Low-altitude aircraft intelligent navigation and obstacle avoidance method

Abstract

The application provides a low-altitude aircraft intelligent navigation and obstacle avoidance method, and belongs to the technical field of unmanned aerial vehicles, and specifically comprises the following steps: determining an interruption control scheme of an optimization target base station according to nearby base stations of the optimization target base station and analysis results of communication data of the optimization target base station; performing interruption control processing of the optimization target base station based on the interruption control scheme, obtaining access data of access users of the optimization target base station in nearby base stations in an interruption control processing period, determining an inspection control optimization base station in the optimization target base station according to analysis results of the access data of the nearby base stations, and determining an obstacle avoidance control method of a low-altitude aircraft based on data of the inspection control optimization base station and in combination with inspection data of nearby base stations of the inspection control optimization base station, so that the reliability of the inspection processing is improved.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, and particularly relates to an intelligent navigation and obstacle avoidance method for low-altitude aircraft. Background Technology

[0002] With the maturity of low-altitude aircraft (drones) technology, their application in communication base station inspection is becoming increasingly widespread. Traditional inspection drones mostly use pre-programmed GPS waypoint flight modes, which have significant drawbacks: First, they lack the ability to avoid obstacles at close range from complex structures such as base station antennas, feeders, and support poles, relying on the pilot's visual operation, which is risky.

[0003] To address the aforementioned technical issues, the invention patent application CN120973056A, "An Airborne 5G Radar Low-Altitude Intelligent Sensing System for Unmanned Aerial Vehicles," integrates millimeter-wave radar 3D point cloud data with centimeter-level positioning information from a 5G integrated sensing base station in real time. This enables local path replanning to be completed in a short time, and end-to-end control closed loop is achieved through 5G-AuRLLC slicing, resulting in a higher obstacle avoidance success rate than traditional technologies. However, the above technical solution has the following technical problems: When using low-altitude aircraft for inspection and control, if the obstacle avoidance capability of the low-altitude aircraft is not effectively verified, it may damage the low-altitude aircraft and affect the inspection reliability of other base stations if obstacle avoidance control is performed in communication base stations with many obstacles. Therefore, how to determine the obstacle avoidance control strategy of low-altitude aircraft in different base stations based on the obstacle data of the base station and whether the base station can effectively switch to a nearby base station for communication processing, in order to improve the effectiveness of obstacle avoidance capability verification and the reliability of inspection processing, has become an urgent technical problem to be solved.

[0004] Therefore, there is an urgent need for an intelligent navigation and obstacle avoidance method for low-altitude aircraft. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides an intelligent navigation and obstacle avoidance method for low-altitude aircraft, which includes: S1 determines the optimal target base station in the communication base station based on the obstacle avoidance control data of the low-altitude aircraft during the inspection of the communication base station. When the distribution data of the optimal target base station in the inspection route does not meet the requirements, proceed to the next step. S2 determines the interruption control scheme for the optimized target base station based on the parsing results of the neighboring base stations and the communication data of the optimized target base station, performs interruption control processing on the optimized target base station based on the interruption control scheme, obtains the access data of the access users of the optimized target base station in the neighboring base stations during the interruption control processing period, and determines the inspection control optimized base station in the optimized target base station based on the parsing results of the access data of the neighboring base stations. S3 determines the obstacle avoidance control method for low-altitude aircraft based on the inspection control optimization base station data and the inspection data of neighboring base stations of the inspection control optimization base station.

[0006] The beneficial effects of this invention are as follows: An interruption control scheme based on network redundancy analysis was designed for high-risk base stations. The interruption effect was evaluated empirically by the ratio of transferred users and data processing delay. In this way, "inspection control optimized base stations" were selected, which are base stations with many obstacles and adjacent base stations that can effectively carry the communication needs of users of the base station. This lays the foundation for low-altitude aircraft to reduce the inspection processing frequency in base stations with many obstacles and improve the reliability of inspection processing.

[0007] This invention creatively transforms the service redundancy capabilities of communication networks (nearby base stations, idle periods) into a safety guarantee resource for UAV inspection operations. By analyzing and utilizing the user carrying capacity of nearby base stations during specific periods, a safe "service interruption window" is created for UAVs to inspect high-difficulty base stations, achieving capability synergy and risk hedging between the communication network and the UAV operation and maintenance network.

[0008] On a predetermined inspection route, intelligent decision-making determines the obstacle avoidance control strategies that low-altitude drones (UAVs) should adopt for different types of base stations, aiming to achieve a balance between overall inspection efficiency and safety. The fundamental logic is: prioritize the use of "other target base stations" (high-difficulty, non-optimized stations) to fully verify and train the UAV's obstacle avoidance capabilities; for "inspection control optimized base stations" (stations with smooth user migration and low interruption risk), rely on recent inspection data from neighboring base stations as indirect health evidence. In the absence of alarms, direct high-risk obstacle avoidance inspections of these stations can be reduced or skipped, thereby saving resources and lowering overall operational risk.

[0009] Furthermore, the obstacle avoidance control data includes determining the obstacles that the low-altitude aircraft needs to avoid during the inspection of the communication base station, specifically based on the number and distribution of obstacles within the target range centered on the communication base station.

[0010] Furthermore, the method for determining the optimized target base station in the communication base station is as follows: Using obstacle avoidance control data from the inspection process of a low-altitude aircraft over a communication base station, the obstacles that the communication base station needs to avoid during the inspection process are determined and used as obstacle avoidance targets. Based on the obstacle avoidance target data of the communication base station, determine whether the communication base station belongs to the optimized target base station.

[0011] Furthermore, the neighboring base station of the optimized target base station is a communication base station whose distance from the optimized target base station is less than a preset distance threshold, specifically a base station that can carry users of the optimized target base station.

[0012] Furthermore, the method for determining the interruption control scheme of the optimized target base station is as follows: The number of neighboring base stations of the optimized target base station is determined based on the neighboring base station data. Based on the parsing results of the communication data of the nearby base station, the time period in which the communication status of the nearby base station is idle is determined and is taken as the idle time period; The interruption control scheme for the optimized target base station is determined based on the number of neighboring base stations and the idle time periods of the neighboring base stations.

[0013] Furthermore, the method for determining the obstacle avoidance control method for the low-altitude aircraft is as follows: Based on the inspection control optimization base station data, the optimized target base stations in the inspection route, excluding the inspection control optimization base stations, are determined and used as other target base stations. Based on the inspection data of the neighboring base stations of the inspection control optimization base station, the inspection data of the neighboring base stations of the inspection control optimization base station in the most recent preset time period is determined, and the neighboring base stations that are inspected in the most recent preset time period are designated as inspection base stations. The obstacle avoidance control method for the low-altitude aircraft is determined based on other target base stations along the inspection route and the inspection base stations adjacent to the inspection control optimization base station.

[0014] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0017] Figure 1This is a flowchart of an intelligent navigation and obstacle avoidance method for low-altitude aircraft; Figure 2 This is a flowchart of a method for determining the optimal target base station in a communication base station. Figure 3 This is a flowchart for determining if the distribution data of the optimized target base stations in the inspection route does not meet the requirements; Figure 4 This is a flowchart illustrating the method for determining the interruption control scheme for the target base station. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0019] Example 1 like Figure 1 As shown, this application provides an intelligent navigation and obstacle avoidance method for low-altitude aircraft, specifically including: S1 determines the optimal target base station in the communication base station based on the obstacle avoidance control data of the low-altitude aircraft during the inspection of the communication base station. When the distribution data of the optimal target base station in the inspection route does not meet the requirements, proceed to the next step. S2 determines the interruption control scheme for the optimized target base station based on the parsing results of the neighboring base stations and the communication data of the neighboring base stations, performs interruption control processing on the optimized target base station based on the interruption control scheme to obtain the access data of the neighboring base stations, and determines the inspection control optimized base station in the optimized target base station based on the parsing results of the access data of the neighboring base stations. S3 determines different navigation control methods for the target base station based on the inspection control optimization base station data and in combination with the inspection data of the neighboring base stations of the inspection control optimization base station.

[0020] When to optimize the inspection control of the target base station, and when to optimize the inspection process of the base station.

[0021] Furthermore, the obstacle avoidance control data includes determining the obstacles that the low-altitude aircraft needs to avoid during the inspection of the communication base station, specifically based on the number and distribution of obstacles within the target range centered on the communication base station.

[0022] Specifically, such as Figure 2As shown, the method for determining the optimized target base station in the communication base station is as follows: Based on historical data from automated inspections of communication base stations by low-altitude aircraft, the system intelligently identifies base stations with complex surrounding environments, making obstacle avoidance during inspections difficult and inefficient. These base stations are marked as "optimization target base stations," allowing for priority optimization of inspection paths or strategies. The fundamental logic is that the more obstacles a base station needs to avoid, the more complex the path planning becomes during inspections, resulting in more hovering and maneuvering maneuvers, leading to increased inspection time, energy consumption, and even a higher risk of collision. Prioritizing the optimization of these stations most effectively improves overall inspection efficiency and safety.

[0023] S11 uses obstacle avoidance control data from the low-altitude aircraft during the inspection of the communication base station to determine the obstacles that the communication base station needs to avoid during the inspection and uses them as obstacle avoidance targets. Obstacle avoidance control data refers to the information on all objects that need to be actively avoided, which is perceived and recorded in real time by the onboard sensors (such as vision and lidar) of a low-altitude aircraft when performing an inspection mission on a specific communication base station, or provided by geographic information system (GIS) data pre-loaded before the mission. The obstacle avoidance target refers to the specific obstacle entity extracted from the above data that exists within the inspection range of the base station target (e.g., a hemispherical airspace and ground area with a radius of 50 meters centered on the base station tower), such as: high-voltage power lines, nearby tall buildings, trees, billboards, other communication antennas, etc.

[0024] This step forms the basis for quantifying the complexity of the inspection environment. The abstract concept of "environmental complexity" is transformed into a concrete and measurable list of "obstacle avoidance targets." By accurately identifying and statistically analyzing these targets, the system can objectively compare the "danger" of the environments surrounding different base stations. Its significance lies in transforming the operational challenges faced by aircraft in actual operations into data indicators that can be prioritized and ordered, providing an objective basis for subsequent optimization decisions.

[0025] Specific example: Analyzing historical inspection data of communication base station "Tower A". The system extracted the following objects that the aircraft needs to avoid within its target inspection range: one 220kV high-voltage power line (passing through the tower 15 meters east of it), one 30-story office building (located 25 meters south of it), and three very tall trees (their canopies encroaching on the inspection airspace). These objects are identified as obstacle avoidance targets for Tower A, totaling four.

[0026] S12 determines whether the communication base station belongs to the optimized target base station based on the obstacle avoidance target data of the communication base station.

[0027] It is understandable that if the number of obstacle avoidance targets of the communication base station does not meet the requirements, then the communication base station is determined to be an optimized target base station because the number of obstacle avoidance targets of the communication base station is relatively large.

[0028] Decision logic: Determine whether the number of obstacle avoidance targets for this base station is "insufficient to meet the requirements". Here, "insufficient to meet the requirements" should be understood as "the number is too large, exceeding the acceptable standard".

[0029] "Not meeting requirements" / "Too many" is a quantitative standard defined by a preset threshold. For example, a threshold for the number of obstacle avoidance targets can be set (e.g., 3). When the number of obstacle avoidance targets for a base station exceeds this threshold, it is judged as "too many" or "not meeting requirements".

[0030] This rule is a simple and efficient priority selection mechanism. Setting a clear threshold is based on engineering experience: when the number of obstacles exceeds a certain threshold, the computational complexity of the automatic inspection path algorithm increases exponentially, the success rate of finding a safe and efficient path decreases, and flight risks increase significantly. Therefore, prioritizing these sites as "optimization target base stations" and concentrating resources on their specialized optimization (e.g., customizing more detailed laser point cloud maps, pre-setting multiple alternative inspection routes, adjusting aircraft approach angles, etc.) can achieve the greatest improvement in overall inspection safety and efficiency with minimal optimization cost. Its significance lies in achieving precise allocation of optimization resources, adhering to the principle of "solving the most difficult problems first."

[0031] Specific Example (Continued): Continuing from the previous example, the number of obstacle avoidance targets for Tower A is 4. Assume the system sets a threshold of 3 for the number of obstacle avoidance targets. Since 4 > 3, it is determined that the number of obstacle avoidance targets "does not meet the requirements" (i.e., the number is too large). Decision: The communication base station "Tower A" is determined to be an optimized target base station. The system will add Tower A to the optimization task queue, and subsequently arrange for dedicated inspection path optimization or environmental adaptation strategy adjustments.

[0032] It should be noted that the inspection route is the flight route of the low-altitude aircraft during the inspection of the communication base station.

[0033] Specifically, such as Figure 3 As shown, it is determined that the distribution data of the optimized target base stations in the inspection route does not meet the requirements, specifically including: The system assesses whether the distribution of "optimized target base stations" (high-difficulty sites) along the inspection route poses a systemic risk to the safety and reliability of the overall inspection task. The core logic extends to assessing not only the operational difficulty of individual base stations but also the cumulative and cascading impact on all subsequent base stations to be inspected should an accident or severe delay occur at a high-difficulty base station. If this impact could jeopardize most subsequent tasks, the current route distribution is deemed unacceptable.

[0034] S21 uses the distribution data of the optimized target base stations in the inspection route to determine the distribution location of the optimized target base stations in the inspection route; Distribution location refers to the sequential number and precise geographical coordinates of each optimized target base station within the inspection route sequence. This determines the precise anchor point of each high-difficulty site in the mission timeline and space.

[0035] This forms the spatiotemporal basis for impact propagation analysis. Simply knowing which obstacles exist is insufficient; it's essential to know their position within the task sequence and their physical location to simulate how an incident at that point would affect subsequent processes. Its significance lies in transforming the abstract concept of "distribution" into concrete parameters that can be used for simulating subsequent impacts.

[0036] Specific example: The inspection route sequence is: Base Station A -> Base Station B (Difficult) -> Base Station C -> Base Station D (Difficult) -> Base Station E -> Base Station F -> Base Station G (Difficult) -> Base Station H. Among them, B, D, and G are marked as the target base stations for optimization. Their distribution locations are: B (Station 2, coordinates (x1, y1)), D (Station 4, coordinates (x2, y2)), and G (Station 7, coordinates (x3, y3)).

[0037] Based on the distribution location, S22 determines the communication base station following the optimized target base station in the inspection route; In this embodiment, "subsequent base stations" is defined as the set of all base stations (including other optimized target base stations and ordinary base stations) that are ranked after a specific optimized target base station in the inspection route sequence. It is an affected target group.

[0038] This step aims to clearly define the potential scope of an incident's impact. When a serious incident (such as a crash or prolonged failure) occurs at a target base station (e.g., base station B), the impact extends beyond the next adjacent base station to all subsequent base stations that have not yet performed inspection tasks. This is because incident handling (search and rescue, site clearing, investigation) could lead to prolonged interruptions or even cancellation of the entire mission. Therefore, it is essential to assess the potential impact on subsequent tasks when an incident occurs at each high-risk site. This approach elevates risk analysis from a "point-to-point" to a "point-to-line" approach, better aligning with operational realities.

[0039] Specific examples (continuous): For the target base station B, its subsequent base station set is {C, D (difficult), E, ​​F, G (difficult), H}, a total of 6; for the target base station D, its subsequent base station set is {E, F, G (difficult), H}, a total of 4; and for the target base station G, its subsequent base station set is {H}, a total of 1.

[0040] S23 determines whether the distribution data of the optimized target base stations in the inspection route meets the requirements based on the optimized target base station data and the communication base stations after the optimized target base stations.

[0041] It should be noted that if the number of optimized target base stations in the inspection route is greater than the preset base station number threshold, then if the inspection optimization control of some optimized target base stations is not performed, the low-altitude aircraft may be damaged due to obstacle avoidance problems, thus making it difficult to meet the inspection reliability requirements. Therefore, it is determined that the distribution data of optimized target base stations in the inspection route does not meet the requirements.

[0042] First-level judgment: Based on the worst-case task loss, the decision logic is as follows: calculate the proportion of all optimized target base stations to the total number of base stations. If this proportion is greater than a certain preset threshold (e.g., 50%), the route is directly judged as "not meeting the requirements".

[0043] This is a baseline test of risk tolerance. If the ratio is too high, it means that there are a large number of target base stations to be optimized in the entire mission, which will have a significant impact and require reconstruction.

[0044] Specific example (continuous): The optimized target base stations include B / D / G, with a ratio of 3 / 8 ≈ 37.5%. Assuming a threshold of 50%, 37.5% is no greater than 50%. Therefore, this alone is insufficient to determine whether the route distribution data meets the requirements.

[0045] Additionally, it can be understood that if the proportion of the number of optimized target base stations in the inspection route is not greater than the preset base station number threshold, in step S231, the number of communication base stations after different optimized target base stations is obtained, and it is determined whether the number of communication base stations after different optimized target base stations is less than the preset value of the number of communication base stations. If so, it is determined that the distribution data of optimized target base stations in the inspection route meets the requirements; otherwise, step S232 is entered. Iterate through each target base station and calculate the size of its subsequent base station set.

[0046] Judgment criteria: Check whether the number of subsequent base stations for all optimized target base stations is less than a "disaster recovery threshold" (e.g., 3). This means that no matter which difficult base station the accident occurs at, it will only affect the tasks of a maximum of 3 subsequent base stations, and the overall task framework is relatively robust.

[0047] This rule assesses the degree of risk dispersion along a route. An ideal route should place high-risk sites as far back as possible, with sufficient "safety margin" (i.e., ordinary base stations) between them and between them and the destination. In this way, the impact of a single incident is limited to a localized area.

[0048] Specific example (assuming B is not the first stop): Assume the route is A->C->E->B (difficult)->F->D (difficult)->G->H.

[0049] The successor set of B is {F, D(difficult), G, H}, with a size of 4. The successor set of D is {G, H}, with a size of 2.

[0050] Let the disaster recovery threshold be 3. The size of the subsequent set of B is 4 > 3, so the condition "all less than" is not true, proceed to the next step.

[0051] The optimized target base station whose number of communication base stations after the optimized target base station is not less than the preset value of the number of communication base stations is regarded as the affected base station. It is determined whether the number of the affected base stations is greater than the preset threshold of the number of affected base stations. If yes, it is determined that the distribution data of the optimized target base stations in the inspection route meets the requirements. If no, proceed to step S233. The optimized target base stations identified in S231 that have a number of subsequent base stations exceeding the disaster recovery threshold are marked as "critical risk nodes". If the number of "critical risk nodes" exceeds a preset number threshold (e.g., 1), the route is determined to "not meet the requirements".

[0052] This rule controls the number of high-risk nodes. The presence of one critical risk point on a route that could impact a large portion of subsequent tasks might be acceptable (by strengthening safeguards at that point). However, if multiple such points exist, the risks along the entire route become a complex, interconnected network, making it extremely vulnerable.

[0053] Specific example (continuous): In the example above, only B is marked as a "critical risk node," with a quantity of 1. Assuming a quantity threshold of 1, 1 is not greater than 1 (equal to), so the condition "greater than" is not met. Proceed to the final comprehensive evaluation.

[0054] Based on the number of optimized target base stations before different communication base stations, the influence weight value of different communication base stations is determined. Based on the influence weight value of different communication base stations, it is determined whether the distribution data of optimized target base stations in the inspection route meets the requirements.

[0055] Specifically, the influence weight value is determined based on the number of optimized target base stations before the inspection route of the communication base station, wherein the more optimized target base stations before the inspection route of the communication base station, the greater the influence weight value.

[0056] An "influence weight value" is calculated for each base station on the route. The calculation method is as follows: for any base station X, its influence weight value = the number of all optimized target base stations preceding X in the inspection route sequence. This means that the further back a base station is in the sequence, the more pre-existing risks it "bears" accumulates.

[0057] Judgment: Calculate the sum of the impact weights of all base stations. If this sum exceeds a preset total weight threshold, the route is deemed "unsatisfactory." This rule quantifies the cumulative effect of risk. Even if the impact range of each problematic base station is small, a large number of problematic base stations, especially if they are located near the front of the route, will cause each subsequent base station to "bear" the potential impact of multiple previous incident points, placing the entire task under high pressure. The total weight is a comprehensive indicator measuring the "pressure" or "vulnerability" of the entire route.

[0058] Specific example (continuous): Calculate the influence weight of the route A->C->E->B(difficult)->F->D(difficult)->G->H.

[0059] A: 0 difficult stations ahead, weight = 0; C: 0; E: 0; B (difficult): 0 (not included); F: 1 difficult station ahead (B), weight = 1; D (difficult): 1 difficult station ahead (B), weight = 1; G: 2 difficult stations ahead (B, D), weight = 2; H: 2 difficult stations ahead (B, D), weight = 2; total weight sum = 0 + 0 + 0 + 0 + 1 + 1 + 2 + 2 = 6.

[0060] Assuming the preset total weight threshold is 5, and 6 > 5, therefore, the inspection route distribution data is determined to be unsatisfactory.

[0061] It is understandable that when the sum of the influence weights of different communication base stations is greater than the preset weight threshold, the distribution data of the optimized target base stations in the inspection route is determined to be unsatisfactory.

[0062] Furthermore, the neighboring base station of the optimized target base station is a communication base station whose distance from the optimized target base station is less than a preset distance threshold, specifically a base station that can carry users of the optimized target base station.

[0063] Specifically, such as Figure 4 As shown, the method for determining the interruption control scheme of the optimized target base station is as follows: Before conducting high-difficulty inspections of the "target base station" by low-altitude aircraft, its network redundancy capability is assessed, a secure service interruption control scheme is formulated, and based on this, the possibility of reducing the frequency of routine inspections of the base station is evaluated. The fundamental logic is: by analyzing the number of nearby base stations and their regular idle periods, it is assessed whether they can effectively take over user services when the target base station is interrupted. If the redundancy capability is strong, the interruption control window is wide, and the impact of the base station's failure on the overall service is small; therefore, the frequency of routine inspections can be reduced until the obstacle avoidance strategy is highly reliable, at which point routine maintenance is necessary.

[0064] S31 uses the neighboring base station data of the optimized target base station to determine the number of neighboring base stations of the optimized target base station; Nearby base stations refer to adjacent base stations that are geographically within a preset threshold (e.g., 500 meters) of the target base station, have overlapping wireless coverage, and are capable of actually serving users of the target base station. The number of nearby base stations is a core indicator for measuring network structural redundancy.

[0065] This step forms the basis for assessing the feasibility of outage control and the possibility of reducing the inspection frequency. Nearby base stations serve as a "service backup pool" during faults or outages. A quantity of zero means no backup; any interruption or fault will result in service disruption, necessitating a high inspection frequency to ensure absolute reliability. A larger quantity provides stronger service sharing capabilities and a greater likelihood of reducing reliance on the target base station's reliability (i.e., inspection frequency).

[0066] Specific example: Optimize the target base station "Stadium Main Coverage Station P". Network topology analysis shows that within a 300-meter radius, "Commercial Building Micro Station Q" and "Metro Entrance Micro Station R" provide effective signal coverage to most of the area. Therefore, the number of nearby base stations for station P is determined to be 2.

[0067] S32 determines the time period during which the communication status of the nearby base station is idle based on the parsing results of the communication data of the nearby base station, and takes it as the idle time period; Idle periods refer to regular time periods identified by analyzing historical call data where the utilization rate of nearby base stations' own resources (such as PRB utilization rate and number of users) is consistently lower than a low threshold (such as 20%).

[0068] This step assesses the temporal availability of redundant resources. Physical coverage alone is insufficient; available capacity must be available when needed. Analyzing regular idle periods helps identify deterministic time windows for interruption control and assessing the impact of faults. Regular idle periods mean that if a fault occurs during the corresponding time period, nearby base stations have a high probability of being able to readily take over the service, and the impact of the fault is controllable.

[0069] Specific example (continuous): Analyze the call data of stations Q and R over the past 30 days. Station Q has a PRB utilization rate of <15% and fewer than 10 users between 1:00 AM and 5:00 AM daily. Station R has a PRB utilization rate of <10% and fewer than 5 users between 12:30 AM and 4:30 AM daily. Taking the intersection, we determine that the common, regular idle period for both is from 1:00 AM to 4:30 AM.

[0070] S33 determines the interruption control scheme for the optimized target base station based on the number of neighboring base stations and the idle time periods of the neighboring base stations.

[0071] It should be noted that if the optimized target base station does not have a neighboring base station, then the interruption control scheme of the optimized target base station is determined to be without interruption control processing. During the interruption control process, it is determined whether the neighboring base station can effectively carry the users of the optimized target base station.

[0072] (Basic judgment: No redundancy, then no interruption, high inspection), Decision: If the number of nearby base stations is 0, then the interruption control scheme is "no interruption control required".

[0073] Additionally, it should be noted that if the target base station for optimization has neighboring base stations, the following content is also included: S331 Determine the number of nearby base stations, and determine whether the number of nearby base stations is greater than a preset threshold for the number of nearby base stations. If yes, determine the interruption control scheme for the optimized target base station, and perform interruption control processing during the period when the average number of users accessing the network is less than the first access number threshold. If no, proceed to step S332. S331: Relaxed strategy in strong redundancy scenarios. Decision logic: If the number of nearby base stations > the preset threshold for the number of nearby base stations (set to 2).

[0074] Interruption control scheme: It can be implemented during periods when "the average number of users accessing the network is less than the first access threshold (which is relatively high, such as 40)". This means that the window is wide, thus ensuring communication quality as much as possible even when users are experiencing base station outages.

[0075] Specific example (continuous): If the number of neighboring base stations of station P is 2, which is not greater than the threshold (equal to), this policy will not be triggered.

[0076] S332 uses the idle time period data of the nearby base station to determine whether there is a nearby base station with an idle time period. If yes, proceed to step S333. If no, determine the interruption control scheme of the optimized target base station. During the time period when the average number of users accessing the station is less than the second access number threshold, interruption control processing is performed to ensure the communication reliability of users of the optimized target base station during the interruption control processing period. If the number of nearby base stations does not meet the standard, check if there are nearby base stations with regular idle periods. Even if the number of redundant base stations is not large, if there are deterministic and regular idle periods, then the nearby base stations are relatively idle, thus increasing the reliability of user communication during the test. Therefore, a wider range of interruption control processing can be adopted.

[0077] Specific example (continuous): Both of station P's two neighboring base stations (Q and R) have regular idle periods (1:00 AM - 4:30 AM), the condition is met, and the system enters S333.

[0078] S333 determines the idle time period coefficient of the neighboring base stations of the optimized target base station by the average percentage of the number of idle time periods of neighboring base stations on different dates, and determines the interruption control scheme of the optimized target base station based on the idle time period coefficient.

[0079] It is understood that when the idle time period coefficient is greater than the preset idle coefficient threshold, the interruption control scheme of the optimized target base station is determined, and interruption control processing is performed during the time period when the average number of users accessing the base station is less than the first access number threshold. When the idle time period coefficient is not greater than the preset idle coefficient threshold, the interruption control scheme of the optimized target base station is determined, and interruption control processing is performed during the time period when the average number of users accessing the base station is less than the second access number threshold.

[0080] Idle Time Coefficient = (Average of the total length of time during which all neighboring base stations are simultaneously idle on different dates) / 24 hours. It quantifies the time coverage of redundancy assurance.

[0081] Decision logic: Calculation coefficient: The neighboring base stations Q and R of station P have a total of 3.5 hours of idle time per day (12:30-5:00). Idle time coefficient = 4.5 / 24 ≈ 0.1875.

[0082] Set threshold: Set the preset idle coefficient threshold to 0.2 (that is, about 4.8 hours of full redundancy coverage per day on average).

[0083] Judgment and Solution Formulation: Since this falls under the category of "coefficient not exceeding the threshold," a conservative strategy is adopted. Interruption control is only implemented during periods when "the average number of users accessing the network is less than the second access threshold (lower, such as 0.3 times the average number of users accessing the target base station)." This period is typically shorter and has lower traffic volume than the period corresponding to the first threshold to ensure safety.

[0084] It should be noted that the first access quantity threshold (e.g., 0.5 times the average number of access users of the target base station) is greater than the second access quantity threshold.

[0085] It should be noted that the users accessing the optimized target base station are those who access the optimized target base station within a preset time period before the interruption control processing period.

[0086] Specifically, the method for determining the inspection control optimization base station in the optimized target base station is as follows: After optimizing the interruption control of the target base station (serving the high-difficulty inspection by drones), the effects of user migration to nearby base stations and user experience are analyzed to identify base stations with smooth user migration and minimal service impact, and these are marked as "inspection control optimized base stations". The fundamental logic is: a base station that can achieve a high proportion of low-latency seamless migration of users during an interruption proves that its network redundancy design is excellent and its parameter configuration is reasonable. This allows the base station to reduce the inspection frequency even before the reliability of the obstacle avoidance control strategy has been effectively verified. Therefore, it can serve as a model site for "optimized" inspection control, thereby improving the overall inspection reliability.

[0087] S51 uses the parsing results of access data from nearby base stations to determine the users who are connected to the target base station and who are connected to nearby base stations during the interruption control processing period, and treats them as transferred users. The interruption control processing period refers to the predetermined time period during which service degradation or shutdown of the target base station is proactively implemented for drone safety inspections. Access users of the target base station refer to all user terminals currently connected to that base station within a preset duration (e.g., 5 minutes) prior to the start of the interruption period. Transferred users refer to users who successfully switch from the target base station to any nearby base station during the interruption control period.

[0088] This step aims to quantify the direct impact and success rate of outage control. By accurately identifying the actual users serving the moment before the outage and tracking how many successfully switched to the backup base station, it's possible to directly assess whether the outage action was "unnoticed" by users. Its significance lies in transforming abstract outage control into a concrete and measurable set of "user migration" events, providing real sample data for subsequent evaluation.

[0089] Specific example: The target base station "Transportation Hub Station T" underwent an interruption control operation from 1:00 AM to 1:15 AM on June 10th. During the period from 0:55 AM to 1:00 AM (preset duration), 120 users accessed station T. After the interruption began, the network monitoring system showed that 115 of these users successfully switched to the nearby "Metro Station M" or "Shopping Mall Station N". These 115 users were marked as the users who were transferred during this interruption event.

[0090] S52 determines the data processing delay of the transferred user based on the access data of the transferred user during the interruption control processing period; Data processing latency specifically refers to the time interval from the moment the target base station begins executing interruption control (such as initiating a handover command or shutting down a signal) to the moment the transferred user successfully establishes a new data connection on a nearby target base station and begins normal data transmission. It measures the duration of user service interruption.

[0091] This step is a key quality indicator for evaluating user experience. A high user migration rate does not necessarily mean a good experience. If the switching process takes several seconds, it can cause video stuttering, call interruptions, and game disconnections. Data processing latency directly reflects the smoothness of the migration process. Low latency means a "seamless switch" for users, which is a sign of high-quality interruption control. Its significance lies in supplementing the "quantity" dimension with the "quality" dimension, comprehensively assessing the actual impact of interruption control on users.

[0092] Specific example (continuous): The system records the switching process for each of the 115 transferred users mentioned above. Calculations show that the average data processing latency for these 115 users was 85 milliseconds, with a maximum of 200 milliseconds. This means that the vast majority of users had their services restored in less than 0.1 seconds, with minimal impact on user experience.

[0093] S53 determines whether the optimized target base station is an inspection control optimized base station based on the transferred users in different interruption control processing periods and the data processing delay of different transferred users.

[0094] It should be noted that, based on the transferred users during different interruption control processing periods and the data processing delays of different transferred users, determining whether the optimized target base station is an inspection control optimized base station specifically includes: S531 uses the proportion of users transferring during the interruption control processing period to the access users of the optimized target base station as the transfer ratio during the interruption control processing period, and determines whether there is an interruption control processing period with a transfer ratio not less than a preset ratio threshold. If yes, proceed to step S532; otherwise, determine that the optimized target base station does not belong to the inspection control optimized base station. Based on the initial screening of high migration rates, the decision logic is as follows: Calculate the migration rate (number of migrated users / number of users connected before the interruption) for each interruption event. Check if there are any interruption periods where the migration rate is not less than a preset threshold (e.g., 95%).

[0095] A high migration rate is a fundamental prerequisite for "optimization." If even users cannot be effectively migrated out, it indicates a fundamental problem with network redundancy or parameters, disqualifying it as an example of "optimization." This step filters out outage events that meet the "quantity" target.

[0096] Specific example (continuous): The transfer rate of station T during the interruption event on June 10th = 115 / 120 ≈ 95.8%. Assuming a preset threshold of 95%, 95.8% ≥ 95%, the condition is met. This period is marked as a potentially effective transfer period.

[0097] S532 takes the interruption control processing period with a transfer ratio not less than a preset ratio threshold as the effective transfer period, and determines whether the number of the effective transfer periods is greater than the preset transfer period number threshold. If yes, it is determined that the optimized target base station belongs to the inspection control optimized base station. If no, it proceeds to step S533. Historically, count the number of "valid handover periods" that meet the S531 condition among all interruption control events of this base station. Determine if this number exceeds a preset threshold for the number of handover periods (e.g., requiring at least 3 successful experiences).

[0098] A single success may be accidental. A truly "optimized" base station should be able to reliably and repeatedly achieve efficient user migration. This step requires the base station to prove its capabilities in multiple trials, ensuring that its experience is replicable and reliable.

[0099] Specific example (continuous): Querying the historical outage records of station T, we find that besides June 10th, the transfer rates for outage control implemented on May 15th and April 20th were 96.2% and 94.9% respectively (both ≥95%). Therefore, the number of effective transfer periods = 3. Assuming a threshold of 2, 3 > 2, the condition is met. Therefore, we can directly determine that station T belongs to the inspection control optimization base station category.

[0100] S533 determines whether the average transfer ratio in different interruption control processing periods is greater than a preset ratio threshold. If yes, it determines that the optimized target base station belongs to the inspection control optimized base station. If no, it proceeds to step S534. If the number of valid events is insufficient, then as a second-best approach, calculate the average transfer rate for all historical outage periods of the base station. If the average rate is still higher than the threshold, then its overall performance is considered excellent.

[0101] Some base stations experience fewer outages but perform consistently well. By calculating the average outage rate, these base stations can be given a chance to be recognized.

[0102] Specific example (assuming S532 is not triggered): Assume station T has only 2 valid transfer periods (not reaching the quantity threshold of 3). Calculate the average transfer rate of all 2 historical interruptions, which is 94%. Set the average rate threshold to 93%, 94% > 93%, the condition is met. It can be determined that this is a base station under inspection control optimization.

[0103] S534 determines the average data processing delay in different interruption control processing periods based on the data processing delay in different interruption control processing periods, and determines whether the optimized target base station belongs to the inspection control optimized base station based on the average data processing delay in different interruption control processing periods.

[0104] It is understood that if there is no interruption control processing period where the average data processing delay is less than the preset duration threshold, then the optimized target base station is determined not to be an inspection control optimized base station; otherwise, it is an inspection control optimized base station.

[0105] If none of the above conditions based on "proportion" are met, the evaluation focus shifts to "quality," i.e., user experience. The average latency for transferring user data is calculated across all outage events at the base station.

[0106] Final ruling: If there are no interruption events and the average data processing latency is less than a preset duration threshold (e.g., 100 milliseconds), then the base station is determined not to be an inspection control optimization base station. This means that the user migration process at this base station is always accompanied by a noticeable service interruption, resulting in a poor user experience.

[0107] In other cases (i.e., the average latency of at least one interruption event is < 100 milliseconds), the base station is determined to be an optimized base station for inspection control. This acknowledges that the base station has achieved an excellent seamless handover experience at least during certain periods, and its parameter configuration or network environment has merits.

[0108] Specific example (assuming all prerequisites are not met): Base station T's average transfer rate is only 90% (below the threshold), but an inspection revealed that during the outage on June 10th, the average data processing latency was 85 milliseconds (<100 milliseconds). According to this rule, base station T can still be determined to be an inspection control optimization base station.

[0109] Specifically, the method for determining the obstacle avoidance control method for the low-altitude aircraft is as follows: On a predetermined inspection route, intelligent decision-making determines the obstacle avoidance control strategies that low-altitude drones (UAVs) should adopt for different types of base stations, aiming to achieve a balance between overall inspection efficiency and safety. The fundamental logic is: prioritize the use of "other target base stations" (high-difficulty, non-optimized stations) to fully verify and train the UAV's obstacle avoidance capabilities; for "inspection control optimized base stations" (stations with smooth user migration and low interruption risk), rely on recent inspection data from neighboring base stations as indirect health evidence. In the absence of alarms, direct high-risk obstacle avoidance inspections of these stations can be reduced or skipped, thereby saving resources and lowering overall operational risk.

[0110] Furthermore, during the process of identifying the optimized target base station during the inspection and control of the optimized base station, the inspection process of the optimized target base station is temporarily suspended.

[0111] S61 uses the inspection control optimization base station data as a basis to determine the optimized target base station in the inspection route, excluding the inspection control optimization base station, and uses it as other target base stations. The inspected and controlled optimized base station refers to the optimized target base station that, after evaluation (S53), has demonstrated a high user migration rate and low processing latency in historical outage control. Other target base stations refer to the remaining optimized target base stations on the same inspection route, excluding the aforementioned optimized stations (i.e., base stations with similarly complex environments but whose user migration performance is not optimal or has not been fully verified).

[0112] This step involves categorizing high-difficulty stations along the inspection route based on their "risk and value." It distinguishes between verified "optimized stations" with minimal disruption impact and unverified or poorly performing "other stations," providing a basis for differentiated obstacle avoidance strategies. Its significance lies in avoiding a "one-size-fits-all" approach of applying the same aggressive (high-risk) obstacle avoidance inspections to all high-difficulty stations.

[0113] Specific example: An inspection route includes 5 target base stations for optimization: B1, B2, B3, B4, and B5. Among them, B2 and B4 are certified as inspection control optimization base stations based on historical data. Therefore, the other target base stations are: {B1, B3, B5}.

[0114] S62 determines the inspection data of the neighboring base stations of the inspection control optimization base station within the most recent preset time period based on the inspection data of the neighboring base stations of the inspection control optimization base station, and takes the neighboring base station that was inspected within the most recent preset time period as the inspection base station. The most recent preset time period refers to a review cycle, such as "the past 7 days". The inspected base station refers to the nearby base station that has completed drone obstacle avoidance inspection within this time period.

[0115] This step aims to find indirect health indicators for "inspection control of optimized base stations." The health status of a base station can be obtained not only through direct inspection but also inferred from the health status of its closely connected neighboring base stations (for example, intact antennas on neighboring base stations indicate that no large-scale natural disasters or human-caused damage have occurred in the area, and the optimized base station is less likely to be damaged). Its significance lies in exploring how to leverage network topology correlations to replace or reduce the likelihood of high-risk direct inspections of "complex sites" (optimized base stations) with inspections of "simple sites" (where neighboring base stations typically have simpler environments).

[0116] Specific example (continuous): For inspection control optimization base station B2, its neighboring base stations are N1 and N2. Querying the inspection records for the past 7 days, it is found that N1 was inspected 3 days ago, while N2 was not inspected. Therefore, the base station B2 inspects is {N1}, and the number is 1.

[0117] S63 determines the obstacle avoidance control method for the low-altitude aircraft based on other target base stations in the inspection route and the inspection base stations in the vicinity of the inspection control optimization base station.

[0118] Furthermore, if the number of other target base stations in the inspection route is greater than the preset target base station number threshold, the low-altitude aircraft can use other target base stations in the inspection route for obstacle avoidance control processing, which can effectively verify the obstacle avoidance capability of the low-altitude aircraft. Therefore, for the inspection control optimization base station, when there is no alarm information, obstacle avoidance control processing is not performed, that is, inspection processing is not performed.

[0119] First-level decision: There are enough high-risk sites along the route. Prioritize verifying obstacle avoidance capabilities. Decision logic: If the number of other target base stations is greater than the preset target base station number threshold (set to 2).

[0120] Obstacle avoidance control methods: For other target base stations (B1, B3, B5): Strictly implement complete obstacle avoidance control inspections. The UAV must fly to the vicinity of the base station and perform complex maneuvers such as circling and hovering detection to fully verify and improve its obstacle avoidance capabilities in extreme environments.

[0121] For inspection control optimization base stations (B2, B4): Implement a "state-based inspection exemption" strategy. That is, as long as these base stations do not generate any equipment alarms or performance degradation alarms in the network management system, the current inspection task will skip direct obstacle avoidance control processing for them (i.e., no high-risk close-range inspections will be performed). Drones can fly directly over them along a safe high-altitude flight path.

[0122] When there are enough high-risk sites (other target base stations) along the route, the drone's obstacle avoidance capabilities have been trained and validated. At this point, skipping those optimized base stations that have been proven to have minimal impact even if interrupted and currently have no alarms can significantly shorten the total mission time, reduce overall flight risk, and save drone energy, while not sacrificing coverage of critical network risks. This embodies the resource allocation principle of "using resources wisely."

[0123] Specific example (continuous): When the number of other target base stations is 3 (B1, B3, B5) > threshold 2, this strategy is triggered. Assuming that B2 and B4 currently have no alarms, the drone will only perform high-difficulty obstacle avoidance inspections on B1, B3, and B5 in this mission, and will only perform remote signal status confirmation on B2 and B4 before skipping them.

[0124] Additionally, it should be noted that if the number of other target base stations in the inspection route is not greater than the preset target base station number threshold, then it is determined whether there is an inspection base station among the neighboring base stations of the inspection control optimization base station. If yes, then proceed to the next step. If no, then it is determined that the low-altitude aircraft has not performed inspection processing in the inspection control optimization base station in the most recent preset time period, then the low-altitude aircraft is controlled to perform obstacle avoidance control processing in the inspection control optimization base station, that is, to perform inspection processing. The route has insufficient high-risk sites and relies on evidence from inspections of nearby sites. Applicable scenarios: if the number of other target base stations is less than or equal to the threshold (i.e., the route is mainly composed of optimized base stations or there are very few high-risk sites).

[0125] Decision logic (S631): Check related evidence. Determine whether there is a recently inspected base station among the neighboring base stations of each inspection control optimization base station.

[0126] If none of the neighboring base stations of an optimized base station have been inspected recently, then the base station is considered to lack any form of indirect health evidence and must be inspected for obstacle avoidance control.

[0127] Specific examples (assuming other target base stations only have B1, and the number is 1≤2): For optimized base station B2: its neighboring station N1 is an inspected base station (inspected 3 days ago), so the condition is met.

[0128] For base station B4, none of its neighboring stations were inspected within the past 7 days, so the condition is not met. Therefore, B4 has not been inspected in the past week and must be added to the inspection list.

[0129] Based on the proportion of the number of inspection base stations among the neighboring base stations of the inspection control optimization base station, the inspection reliability ratio of the neighboring base stations is determined. It is then determined whether the inspection reliability ratio is greater than a preset reliability ratio threshold. If so, obstacle avoidance control processing is not performed for the inspection control optimization base station when there is no alarm information, i.e., inspection processing is not performed. If not, obstacle avoidance control processing is performed on the inspection control optimization base station with the lowest inspection reliability ratio and where the low-altitude aircraft has not performed inspection processing in the most recent preset time period, i.e., inspection processing is performed.

[0130] The third layer of decision-making: sorting and sampling based on the reliability of associated evidence. Decision logic (S632): For an optimized base station (such as B2) that has a patrol base station in its vicinity, calculate the "patrol reliability ratio" = (the number of patrol base stations among all the neighboring base stations of the optimized base station) / (the total number of neighboring base stations).

[0131] Judgment: If the inspection reliability ratio is greater than the preset reliability ratio threshold (e.g., 0.7): the indirect evidence is considered strong enough, and the "no alarm, no inspection" strategy is adopted for the optimized base station.

[0132] If the inspection reliability ratio is less than or equal to the threshold, the indirect evidence is considered weak. The system will select the base station with the lowest inspection reliability ratio and which has not been inspected recently from all optimized base stations that meet this condition, and add it to the obstacle avoidance inspection list for random inspection.

[0133] Specific examples (in a coherent manner): For B2: the total number of nearby base stations is 2, the number of base stations inspected is 1, the inspection reliability ratio is 0.5, and the reliability ratio threshold is set to 0.7, 0.5 ≤ 0.7.

[0134] System checks revealed that B2 had not been inspected in the last 30 days, while another optimized base station, B4, had been inspected 10 days ago.

[0135] Decision: The site with the lowest inspection reliability (none of the neighboring sites have been inspected) and which has not been inspected in the most recent week (B4) will be added to the obstacle avoidance inspection list. The drone will perform obstacle avoidance control on B1, B3, B5 (other target base stations), and B4 (lacking any evidence of neighboring sites).

[0136] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0137] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0138] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for intelligent navigation and obstacle avoidance of low-altitude aircraft, characterized in that, Specifically, it includes: Based on obstacle avoidance control data from the low-altitude aircraft during the inspection of communication base stations, the optimal target base station in the communication base station is determined. When the distribution data of the optimal target base station in the inspection route does not meet the requirements, proceed to the next step. Based on the parsing results of the neighboring base stations of the optimized target base station and the communication data of the optimized target base station, the interruption control scheme of the optimized target base station is determined, and the interruption control processing of the optimized target base station is performed based on the interruption control scheme. During the interruption control processing period, the access data of the access users of the optimized target base station in the neighboring base stations is obtained, and the inspection control optimized base station in the optimized target base station is determined based on the parsing results of the access data of the neighboring base stations. Based on the inspection control optimization base station data and combined with the inspection data of the neighboring base stations of the inspection control optimization base station, the obstacle avoidance control method for low-altitude aircraft is determined.

2. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 1, characterized in that, The obstacle avoidance control data includes the determination of obstacles that the low-altitude aircraft needs to avoid during the inspection of communication base stations.

3. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 1, characterized in that, The method for determining the optimized target base station in the communication base station is as follows: Using obstacle avoidance control data from the inspection process of a low-altitude aircraft over a communication base station, the obstacles that the communication base station needs to avoid during the inspection process are determined and used as obstacle avoidance targets. Based on the obstacle avoidance target data of the communication base station, determine whether the communication base station belongs to the optimized target base station.

4. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 3, characterized in that, If the number of obstacle avoidance targets of the communication base station does not meet the requirements, the communication base station is therefore determined to be an optimized target base station.

5. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 1, characterized in that, The inspection route is the flight path of the low-altitude aircraft during the inspection of the communication base station.

6. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 1, characterized in that, The distribution data of the optimized target base stations in the inspection route is determined to be unsatisfactory, specifically including: Based on the distribution data of the optimized target base stations in the inspection route, determine the distribution location of the optimized target base stations in the inspection route; Based on the distribution location, determine the communication base station following the optimized target base station in the inspection route; Based on the optimized target base station data in the inspection route and the communication base stations after the optimized target base station, determine whether the distribution data of the optimized target base station in the inspection route meets the requirements.

7. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 6, characterized in that, If the number of optimized target base stations in the inspection route is greater than the preset base station number threshold, it is determined that the distribution data of optimized target base stations in the inspection route does not meet the requirements.

8. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 1, characterized in that, The neighboring base station of the optimized target base station is a communication base station whose distance from the optimized target base station is less than a preset distance threshold, specifically a base station that can carry users of the optimized target base station.

9. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 1, characterized in that, The method for determining the obstacle avoidance control method for the low-altitude aircraft is as follows: Based on the inspection control optimization base station data, the optimized target base stations in the inspection route, excluding the inspection control optimization base stations, are determined and used as other target base stations. Based on the inspection data of the neighboring base stations of the inspection control optimization base station, the inspection data of the neighboring base stations of the inspection control optimization base station in the most recent preset time period is determined, and the neighboring base stations that are inspected in the most recent preset time period are designated as inspection base stations. The obstacle avoidance control method for the low-altitude aircraft is determined based on other target base stations along the inspection route and the inspection base stations adjacent to the inspection control optimization base station.

10. The intelligent navigation and obstacle avoidance method for low-altitude aircraft as described in claim 9, characterized in that, If the number of other target base stations in the inspection route is greater than the preset target base station number threshold, then for the inspection control optimization base station, obstacle avoidance control processing will no longer be performed when there is no alarm information, that is, inspection processing will no longer be performed.

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