Unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system

Abstract

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a method and system for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles. Background Art

[0002] With the large-scale application of drones in logistics and distribution, agricultural plant protection, security inspection and other fields, low-altitude airspace traffic management faces some challenges. For example, some existing technologies have the following defects: Fixed geographic fences cannot be dynamically adjusted based on the real-time occupancy rate of the airspace, resulting in less than 40% utilization of idle airspace during non-peak hours (such as the night-time flight ban period for agricultural plant protection), causing serious waste of airspace resources. The existing monitoring platform only displays the location of drones and lacks an active early warning mechanism for potential conflicts (such as no alarm when the drone is less than 100 meters from the no-fly zone). Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles, so as to maximize the utilization of airspace resources while ensuring safety.

[0004] In order to solve the above technical problems, the technical solutions of the present invention are as follows: First, the method for dynamic airspace control of low-altitude intelligent traffic of drones includes: Step 1: Receive a drone flight mission application and generate a comprehensive risk value. Based on the comprehensive risk value, if the comprehensive risk value is lower than a preset threshold, issue a second-level approval instruction. Step 2: Using the approval permission instruction output in step 1 as a trigger condition, obtain airspace occupancy rate and flight mission priority data in real time; generate an electronic fence dynamic adjustment instruction based on the airspace occupancy rate and flight mission priority data, and output an updated electronic fence geographic coordinate set; Step 3: Use the geo-fence coordinates output from step 2 as the monitoring baseline boundary, integrate ADS-B data, meteorological information, and drone equipment status, generate a real-time risk heat map, and output graded alarm instructions; Step 4: Receive the real-time risk heat map and graded alarm instructions output in step 3, combine them with wind speed forecast and dynamic airspace occupancy data; when the graded alarm instruction is triggered, perform the following operations: Step 41, constructing a feasible solution space for the flight route using the no-fly zones and high-risk areas in the risk heat map as constraints; In step 42, with the minimum energy consumption as the optimization goal, the path population is iteratively evolved through the selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance and detour path, and a route update instruction is issued to the UAV flight control system.

[0005] Secondly, the UAV low-altitude intelligent traffic dynamic airspace control system includes: The receiving module is used to receive drone flight mission applications and generate a comprehensive risk value. When the comprehensive risk value is lower than a preset threshold, the module outputs a second-level approval instruction. A judgment module is used to obtain airspace occupancy and flight mission priority data in real time based on the output approval permission instruction; generate an electronic fence dynamic adjustment instruction based on the airspace occupancy and flight mission priority data, and output an updated electronic fence geographic coordinate set; The generation module is used to use the output geo-fence geographic coordinate set as the monitoring reference boundary, integrate ADS-B data, meteorological information and drone equipment status, generate a real-time risk heat map and output graded alarm instructions; The execution module is used to receive the output of real-time risk heat maps and graded warning instructions, combined with wind speed forecasts and dynamic airspace occupancy data; when the graded warning instructions are triggered, the following operations are performed: using the no-fly zones and high-risk areas in the risk heat map as constraints, a feasible solution space for the route is constructed; with the lowest energy consumption as the optimization goal, the path population is iteratively evolved through the selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance and detour path, and a route update instruction is issued to the UAV flight control system.

[0006] The above solution of the present invention includes at least the following beneficial effects: Through the automatic decision-making mechanism of the multi-dimensional risk assessment model, manual approval delays are completely eliminated, meeting the urgent need for real-time airspace access for emergency tasks; while ensuring safety and compliance, the airspace application process is compressed from "hourly level" to "instant response."

[0007] Dynamically adjust electronic fences based on real-time task requirements and airspace load status to solve the resource rigidity problem caused by static fences; release idle airspace for high-priority tasks, and significantly optimize the time and space utilization of airspace resources.

[0008] The risk heat map generated by integrating multi-source data can realize early warning of no-fly zone intrusion, weather threats and equipment failure; the graded warning mechanism (early warning / forced intervention) forms a progressive risk response closed loop, significantly reducing the probability of collision accidents.

[0009] Plan detour routes in real time under the constraints of risk heat maps, and simultaneously avoid dynamic airspace obstacles and meteorological threats; combine aircraft performance and energy consumption models to generate economical routes and extend the effective operation time of drones.

[0010] Deeply integrated with civil aviation regulatory standard interfaces to meet the mandatory requirements of airspace management regulations, the collaborative mechanism of dynamic fencing and risk assessment forms a technical protection network that is difficult to circumvent. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flow chart of the method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles provided by an embodiment of the present invention.

[0012] Figure 2 It is a schematic diagram of a dynamic airspace control system for low-altitude intelligent traffic of unmanned aerial vehicles provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0014] like Figure 1 As shown, an embodiment of the present invention proposes a method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles, including: Step 1: Receive a drone flight mission application and generate a comprehensive risk value. Based on the comprehensive risk value, if the comprehensive risk value is lower than a preset threshold, issue a second-level approval instruction. Step 2: Using the approval permission instruction output in step 1 as a trigger condition, obtain airspace occupancy rate and flight mission priority data in real time; generate an electronic fence dynamic adjustment instruction based on the airspace occupancy rate and flight mission priority data, and output an updated electronic fence geographic coordinate set; Step 3: Use the geo-fence coordinates output from step 2 as the monitoring baseline boundary, integrate ADS-B data, meteorological information, and drone equipment status, generate a real-time risk heat map, and output graded alarm instructions; Step 4: Receive the real-time risk heat map and graded alarm instructions output in step 3, combine them with wind speed forecast and dynamic airspace occupancy data; when the graded alarm instruction is triggered, perform the following operations: Step 41, constructing a feasible solution space for the flight route using the no-fly zones and high-risk areas in the risk heat map as constraints; In step 42, with the minimum energy consumption as the optimization goal, the path population is iteratively evolved through the selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance and detour path, and a route update instruction is issued to the UAV flight control system.

[0015] In this embodiment of the present invention, a comprehensive risk value quantitative assessment (step 1) replaces the traditional manual review process, enabling automated and rapid approval of drone flight missions. Approval times are reduced to seconds, significantly shortening mission wait times. This approach is particularly suitable for time-sensitive scenarios such as emergency rescue and logistics distribution. A dynamic matching mechanism for risk thresholds can be flexibly adjusted based on airspace management policies to ensure a balance between safety and efficiency. For example, lower risk thresholds can be set for urban core areas, while approval requirements can be relaxed for low-risk areas such as suburban areas. Based on real-time monitoring of airspace occupancy and task priority grading (step 2), electronic fences can be dynamically expanded or contracted, avoiding the waste of airspace resources caused by static fences. Dynamically demarcating no-fly zones during large-scale events or temporarily creating priority flight corridors for medical emergency drones enhances the flexibility and fairness of airspace use. A real-time update mechanism for geographic coordinate sets seamlessly integrates with the drone's navigation system, ensuring that flight paths always comply with current airspace control requirements and reducing the risk of violations caused by lagging regulations.

[0016] By integrating multi-dimensional data such as ADS-B data, weather information, and equipment status (step 3), a dynamic risk heat map is constructed, enabling visual prediction of potential airspace hazards such as collision risks, meteorological disasters, and equipment failures. For example, by overlaying wind speed and precipitation data, drones are warned to avoid areas with severe weather. A graded warning mechanism (such as blue, yellow, orange, and red warnings) enables the control system to adopt differentiated response strategies based on risk level. Low-risk scenarios only require a warning, while high-risk scenarios automatically trigger obstacle avoidance path planning, improving the accuracy and efficiency of emergency response. A genetic algorithm-based path optimization model (step 4) aims to minimize energy consumption, incorporating constraints such as no-fly zones and high-risk areas, and generating a globally optimal obstacle avoidance path through population iteration. Compared to traditional manual or rule-based obstacle avoidance, this method reduces detour distances and energy consumption, making it particularly suitable for route optimization of long-endurance logistics drones. The real-time infusion of dynamic airspace occupancy data enables path planning to adapt to multi-drone coordination scenarios within the airspace, avoiding secondary congestion caused by simultaneous route adjustments by multiple drones and improving the robustness and coordination of low-altitude traffic.

[0017] In a preferred embodiment of the present invention, step 1, receiving a drone flight mission application and generating a comprehensive risk value, includes: Step 11: Obtain the real-time number and distribution coordinates of aircraft in the target airspace; Step 12: Calculate the airspace density index based on the real-time number and distribution coordinates of aircraft and the preset aircraft spacing safety threshold; Step 13: Receive the airspace density index from step 12, associate it with the corresponding airspace wind speed, precipitation, and visibility data, and output the meteorological risk level based on the wind resistance level of the UAV model; Step 14: Based on the meteorological risk level, retrieve the electronic identification code of the drone being applied for, verify the drone's communication module certification status and battery life threshold, and output the device compliance coefficient; In step 15, a comprehensive risk value is generated through a weighted decision matrix according to the airspace density index, meteorological risk level, and equipment compliance coefficient.

[0018] In the embodiment of the present invention, the above steps, when specifically applied, can be implemented through the following specific steps: In step 11 above, the target airspace is scanned in real time through the use of drone automatic dependent surveillance-broadcast (ADS-B) equipment, airspace monitoring radar and other equipment. The system receives and records the unique identification codes and real-time location coordinates of all aircraft in the target airspace, and counts the number of aircraft to form a list of aircraft distribution in the airspace.

[0019] In step 12 above, the target airspace is divided into multiple grid areas. The aircraft distribution coordinates obtained in step 11 are mapped to each grid area. For each grid area, the number of aircraft within it is counted and divided by the grid area to obtain the local density of that grid area. The local densities of all grid areas are combined to calculate the average density of the entire target airspace. This average density is then compared with the preset aircraft spacing safety threshold to determine the airspace density index. If the actual density approaches or exceeds the safety threshold, the airspace density index is high, indicating congestion; otherwise, the index is low.

[0020] In step 13 above, real-time meteorological data released by the meteorological department is received, and wind speed, precipitation, and visibility data for the corresponding airspace are obtained. The obtained wind speed data is compared with the wind resistance rating of the drone model being applied for flight. If the wind speed approaches or exceeds the drone's wind resistance rating, the wind will have a significant impact on the flight. Based on the precipitation intensity and visibility, the meteorological risk is classified into low, medium, and high levels according to pre-set meteorological risk assessment rules. For example, if there is heavy precipitation and low visibility, and the wind speed is close to the drone's wind resistance rating, the meteorological risk level is determined to be high.

[0021] In step 14 above, based on the meteorological risk level determined in step 13, the electronic identification code of the drone being applied for is retrieved. The drone's communication module is then verified through the relevant certification system to determine if it is certified and its communication functions are compliant and operational. Furthermore, the drone's current battery charge and endurance are checked and compared with the estimated mission duration to assess whether the battery life meets the mission requirements. Based on the communication module certification status and battery life, a device compliance coefficient is calculated according to specific evaluation criteria. If the communication module is certified correctly and the battery life is sufficient, the device compliance coefficient is high; otherwise, it is low.

[0022] In step 15, the airspace density index obtained in step 12, the meteorological risk level obtained in step 13, and the equipment compliance coefficient obtained in step 14 are substituted into a pre-set weighted decision matrix. The airspace density index, meteorological risk level, and equipment compliance coefficient are weighted differently based on their impact on flight safety. Through a weighted calculation, the values of these three factors are combined to ultimately determine the overall risk value for the UAV mission.

[0023] In the embodiment of the present invention, calculation and evaluation are performed from multiple dimensions such as airspace density, meteorological conditions, and equipment compliance to avoid the one-sidedness of single-factor evaluation; real-time acquisition of dynamic data such as the number of aircraft and weather can promptly respond to changes in airspace and environment, making risk assessment results more in line with actual conditions and ensuring that flight risks can be effectively assessed in different scenarios; strict verification of drone equipment compliance is carried out to prevent flight accidents caused by equipment problems such as communication failures and insufficient power; combined with meteorological conditions and airspace density assessment, risks caused by bad weather and airspace congestion are avoided in advance to maximize drone flight safety; the automated calculation process quickly generates a comprehensive risk value, which greatly improves risk assessment efficiency compared to manual evaluation, speeds up flight mission approval, and is conducive to the efficient development of drone business.

[0024] In a preferred embodiment of the present invention, based on the comprehensive risk value, when the comprehensive risk value is lower than a preset threshold, a second-level approval permission instruction is output, including: Step 16: receiving the comprehensive risk value generated in step 15 and comparing the comprehensive risk value with a preset airspace safety threshold in real time; Step 17: When the comprehensive risk value is lower than the airspace safety threshold, the automatic approval channel is triggered. When the comprehensive risk value is higher than the airspace safety threshold, the application is transferred to the manual review queue. Step 18: After the automatic approval channel is triggered, the time and space coordinates and device ID of the flight mission application are extracted to generate a second-level approval permission instruction containing an encrypted digital certificate.

[0025] In the embodiment of the present invention, when the above steps are specifically applied, they can be implemented through the following specific steps: In step 16 above, the comprehensive risk value for the drone flight mission generated in step 15 is received and the pre-set airspace safety threshold is retrieved. This threshold is set differently based on different airspace types (e.g., urban core areas, suburban areas, and areas surrounding no-fly zones), flight times (daytime, nighttime), and regulatory requirements. The system compares the comprehensive risk value with the airspace safety threshold for the corresponding scenario in real time, bit by bit, to determine whether the comprehensive risk value is below the threshold.

[0026] In step 17 above, based on the comparison results from step 16, if the comprehensive risk value is below the airspace safety threshold, the system automatically triggers the automated approval channel, marking the flight mission application as "low risk" and placing it in the expedited approval process. If the comprehensive risk value is above the airspace safety threshold, the flight mission application is transferred to the manual review queue and marked as "high risk," awaiting further evaluation and approval by professionals. This process is automated through the system's built-in conditional judgment logic, eliminating the need for human intervention and ensuring rapid approval flow.

[0027] In step 18 above, once the automatic approval channel is triggered, the system immediately extracts key information from the flight mission request, including the planned flight start and end times, the coordinates of the specific flight area (longitude, latitude, and altitude range), and the drone device ID. Using an encryption algorithm, the system combines this information with a pre-defined digital certificate template to generate a second-by-second approval instruction containing an encrypted digital certificate. The encrypted digital certificate ensures the security and immutability of the instruction, while also facilitating authentication and instruction recognition between the drone device and the regulatory system.

[0028] In an embodiment of the present invention, through automated risk value comparison and approval channel determination, low-risk flight missions can be approved in seconds, significantly shortening the approval time, meeting the needs of scenarios with extremely high timeliness requirements such as emergency rescue and express delivery, and improving the operational efficiency of drones; flight missions are automatically diverted according to risk levels, low-risk tasks go through automatic approval channels, and high-risk tasks are manually reviewed, so that limited human resources can be focused on high-risk and complex tasks, avoiding the waste of manual review resources and improving overall management efficiency; pre-set airspace safety thresholds and encrypted digital certificate mechanisms ensure that low-risk tasks are passed quickly while strictly adhering to the safety bottom line, and ensure the security of approval information transmission through encrypted instructions, preventing instructions from being tampered with or misused, and ensuring the standardization and safety of drone flight management; the generation of second-level approval permission instructions provides drone operating companies and users with a convenient and efficient service experience, reduces waiting time, and enhances users' trust in the low-altitude traffic management system.

[0029] In a preferred embodiment of the present invention, step 2, using the approval permission instruction output in step 1 as a trigger condition, obtains airspace occupancy rate and flight mission priority data in real time, including: Step 21: Receive the approval instruction, parse the encrypted digital certificate, verify the certificate validity period and device ID binding status, and output a verification pass signal; Step 22: Based on the verification pass signal, locate the target airspace coordinates in the approval permission instruction, divide the target airspace into 1km×1km spatial grid units, and output the grid index code; Step 23: For the grid index encoding, perform the following parallel operations: Count the number of real-time aircraft in each grid and calculate the airspace occupancy rate; Retrieve the flight mission database and output the mission priority queue according to the urgency weight.

[0030] In the embodiment of the present invention, when the above steps are specifically applied, they can be implemented through the following specific steps: In step 21 above, after receiving the approval instruction, the encrypted digital certificate in the instruction is first parsed. Using the pre-stored encryption key and verification algorithm, the digital certificate signature is checked for validity and the certificate is within its validity period. The device ID in the instruction is then compared with the drone registration database to confirm the binding status of the device ID to the actual drone. If the digital certificate is valid and the device ID is correctly bound, a verification pass signal is output. If the certificate is expired, the signature is invalid, or the device ID does not match, a verification fail signal is output, preventing further execution.

[0031] In step 22 above, after receiving the verification pass signal from step 21, the system extracts the target airspace coordinates (including longitude, latitude, and altitude range) from the approval permit. The target airspace is divided horizontally into a grid using a standard 1km×1km grid size. Each grid is assigned a unique index code, which contains the grid's row and column position within the airspace. Simultaneously, the vertical division is also layered based on the target airspace's altitude range, ensuring that each grid is clearly identified in three-dimensional space. Once the division is complete, the system outputs the index codes for all grids, providing a basis for subsequent data statistics.

[0032] In the above step 23, for each grid index code outputted in step 22, the system performs two operations in parallel.

[0033] Airspace occupancy calculation uses ADS-B equipment, radar, and other monitoring methods to obtain real-time information on the number of aircraft within each grid. The number of aircraft within each grid is compared with the maximum number of aircraft that grid can accommodate (pre-set based on safety spacing and other standards) to calculate the airspace occupancy ratio for each grid. The occupancy ratios of all grids are aggregated and the airspace occupancy rate for the entire target airspace is calculated using methods such as weighted average.

[0034] To determine mission priority, the system accesses the flight mission database and searches for detailed information on all approved flight missions within the target airspace. Each mission is assigned a weight based on factors such as its urgency (e.g., emergency rescue missions are assigned a high weight, while general inspection missions are assigned a low weight) and its type (e.g., medical supply transportation, commercial filming). Tasks are then sorted from highest to lowest weight, creating a task priority queue that clearly defines the order in which each task is prioritized within the airspace.

[0035] The strict verification of approval and permit instructions in step 21 of the present invention ensures that only legal and valid instructions can proceed to subsequent processes, preventing management confusion and security risks caused by illegal or tampered instructions, and safeguarding the security and authority of the low-altitude traffic management system. The grid-based division in step 22 meticulously segments the target airspace, enabling more precise monitoring and management of airspace usage. Combined with the airspace occupancy rate calculated in step 23, this allows real-time monitoring of airspace resource usage in each area, preventing waste and overcrowding, and improving airspace resource utilization efficiency. The task priority queue generated in step 23 provides a clear basis for scheduling UAV flight missions. When airspace resources are scarce, high-priority tasks are prioritized, ensuring the smooth completion of critical tasks such as emergency rescue and the transportation of important supplies, thereby improving the overall coordination and service quality of low-altitude traffic management. Real-time airspace occupancy and task priority data provide the management system with a rich basis for decision-making. Based on this data, the system can dynamically adjust electronic fences and optimize flight paths, enabling intelligent and dynamic management of UAV flights and better adapting to the complex and ever-changing low-altitude traffic environment.

[0036] In a preferred embodiment of the present invention, a dynamic adjustment instruction for an electronic fence is generated based on airspace occupancy and flight mission priority data, and an updated set of electronic fence geographic coordinates is output, including: Step 24: Receive the airspace occupancy rate and the task priority queue. If the occupancy rate is less than 40% and there is a first-priority task, generate an expansion instruction; if the occupancy rate is greater than 80%, generate a contraction instruction. Step 25: Perform differentiated operations based on the expansion instruction and contraction instruction types: Taking the original electronic fence center as the reference, extend the boundary toward the first occupancy grid. The extension distance = task priority coefficient × basic extension radius. Remove the grid cells with the highest idle rate to generate a compact polygon boundary; Step 26: Input the compact polygon boundary into the GIS engine to obtain an updated geo-fence geographic coordinate set consisting of a sequence of longitude and latitude coordinate points.

[0037] In the embodiment of the present invention, the above steps, when specifically applied, can be implemented through the following specific steps: Step 24 receives the airspace occupancy and task priority queue data output from step 23. The airspace occupancy value is first determined and compared to pre-set thresholds (40% and 80%). If the airspace occupancy is less than 40%, the task priority queue is further checked for any top-priority tasks (such as emergency rescue or medical supply transportation). If so, an electronic fence extension command is generated to ensure smooth execution of high-priority tasks. If the airspace occupancy is greater than 80%, indicating airspace resource constraints, an electronic fence retraction command is generated to prevent the concentration of excessive drones and potential safety hazards.

[0038] In step 25 above, upon receiving the extension command, the system uses the geometric center of the original geo-fence as a reference point, identifies the grid cell with the highest occupancy in the current airspace (i.e., the first occupancy grid), and determines the direction of extension of the geo-fence. The task priority coefficient is set based on the urgency of the task (for example, a coefficient of 3 for a first-priority task and 2 for a second-priority task), and the basic extension radius is a pre-set fixed distance (e.g., 500 meters). The system multiplies the task priority coefficient by the basic extension radius to determine the actual extension distance, and then extends the geo-fence boundary in the direction of the first occupancy grid.

[0039] If a contraction command is received, the system iterates through all grid cells and calculates the idle rate (i.e., the percentage of time spent without an aircraft) for each grid cell. The grid cells with the highest idle rates are selected and removed from the geo-fence. The geo-fence is contracted by connecting the boundary points of the remaining grid cells to reconstruct a compact polygonal boundary.

[0040] In step 26 above, the compact polygon boundary data adjusted in step 25 is input into a geographic information system (GIS) engine. The GIS engine performs geographic coordinate conversion on the polygon vertices, converting them into a sequence of longitude and latitude coordinate points. Each coordinate point precisely corresponds to a key location on the geo-fence boundary, ultimately generating an updated geo-fence geographic coordinate set consisting of a series of longitude and latitude coordinate points. This coordinate set can be directly used in the drone navigation system and monitoring platform to achieve real-time updates of the geo-fence.

[0041] The present invention dynamically adjusts the electronic fence according to the airspace occupancy rate and task priority, avoiding resource waste or overcrowding caused by static fences. For example, when the airspace is idle, the flight space is expanded for high-priority tasks, and when the airspace is busy, the range is reduced to reduce the risk of conflict, significantly improving the utilization rate of airspace resources; for the expansion mechanism of the first priority task, a dedicated flight channel can be temporarily opened or the operation area can be expanded to ensure that key tasks such as emergency rescue and medical transportation are not restricted by airspace, shortening the task execution time and improving the efficiency of public service response; through the contraction instruction, the range of drone activities in high-density airspace is timely reduced to reduce the risk of collision; the operation of removing idle grids avoids the occupation of invalid areas, making the electronic fence more in line with actual needs and forming a safer and more orderly flight environment; the automatic dynamic adjustment of the electronic fence does not require human intervention and is seamlessly connected with the real-time monitoring data and task priority system.

[0042] In a preferred embodiment of the present invention, step 3 uses the geo-fence geographic coordinate set output in step 2 as the monitoring reference boundary, integrates ADS-B data, meteorological information, and drone equipment status, generates a real-time risk heat map, and outputs graded alarm instructions, including: Step 31, receiving the geo-fence geographic coordinate set outputted in step 26, and loading it into a geographic information system engine; converting the coordinate set into a polygonal geo-fence reference boundary, and outputting a boundary topology diagram; Step 32: Based on the boundary topology diagram, perform the following real-time data mapping: Project the aircraft position in the ADS-B data onto the grid within the fence boundary, overlay the wind speed vector and precipitation intensity layers from the meteorological information, and correlate the drone equipment status to obtain fused data; Step 33: perform spatial interpolation calculation on the fused data: Generate static high-risk zones with no-fly zones as the core, dynamically generate storm migration high-risk zones based on meteorological data, generate equipment failure warning zones based on equipment status, and output a real-time risk heat map that overlays all risk layers; Step 34, hierarchical alarm triggering sub-step: When the drone enters the area with a risk value ≥ 0.6 in the thermal map, a yellow warning command is triggered; When the risk value is ≥0.9 or enters the static high-risk zone, a red forced return command is triggered.

[0043] In the embodiment of the present invention, when the above steps are specifically applied, they can be implemented through the following specific steps: In step 31 above, the geo-fence coordinate set output from step 26 is received and imported into a geographic information system (GIS) engine. The GIS engine parses the longitude and latitude coordinates in the coordinate set, connecting each point sequentially in coordinate order to construct a polygonal geo-fence. During this construction process, the GIS engine automatically identifies and records information such as the connection relationships of each polygon's edges and vertex coordinates, generating a boundary topology diagram. This topology diagram clearly defines the geo-fence's shape, extent, and spatial relationship with the surrounding geographic area, providing a foundational framework for subsequent data mapping and risk analysis.

[0044] In step 32, based on the boundary topology diagram generated in step 31, the system processes the multi-source data acquired in real time. First, the real-time location information of each aircraft recorded in the ADS-B data is projected onto the corresponding grid cells within the geo-fence boundary according to their latitude and longitude coordinates to achieve spatial localization of the aircraft's position. Simultaneously, wind speed vector data and precipitation intensity data from meteorological information are retrieved and overlaid as layers within the geo-fence area, visually displaying the spatial distribution of meteorological conditions. Furthermore, the system associates each drone's device status data (such as battery charge, communication signal strength, and sensor operating status) with the aircraft's location information. Finally, the three types of data—aircraft location, meteorological information, and device status—are integrated to form a fused, comprehensive data set that comprehensively reflects the real-time conditions within the geo-fence.

[0045] In step 33 above, spatial interpolation is performed on the fused data from step 32 to estimate the risk value for each area within the electronic fence. First, a static high-risk zone is defined within a certain range around the no-fly zone. The risk value in this zone is set to the highest level, prohibiting drones from entering. Next, based on meteorological data such as wind speed, precipitation, and storm movement paths, a high-risk zone for storm migration is dynamically generated. The scope and location of this zone are updated in real time as meteorological conditions change. Then, combined with drone device status data, an equipment failure warning zone is generated for the locations and surrounding areas of drones experiencing low battery, communication anomalies, or sensor failures. Finally, the system overlays different risk zones—static high-risk zones, storm migration high-risk zones, and equipment failure warning zones—and generates a real-time risk heat map using visualization methods such as color depth and transparency. Darker areas in the heat map indicate higher risk values, allowing managers to quickly identify high-risk areas.

[0046] In step 34 above, the drone's position in the risk heat map and the corresponding risk value are monitored in real time. If the drone enters an area in the heat map with a risk value greater than or equal to 0.6, a yellow warning is triggered, and a warning message is sent to the drone operator via the management platform, reminding them to be aware of flight risks and to operate with caution. If the drone enters an area with a risk value greater than or equal to 0.9, or enters a static high-risk zone, a red forced return command is triggered. The system automatically sends a return command to the drone's flight control system, controlling the drone to immediately return to a designated safe location to avoid a flight accident.

[0047] By integrating ADS-B data, meteorological information, and drone equipment status, the present invention achieves comprehensive monitoring of various risk factors within the electronic fence, avoiding the limitations of a single data source and enabling managers to grasp the complex conditions within the airspace in real time. The real-time risk heat map displays the risk distribution in an intuitive and visual way, allowing managers to quickly locate high-risk areas and take preventative measures in advance. Compared with traditional text or data alarms, this greatly improves the efficiency of risk identification and processing. The graded alarm instructions provide differentiated responses based on the severity of the risk. Yellow warnings give operators room for independent decision-making, while red forced return signals force drone safety in high-risk situations, ensuring both flight flexibility and a safety bottom line in emergencies. The system dynamically generates risk areas based on meteorological conditions and updates warnings in real time based on equipment status, enabling the system to adapt to the ever-changing flight environment, effectively respond to sudden risks, and enhance the intelligence and reliability of low-altitude traffic management. Through measures such as early warnings and forced returns, the accident rate of drones caused by collisions, meteorological disasters, and equipment failures is significantly reduced.

[0048] In a preferred embodiment of the present invention, step 41, using the no-fly zones and high-risk areas in the risk heat map as constraints, constructs a feasible solution space for the route, including: Step 411: Receive a real-time risk heat map, extract coordinates of all regions with risk values ≥ 0.5 to obtain a first risk region; convert the boundary of the first risk region into a set of no-fly cubes in three-dimensional space; Step 412: Based on the equipment failure warning zone data in the heat map, retrieve the maximum climb rate and minimum turning radius of the current UAV to output the UAV maneuverability parameter set; Step 413 integrates the no-fly cube set and the maneuverability parameter set, uses the line connecting the start point and the end point as the reference axis, expands the safety buffer zone along both sides of the axis, removes the invalid space overlapping with the no-fly cube, and outputs a network topology diagram of the route feasible solution space consisting of the remaining space.

[0049] In the embodiment of the present invention, the above steps, when specifically applied, can be implemented through the following specific steps: In step 411, the real-time risk heat map generated in step 33 is received. All areas with a risk value greater than or equal to 0.5 are screened, marked as first-risk areas, and their boundary coordinates are extracted. Because drone flight is a three-dimensional activity, the system vertically extends the first-risk area and, based on the actual flight altitude range, converts the two-dimensional plane boundary into multiple three-dimensional no-fly cubes. Each no-fly cube clearly defines the spatial range within which drones are prohibited from entering within a specific altitude range. This complete set of three-dimensional no-fly cubes provides clear spatial constraints for subsequent route planning.

[0050] In step 412, drones in a warning state are located based on the equipment failure warning zone data in the risk heat map. For these drones, parameters such as the maximum climb rate (the maximum altitude the drone can reach per unit time) and the minimum turning radius (the minimum radius of space required for the drone to complete a turn) recorded in their registration information are retrieved. These parameters are dynamically adjusted based on the drone's current flight status (such as speed and attitude) to ensure accuracy. Ultimately, a set of parameters reflecting the drone's current maneuverability is generated, which will be used to assess the drone's flyability in different spaces.

[0051] In step 413, the no-fly cubes generated in step 411 are integrated with the drone maneuverability parameter set obtained in step 412. First, using the line connecting the start and end points of the drone's flight mission as the reference axis, a three-dimensional safety buffer zone is extended on both sides of the axis according to the drone's minimum turning radius and safety spacing requirements. This buffer zone represents the spatial range within which the drone can ideally fly. Next, the safety buffer zone is spatially compared with the no-fly cubes, eliminating any invalid space that overlaps with the no-fly cubes. After this processing, the remaining spatial regions constitute the feasible solution space for the route. The system then connects and topologically analyzes these feasible space regions to generate a network topology diagram of the feasible solution space for the route. This diagram clearly displays the connections between the feasible spaces and the path directions, providing a foundational framework for subsequent route optimization.

[0052] By converting high-risk areas in the risk heat map into a three-dimensional no-fly cube, the present invention can fully cover the risk space that drones may encounter during flight, avoiding the risk of three-dimensional space collisions caused by two-dimensional planning. The feasible solution space is constructed by combining the drone's own maximum climb rate, minimum turning radius and other maneuverability parameters to ensure that the planned route meets the drone's actual control performance, avoiding flight out-of-control problems caused by exceeding the capability range, and improving flight reliability. The network topology diagram of the feasible solution space of the route provides a clear search space for subsequent path optimization algorithms, reducing the exploration of invalid paths and significantly improving path planning efficiency. At the same time, the connection relationship in the topology diagram helps to quickly generate multiple alternative routes. The construction method can dynamically adjust the feasible solution space according to different risk heat maps and drone models, and can quickly generate a feasible flight space that meets safety requirements, whether in complex urban airspace or remote mountainous areas.

[0053] In a preferred embodiment of the present invention, step 42, with the minimum energy consumption as the optimization goal, iteratively evolves the path population through selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance path, and issues a route update instruction to the UAV flight control system, including: Step 421: randomly generate N initial paths connecting the start and end points in the network topology of the route feasible solution space; each path is represented by a sequence of waypoints, where the distance between the points is ≥ the minimum turning radius; Step 422, for each path, execute: Extract the mean of the real-time risk heat map of the path crossing area, calculate the total path length and wind speed vector projection resistance, and integrate the risk value and energy consumption factor to generate a fitness score; The loop is executed until convergence: Select and retain the top 30% of the fitness scores as parents; Crossover, swap and reorganize the parent path waypoints; Mutation, randomly adjust 5% of the waypoint positions; When the number of iterations reaches the upper limit, the corresponding path in the contemporary population is extracted, the path waypoints are smoothed and the data volume is compressed, and the route update command is sent to the flight control system through the air-ground communication link.

[0054] In the embodiment of the present invention, when the above steps are specifically applied, they can be implemented through the following specific steps: In step 421, based on the network topology of the route feasible solution space output in step 413, N initial paths are randomly generated between the starting and ending points. When generating these paths, the system strictly adheres to the drone's minimum turning radius constraint, ensuring that the distance between adjacent waypoints is greater than or equal to the minimum turning radius to ensure that the drone can safely and smoothly complete turns. Each path consists of an ordered sequence of waypoints, each containing precise latitude, longitude, and altitude information. These initial paths constitute the initial path population used for subsequent optimization.

[0055] In step 422 above, for each path in the initial path population, the system performs the following operations. First, the risk value of the area crossed by the path in the real-time risk heat map is extracted, and the risk mean of the area is calculated to quantify the safety risk faced by the path. Next, the total length of the path is calculated. At the same time, combined with the wind speed vector data in the meteorological information, the angle between the path direction and the wind speed direction is analyzed, and the projected resistance of the wind speed in the path direction is calculated to evaluate the energy required for the path flight. Finally, the risk value and the energy consumption factor are integrated according to the pre-set weights to generate a fitness score for each path. The higher the score, the better the overall performance of the path in terms of safety and energy consumption.

[0056] The path population is sorted according to fitness scores, the top 30% of the paths are retained as parent paths, and the remaining paths are eliminated. This operation simulates the "survival of the fittest" law in nature, ensuring that excellent path characteristics can be passed on to the next generation.

[0057] The crossover operation randomly swaps and recombines waypoint segments from the selected parent paths. For example, the first half of one parent path's waypoints can be combined with the second half of another parent path to generate a new child path. The crossover operation helps integrate the advantages of different paths, resulting in a more optimal path combination.

[0058] Mutation randomly adjusts the waypoints in the offspring path. Each time, approximately 5% of the waypoints are randomly selected and their positions are fine-tuned while satisfying the minimum turning radius and feasible solution space constraints. Mutation introduces new path features, preventing the algorithm from falling into local optimal solutions and increasing population diversity.

[0059] After the iteration terminates and the command is issued, the system continues to loop through selection, crossover, and mutation operations until the preset upper limit of iterations is reached or the population fitness score stabilizes (i.e., converges). At the end of the iteration, the path with the highest fitness score is selected from the current population. The waypoints on this path are smoothed, redundant points are removed, and data is compressed to reduce the communication burden. Finally, the optimized route update command is sent to the drone's flight control system via an air-to-ground communication link, guiding the drone to execute the new flight path.

[0060] By integrating risk values with energy consumption factors to calculate fitness scores, this approach balances flight safety and energy consumption when planning routes. This avoids risking high-risk areas in pursuit of low energy consumption, or significantly increasing energy consumption due to excessive risk aversion, thus ensuring efficient and safe UAV flight. The genetic algorithm's iterative optimization mechanism rapidly searches for the optimal global solution within a complex feasible solution space. Compared to traditional path planning methods, this significantly reduces path planning time and generates more reasonable and efficient paths, making it particularly suitable for dynamically changing low-altitude environments. Crossover and mutation operations enable the algorithm to continuously explore new path combinations, adapting to varying airspace environments, weather conditions, and UAV models. Even if airspace risks or weather conditions change during flight, the algorithm can quickly replan an appropriate route, improving the UAV's ability to navigate complex scenarios. Smoothing and data compression on the optimized path reduces the data volume of route update commands, alleviating the transmission pressure on the air-to-ground communication link, and alleviating the computational burden on the UAV's flight control system, thereby improving the overall system's operational efficiency and stability.

[0061] like Figure 2 As shown in the figure, the UAV low-altitude intelligent traffic dynamic airspace control system includes: The receiving module is used to receive drone flight mission applications and generate a comprehensive risk value. When the comprehensive risk value is lower than a preset threshold, the module outputs a second-level approval instruction. A judgment module is used to obtain airspace occupancy and flight mission priority data in real time based on the output approval permission instruction; generate an electronic fence dynamic adjustment instruction based on the airspace occupancy and flight mission priority data, and output an updated electronic fence geographic coordinate set; The generation module is used to use the output geo-fence geographic coordinate set as the monitoring reference boundary, integrate ADS-B data, meteorological information and drone equipment status, generate a real-time risk heat map and output graded alarm instructions; The execution module is used to receive the output of real-time risk heat maps and graded warning instructions, combined with wind speed forecasts and dynamic airspace occupancy data; when the graded warning instructions are triggered, the following operations are performed: using the no-fly zones and high-risk areas in the risk heat map as constraints, a feasible solution space for the route is constructed; with the lowest energy consumption as the optimization goal, the path population is iteratively evolved through the selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance and detour path, and a route update instruction is issued to the UAV flight control system.

[0062] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles, characterized by: The method comprises: Step 1: Receive a drone flight mission application and generate a comprehensive risk value. Based on the comprehensive risk value, if the comprehensive risk value is lower than a preset threshold, issue a second-level approval instruction. Step 2: Using the approval permission instruction output in step 1 as a trigger condition, obtain airspace occupancy rate and flight mission priority data in real time; generate an electronic fence dynamic adjustment instruction based on the airspace occupancy rate and flight mission priority data, and output an updated electronic fence geographic coordinate set; Step 3: Use the geo-fence coordinates output from step 2 as the monitoring baseline boundary, integrate ADS-B data, meteorological information, and drone equipment status, generate a real-time risk heat map, and output graded alarm instructions; Step 4: Receive the real-time risk heat map and graded alarm instructions output in step 3, combine them with wind speed forecast and dynamic airspace occupancy data; when the graded alarm instruction is triggered, perform the following operations: Step 41, constructing a feasible solution space for the flight route using the no-fly zones and high-risk areas in the risk heat map as constraints; In step 42, with the minimum energy consumption as the optimization goal, the path population is iteratively evolved through the selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance and detour path, and a route update instruction is issued to the UAV flight control system.

2. The method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles according to claim 1 is characterized in that: Step 1: Receive the drone flight mission application and generate a comprehensive risk value, including: Step 11: Obtain the real-time number and distribution coordinates of aircraft in the target airspace; Step 12: Calculate the airspace density index based on the real-time number and distribution coordinates of aircraft and the preset aircraft spacing safety threshold; Step 13: Receive the airspace density index from step 12, associate it with the corresponding airspace wind speed, precipitation, and visibility data, and output the meteorological risk level based on the wind resistance level of the UAV model; Step 14: Based on the meteorological risk level, retrieve the electronic identification code of the drone being applied for, verify the drone's communication module certification status and battery life threshold, and output the device compliance coefficient; In step 15, a comprehensive risk value is generated through a weighted decision matrix according to the airspace density index, meteorological risk level, and equipment compliance coefficient.

3. The method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles according to claim 2 is characterized in that: Based on the comprehensive risk value, when it is lower than the preset threshold, the system will issue approval instructions within seconds, including: Step 16: receiving the comprehensive risk value generated in step 15 and comparing the comprehensive risk value with a preset airspace safety threshold in real time; Step 17: When the comprehensive risk value is lower than the airspace safety threshold, the automatic approval channel is triggered. When the comprehensive risk value is higher than the airspace safety threshold, the application is transferred to the manual review queue. Step 18: After the automatic approval channel is triggered, the time and space coordinates and device ID of the flight mission application are extracted to generate a second-level approval permission instruction containing an encrypted digital certificate.

4. The method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles according to claim 3 is characterized in that: Step 2: Using the approval permission command output in step 1 as a trigger, obtain airspace occupancy rate and flight mission priority data in real time, including: Step 21: Receive the approval instruction, parse the encrypted digital certificate, verify the certificate validity period and device ID binding status, and output a verification pass signal; Step 22: Based on the verification pass signal, locate the target airspace coordinates in the approval permission instruction, divide the target airspace into 1km×1km spatial grid units, and output the grid index code; Step 23: For the grid index encoding, perform the following parallel operations: Count the number of real-time aircraft in each grid and calculate the airspace occupancy rate; Retrieve the flight mission database and output the mission priority queue according to the urgency weight.

5. The method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles according to claim 4 is characterized in that: Generates dynamic geo-fence adjustment instructions based on airspace occupancy and flight mission priority data, and outputs an updated geo-fence geographic coordinate set, including: Step 24: Receive the airspace occupancy rate and the task priority queue. If the occupancy rate is less than 40% and there is a first-priority task, generate an expansion instruction; if the occupancy rate is greater than 80%, generate a contraction instruction. Step 25: Perform differentiated operations based on the expansion instruction and contraction instruction types: Taking the original electronic fence center as the reference, extend the boundary toward the first occupancy grid. The extension distance = task priority coefficient × basic extension radius. Remove the grid cells with the highest idle rate to generate a compact polygon boundary; Step 26: Input the compact polygon boundary into the GIS engine to obtain an updated geo-fence geographic coordinate set consisting of a sequence of longitude and latitude coordinate points.

6. The method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles according to claim 5 is characterized in that: Step 3: Use the geo-fence coordinates output from step 2 as the monitoring baseline boundary, integrate ADS-B data, meteorological information, and drone equipment status, generate a real-time risk heat map, and output graded warning instructions, including: Step 31, receiving the geo-fence geographic coordinate set outputted in step 26, and loading it into a geographic information system engine; converting the coordinate set into a polygonal geo-fence reference boundary, and outputting a boundary topology diagram; Step 32: Based on the boundary topology diagram, perform the following real-time data mapping: Project the aircraft position in the ADS-B data onto the grid within the fence boundary, overlay the wind speed vector and precipitation intensity layers from the meteorological information, and correlate the drone equipment status to obtain fused data; Step 33: perform spatial interpolation calculation on the fused data: Generate static high-risk zones with no-fly zones as the core, dynamically generate storm migration high-risk zones based on meteorological data, generate equipment failure warning zones based on equipment status, and output a real-time risk heat map that overlays all risk layers; Step 34, hierarchical alarm triggering sub-step: When the drone enters the area with a risk value ≥ 0.6 in the thermal map, a yellow warning command is triggered; When the risk value is ≥0.9 or enters the static high-risk zone, a red forced return command is triggered.

7. The method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles according to claim 6 is characterized in that: Step 41, using the no-fly zones and high-risk areas in the risk heat map as constraints, constructs a feasible solution space for the route, including: Step 411: Receive a real-time risk heat map, extract coordinates of all regions with risk values ≥ 0.5 to obtain a first risk region; convert the boundary of the first risk region into a set of no-fly cubes in three-dimensional space; Step 412: Based on the equipment failure warning zone data in the thermal map, retrieve the maximum climb rate and minimum turning radius of the current UAV to output the UAV maneuverability parameter set; Step 413 integrates the no-fly cube set and the maneuverability parameter set, uses the line connecting the start point and the end point as the reference axis, expands the safety buffer zone along both sides of the axis, removes the invalid space overlapping with the no-fly cube, and outputs a network topology diagram of the route feasible solution space consisting of the remaining space.

8. The method for dynamic airspace control of low-altitude intelligent traffic of unmanned aerial vehicles according to claim 7 is characterized in that: Step 42, with the minimum energy consumption as the optimization goal, iteratively evolves the path population through the selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance path and issue a route update instruction to the UAV flight control system, including: Step 421: randomly generate N initial paths connecting the start and end points in the network topology of the route feasible solution space; each path is represented by a sequence of waypoints, where the distance between the points is ≥ the minimum turning radius; Step 422, for each path, execute: Extract the mean of the real-time risk heat map of the path crossing area, calculate the total path length and wind speed vector projection resistance, and integrate the risk value and energy consumption factor to generate a fitness score; The loop is executed until convergence: Select and retain the top 30% of the fitness scores as parents; Crossover, swap and reorganize the parent path waypoints; Mutation, randomly adjust 5% of the waypoint positions; When the number of iterations reaches the upper limit, the corresponding path in the contemporary population is extracted, the path waypoints are smoothed and the data volume is compressed, and the route update command is sent to the flight control system through the air-ground communication link.

9. The UAV low-altitude intelligent traffic dynamic airspace control system is characterized by: The system is used to perform the method according to any one of claims 1 to 8, comprising: The receiving module is used to receive drone flight mission applications and generate a comprehensive risk value. When the comprehensive risk value is lower than a preset threshold, the module outputs a second-level approval instruction. A judgment module is used to obtain airspace occupancy and flight mission priority data in real time based on the output approval permission instruction as a trigger condition; generate an electronic fence dynamic adjustment instruction based on the airspace occupancy and flight mission priority data, and output an updated electronic fence geographic coordinate set; The generation module is used to use the output geo-fence geographic coordinate set as the monitoring reference boundary, integrate ADS-B data, meteorological information and drone equipment status, generate a real-time risk heat map and output graded alarm instructions; The execution module is used to receive the output of real-time risk heat maps and graded warning instructions, combined with wind speed forecasts and dynamic airspace occupancy data; when the graded warning instructions are triggered, the following operations are performed: using the no-fly zones and high-risk areas in the risk heat map as constraints, a feasible solution space for the route is constructed; with the lowest energy consumption as the optimization goal, the path population is iteratively evolved through the selection, crossover, and mutation operations of the genetic algorithm to output the final global obstacle avoidance and detour path, and a route update instruction is issued to the UAV flight control system.

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