Low-altitude safety corridor generation method and device based on unmanned aerial vehicle performance

Abstract

The invention discloses a low-altitude safety corridor generation method and device based on unmanned aerial vehicle performance, and the method comprises the steps: dividing the unmanned aerial vehicle performance into three performance levels according to the performance data of all unmanned aerial vehicles in a target low-altitude airspace; constructing a three-channel low-altitude safety corridor corresponding to the performance grade and a whole-process operation rule of the unmanned aerial vehicle in the low-altitude safety corridor; a multi-objective optimization model of the low-altitude safety corridor is constructed, the maximum traffic capacity and the minimum operation risk are taken as objective functions, and the performance limit of the unmanned aerial vehicle is taken as a constraint condition; solving the multi-objective optimization model to obtain a final value of a decision vector; the decision vector comprises the channel width, the vertical height, the lower limit and the upper limit of the speed-limiting speed interval and the dynamic minimum longitudinal safety interval of each channel; and generating a low-altitude safety corridor according to the final value of the decision vector. By constructing a multi-channel and differentiated speed limiting rule, performance conflicts are effectively isolated, and the overall operation capacity and safety level of a corridor are improved.

Description

Technical Field

[0001] This application belongs to the field of low-altitude airway structure design technology, specifically relating to a method and apparatus for generating low-altitude safe corridors based on the performance of unmanned aerial vehicles (UAVs). Background Technology

[0002] In recent years, the drone industry has developed rapidly, with a surge in low-altitude drone flights. However, the high-density dynamic traffic demands have also brought challenges to the large-scale management of urban low-altitude airspace: drones with different performance characteristics are difficult to integrate and operate efficiently; the overlapping flight paths of multiple drones increase the probability of collisions; and the mixed operation of manned and unmanned aircraft in some areas increases risks. These challenges demonstrate that traditional airspace management methods are no longer adequate for the complex characteristics of low-altitude operations.

[0003] Currently, research on the design of low-altitude safety corridors employs methods such as fixed-height layering, unified route planning, and static spacing standards. However, the corridor structures designed using these methods are typically single or fixed-layered, and their operational rules are mostly standardized. Considering the significant differences in core performance characteristics of UAVs such as speed, range, and maneuverability, when UAVs of different performance characteristics operate in a traditional single corridor, lower-performance UAVs can severely restrict the operational efficiency of high-performance UAVs, creating a "bottleneck effect" and leading to a decrease in the overall corridor's capacity. Therefore, these methods cannot accurately meet the complex needs of multi-aircraft mixed operations in the real world and fail to align with the planning goal of improving the overall efficiency of low-altitude transportation systems. Summary of the Invention

[0004] Objective: In view of at least one of the above technical problems, this application provides a method and apparatus for generating low-altitude safe corridors based on UAV performance. It considers the significant differences in speed and maneuverability of UAVs of different performance levels on mixed traffic flow, and effectively isolates performance conflicts by constructing multi-channel and differentiated speed limit rules, thereby improving the overall operational capacity and safety level of the corridor.

[0005] The technical solution adopted in this application is as follows:

[0006] In a first aspect, this application provides a method for generating low-altitude safe corridors based on UAV performance, including:

[0007] S1: Based on the performance data of all UAVs in the target low-altitude airspace, the performance of UAVs is divided into three performance levels;

[0008] S2: Construct a three-channel low-altitude safety corridor corresponding to the performance level, and establish full-process operation rules for UAVs in the low-altitude safety corridor;

[0009] S3: Construct a multi-objective optimization model for the low-altitude safety corridor. The multi-objective optimization model takes maximizing traffic capacity and minimizing operational risk as the objective function and the performance limit of the UAV as the constraint condition.

[0010] S4: Solve the multi-objective optimization model to obtain the final value of the decision vector; wherein, the decision vector includes the channel width, vertical height, lower limit and upper limit of the speed limit range, and dynamic minimum longitudinal safety interval of each channel of the low-altitude safety corridor.

[0011] S5: Generate a low-altitude safety corridor based on the final value of the decision vector.

[0012] Secondly, this application provides a low-altitude safe corridor generation device based on UAV performance, including a processor and a storage medium;

[0013] The storage medium is used to store instructions;

[0014] The processor is configured to operate according to the instructions to execute the method according to the first aspect.

[0015] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0016] Fourthly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0017] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0018] Beneficial Effects: The method and apparatus for generating low-altitude safe corridors based on UAV performance provided in this application have the following advantages: This application considers the physical differences in speed and maneuverability between different UAV models. By quantifying the performance differences of UAVs and constructing a multi-channel low-altitude safe corridor spatial model, it scientifically solves the "bottleneck effect" problem in the mixed operation of high- and low-speed UAVs. By setting dynamic safety intervals and speed transition buffer zones, it significantly reduces the risk of conflict while improving the utilization rate of airspace resources, achieving synergistic optimization of low-altitude traffic capacity and safety level, and providing a dynamic and reasonable guarantee scheme for the large-scale operation of UAVs in complex urban environments. Through multi-channel isolation and dynamic speed limit rule setting, it effectively eliminates the "bottleneck effect" in mixed operation and makes up for the shortcomings of low traffic efficiency caused by the single structure and uniform operation rules of existing airspace management methods. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for generating a low-altitude safe corridor based on UAV performance according to an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the entire process of the low-altitude safety corridor operation rules in one embodiment of this application.

[0021] Figure 3 This is a schematic diagram comparing the passage time of the same number of drones through a corridor in one embodiment of this application;

[0022] Figure 4 This is a schematic diagram comparing corridor capacity under the same operating time in one embodiment of this application. Detailed Implementation

[0023] The present application will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and should not be used to limit the scope of protection of the present application.

[0024] In the description of this application, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0025] In the description of this application, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0026] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0027] This application aims to maximize traffic capacity and minimize operational risks by establishing a low-altitude safe corridor generation model based on UAV performance. By classifying UAV performance data into performance levels, it effectively identifies the operational conflict characteristics of different performance models, reduces the rule calculation space in complex dynamic environments, and improves the speed of traffic flow simulation and scheme optimization for multi-model mixed-flow operations. This application provides model support and a rule foundation for the safe and efficient operation of multiple UAV types in low-altitude airspace by constructing differentiated speed transition buffer zones and dynamic safety interval standards, thus providing strong support for improving the systematic management level and large-scale operation capability of urban low-altitude traffic systems.

[0028] Example 1: This example provides a method for generating low-altitude safe corridors based on UAV performance, such as... Figure 1 As shown, it includes:

[0029] S1: Based on the performance data of all UAVs in the target low-altitude airspace, the performance of UAVs is divided into three discrete performance levels;

[0030] It should be noted that, in this embodiment, the performance data includes the maximum cruising speed. Standard cruising speed Maximum climb rate and maneuver response time ;in Maximum cruising speed represents the drone's limit of horizontal speed capability; Standard cruise speed represents the drone's ability to cruise sustainably and efficiently in typical missions. The maximum rate of climb represents the vertical velocity capability of the drone. Maneuver response time represents the time required for a UAV to complete a standard speed or heading change maneuver from receiving a command. It reflects the UAV's speed control and maneuverability in complex airspace. These four parameters can be readily extracted from publicly available technical specifications or certification documents of various UAV models and are used to quantify the speed-related performance of UAVs.

[0031] In this embodiment, step S1 may specifically include:

[0032] For each drone, a comprehensive performance feature vector is obtained by weighted fusion of performance data. ;

[0033] Cluster analysis based on comprehensive performance feature vectors is used to divide the performance of UAVs into three discrete performance levels.

[0034] Furthermore, the clustering analysis is achieved by minimizing the sum of intra-cluster distances:

[0035] ;

[0036] In the formula, In this embodiment, the number of performance levels is 3. Let be the comprehensive performance feature vector of the i-th UAV. For the first The mean of the comprehensive performance feature vectors of drones at each performance level, summed in the inner layer. Indicates belonging to the first Summing all drones in the class, summing the outermost layer. This indicates that the summation of three performance level categories will automatically output the classification results of high performance, medium performance, and low performance. In this embodiment, the specific definitions of the three performance levels are as follows:

[0037] High-speed performance class: Drones with high cruise speed, high climb rate and short response time.

[0038] Medium-speed performance level: Drones with speed performance parameters at a medium level.

[0039] Low-speed performance class: Drones with lower cruise speed, climb rate, and relatively longer response time.

[0040] S2: Construct a three-channel low-altitude safety corridor corresponding to the performance level, and establish full-process operation rules for UAVs in the low-altitude safety corridor;

[0041] The performance levels are divided into high-performance, medium-performance, and low-performance levels.

[0042] In step S2, the low-altitude safety corridor corresponding to the performance level includes three channels: a high-performance channel, a medium-performance channel, and a low-performance channel.

[0043] No. The length, width, and thickness of the channel are respectively Channel width With channel thickness The initial value is: ,in, and The first The average envelope width and height of a single-channel UAV This is the safety margin factor;

[0044] The channels are arranged horizontally from low to high performance level, forming a layout of "right-low performance channel, medium-medium performance channel, and left-high performance channel".

[0045] In some embodiments, furthermore, based on this multi-channel low-altitude safety corridor, a multi-layered corridor structure with clearly defined hierarchical levels and responsibilities can be systematically constructed. This structure can not only provide matching flight space according to the inherent performance characteristics of different levels of UAVs in terms of cruise speed, maneuverability, and physical size, but also adapt to the differentiated needs of various application scenarios such as urban logistics, regional inspection, and emergency response in terms of airspace utilization efficiency, safety redundancy, and mission adaptability by flexibly configuring channel geometry parameters and operating rules.

[0046] In some embodiments, in step S2, such as Figure 2 As shown, the full-process operation rules for the UAV in the low-altitude safety corridor include:

[0047] The operation of drones in the corridor is divided into three stages: before entering the corridor, during flight in the corridor, and after exiting the corridor.

[0048] Step 2.1: The operating rules before entering the channel include:

[0049] Step 2.1.1: Submit flight information data set to the low-altitude safety corridor in advance before the UAV takes off. Flight information data set Includes identity information tuples Flight plan information tuple and emergency landing information tuple , is represented as:

[0050] ;

[0051] Among them, identity information tuple Includes drones Serial number ,model and related performance parameters Name of operating unit and operator number , is represented as:

[0052] ;

[0053] Among them, the flight plan information tuple Includes drones Latitude and longitude information of take-off and landing points Preset corridor entrance / exit and time Preset flight path Total estimated flight time and the expected total flight distance , is represented as:

[0054] ;

[0055] Among them, the latitude and longitude information of the take-off and landing points Includes drones Latitude and longitude information of takeoff and landing sites and landing field latitude and longitude information , is represented as:

[0056] ;

[0057] Among them, the preset corridor entrance / exit and time are specified. Including drones You need to select the nearest corridor entrance point based on your starting point. Select the nearest corridor exit point based on your flight destination. and submit the estimated time to enter the corridor. and expected departure time from the corridor , is represented as:

[0058] ;

[0059] Among them, the preset flight path All waypoints included in the preset flight path are represented as follows:

[0060] , ; , , drones The first waypoint, the j-th waypoint, and the last waypoint in the preset flight path, where s is the UAV. Total number of waypoints for the preset flight path;

[0061] The total expected flight time is as follows: , is represented as: That is, the total time from when the drone enters the corridor to when it leaves;

[0062] Among them, the estimated total flight distance The preset path length from the corridor entrance to the corridor exit, including adjacent waypoints along the preset flight path. The sum of the distances between them is obtained as follows:

[0063] ;

[0064] Submit emergency landing information tuple A pre-designated flight path for emergency use near the preset flight path, consisting of multiple waypoints along the alternate flight path:

[0065] ;

[0066] Where z represents the drone Total number of waypoints on alternate flight paths The last waypoint on the alternate flight path;

[0067] Step 2.1.2: The low-altitude safety corridor receives the flight information data set submitted by the UAV. Subsequently, the flight plan information tuple The system will review the data to ensure there are no conflicts with the approved flight plan information set. If conflicts exist, the low-altitude safety corridor will dynamically adjust the entry time or flight path of the conflicting drones and generate a set of negotiated solutions. :

[0068] ;

[0069] ;

[0070] in, For the first A negotiated solution, For the latitude and longitude information of the take-off and landing points, For the first Preset corridor entrance / exit and time for each negotiated solution For the first The pre-defined flight path of each negotiated solution For the first The estimated total flight time for each negotiated solution, For the first The estimated total flight distance for each negotiated solution;

[0071] If there is a conflict between the preset corridor entrance / exit and the time, the low-altitude safety corridor will send a coordinated preset corridor entrance / exit and time. including drones The The nearest corridor entrance to the negotiation solution The nearest corridor exit Expected time to enter the corridor and expected departure time from the corridor , is represented as:

[0072] ;

[0073] If there is a conflict between preset flight paths, adjustments will be made to the original preset flight paths to generate a new preset flight path. :

[0074] ;

[0075] For drones The In the negotiated solution, the first waypoint, the j-th waypoint, and the last waypoint are preset in the flight path; s represents the UAV. Total number of waypoints for the preset flight path;

[0076] Updated estimated total flight time: based on the A conflict coordination scheme recalculates the drone Total estimated flight time ;

[0077] Updated estimated total flight distance: based on the A conflict coordination scheme recalculates the drone The estimated total flight distance ;

[0078] Step 2.2: The operating rules during channel flight are as follows:

[0079] Step 2.2.1: Monitor the operational safety of UAVs within the low-altitude safety corridor, assuming the UAV... The maximum horizontal dimension is The corresponding radius of the body cylinder is The height is circumscribed sphere radius for: ;

[0080] When two adjacent drones When the collision protection zones overlap, the system determines that a collision has occurred:

[0081] ; For two adjacent drones The distance;

[0082] Step 2.2.2: Monitor the traffic flow safety of drones within the low-altitude safety corridor through statistical analysis. Time of the first Instantaneous number of drones in the channel and the instantaneous speed of each drone , obtained the first corridor Instantaneous average velocity of the channel , is represented as: ;

[0083] No. Density of channels Represented as: ;

[0084] Low-altitude safety corridor via real-time monitoring channel Traffic flow density Assess the operational status of the corridor, when traffic flow density... When the preset threshold is reached (approaching saturation), the low-altitude safety corridor will proactively implement preventative flow control, limiting the amount of flight information data that UAVs must submit before entering the corridor. The approval rate is used to limit the number of applications. Number of drones per channel in an instant Thus adjusting the first Density of channels This ensures the stable operation of the entire corridor.

[0085] Step 2.2.3: Establish a database of abnormal events for handling low-altitude safety corridors to handle abnormal situations occurring within the corridors in real time. It is a time The accumulated database includes anomaly-aware tuples. Abnormal diagnostic tuple and handling response tuple , is represented as:

[0086] ;

[0087] Among them, the anomaly-aware tuple Used to record drones The detected anomalies help identify the source of unusual events in the low-altitude safety corridor, including drones. identity information tuple The timestamp of the low-altitude safety corridor detecting anomalies Abnormal situation categories Performance status and flight status , is represented as: ;

[0088] Among them, abnormal diagnostic tuples This is used to refine and classify identified anomalies, combining the anomaly categories. Inferring the root cause classification of abnormal situations and define the exception level. : ;

[0089] Among them, the handling response tuple After providing a negotiated solution for the low-altitude safety corridor, the final anomaly handling plan is determined through human intervention, generating a corresponding handling response record; the handling response tuple Includes timestamps of instructions issued based on the final disposal plan. drones identity information tuple Response instructions And the evaluation level of the response to this anomaly. , is represented as: ;

[0090] Step 2.3: The operating rules for exiting the channel are as follows:

[0091] When drones Complete flight plan and prepare to exit the passage, fly to the designated corridor exit point. When the drone exits the corridor, the exit procedure will be automatically triggered, submitting an exit application to the low-altitude safety corridor. After confirming there is no conflict, the low-altitude safety corridor will issue an exit permit and initiate a handover procedure for drone operation and control. Upon receiving the exit permit, the drone will exit from the preset corridor exit point. Exit the corridor and submit the exact time you left the corridor. The low-altitude safety corridor has completed its monitoring mission, and based on the perception, diagnosis, and response triad generated during flight... Abnormal event handling database Incremental updates will be implemented. The emergency response plan instruction library will be continuously optimized to promote the accumulation and updating of low-altitude safety corridors.

[0092] Step 2.4: Construct speed limit zones and dynamic minimum longitudinal safety intervals for each passage of the low-altitude safety corridor, specifically including:

[0093] Based on the performance level of the drones served by each channel, the settings are differentiated. Speed ​​limit zone , Let these be the lower and upper speed limits for the nth channel;

[0094] The following velocity overlap constraints must be satisfied between adjacent channels: , This is the velocity overlap region. This is the transition buffer coefficient. , The design average speed for the nth and (n+1)th channels;

[0095] To prevent rear-end collisions, a dynamic minimum longitudinal safety separation is set between the vehicles. , , As the reaction time factor, As a braking performance compensation factor, This represents the maximum deceleration of a drone of this performance level.

[0096] Step 2.5: Model the operation of the drone in the corridor using the Manhattan Motion Model.

[0097] At a given moment drones Located in The speed is The safe interval distance is drones The drones in the rear and front are respectively referred to as drones. and drones Their positions are respectively represented as and Their speeds are expressed as follows: and drones and drones The distance between them is expressed as ;

[0098] Constructing a speed evolution model based on performance constraints: Unmanned Aerial Vehicles In the next moment The speed update logic follows its location Speed ​​limit zone of the channel and the motion status of the front machine: ,in, The acceleration is set according to the corresponding performance level of the drone. This represents the interval between adjacent moments; simultaneously, to ensure flight safety, the constraints of the car-following principle must be satisfied: if Time Drone and the adjacent drone in front Distance between Less than the dynamic minimum longitudinal safety interval Then drone To perform a deceleration evasion maneuver, the deceleration rate depends on the maximum deceleration rate of the drone at this performance level. .

[0099] Step 2.6: Construct a traffic efficiency evaluation system for the low-altitude safety corridor.

[0100] Step 2.6.1: Calculate traffic capacity Based on unit time Total number of drone sorties passing through the designated section of the corridor Obtain traffic capacity , represented as ;

[0101] To verify the efficiency of the multi-channel low-altitude safety corridor structure, this embodiment conducts a comparative simulation experiment of typical and improved scenarios based on traffic capacity. The typical scenario refers to a corridor operation scenario without UAV speed allocation, where one corridor contains three channels with the same speed limit range, and the upper and lower speed limits of the corridor are consistent with the improved scenario. The improved scenario is the three-channel low-altitude safety corridor structure designed above, where one corridor contains three corridor channels with different speed limit ranges.

[0102] The specific parameter settings are as follows: the maximum number of drone swarms is set at 5000, and the corridor length is... Speed ​​limits: Mixed corridor 20-150 km / h, low-speed corridor 20-40 km / h, medium-speed corridor 40-100 km / h, high-speed corridor 100-150 km / h, drone speed. From the speed range of the corresponding corridor The sampling is uniform, and the arrival time at the corridor entrance follows a Poisson distribution.

[0103] Experimental results are as follows Figure 3 , Figure 4 As shown, the improved scenario of this application is more efficient in the corridor operation process, and the speed-limited three-channel corridor structure proposed in this application can significantly improve the corridor operation efficiency.

[0104] Step 2.3.2: Calculate operational risks .

[0105] Operational risks Represented as: ; To mitigate the risk of speed deviation, To mitigate the risk of channel deviation, These are the corresponding weight coefficients, and they satisfy... The weights can be adjusted according to the security priorities of different operating scenarios.

[0106] Speed ​​deviation risk This measure indicates the degree to which a drone's actual operating speed deviates from its designated speed limit zone. The greater the speed deviation and the higher the frequency, the higher the risk.

[0107] ;

[0108] in, The actual speed within the speed limit range of this channel during the statistical period. The number of drones inside, This represents the total number of drones operating within the channel.

[0109] Channel deviation risk Assess the extent to which the drone deviates horizontally from its designated channel centerline. Excessive lateral deviation may encroach on adjacent channels or obstacle areas.

[0110] ;

[0111] in, This represents the total number of drones operating within the channel. For the first The actual horizontal position of the drone. The location is the centerline of the passage. This represents the channel width. The closer this value is to 1, the closer the drone is to the channel boundary on average.

[0112] S3: Construct a multi-objective optimization model for the low-altitude safety corridor. The multi-objective optimization model takes maximizing traffic capacity and minimizing operational risk as the objective function and the performance limit of the UAV as the constraint condition.

[0113] The objective function of the multi-objective optimization model for:

[0114] ;

[0115] In the formula, To reflect the total number of drones passing through the cross section per unit time; To reflect the operational risks of the corridor, , respectively Passage capacity Operational risks Weighting factors;

[0116] Constraints: To ensure the safety of the generated scheme, the speed range of each channel should be strictly limited to the theoretical capability of the UAV performance level served by that channel: , To achieve the maximum cruising speed, ensure that the upper limit of the speed limit zone in each channel does not exceed the maximum cruising speed of all drones in this performance class.

[0117] S4: Solve the multi-objective optimization model to obtain the decision vector. The final value; wherein, the decision vector The width of each passage in the low-altitude safety corridor Vertical height Lower limit of the speed limit range Upper limit and dynamic minimum longitudinal safety interval ;

[0118] In this embodiment, the multi-objective optimization model is solved using an improved non-dominated sorting genetic algorithm (NSGA-II) to realize the decision vector. Parameter optimization specifically includes:

[0119] Population initialization: randomly generated Individuals containing corridor geometry and velocity rule parameters form the initial population;

[0120] Parallel simulation evaluation: The traffic flow simulation model of the constructed low-altitude safety corridor is invoked to simulate the parameter schemes for each individual in the population, and the traffic capacity corresponding to each scheme is calculated and recorded in real time. and operational risks The objective function value is calculated.

[0121] Selection and Evolution: A fast non-dominated sorting algorithm is used to stratify individuals and a crowding comparison operator is introduced to maintain the diversity of the solution set; by simulating the crossover and mutation operations of biological evolution, the population is made to continuously move closer to the Pareto front.

[0122] Convergence criterion: The calculation stops when the optimal frontier of the population remains stable over multiple consecutive iterations or when a preset number of iterations (e.g., 500) is reached.

[0123] Pareto optimal solution set selection and scenario matching. The algorithm ultimately outputs a set of mutually non-dominated decision vectors. Optimal parameter solution set.

[0124] In this embodiment, by introducing traffic capacity Operational risks Weighting factors , Decision support, including:

[0125] Efficiency-first scenario: When At that time, the passage capacity is automatically selected from the obtained solution set. The largest solution is suitable for high-frequency operation needs during peak logistics periods;

[0126] Safe and robust scenario: When At that time, identify operational risks. The lowest-cost option is suitable for operational needs in complex weather or mixed airspace conditions.

[0127] S5: Based on the decision vector The final value generates a low-altitude safety corridor.

[0128] In step S5, the scheme is generated and the digital twin is exported. The selected decision vectors are then... The final value (optimal decision vector) The system is standardized and encapsulated to generate a low-altitude safety corridor technical document that includes the three-dimensional spatial definition of the corridor's three channels, a dynamic speed limit interval instruction set, and a layered dynamic minimum longitudinal safety separation standard. Simultaneously, this solution is converted into a digital twin model file and directly imported into the UAV air traffic management system as the execution benchmark for low-altitude flight flow control and conflict management.

[0129] Example 2: Based on Example 1, this example provides a low-altitude safe corridor generation device based on UAV performance, including a processor and a storage medium;

[0130] The storage medium is used to store instructions;

[0131] The processor is configured to operate according to the instructions to execute the method according to Embodiment 1.

[0132] Example 3: Based on Example 1, this example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Example 1.

[0133] Example 4: Based on Example 1, this example provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in Example 1.

[0134] Example 5: Based on Example 1, this example provides a computer program product, including a computer program that, when executed by a processor, implements the method described in Example 1.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating low-altitude safe corridors based on UAV performance, characterized in that, include: S1: Based on the performance data of all UAVs in the target low-altitude airspace, the performance of UAVs is divided into three performance levels; S2: Construct a three-channel low-altitude safety corridor corresponding to the performance level, and establish full-process operation rules for UAVs in the low-altitude safety corridor; S3: Construct a multi-objective optimization model for the low-altitude safety corridor. The multi-objective optimization model takes maximizing traffic capacity and minimizing operational risk as the objective function and the performance limit of the UAV as the constraint condition. S4: Solve the multi-objective optimization model to obtain the final value of the decision vector; wherein, the decision vector includes the channel width, vertical height, lower limit and upper limit of the speed limit range, and dynamic minimum longitudinal safety interval of each channel of the low-altitude safety corridor. S5: Generate a low-altitude safety corridor based on the final value of the decision vector.

2. The method according to claim 1, characterized in that, Based on the performance data of all UAVs within the target low-altitude airspace, the UAV performance is divided into three performance levels, including: For each drone, a comprehensive performance feature vector is obtained by weighted fusion of performance data. The performance data includes maximum cruising speed. Standard cruising speed Maximum climb rate and maneuver response time ; Cluster analysis based on comprehensive performance feature vectors is used to classify UAV performance into three discrete performance levels.

3. The method according to claim 1, characterized in that, The performance levels include high performance, medium performance, and low performance. In step S2, the low-altitude safety corridor corresponding to the performance level includes three channels: a high-performance channel, a medium-performance channel, and a low-performance channel. No. The length, width, and thickness of the channel are respectively Channel width With channel thickness The initial value is: ,in, and The first The average envelope width and height of a single-channel UAV This is the safety margin factor; The channels are arranged horizontally from low to high performance level, forming a layout of "right-low performance channel, medium-medium performance channel, and left-high performance channel".

4. The method according to claim 1, characterized in that, In step S2, the full-process operation rules for the UAV in the low-altitude safety corridor include: The operation of drones in the corridor is divided into three stages: before entering the corridor, during flight in the corridor, and after exiting the corridor. (1) The operating rules before entering the channel include: The drone must submit its flight information data set to the low-altitude safety corridor in advance before takeoff. Flight information data set Includes identity information tuples Flight plan information tuple and emergency landing information tuple , represented as: ； Among them, identity information tuple Includes drones Serial number ,model and related performance parameters Name of operating unit and operator number , represented as: ； Among them, the flight plan information tuple Includes drones Latitude and longitude information of take-off and landing points Preset corridor entrance / exit and time Preset flight path Total estimated flight time and the expected total flight distance , represented as: ； Among them, the latitude and longitude information of the take-off and landing points Includes drones Latitude and longitude information of takeoff and landing sites and landing field latitude and longitude information , represented as: ； Among them, the preset corridor entrance / exit and time are specified. Including drones You need to select the nearest corridor entrance point based on your starting point. Select the nearest corridor exit point based on your flight destination. and submit the estimated time to enter the corridor. and expected departure time from the corridor , represented as: ； Among them, the preset flight path All waypoints included in the preset flight path are represented as follows: , ; , , drones The first waypoint, the j-th waypoint, and the last waypoint in the preset flight path, where s is the UAV. Total number of waypoints for the preset flight path; The total expected flight time is as follows: , represented as: ; Among them, the estimated total flight distance The preset path length from the corridor entrance to the corridor exit, including adjacent waypoints along the preset flight path. The sum of the distances between them is obtained as follows: ； Step 2.1.3: Submit the emergency landing information tuple A pre-designated flight path for emergency use near the preset flight path, consisting of multiple waypoints along the alternate flight path: ； Where z represents the drone Total number of waypoints on alternate flight paths , These are the first, second, and last waypoints on the alternate flight path, respectively. The low-altitude safety corridor receives flight information data sets submitted by drones. Subsequently, the flight plan information tuple The system will review the data to ensure there are no conflicts with the approved flight plan information set. If conflicts exist, the low-altitude safety corridor will dynamically adjust the entry time or flight path of the conflicting drones and generate a set of negotiated solutions. : ； ； in, For the first A negotiated solution, For the latitude and longitude information of the take-off and landing points, For the first Preset corridor entrance / exit and time for each negotiated solution For the first The pre-defined flight path of each negotiated solution For the first The estimated total flight time for each negotiated solution, For the first The estimated total flight distance for each negotiated solution; If there is a conflict between the preset corridor entrance / exit and the time, the low-altitude safety corridor will send a coordinated preset corridor entrance / exit and time. including drones The The nearest corridor entrance to the negotiation solution The nearest corridor exit Expected time to enter the corridor and expected departure time from the corridor , represented as: ； If there is a conflict between preset flight paths, adjustments will be made to the original preset flight paths to generate a new preset flight path. : ； For drones The In the negotiated solution, the first waypoint, the j-th waypoint, and the last waypoint are preset in the flight path; s represents the UAV. Total number of waypoints for the preset flight path; Updated estimated total flight time: based on the A conflict coordination scheme recalculates the drone Total estimated flight time ; Updated estimated total flight distance: based on the first A conflict coordination scheme recalculates the drone The expected total flight distance ; (2) The operational rules for flight in the passageway include: Monitoring the operational safety of drones within the low-altitude safety corridor; drones The maximum horizontal dimension is The corresponding radius of the body cylinder is The height is circumscribed sphere radius ; When two adjacent drones The collision protection zones overlapped, and two adjacent drones... Distance between The system determines that a collision has occurred; Monitoring the traffic flow safety of drones within the low-altitude safety corridor, and through statistical analysis. Time of the first Instantaneous number of drones in the channel and the instantaneous speed of each drone , obtained the first corridor Instantaneous average velocity of the channel and traffic flow density , represented as: ， ； Low-altitude safety corridor via real-time monitoring channel Traffic flow density Assess the operational status of the corridor, when traffic flow density... When a preset threshold is reached, the low-altitude safety corridor will proactively implement preventative flow control, restricting the submission of flight information data sets by drones before they enter the corridor. The approval rate is used to limit the number of applications. Number of drones per channel in an instant Thus adjusting the first Traffic flow density of the corridor This ensures the stable operation of the entire corridor. Establish a database of abnormal incidents for low-altitude safety corridors to handle abnormal situations that occur within the corridors in real time. It is a time The accumulated database includes anomaly-aware tuples. Abnormal diagnostic tuple and handling response tuple , represented as: ; Among them, the anomaly-aware tuple Used to record drones The detected anomalies help identify the source of unusual events in the low-altitude safety corridor, including drones. identity information tuple The timestamp of the low-altitude safety corridor detecting anomalies Abnormal situation categories Performance status and flight status , represented as: ; Among them, abnormal diagnostic tuples This is used to refine and classify identified anomalies, combining the anomaly categories. Inferring the root cause classification of abnormal situations and define the exception level. : ; Among them, the disposal response tuple After providing a negotiated solution for the low-altitude safety corridor, the final anomaly handling plan is determined through human intervention, generating a corresponding handling response record; the handling response tuple Includes timestamps of instructions issued based on the final disposal plan. drones identity information tuple Response instructions And the evaluation level of the response to this anomaly. , represented as: ; (3) The operating rules for exiting the channel include: When drones Complete flight plan and prepare to exit the passage, fly to the designated corridor exit point. When the drone is in use, the exit corridor procedure will be automatically triggered, and an exit application will be submitted to the low-altitude safety corridor. After confirming that there is no conflict, the low-altitude safety corridor will issue an exit permission instruction. After receiving the exit permission instruction, the drone will exit from the preset corridor exit point. Exit the corridor and submit the exact time you left the corridor. The low-altitude safety corridor has completed its monitoring mission, and based on the perception, diagnosis, and response triad generated during flight... Abnormal event handling database Perform incremental updates.

5. The method according to claim 1, characterized in that, In step S2, the low-altitude safety corridor further includes: Based on the performance level of the drones served by each channel, the settings are differentiated. Speed ​​limit zone , Let these be the lower and upper speed limits for the nth channel; The following velocity overlap constraints must be satisfied between adjacent channels: , This is the velocity overlap region. This is the transition buffer coefficient. , The design average speed for the nth and (n+1)th channels; Dynamic minimum longitudinal safety separation between machines , , As the reaction time factor, As a braking performance compensation factor, This represents the maximum deceleration of a drone of this performance level. The Manhattan Movement Model is used to model the operation of UAVs within a low-altitude safety corridor: at a given moment... drones Located in The speed is drones The drones in the rear and front are respectively referred to as drones. and drones The corresponding positions are respectively represented as and The corresponding speeds are expressed as follows: and drones and drones The distance between them is expressed as ; Constructing a speed evolution model based on performance constraints: Unmanned Aerial Vehicles In the next moment The speed update logic follows its location Speed ​​limit zone of the channel and the motion status of the preceding machine: ,in, The acceleration is set according to the corresponding performance level of the drone. This represents the interval between adjacent moments; simultaneously, to ensure flight safety, the constraints of the car-following principle must be satisfied: if Time Drone and the adjacent drone in front Distance between Less than the dynamic minimum longitudinal safety interval Then drone To perform a deceleration evasion maneuver, the deceleration rate depends on the maximum deceleration rate of the drone at this performance level. .

6. The method according to claim 1, characterized in that, Establish a low-altitude safe corridor traffic efficiency assessment system, including traffic capacity. and operational risks ; According to unit time Total number of drone sorties passing through the designated section of the corridor Obtain traffic capacity , represented as ; Operational risks Represented as: ; To mitigate the risk of speed deviation, To mitigate the risk of channel deviation, These are the corresponding weight coefficients, and they satisfy... ; ； in, The actual speed within the speed limit range of this channel during the statistical period. The number of drones inside, This represents the total number of drones operating within the passageway. ； in, This represents the total number of drones operating within the channel. For the first The actual horizontal position of the drone. The location is the centerline of the passage. This represents the channel width.

7. The method according to claim 1, characterized in that, The decision vector The width of each passage in the low-altitude safety corridor Vertical height Lower limit of the speed limit range Upper limit and dynamic minimum longitudinal safety interval , represented as ; The objective function of the multi-objective optimization model Represented as: ； In the formula, To reflect the total number of drones passing through the cross section per unit time; To reflect the operational risks of the corridor, , respectively Passage capacity Operational risks Weighting factors; The constraint condition of the multi-objective optimization model is: the upper limit of the velocity of the nth channel. Do not exceed the maximum cruising speed of the drone in this performance class , represented as: .

8. A low-altitude safe corridor generation device based on UAV performance, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

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