module controls the UAV to dynamically switch from the main route to an alternate route.

10. An electronic device, characterized in that, include: Memory; The processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the UAV three-dimensional traffic route dynamic generation method according to any one of claims 1 to 8.

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