Unmanned aerial vehicle route control method and related device

Abstract

The application provides a UAV route control method and related equipment, which can deploy a backup landing point in a target UAV inspection route according to a preset inspection route of the target UAV; so that when the target UAV is insufficient in power to complete all inspection tasks during the execution of the inspection task, the target UAV can perform a safe backup landing at any time, and the remaining power of the battery of the target UAV is determined and recorded in real time according to the voltage of the battery; so as to analyze the total amount of the battery required by the target UAV to complete the inspection route to be executed and the flight action; and then, according to the power consumption model of the UAV inspection action and the remaining power, it is judged in real time whether the target UAV needs to perform a backup landing and whether there is a backup landing condition; wherein, the model can be dynamically adjusted according to external conditions, if yes, the related alarm parameters of the target UAV are reported, so that the backup landing strategy of the target UAV can be determined based on the alarm parameters, to realize the safe backup landing of the target UAV.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a UAV flight path control method and related equipment. Background Technology

[0002] Recently, drone technology has made rapid progress, and its applications are becoming increasingly widespread. In the logistics and delivery sector, drones, with their high efficiency and speed, can deliver goods quickly, especially in areas with inconvenient transportation or remote locations, greatly improving delivery efficiency. In emergency rescue and disaster relief scenarios, drones can penetrate deep into disaster areas, transmitting real-time information about the situation and providing crucial information for rescue decisions. Flight path control, as the core technology of autonomous drone flight, plays a decisive role in the execution of various tasks by drones. Efficient flight path planning allows drones to complete tasks in the shortest possible time; reasonable flight path control can effectively avoid dangerous situations such as collisions between drones and obstacles.

[0003] However, existing drone flight path control methods have limitations and cannot fully guarantee the reliability of drones during flight. This problem is particularly prominent in indoor operating scenarios. Indoor environments are often characterized by limited space and numerous obstacles, placing higher demands on drone flight path control. Furthermore, indoor operating scenarios are often high-value environments, such as inside rail transit vehicles where equipment is sophisticated and expensive; a collision caused by a drone due to flight path control errors could result in severe economic losses. Similarly, in the instrument cabinet areas of power plants, improper drone flight could interfere with the normal operation of instruments, threatening the stable operation of the power plant. In these high-value indoor scenarios, employing multiple measures to ensure that indoor drones can assess battery power in real time during flight to make timely and appropriate decisions, while simultaneously planning flight paths accurately and designing scientifically suitable alternate landing points, is crucial for ensuring the safe and efficient operation of drones. Therefore, there is an urgent need for an innovative method to address these issues and meet the application needs of indoor drones in complex, high-value scenarios. Summary of the Invention

[0004] This application aims to at least solve one of the aforementioned technical defects. In view of this, this application provides a method and related equipment for controlling the flight path of a drone, which solves the technical defect of difficulty in controlling the flight path of a drone in the prior art.

[0005] A method for controlling the flight path of an unmanned aerial vehicle (UAV) includes: deploying backup landing points along a pre-defined inspection route of the target UAV; determining and recording the remaining battery power of the target UAV in real time based on its battery voltage; analyzing the total battery power required for the target UAV to complete the inspection route and flight maneuvers based on a pre-defined power consumption model for UAV inspection actions, wherein the pre-defined power consumption model for UAV inspection actions is trained using flight inspection data of a training UAV as training samples and the total battery power required to complete the training inspection route and flight maneuvers included in the flight inspection data of the training UAV as sample labels; determining in real time whether the target UAV needs to perform an emergency landing based on the total battery power required for the target UAV to complete the inspection route and flight maneuvers and the remaining battery power of the target UAV; if it is determined that the target UAV needs to perform an emergency landing, reporting relevant alarm parameters of the target UAV and performing an emergency landing for the target UAV based on the pre-defined backup landing points along the inspection route of the target UAV.

[0006] Preferably, the step of deploying backup landing points in the inspection route of the target UAV according to the preset inspection route of the target UAV includes: determining the backup landing point layout scheme of the target UAV according to the preset inspection route of the target UAV; and deploying the backup landing points in the inspection route of the target UAV according to the backup landing point layout scheme of the target UAV.

[0007] Preferably, the step of determining the alternative landing point layout scheme for the target UAV based on the preset inspection route of the target UAV includes: constructing map data corresponding to the preset inspection route of the target UAV in real time; marking the alternative landing points of the target UAV during the execution of the preset inspection route based on the constructed map data and the preset alternative landing point marking rules; wherein, the selection of alternative landing points is related to the scenario corresponding to the preset inspection route, and the safety factors of the scenario are used as the first selection factor for alternative landing points; the interval of each alternative landing point is set based on the on-site route corresponding to each scenario.

[0008] Preferably, the step of determining and recording the remaining battery power of the target drone in real time based on the battery voltage includes: acquiring temperature data of the flight environment corresponding to the target drone performing a preset inspection task in real time; determining the corresponding discharge curve relationship between the target drone's battery and the flight environment temperature based on the temperature data of the flight environment corresponding to the target drone performing the preset inspection task and the target drone's battery data; monitoring the target drone's battery voltage in real time; and determining and recording the remaining battery power of the target drone in real time based on the target drone's battery voltage and by comparing the corresponding discharge curve between the target drone's battery and the flight environment temperature.

[0009] Preferably, the step of determining in real time whether the target drone needs to make an emergency landing based on the total battery capacity required for the target drone to complete the inspection route and flight maneuvers to be executed and the remaining battery power of the target drone includes: determining the total battery capacity of the target drone; determining the battery capacity required for the target drone to complete the flight maneuvers corresponding to the currently incomplete inspection route; and determining whether the ratio between the difference between the current remaining battery capacity of the target drone and the battery capacity required for the target drone to complete the flight maneuvers corresponding to the currently incomplete inspection route and the total battery capacity of the target drone is less than a preset battery alarm threshold; if so, it is determined that the target drone needs to initiate an emergency landing.

[0010] Preferably, the step of analyzing the total battery capacity required for the target UAV to complete the inspection route and flight maneuvers based on the target UAV's planned inspection route and flight maneuvers includes: determining the mapping relationship between the power required for the target UAV to complete the inspection route and flight maneuvers and the target UAV's total battery capacity, based on the target UAV's planned inspection route and flight maneuvers; wherein the mapping relationship between the power required for the target UAV to complete the inspection route and flight maneuvers and the target UAV's total battery capacity is as follows: ;in, This indicates the energy consumption per milliampere-hour per unit flight time. Indicates the duration of the corresponding flight behavior; This represents the weighting coefficients under different temperature and altitude conditions. Less than 1; This indicates the battery capacity required for the target drone to complete the inspection route and flight maneuvers to be performed.

[0011] Preferably, the step of rescuing the target UAV from the backup landing point of the preset inspection route of the target UAV includes: determining at least one target backup landing point closest to the current position of the target UAV based on the current position of the target UAV and the layout scheme of the backup landing point corresponding to the target UAV; comparing the safety factors of each determined target backup landing point, and selecting the target backup landing point that is closest to the target UAV and has the highest safety factor for the target UAV to rescue.

[0012] A drone flight path control device includes: a backup landing point deployment unit, used to deploy backup landing points in the target drone's pre-set inspection flight path according to the target drone's preset inspection flight path; a battery power determination unit, used to determine and record the remaining battery power of the target drone in real time based on the battery voltage of the target drone; an analysis unit, used to analyze the total battery power required by the target drone to complete the inspection route and flight actions according to a pre-set drone inspection action power consumption model, wherein the pre-set drone inspection action power consumption model is trained using flight inspection data of a training drone as training samples, and using the total battery power required to complete the training inspection route and flight actions included in the flight inspection data of the training drone as sample labels; a judgment unit, used to determine in real time whether the target drone needs to perform a backup landing based on the total battery power required by the target drone to complete the inspection route and flight actions and the remaining battery power of the target drone; and a reporting unit, used to report relevant alarm parameters of the target drone when the judgment unit determines that the target drone needs to perform a backup landing, and to perform a backup landing for the target drone according to the backup landing point in the target drone's preset inspection flight path.

[0013] A drone flight path control device includes: one or more processors and a memory; the memory stores computer-readable instructions, which, when executed by the one or more processors, implement the steps of any of the drone flight path control methods described above.

[0014] A readable storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of any of the unmanned aerial vehicle (UAV) flight path control methods described above.

[0015] As can be seen from the above introduction, when using drones to perform inspection tasks, in order to ensure that the drones can safely complete all inspection tasks, this application can deploy backup landing points along the target drone's preset inspection route. This allows the target drone to safely land if its battery power is insufficient to complete all inspection tasks. The target drone's battery voltage reflects its battery capacity. To confirm whether the target drone needs to make a backup landing, the remaining battery power can be determined and recorded in real time based on the target drone's battery voltage. Furthermore, based on a preset drone inspection action power consumption model, the total battery power required for the target drone to complete the inspection route and flight actions can be analyzed. The preset drone inspection action power consumption model can be trained... The training drone's flight inspection data serves as the training sample. The total battery power required to complete the training inspection route and flight maneuvers included in the training drone's flight inspection data is used as the sample label for training. In particular, different flight maneuvers consume different amounts of power. To confirm whether the target drone's remaining power is sufficient to complete the inspection route and flight maneuvers to be executed, the total battery power required to complete the inspection route and flight maneuvers and the target drone's remaining battery power can be used to determine in real time whether the target drone needs to make an emergency landing. If it is determined that the target drone needs to make an emergency landing, the relevant alarm parameters of the target drone are reported to determine the emergency landing strategy based on the alarm parameters, so as to achieve a safe emergency landing for the target drone.

[0016] As described above, when using drones for inspection tasks, this application effectively ensures safe flight by rationally deploying backup landing points along the target drone's inspection route and by real-time monitoring and analysis of battery power. Pre-deploying backup landing points allows for safe emergency landings when the drone's battery is insufficient to complete all inspection tasks, preventing crashes due to battery depletion, reducing equipment damage risks, and protecting the drone itself and the equipment in the inspection area. The remaining battery power is determined and recorded in real-time based on battery voltage, and the required power is analyzed based on the inspection route and flight maneuvers. Considering the varying power consumption of different flight maneuvers, this precise power analysis method more accurately judges the drone's battery status, providing a scientific basis for emergency landings. The system determines whether an emergency landing is necessary based on the required and remaining power; if an emergency landing is required, alarm parameters are reported to facilitate timely emergency landing strategy development. This allows the drone to respond promptly to insufficient power during flight, making reasonable decisions and improving the reliability of mission execution. By accurately managing battery power and making real-time emergency landing decisions, the system ensures that drones can complete inspection tasks as much as possible when battery power is sufficient, and can safely make emergency landings when battery power is insufficient. This effectively balances the relationship between task execution and flight safety, and improves the overall efficiency and quality of inspection tasks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort. Figure 1 A flowchart illustrating a method for implementing unmanned aerial vehicle (UAV) flight path control; Figure 2 This is a schematic diagram of an indoor unmanned aerial vehicle (UAV) system architecture. Figure 3 This is a diagram showing the relationship between a drone's flight actions and flight duration. Figure 4 A schematic diagram of a drone flight path control device; Figure 5 This is a hardware structure block diagram of a drone flight path control device. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] Given that most current UAV flight path control schemes are ill-suited to complex and ever-changing operational needs, this applicant has developed a UAV flight path control scheme. This scheme can promptly address low battery situations during UAV flight, making appropriate decisions and improving mission reliability. Through precise battery management and real-time emergency landing decisions, it ensures that the UAV completes inspection tasks as much as possible when battery power allows, and can safely land when battery power is insufficient. This effectively balances mission execution and flight safety, improving the overall efficiency and quality of inspection missions.

[0020] The method provided in this application can be used in numerous general-purpose or special-purpose computing device environments or configurations. For example, personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices, etc. This application provides a method for controlling the flight path of a drone. This method can be applied to various drone inspection and management systems, as well as to various computer terminals or smart terminals. The executing entity can be the processor or server of the computer terminal or smart terminal.

[0021] The following is combined with Figure 1 This application describes the flow of the UAV flight path control method according to embodiments, such as... Figure 1 As shown, the process may include the following steps:

[0022] Step S101: Deploy backup landing points in the target UAV's pre-set inspection route according to the target UAV's preset inspection route.

[0023] Specifically, drone inspections play a crucial role in numerous scenarios due to their flexibility, efficiency, and ability to operate in complex environments. Drones can conduct inspections even in adverse weather conditions, ensuring that inspection work is unaffected by weather and improving the timeliness and accuracy of inspections. Therefore, drones can conduct inspections in both outdoor and indoor environments. In practical applications, during flight in either indoor or outdoor environments, drones may encounter sudden drops in battery power or reduced battery capacity after prolonged operation, leading to an inability to complete the designed flight path, failure to return to the drone's home, or even a crash, affecting the safety of the inspection scenario. For example, in the scenario of inspecting rail transit vehicles in a depot, the one-way flight path is 140 meters, and the two-way flight path is nearly 300 meters. When the drone's battery capacity decreases or an abnormality occurs, the battery energy may drop to zero, and the drone may land on sensitive locations such as the pantograph or roof of the vehicle, affecting the safe operation of the vehicle.

[0024] To avoid the above situation, the reliability of indoor drones can be optimized by deploying backup landing points along the target drone's preset inspection route in the drone's management system. For example... Figure 2 As shown, an indoor drone system generally consists of three parts: the indoor drone, the drone nest control board (indoor drone nest), and the indoor drone inspection system (inspection backend). The indoor drone is the main component for flight functionality, achieving environmental perception in specific locations through different attachments, including machine vision, laser, and thermal sensing. It is typically a multi-rotor drone, with a basic requirement of 4 rotors and an enhanced requirement of 8 rotors. The indoor drone is divided into a flight control subsystem (flight control board) and a management subsystem (control board). The flight control board is responsible for implementing the motor control signals (PWM) and flight control algorithms for the multi-rotor drone; its real-time performance is a key indicator, and it generally uses an RTOS operating system. The indoor drone's control board is mainly responsible for indoor positioning, indoor flight according to planned routes, and executing various flight maneuvers. The flight control board is controlled by the control board. The drone control board is responsible for monitoring battery power and analyzing the completion of flight maneuvers; it is part of the drone's MCU microprocessor module. The drone nest control board is mainly responsible for issuing control commands, flight routes, and real-time status reports for drones, while also monitoring whether the drones have returned to the nest on time. The inspection backend is mainly responsible for designing multiple flight routes and their inspection actions, and issuing them to the indoor drone nest to manage drone flight activities.

[0025] Generally, the areas where drones perform inspection missions may be wide and the environment complex. Therefore, in practical applications, to ensure that the target drone can successfully perform its inspection mission, backup landing points can be deployed along the target drone's preset inspection route. Setting backup landing points along the preset inspection route ensures that no matter where the target drone encounters low power or other emergency landing situations at any point along the route, it can find a safe landing location within a reasonable distance. For example, when the target drone is conducting inspections in the field, there may not be suitable flat areas nearby for it to land freely. Planning backup landing points closely related to the target drone's inspection route in advance can prevent the target drone from crashing due to the inability to find a suitable landing spot. Secondly, the preset inspection routes of the target drone usually cover critical areas or equipment, such as substations and transmission lines. If the target drone encounters power problems in these important areas and is unable to complete its mission, it can land in time at a backup landing point near the preset inspection route. This can prevent the target drone from damaging critical facilities due to loss of control and can also avoid safety accidents caused by crashes, thereby improving the safety and reliability of the entire inspection process. Furthermore, matching backup landing points with pre-set inspection routes facilitates rapid maintenance and re-launch of the target drone, allowing it to continue unfinished inspection tasks. It also reduces mission downtime due to unforeseen circumstances, improving inspection efficiency and ensuring mission continuity. For example, if a target drone lands at a backup point due to low battery, staff can quickly replace the battery or perform a simple check, enabling it to resume the remaining inspection tasks as soon as possible. Additionally, some inspection environments may present complex factors such as strong winds and electromagnetic interference, potentially leading to unexpected power consumption even with normal power planning. Deploying backup landing points based on the target drone's pre-set inspection routes better addresses these unforeseen environmental challenges, ensuring the target drone has a safe landing option in case of emergencies.

[0026] The preset inspection route for a target UAV is generally set based on the inspection tasks and safety requirements it needs to perform. The preset route determines where the target UAV takes off, how it flies, and where it goes. The preset inspection route may include task information related to the inspection task, equipment identification information, core spatial path information, flight parameters and safety rules (such as the control logic of the inspection route), payload coordination control information (such as the logic for executing the inspection task), and environmental adaptation and redundancy information for the inspection route. The task information may include the inspection task name, number, execution schedule or time period, task manager, and contact information. Equipment identification information may include the target UAV model, serial number, and the type and parameters of the inspection payload carried by the target UAV. Core spatial path information may include key coordinate point data in the route, waypoint type information, and path connection rules, such as latitude and longitude, relative or absolute altitude, takeoff or landing point, and information related to inspection points or transition points. The path connection rules define the flight trajectory type between each waypoint to ensure path stability and compliance with inspection requirements. Flight parameters can include flight status parameters, such as flight speed, waypoint dwell time, and flight mode. Flight speed is generally set according to the type of inspection target. Waypoint dwell time mainly includes the duration of stay at the "inspection point" to ensure that the payload completes data acquisition. Flight mode mainly sets the control mode of the UAV. Safety rules can include safety constraint rules, such as the preset route must automatically avoid marked no-fly zones and restricted flight zones. If the route approaches a no-fly zone, "automatic return" or "detour turning point" must be set. The maximum and minimum flight altitudes of the route can be set. Other emergency rules include low battery emergency (such as automatically aborting the mission and returning to the nearest take-off and landing point when the remaining battery is below 20%), signal loss emergency, and obstacle avoidance trigger emergency. Payload collaborative control information can include payload trigger conditions, such as setting the timing of payload start / stop. For example, the waypoint trigger condition can be set to automatically start the camera to take pictures or record videos and the sensor to detect when the UAV arrives at the "inspection point". The distance trigger condition can be set to start the payload when the distance between the UAV and the inspection target is less than a set value. The time trigger can be set to start the payload when the route is executed to a specific time period. Payload parameter adaptation can be adjusted according to flight path altitude and speed. For example, the higher the flight altitude, the larger the focal length needs to be (e.g., when inspecting at an altitude of 100 meters, the focal length is set to 50mm to ensure clear target). The infrared thermal imager temperature measurement range is set to -20℃ to 200℃ when inspecting electrical equipment, and to 0℃ to 80℃ when inspecting photovoltaic panels to avoid exceeding the range and causing invalid data. Environmental adaptation and redundancy information for the inspection route can include meteorological adaptation parameters: preset routes need to be associated with meteorological thresholds, such as setting a wind speed threshold, such as automatically pausing the route when the wind speed exceeds 8m / s; setting precipitation / visibility thresholds, such as prohibiting the route from starting in rainy or foggy weather to prevent payload failure or collision risks.Backup waypoints can be set in key inspection areas. If the main waypoint cannot be located due to signal interference, the drone will automatically switch to the backup waypoint to ensure that the inspection is not interrupted.

[0027] Therefore, in the process of deploying backup landing points along the target UAV's preset inspection route, a backup landing point layout plan can be determined first based on the target UAV's preset inspection route. This allows for the deployment of backup landing points along the target UAV's inspection route according to the backup landing point layout plan. This can build a "safety redundancy system" for UAV inspection operations, ensuring the survivability of UAVs under risks such as sudden failures and environmental changes, while also ensuring the continuity and efficiency of inspection tasks. Simultaneously, it reduces risks of equipment loss, personnel safety, and environmental interference, helping to avoid "unexpected risks," ensuring the continuity of inspection tasks, reducing "task interruption losses," reducing task restart costs, avoiding missed inspections of critical areas, optimizing inspection task efficiency, and reducing labor and management costs.

[0028] Step S102: Determine and record the remaining battery power of the target drone in real time based on the battery voltage of the target drone.

[0029] Specifically, to ensure the target drone safely completes its inspection and smoothly arrives at the backup point, avoiding loss of control and crash, after designing a backup landing point for the target drone, the remaining battery power of the target drone is determined and recorded in real time based on the battery voltage. Designing a backup landing point for the target drone provides a safety backup option in case of emergencies. However, having a backup point does not guarantee a safe landing. Whether the target drone can reach the backup point depends on whether its remaining battery power can support the flight power consumption from its current location to the backup landing point. Battery voltage is the most direct and easily monitored indicator of remaining battery power in real time. In practical applications, drone batteries are mostly lithium batteries, and voltage and remaining capacity have a clear correlation. For example, a single lithium polymer battery cell voltage of 3.7V is approximately 50% of its capacity, while below 3.2V it is close to being depleted. If the battery voltage of the drone is not monitored in real time and the remaining battery power is not calculated, the drone may experience a sudden malfunction during inspection and need to land at an alternate landing point. However, the actual remaining battery power of the target drone may not be sufficient to support the flight distance to the desired alternate landing point, resulting in a power outage and crash midway. In reality, battery aging may cause the voltage to be falsely high, and low temperature environments may also cause a sudden drop in voltage. Therefore, if the battery voltage of the target drone is not tracked in real time, it may be mistakenly judged that the remaining battery power of the target drone is sufficient, and the inspection mission may continue, ultimately resulting in the target drone running out of power before reaching the intended landing point.

[0030] Furthermore, based on the target drone's battery voltage, real-time determination and recording of the remaining battery power can also guide flight decisions and avoid the risk of over-power depletion. Drone inspections typically involve fixed routes with relatively fixed flight paths, altitudes, and speeds. In practice, voltage data can be used to calculate the drone's "maximum flight distance / time corresponding to remaining battery power" in real time and dynamically compare it with the "distance from the current location to the backup point." If the drone's remaining battery power is greater than or equal to the sum of the power consumed during flight to the backup point and the redundant power (usually 10%-15% reserved to cope with additional consumption due to sudden turbulence), the drone can choose to continue completing the current inspection segment according to the inspection task requirements, or smoothly land at the backup point if an anomaly occurs. If the drone's remaining battery power is less than the power consumed during flight to the backup point, it may be necessary to immediately trigger a "priority decision": either interrupt the current inspection and prioritize returning to the nearest backup point (rather than trying to complete the task), or call ground personnel to assist in troubleshooting battery problems (such as whether there is a cell failure causing abnormal voltage). For example, if a power line inspection drone detects during flight that its remaining power is only 90% enough to support the journey from the current location to the backup point, it must immediately terminate the subsequent inspection route and turn to the nearest backup point to land, in order to avoid running out of power.

[0031] In practical applications, backup landing points are not absolutely fixed. If a drone needs to switch to a second backup point during inspections due to mission adjustments (such as temporarily adding inspection points) or environmental changes (such as the backup point being obstructed), it is necessary to use the drone's real-time battery data to determine whether the distance to the new backup point is within the range supported by the drone's remaining battery power. If switching to a second backup point, should the drone adjust its flight speed (e.g., reduce speed to decrease battery consumption)? Without real-time battery data, blindly switching backup points may lead to a "double risk": deviating from the original plan and failing to reach the new backup point due to insufficient battery power. Therefore, real-time recording of the drone's battery data provides a basis for subsequent fault tracing and battery management. Real-time recording of the corresponding data of "voltage-remaining battery power-flight time / location" is a key aspect of drone operation and maintenance. If the drone ultimately experiences battery-related problems (such as crashing or failing to reach the backup point), the cause can be investigated through historical battery data to determine whether it is due to battery voltage monitoring errors, battery aging (such as uneven cell voltage), or an underestimation of flight consumption during backup point design. By recording voltage change curves over a long period (such as the rate of voltage drop during each flight), the battery degradation can be assessed (e.g., new batteries experience a gradual voltage drop, while aging batteries experience a steep voltage drop), allowing for timely replacement of aging batteries and preventing misjudgments of battery power during subsequent flights. Based on the drone's historical battery power data, the layout of backup points can be optimized (e.g., adding backup points near inspection sections where power consumption is rapid) and inspection routes can be adjusted (e.g., reducing long-distance route segments without backup points), thereby improving overall inspection safety. Secondly, in drone battery power monitoring, voltage is the most mainstream real-time monitoring parameter, rather than directly measuring the remaining capacity (mAh). This is because voltage can be collected in real time by the battery management system (BMS) (sampling frequency up to 1 time / second), which can quickly reflect changes in power. In contrast, the remaining capacity needs to be estimated using multi-parameter algorithms such as voltage, current, and temperature (i.e., the "coulomb counter" principle). The calculation process has a certain delay, which cannot meet the "second-level decision-making" requirements of drones. Furthermore, the drone flight environment is complex (high temperature, vibration, electromagnetic interference). Voltage monitoring circuits have strong anti-interference capabilities, and their data stability is better than other parameters. When the battery voltage is lower than the "protection voltage" (e.g., a single lithium polymer battery cell is below 3.0V), over-discharge protection will be triggered, forcibly cutting off the power supply. Real-time voltage monitoring can provide early warning before the protection is triggered, avoiding permanent damage caused by over-discharge (over-discharge will shorten battery life and even cause cell bulging).

[0032] Step S103: Based on the preset power consumption model of UAV inspection actions, analyze the total battery amount required for the target UAV to complete the inspection route and flight actions.

[0033] Specifically, after monitoring the remaining battery power of the drone in real time, further analyzing the total battery capacity required for the inspection route and flight maneuvers is crucial to ensuring the remaining battery power matches the task's consumption. Only through dynamic comparison of these two factors can a true balance be achieved between safely completing the task and avoiding battery depletion be realized. This is especially important for the complex nature of drone inspections (such as in power, photovoltaic, and industrial scenarios), and is the core guarantee against mid-mission power outages and task failures. Therefore, after recording the drone's remaining battery power in real time, a pre-defined drone inspection maneuver power consumption model can be used to analyze the total battery capacity required for the target drone to complete the inspection route and flight maneuvers. This pre-defined model can be trained using the drone's flight inspection data as training samples, with the total battery capacity required to complete the training inspection route and flight maneuvers included in the training data serving as sample labels. In practice, the drone inspection maneuver power consumption model can also be dynamically optimized based on external conditions, such as temperature and air pressure, to improve its accuracy.

[0034] The total battery capacity required for the inspection route (distance, altitude, environment) and flight maneuvers (climb, hover, high-speed cruise) is the minimum energy consumption necessary to complete the mission. Different flight maneuvers require different flight durations, and the flight duration for each maneuver can be described as follows: Figure 3 As shown, the relationship between the two directly determines whether the mission can proceed. If we only know how much power is left but not how much is needed to complete the mission, two typical risks may occur. For example, there is the risk of misjudging that there is enough power. In fact, the remaining power may seem sufficient (e.g., showing 50%), but the route to be executed contains a lot of power-intensive actions (e.g., continuous climbs, long-term hovering inspections), and the actual consumption is far greater than expected, which may lead to the power being exhausted midway. For example, if we do not know the actual power consumption of the mission, we may mistakenly judge a low-power-consumption mission as a high-power-consumption mission and interrupt the inspection prematurely (e.g., the remaining power is enough to support the completion of the mission and the landing at the backup point, but due to misjudgment, we have to return to base), resulting in low mission efficiency.

[0035] Drone inspections are typically divided into segments (e.g., segment A: base station 1 → base station 2 → backup point 1). Before initiating each segment, a feasibility decision must be made by comparing the drone's remaining battery power with the battery power required for the segment. Assuming the total battery power required for the task equals the sum of route energy consumption (distance × power consumption per unit distance), flight maneuver energy consumption (climb altitude × power consumption per unit altitude + hovering time × power consumption per unit time), and redundancy energy consumption (10%-15% reserved for unexpected turbulence or temporary adjustments), the decision criterion can be as follows: If the remaining battery power is greater than or equal to the total required for the task, the segment can be initiated normally. Simultaneously, the remaining battery power after completion should be clearly defined (e.g., 50% remaining, 30% required for the task, 20% remaining after completion, sufficient for returning to the backup point). If the remaining battery power is less than the total amount required for the task to be performed, adjustments must be made immediately. This could involve reducing the task scope (e.g., reducing one inspection point to lower the required battery power), replacing the battery with a spare, or prioritizing returning to the nearest spare point and abandoning the flight segment. Furthermore, during drone inspections, the actual energy consumption may exceed the initial estimate due to environmental changes or operational deviations (e.g., extended hovering time). In such cases, adjustments must be made dynamically by combining the real-time remaining battery power with the remaining required battery power for the unfinished task. The core premise of this dynamic adjustment is to clearly understand the total energy consumption requirement of the task to be performed (including the unfinished portion). If this is not clear, it is impossible to determine whether the additional consumption can be handled, and one can only passively wait for the battery power alarm to sound.

[0036] The core function of a backup landing point is to ensure safety in case of mission failure or insufficient battery power. However, completing the mission and reaching the backup point are two consecutive steps. It is crucial to ensure that the total energy consumption (i.e., the energy required for the mission and the energy consumed to reach the backup point) is less than or equal to the remaining battery power. If only the energy required for the mission is analyzed, while ignoring the energy consumption to reach the backup point, there is a risk that the mission may be completed, but there is no power left to reach the backup point. For example, a drone's mission may consume 30% of its battery power, and the journey to the required backup landing point may consume 15%, totaling 45%. If the remaining battery power is 40%, focusing only on the 30% required for the mission might be mistakenly considered sufficient, as only 10% remains after completing the mission, making it impossible to reach the backup point. Only by analyzing the total required 45% can the mission be abandoned early, prioritizing a return to the backup point. Furthermore, in circuit-related inspections of drones, the high-power-consuming actions of the mission constitute a significant portion. Without analyzing the total battery required for the target drone to complete the inspection route and flight maneuvers, uncontrolled energy consumption is highly likely.

[0037] In practice, based on the inspection route and flight maneuvers to be performed by the target drone, the mapping relationship between the power required for the target drone to complete the inspection route and flight maneuvers and the total battery capacity of the target drone can be determined. The mapping relationship between the power required for the target drone to complete the inspection route and flight maneuvers and the total battery capacity of the target drone is as follows: ;in, This indicates the energy consumption per unit flight time in milliampere-hours, such as the energy consumed per milliampere-hour during hovering. Indicates hovering; It is expressed as a speed of 0.5 m / s; This indicates a speed of 1 m / s. Indicates the action of returning to one's roost; Indicates a rotational motion; Indicates the duration of the corresponding flight behavior; This represents the weighting coefficients under different temperature and altitude conditions. Less than 1; The battery capacity required for the target drone to complete the inspection route and flight maneuvers to be performed.

[0038] In particular, when constructing the mapping relationship between the two in practice, interpolation can be used to supplement the data for intermediate state parameters.

[0039] Step S104: Based on the total battery capacity required for the target drone to complete the inspection route and flight maneuvers to be performed, and the remaining battery power of the target drone, determine in real time whether the target drone needs to make an emergency landing.

[0040] Specifically, after analyzing the total battery capacity required for the drone to complete its inspection mission and knowing its real-time remaining battery power, it is still necessary to determine in real time whether the drone needs to make an emergency landing based on these two core data points. Therefore, in order to build a safety redundancy for the inspection mission from the perspective of battery matching and avoid risks such as mission failure, drone loss of contact, or crash due to insufficient battery power, it is possible to further determine in real time whether the target drone needs to make an emergency landing based on the total battery capacity required for the target drone to complete its inspection route and flight maneuvers, as well as the target drone's remaining battery power. This also helps to verify whether the remaining battery power can support the complete mission and a safe emergency landing, preventing battery shortages. The drone's inspection mission is not simply about completing the route; it needs to simultaneously meet two core requirements: "completing the inspection actions to be performed" and "being able to safely reach the backup landing point after the mission is completed (or in case of unforeseen circumstances). Knowing only the remaining battery power and the total amount required for the mission is insufficient to determine their compatibility. Real-time comparison and calculation are necessary to verify if a battery shortage exists. If the remaining battery power is greater than or equal to the sum of the total amount required to complete the inspection mission and the redundancy battery power (redundancy battery power refers to the power consumption from the current mission position to the backup landing point, including additional consumption for takeoff, hovering, and landing), it means that the battery power is sufficient to support a safe backup landing after the mission is completed. There is no need to trigger a backup landing, and the inspection can continue. If the remaining battery power is less than the sum of the total amount required to complete the inspection mission and the redundancy battery power, it means that even if a backup landing is prioritized, the existing battery power may not be enough to support the drone to reach the backup landing point (e.g., the battery runs out midway), or the drone may lose its backup landing capability after forcibly completing part of the mission. In this case, a backup landing must be triggered in real time to prevent the drone from going out of control.

[0041] In actual drone inspections, battery consumption doesn't always follow a fixed, pre-analyzed total. It's influenced by real-time environmental and flight status changes, leading to discrepancies between actual and predicted power consumption. For example, sudden strong winds or headwinds increase motor load, causing power consumption per unit time to far exceed the pre-defined value. Additional hovering or detours (e.g., obstacle avoidance) increase power consumption. Low temperatures temporarily reduce battery capacity, potentially resulting in a lower actual remaining charge than the theoretical value calculated based on voltage. Real-time assessment of whether a diversion is necessary helps in matching the drone's actual remaining charge with the dynamically adjusted total power consumption for the inspection task and diversion. For instance, if the predicted remaining charge is just enough to support the inspection and diversion, but a sudden headwind accelerates power consumption, continuing the mission without a real-time reassessment could quickly lead to a power shortage. Real-time comparison allows for timely detection that insufficient remaining charge will cover the dynamically increasing total power consumption, triggering a diversion early and mitigating risk. This ensures flexibility in mission decision-making and balances mission completion with equipment safety. For example, in inspection missions, ensuring the safety of the drone must be prioritized over completing the preset route. However, it's not always necessary to immediately make an emergency landing when battery power is low. Instead, flexible decisions should be made based on real-time battery comparisons. For instance, if the drone's remaining battery power is only slightly lower than the total power required to complete the inspection mission and make an emergency landing, but the drone is nearing the inspection endpoint (only one target point left), it can be determined to complete that target point first before making an emergency landing. In this case, the total power consumption for completing the final inspection target and making the emergency landing needs to be recalculated to ensure sufficient remaining power. If the remaining battery power is significantly lower than the total power consumption for completing the inspection mission and making an emergency landing, and the current location is close to an alternative landing point, the mission should be immediately aborted for an emergency landing to avoid running out of power due to hesitation. This real-time assessment provides a dynamic decision-making basis, avoiding frequent mission interruptions due to overly conservative approaches (not fully utilizing battery power) and preventing equipment damage due to overly aggressive approaches (ignoring battery shortages), ultimately achieving the goal of maximizing mission completion while ensuring safety.

[0042] Therefore, if it is determined that the target drone needs to make an emergency landing, step S105 can be executed.

[0043] Step S105: Report the relevant alarm parameters of the target UAV and make an alternate landing for the target UAV according to the alternate landing point of the preset inspection route of the target UAV.

[0044] Specifically, after determining that a target drone needs to make an emergency landing, in order to ensure that the emergency landing process is safe, controllable, traceable, and collaborative, relevant alarm parameters of the target drone can be reported, and the target drone can be landed at an alternate landing point based on the preset inspection route. The reported alarm parameters provide crucial information for emergency landing coordination and risk tracing. Drone emergency landing is not an isolated action, especially in professional inspection scenarios, which typically involve collaboration among multiple roles such as the ground command center, inspection team, and airspace management. Reporting alarm parameters clearly conveys the reason, status, and risks of the emergency landing to relevant parties, avoiding coordination chaos or secondary risks caused by information gaps, clarifying the reason for the emergency landing, and preventing misjudgments and ineffective interventions.

[0045] In practice, alarm parameters can include core data that triggers an emergency landing. For example, alarm parameters can be an array with the main title "UAV Emergency Landing Initiation Alarm," containing information such as: emergency landing point information, current flight completion status, remaining battery power of the UAV, and abnormal flight report points (those that would not trigger an emergency landing according to normal flight route planning). For instance, alarm parameters for a target UAV might include its remaining battery power (converted from current voltage), remaining power consumption for the mission, environmental interference factors, and abnormal flight report information. Using these parameters, the ground command center can quickly confirm that the UAV's emergency landing is due to insufficient battery power rather than equipment failure, thus avoiding the accidental activation of equipment failure emergency plans and focusing on the core task of guiding the emergency landing.

[0046] Reporting relevant alarm parameters for the target drone supports ground coordination and ensures a safe alternate landing environment. If there are potential temporary obstacles in the alternate landing area, the ground team can use the reported drone's current location, estimated alternate landing time, flight trajectory, and other parameters to arrive at the alternate landing point in advance to clear the environment and set up warning signs, preventing collisions during drone landing. Simultaneously, if the alternate landing point involves airspace coordination, airspace management personnel can report the alternate landing route in advance based on the alarm parameters to ensure airspace compliance during the drone's alternate landing process. Reporting relevant alarm parameters for the target drone can also retain risk data for post-event review and optimization. Specifically, the alarm parameters record the complete state at the time of the alternate landing trigger. This data can be used for post-event review, such as determining if there is excessive deviation in the predicted mission power consumption, whether the battery voltage calculation of remaining power is accurate, and whether the alternate landing point is set too far away. This allows for optimization of subsequent power calculation models, mission planning logic, or alternate landing point selection, reducing the recurrence of similar alternate landing scenarios. Secondly, landing at a pre-set backup landing point ensures a safe, efficient, and predictable landing. These pre-set backup landing points are optimal landing areas selected in advance during the inspection mission planning phase, based on the drone's range, terrain safety, and takeoff and landing convenience. Choosing these points instead of randomly selecting a landing site is primarily to mitigate the uncertainties and risks associated with temporary site selection. For example, the pre-set backup landing point has already undergone preliminary surveys to eliminate risk factors, such as the absence of loose soil, high-voltage cables or trees, and a flat surface. Abandoning the pre-set landing point and choosing an unknown area for landing could lead to motor damage, fuselage impact, or even more serious accidents like battery fires due to potential ground debris, water accumulation, or hidden obstacles. Furthermore, the coordinates, altitude, and takeoff / landing radius of the pre-set backup landing point are pre-entered into the drone's flight control system. When a backup landing is triggered, the flight control system can directly call upon the pre-set flight path to quickly plan the optimal route from the current location to the backup landing point, reducing flight path deviations caused by errors in temporary path calculations. Meanwhile, the flight control system is more familiar with the landing altitude and hovering position of the preset points, and can accurately execute the "low-altitude hovering → slow descent → smooth landing" maneuver, avoiding operational errors caused by unfamiliarity with the terrain during temporary landings. The preset backup landing point is usually selected in an area that is "easily accessible to the ground team and has convenient transportation." After the backup landing, ground personnel can quickly locate the drone, check the equipment status, and replace the battery. If the mission is not completed, it can be restarted from that point to continue the inspection. If a random area is chosen for landing, it may lead to drone positioning deviation and difficulty for ground personnel to reach the location, which not only increases recovery costs but may also lead to equipment loss or damage from moisture due to prolonged exposure.

[0047] If only alarm parameters are reported without prioritizing a landing site, the ground team, while aware that the drone needs to land, cannot predict its landing location and cannot coordinate support in advance. The drone may be in danger due to a mistake in choosing a landing site, and the ground recovery will lack a clear target, leading to loss of control after landing. If only a landing site is prioritized without reporting alarm parameters, the drone may land safely, but the ground command center will not know the "reason for landing" or "current status," which may lead to a misjudgment that the drone is out of contact, initiating unnecessary search and rescue procedures. At the same time, the landing data cannot be saved for subsequent optimization.

[0048] In the field of civilian drone inspection, relevant regulations clearly require that "when a drone encounters an emergency and needs to make an emergency landing, it should promptly report its status to the ground control station and prioritize selecting a pre-set emergency landing point." Reporting alarm parameters is "fulfilling the obligation to report status," and making an emergency landing at a pre-set point is complying with emergency operation procedures. Failure to perform these two steps may not only result in equipment damage but may also lead to regulatory penalties for violating compliance requirements. Furthermore, if losses are caused to third parties during the emergency landing process, legal liability will also be incurred.

[0049] As described above, when using drones for inspection missions, this application effectively ensures safe flight by rationally deploying backup landing points along the target drone's inspection route and by real-time monitoring and analysis of battery power. Pre-deploying backup landing points allows for safe emergency landings when the drone's battery is insufficient to complete all inspection tasks, preventing crashes due to battery depletion, reducing equipment damage risks, and protecting the drone itself and the equipment in the inspection area. The remaining battery power is determined and recorded in real-time based on battery voltage, and the required power is analyzed based on the inspection route and flight maneuvers. Considering the varying power consumption of different flight maneuvers, this precise power analysis method more accurately judges the drone's battery status, providing a scientific basis for emergency landings. The system determines whether an emergency landing is necessary based on the required and remaining power; if an emergency landing is required, alarm parameters are reported to facilitate timely emergency landing strategy development. This allows the drone to respond promptly to insufficient power during flight, making reasonable decisions and improving mission reliability. By accurately managing battery power and making real-time emergency landing decisions, the system ensures that drones can complete inspection tasks as much as possible when battery power is sufficient, and can safely make emergency landings when battery power is insufficient. This effectively balances the relationship between task execution and flight safety, and improves the overall efficiency and quality of inspection tasks.

[0050] As described above, this application can determine the alternative landing point layout scheme for the target UAV based on the target UAV's preset inspection route. The process will be described below, and may include the following:

[0051] Step S201: Based on the preset inspection route of the target UAV, construct the map data corresponding to the inspection route of the target UAV in real time.

[0052] Specifically, to overcome the information limitations of "relying solely on preset flight path coordinates," dynamic and accurate geographic environmental information provides a basis for decision-making regarding the "safety, accessibility, and effectiveness" of backup landing points, avoiding unreasonable site selection due to missing environmental information. When determining backup landing point placement schemes based on the target UAV's preset inspection flight path, map data corresponding to the target UAV's preset inspection flight path can be constructed in real time. This helps solve the problem of "deviation between preset flight path and actual environment," ensuring the "spatial accuracy" of site selection. In practice, UAV preset inspection flight paths are usually based on prior basic map planning, but they mostly record "static coordinate information," which cannot cover dynamic environmental changes or previously unidentified detailed obstacles that may occur after planning. For example, temporary buildings may be added to the area traversed by the preset flight path after planning, dense trees may obscure previously open areas, and potholes / water accumulation may appear on the ground. If backup landing points are directly placed based on the preset flight path coordinates, areas that "show as open ground on the coordinates but actually have obstacles" may be mistakenly selected as landing points, leading to a significant increase in the risk of collision during emergency UAV landings. By constructing map data corresponding to flight routes in real time, the "real spatial status" along the route can be dynamically updated, ensuring that the coordinates of backup landing points are fully matched with the actual geographical environment, thus avoiding the safety hazard of "coordinates being out of sync with reality" from the root.

[0053] First, based on the target drone's preset inspection route, the system constructs map data corresponding to that route in real time. It can also extract terrain and obstacle features to filter for "basic safety zones" that meet landing conditions. The core requirements for a drone's backup landing point are "no fatal obstacles, gentle terrain, and sufficient buffer space," conditions that require refined map data feature extraction to determine. Real-time map data accurately identifies the terrain slope, obstacle type and height, and ground texture along the route. Without real-time map data, relying solely on the "macro path" of the preset route makes it impossible to determine which areas meet the above safety conditions, potentially leading to landing sites that "appear near the route but actually fail to meet basic landing requirements." The system also calculates the "accessibility of the drone to candidate landing points," ensuring "rapid arrival" in emergencies. The core value of backup landing points is ensuring "safe arrival in the shortest possible time in the event of a sudden drone malfunction." "Accessibility" relies on calculations of the spatial relationship between the flight path and the landing point in real-time map data. This requires confirming through the real-time map whether the "candidate landing point is within the drone's emergency flight range": combining the drone's current remaining battery power, flight speed, and the "shortest path distance from the flight path to the candidate landing point" in the real-time map to determine if the landing point is within the reachable "emergency radius." It also requires confirming whether the "relative position of the landing point to the flight path facilitates maneuverability": for example, if the candidate landing point is directly below the flight path and there are no vertical obstacles, the drone can directly dive to land; if the landing point is to the side of the flight path, it needs to be determined whether there is sufficient airspace to the side for the drone to turn. These calculations all rely on the "path topology" and "elevation data" of the real-time map and cannot be completed using only the linear coordinates of a preset flight path.

[0054] First, based on the target drone's preset inspection route, real-time construction of map data corresponding to the target drone's inspection route helps to coordinate the "redundancy and coverage of multiple backup points," avoiding the lack of emergency options due to "single-point failure." Backup landing point deployment must follow the "redundancy principle," with multiple landing points deployed along the route to ensure that if one landing point becomes unusable due to an emergency, there are still other options. The "rationality of redundant coverage" relies on the "spatial distribution analysis" of the real-time map. The real-time map can calculate the "distance between adjacent backup points"; for example, based on the drone's emergency endurance, the distance can be controlled at 1-2 kilometers (ensuring the drone can fly to the next landing point if the previous one fails), avoiding excessively large distances leading to "mid-flight power outages," or excessively close distances causing resource waste. It can also identify "key coverage of critical nodes on the route": for example, when the route passes through areas with weak signals or high-risk areas, denser deployment of points is needed near these nodes, and the real-time map can accurately locate the "geographical range of these key nodes," ensuring no emergency blind spots in key areas. In practice, SLAM (Simultaneous Localization and Mapping) technology, including but not limited to LiDAR and visual SLAM, can be used to build indoor 3D point cloud maps or raster maps in real time.

[0055] Step S202: Based on the constructed map data and the preset alternate landing point marking rules, mark the alternate landing points of the target UAV during the execution of the preset inspection route.

[0056] Specifically, to better address the critical transition from "available space to precise and usable alternate landing points," rule-based screening, definition, and solidification are employed to ensure that the marked alternate landing points not only meet geographical requirements but also match the functional needs, operational logic, and safety standards for emergency drone landings. This avoids the problem of "potential areas in map data that cannot be directly used as effective alternate landing points." After constructing map data corresponding to the inspection route in real time, alternate landing points for the target drone during the execution of the preset inspection route can be further marked based on the constructed map data and preset alternate landing point marking rules. In practice, real-time constructed map data can only identify potentially safe areas with flat terrain and no obvious obstacles. However, these areas may not fully meet the quantitative standards for drone alternate landings. The preset alternate landing point marking rules (usually rigid requirements formulated in conjunction with drone model and mission scenario) are used as the screening criteria to transform potential areas into effective alternate landing points, avoiding emergency risks caused by qualified areas but substandard details. For example, labeling rules might specify that the minimum area of ​​an alternate landing point must be greater than or equal to twice the drone's wingspan; and that there must be no low obstacles higher than 0.5 meters within 5 meters of the center point of the alternate landing point. Without rule-based labeling, relying solely on the macroscopic safety area in map data might mistakenly identify areas with insufficient area or low obstacles as alternate landing points. By verifying potential areas in the map data one by one using rules, areas that do not meet the detailed standards can be accurately eliminated, ensuring that the labeled alternate landing points fully comply with the quantitative safety standards for emergency drone landings.

[0057] Furthermore, when a drone experiences a sudden malfunction, the operator or the autopilot system needs to quickly determine which alternate landing point is most suitable for the current situation. This requires the alternate landing point to be not only a geographical coordinate but also to include functional attribute labels, which need to be generated through labeling rules combined with map data. For example, labeling rules may require labeling the alternate landing point type based on the ground material of the map data: "Cement ground alternate landing point (priority 1, suitable for all-weather landing, stable ground hardness)", "Grass alternate landing point (priority 2, prone to water accumulation in rainy weather, note that it can only be used in sunny weather)", "Hard dirt road alternate landing point (priority 3, prone to dust in windy weather, note that it should be used with caution when the wind speed is greater than 5m / s)"; and auxiliary information such as surrounding facilities combined with map data to label: "Alternate landing point is 1.2 kilometers away from the nearest manual repair point (note that it is suitable for manual recovery after a malfunction)" and "Alternate landing point is within the coverage area of ​​a signal tower (note that it is suitable for replanning the flight path after the signal is restored)". These functional attributes cannot be obtained directly from map data alone. They must be "translated" into decision-making attribute labels through annotation rules, so that the choice of landing point in an emergency can be changed from blindly selecting coordinates to selecting the optimal one based on attributes.

[0058] Furthermore, during emergency landings of drones, whether autopilot or manual control is used, the alternate landing point needs to have clear spatial markings and clear operational instructions. Marking can transform points on the map into executable target points for the drone. This is mainly because the marking rules require defining the center coordinates of the alternate landing point (for drone positioning), landing orientation (e.g., combining wind direction data on the map, marking 'Recommended to land against the wind, facing 350°'), and safety buffer radius (e.g., marking an area with a radius of 8 meters centered on the center point as a no-fly zone to prevent other objects from entering). The marking rules also combine the flight path data with the relative position of the alternate landing point to mark the arrival path guidance. For example, if the alternate landing point is 200 meters to the right of the current flight path, the drone needs to turn 15° to the right from the flight path, descend to an altitude of 50 meters, and land smoothly. If there are high-voltage power lines directly above the alternate landing point, mark that direct dive is prohibited and drones must fly around to the east to reach the landing point. Without marking, drones only know that there is a safe area on the map, but cannot know "where the center point is, which direction to land in, and how to fly there", which will lead to an inability to arrive accurately in an emergency, or even cause accidents due to chaotic operation.

[0059] Furthermore, real-time map data may not be able to fully identify all hidden risks (such as areas that appear unobstructed but pose electromagnetic interference or temporary activity risks). Pre-defined alternate landing point marking rules typically include risk control clauses, which can be combined with indirect features of map data or external correlation information to exclude such areas with hidden risks. For example, the marking rules might stipulate that if there is a high-voltage power tower within 100 meters of an alternate landing point in the map data (identified by the power facility layer on the map), even if the ground is unobstructed, it must be marked as "No Use" (due to strong electromagnetic interference that could cause the drone to lose control). Another example is when combined with temporary activity information associated with the map data (such as construction in an area during inspection periods (marked as a temporary construction area on the map), the marking rules would require potential alternate landing points in that area to be marked with caution (due to potential temporary obstacles or personnel)). These hidden risks cannot be directly judged based solely on the terrain and obstacle features of the map data; they must be identified and eliminated through the risk correlation logic of the marking rules to ensure that the marked alternate landing points are not only superficially safe but also free of hidden dangers. The selection of alternate landing points is related to the scenarios corresponding to the preset inspection routes, and the safety factors of the scenario should be the primary selection criterion for alternate landing points. The interval between each alternate landing point is set based on the on-site route corresponding to each scenario. The more alternate landing points, the higher the system reliability. This system recommends selecting alternate landing points at 10-meter intervals.

[0060] In the scenario of marking alternate landing points for drone inspections, the marking method needs to be designed in conjunction with "map data type (such as vector map, raster map)," "drone operation logic (automatic / manual)," and "emergency scenario requirements (rapid response, precise positioning)" to ensure that the marked information can be automatically recognized by the system and quickly interpreted by humans. During the marking of alternate landing points, marking can include manual point marking or marking according to three methods: "spatial identification dimension," "information presentation dimension," and "interactive subject dimension."

[0061] Classifying and labeling by "spatial identification dimension" clarifies the geographical location and boundaries of alternate landing points, addressing the question of "where are the alternate landing points and what is their area?". This requires using a coordinate system based on map data (such as WGS84 or UTM coordinate systems) to transform the spatial characteristics of the alternate landing points into precise visual identifiers, forming the basis for subsequent operations. The core area and safety boundaries of the alternate landing point can be clearly defined through "precise coordinates and geometric shapes." First, mark the "center latitude and longitude" or "Cartesian coordinates" of the alternate landing point on the map, typically using a combination of "red dots + coordinate values," serving as the target positioning point for drone navigation. Then, based on the actual usable area of ​​the alternate landing point, mark the boundaries on the map using "red solid-line boxes (rectangles)" or "polygons (irregular areas, such as trapezoidal grasslands)," filling the boxes with semi-transparent red (without obscuring the map background). Simultaneously, mark the "boundary dimensions" and "ground slope (e.g., less than or equal to 8°)," clearly defining the "physical safety range" for drone landing. Furthermore, by combining the relationship between the alternate landing point and surrounding geographical features (such as flight paths, obstacles, and landmarks), markings can help the drone quickly locate the alternate landing point and avoid flight deviations. For example, a "blue dashed line" can be used to connect the center point of the alternate landing point with the nearest point on the inspection flight path, and the "distance from the flight path (e.g., 200m)" and "turning angle (e.g., turning 15° to the right from the flight path)" can be marked to clarify the "shortest path direction from the current flight path to the alternate landing point." A "red triangle warning symbol" can also be marked next to obstacles (such as trees or utility poles) within 50m of the alternate landing point, along with the "obstacle height (e.g., 12m)" and "distance from the alternate landing point (e.g., 35m)" to remind the drone of the "area to avoid during landing." If there are landmarks (such as signal towers or well houses) near the alternate landing point, a "green arrow" can be used to point to the landmark, and the "landmark name (e.g., X signal tower)" and "relative distance (e.g., 80m northeast of the alternate landing point)" can be marked to assist in visual positioning during manual control.

[0062] Categorizing and labeling alternate landing points by "information presentation dimension" assigns decision-making attribute information, addressing the questions of "can this alternate landing point be used, when to use it, and how to use it." This requires converting "map features (such as ground texture and surrounding environment)" into "visualized attribute labels" using pre-set alternate landing point labeling rules to support emergency decision-making. Alternate landing points can be prioritized and assigned applicability based on their "safety level and environmental adaptability," allowing operators or systems to "lock in the optimal option in one second" during emergencies. For example, different colored circular labels can be used to indicate priority (red = level 1 (optimal), orange = level 2, yellow = level 3), placed next to the center point of the alternate landing point; for instance, an alternate landing point with "concrete ground + unobstructed view + signal coverage" is labeled "red level 1," while an alternate landing point with "grassland + rainwater accumulation" is labeled "orange level 2 (note: usable in sunny weather)." Restrictions can be marked with small gray labels (with icons), such as "Disabled when wind speed > 5m / s (with wind level icon)," "Disabled in rain (with raindrop icon)," and "Only usable for manual recovery (with personnel icon)," which can be directly affixed to the outside of the boundary box for easy identification. For example, when a drone suddenly runs out of power (only enough for 1 minute of flight), the system can automatically filter alternate landing points with "red level 1 + no disabling conditions," eliminating the need for individual environmental analysis and shortening decision-making time. Alternative landing points can also be labeled with their "functional attributes" (e.g., whether they support maintenance or signal recovery) and "operational requirements" (e.g., landing orientation, buffer zone) to ensure the drone can land safely and be used effectively. For example, core functions can be marked with blue function icons, such as "Maintenance Support (with wrench icon, note: 1.2km from maintenance point)" and "Signal Recovery (with signal tower icon, note: supports 4G / BeiDou positioning)," all grouped together on the right side of the alternate landing point. Specific operational requirements can also be marked using "black text boxes," such as "Recommended landing orientation: 350° (headwind, with arrow icon)," "Safety buffer radius: 8m (no other equipment allowed, with circle icon)," and "Descent speed limit: ≤2m / s (with speedometer icon)," directly linked to the corresponding position on the bounding box (e.g., the orientation is marked in front of the bounding box). If the drone needs to make an emergency landing due to sensor malfunction, the operator can prioritize selecting the "alternate landing point with wrench icon" (for easy subsequent repair) and operate according to the "landing orientation 350°" guideline to avoid tipping over due to incorrect orientation.

[0063] Classifying and labeling by "interaction subject dimension" can adapt to both "automatic system recognition" and "manual operation" scenarios, balancing the different needs of "drone autopilot systems" and "human operators." Systems require "machine-readable structured data," while humans need "intuitive and understandable visual identifiers," requiring collaboration through labeling. For example, machine-readable labels can support automatic decision-making in autopilot systems. All information about alternate landing points can be converted into "structured data tags" and embedded in map data, allowing the drone's flight control system to automatically read, analyze, and execute them without human intervention. Specifically, structured information can be embedded in the coordinate data of alternate landing points, and "machine-readable QR codes / AR markers" can be added to the ground at alternate landing points (marker locations need to be planned in advance using map data). Before landing, the drone scans the markers with its camera to automatically calibrate coordinate deviations. For example, if a drone experiences a sudden malfunction in "fully autonomous mode," the flight control system can directly read the metadata tags, automatically select the highest-priority alternate landing point, and correct the position using machine vision identifiers to achieve "automatic landing without human intervention." Human-readable labels can also be added to assist operators in making quick judgments. For example, alternate landing point information can be presented through "intuitive graphics, text, and colors," conforming to human visual habits and ensuring that operators can "quickly understand at a glance," suitable for "semi-automatic control" or "emergency manual intervention" scenarios. For instance, a "semi-transparent white information card" can be suspended next to the alternate landing point, containing a title, core information, and color-coded warning indicators, such as "green ring = available," "yellow ring = use with caution (e.g., current wind speed close to 5 m / s)," and "red ring = disabled (e.g., sudden rainfall)." This card can be directly overlaid on the outer boundary of the alternate landing point. For example, if the drone's flight control system indicates "low battery," but the automatic landing path is blocked by a temporary obstacle, the operator can quickly scan the "green ring + level 1" alternate landing points on the map, select the point "closest to the flight path" from the information card, and manually control the landing.

[0064] Regardless of the annotation method, the goal is to "transform the 'raw geographic information' of map data into 'decision-making and operational information adapted to the emergency needs of drones' through pre-defined landing point annotation rules." This aims to ensure that machines can automatically identify and execute the information, while also allowing humans to quickly understand and judge it, achieving the objective of "accurately locating, quickly selecting, and safely using alternate landing points in emergencies." In practice, multiple annotation methods are usually combined to form an alternate landing point annotation system that is "spatially accurate, informationally complete, and facilitates human-machine collaboration."

[0065] As can be seen from the above description, this application can determine the alternative landing point layout scheme of the target UAV based on the preset inspection route of the target UAV, which helps to improve the security of the system.

[0066] As described above, this application can determine and record the remaining battery power of the target drone in real time based on the battery voltage. The process will be described below, and may include the following:

[0067] Step S301: Real-time acquisition of temperature data in the flight environment corresponding to the target UAV performing the preset inspection task.

[0068] Specifically, temperature significantly affects the voltage-capacity relationship of a battery. Therefore, in determining the remaining capacity based on the drone's battery voltage, it is necessary to first obtain real-time flight environment temperature data. This directly determines the accuracy of the capacity calculation, thereby ensuring the safety of the inspection mission and the reliability of the emergency landing decision. For example, the lithium batteries commonly used in drones (such as lithium polymer batteries and lithium-ion batteries) are temperature-sensitive energy storage devices. Their internal electrochemical reaction efficiency and internal resistance characteristics change drastically with the ambient temperature. These changes directly break the static relationship of "fixed voltage corresponding to a certain remaining capacity." For example, low temperatures inhibit the migration rate of lithium ions inside the battery, leading to hindered electrochemical reactions. At this time, even if the actual remaining capacity of the battery is high (e.g., 50%), its output voltage will be "falsely reduced," exhibiting a voltage value similar to that of a low capacity (e.g., 20%). If the capacity is calculated solely based on the voltage, it may be mistakenly judged that the battery is depleted, potentially triggering an emergency landing prematurely or even interrupting the inspection mission when the actual capacity is sufficient. High temperatures accelerate internal side reactions in the battery (such as electrolyte decomposition and electrode material aging) and reduce the battery's internal resistance. At this point, the battery voltage "decline curve will flatten out": even if the actual remaining power is low (e.g., 15%), the voltage may still remain at a high level (close to the voltage value of 30% power). Judging solely by the voltage will lead to a misjudgment that the power is sufficient, which may cause the drone to lose control and crash during inspections due to the actual power being depleted.

[0069] Secondly, temperature affects the battery's "actual usable capacity." In actual calculations, the power calculation benchmark needs to be corrected. The "nominal capacity" of a drone battery is a test value under standard temperature conditions. However, in actual flight, temperature directly changes the battery's "actual usable capacity." For example, at low temperatures, the battery's "actual usable capacity" will shrink significantly. At high temperatures, although the usable capacity may increase slightly in the short term, it will permanently decay due to side reactions in the long term. Furthermore, if the power calculation during flight does not take into account the impact of high temperature on the "safe capacity threshold," it will increase safety risks.

[0070] Drone inspection missions are typically conducted in complex environments, and remaining battery power is a crucial factor in determining whether the current mission segment can be completed and whether an emergency landing is necessary. If a low battery level is misjudged due to low temperatures, triggering an emergency landing prematurely can lead to interrupted inspection routes, reduced mission efficiency, and increased takeoff and landing risks in complex terrain due to frequent emergency landings. Conversely, if a high temperature level is misjudged as sufficient power, failing to land in time can directly result in the drone losing contact and crashing, causing not only equipment damage but also potential secondary accidents. Real-time temperature data acquisition and battery power calculation correction ultimately provide accurate and reliable battery power data for mission endurance assessment and emergency landing timing selection during inspections, balancing mission efficiency and flight safety, and avoiding various risks caused by misjudged battery power.

[0071] Step S302: Based on the temperature data of the flight environment corresponding to the target UAV performing the preset inspection task and the battery data of the target UAV, determine the corresponding discharge curve relationship between the battery of the target UAV and the flight environment temperature.

[0072] Specifically, to establish a dynamic correlation model of "temperature-charge-voltage-flight status," isolated temperature and voltage data are transformed into "battery energy consumption patterns" that can directly guide flight decisions, fundamentally solving the problem of "static parameters failing to match dynamic flight requirements." After acquiring flight environment temperature data and UAV battery data, the corresponding discharge curve relationship between the target UAV's battery and flight environment temperature can be determined based on the temperature data of the flight environment corresponding to the target UAV performing a preset inspection task and the target UAV's battery data. The discharge curve is the "dynamic correlation carrier" of "temperature-charge-voltage," overcoming the limitations of static calculations. Previously, temperature data was acquired to correct the static correspondence between "voltage-charge" (e.g., low voltage at low temperatures does not necessarily mean low charge); however, UAVs are not "statically consuming power" during inspections. Flight speed (cruising / hovering), load (mounted cameras / sensors), and flight attitude (climbing / descending) all change the battery's discharge current in real time, and the discharge current further interacts with temperature, causing the "voltage-charge" relationship to change dynamically with flight status. At this point, the "temperature-corresponding discharge curve" can transform this "multi-variable interaction" into a visualized and calculable pattern. The discharge curve primarily uses "remaining charge (SOC)" as the horizontal axis and "voltage" as the vertical axis, while also indicating the voltage change trend under "different temperatures" and "different discharge currents" (for example, at the same temperature, the voltage drop slope is different when hovering (low current) and climbing (high current)). For instance, at 25℃ and low current (cruising), the voltage gradually decreases from 4.2V to 3.7V when the battery discharges from 100% to 20%; however, at -10℃ and high current (climbing), the voltage drops rapidly from 4.1V to 3.5V when discharging from 100% to 20%. If only a static "temperature-voltage" correspondence table is used, it is impossible to distinguish the voltage change caused by "current difference". The discharge curve integrates the three elements of "temperature + current + charge", upgrading the charge calculation from "static estimation" to "dynamic and accurate calculation".

[0073] Determining the relationship between the target drone's battery discharge curve and the ambient temperature during flight can also adapt to the "dynamic power consumption scenario" of inspection tasks, ensuring that the endurance judgment is realistic. The power consumption mode of drone inspection is not constant. The difference in discharge current in different scenarios will directly affect the battery's "actual endurance." Temperature will amplify this difference. Only through the discharge curve can the impact of "temperature + dynamic power consumption" be transformed into quantifiable endurance data. For example, if a certain inspection segment needs to last for 30 minutes, including 10 minutes of hovering (discharge current 5A) and 20 minutes of cruising (discharge current 3A), and the ambient temperature is 25℃, the discharge curve corresponding to 25℃ shows that "5A discharge for 10 minutes + 3A discharge for 20 minutes" consumes a total of 35% of the power. If the current remaining power is 50%, it is determined that the task segment can be completed. If the ambient temperature is -5℃, the discharge curve corresponding to -5℃ will show that "the voltage drops faster under the same current." The same power consumption mode will consume 48% of the power. If the current remaining power is 50%, it is determined that an emergency landing should be prioritized to avoid insufficient power. Without a discharge curve, relying solely on the "static charge percentage after temperature correction" cannot be combined with the "dynamic power consumption rhythm" of the inspection mission to predict the remaining range, potentially leading to misjudgments such as "theoretically sufficient charge but actual power consumption too fast." Secondly, inspections often require temporary route adjustments (such as bypassing obstacles), which can cause a sudden increase in discharge current. By matching the discharge curve with the current temperature in real time, it's possible to quickly calculate "how much the power consumption rate will increase after the current increases," thereby adjusting subsequent flight plans (such as shortening detour distances or planning nearby alternate landing points in advance) to avoid uncontrolled power loss due to "sudden changes in power consumption rate." Furthermore, the "safe discharge threshold" of lithium batteries (such as minimum safe voltage and maximum permissible discharge current) is not a fixed value but varies with temperature. For example, the minimum safe voltage is 3.0V at 25℃, but needs to be increased to 3.3V at -10℃ (otherwise lithium dendrite precipitation will occur, damaging the battery), and the maximum permissible discharge current needs to be reduced from 10A to 8A at 45℃ (otherwise electrolyte decomposition will be accelerated, causing bulging). The "temperature-corresponding discharge curve" is the basis for defining the "safe discharge boundary." The "lower voltage limit" of the curve indicates the "safe cut-off voltage" at different temperatures: for example, on the discharge curve, the voltage of 20% charge at -10℃ is 3.3V. At this point, a backup landing should be triggered, rather than using the standard of 3.0V corresponding to 20% charge at 25℃, to avoid over-discharge at low temperatures. The "current tolerance zone" of the curve indicates the "maximum safe discharge current" at different temperatures: for example, at 45℃, after the discharge current exceeds 8A, the curve will show a "sudden voltage drop" (indicating that the battery has exceeded the safe load). At this point, the flight attitude should be automatically limited to avoid overcurrent at high temperatures. Without a discharge curve, temperature data alone cannot accurately define the "safe discharge range at the current temperature," which may lead to the risk of "seemingly sufficient charge but already close to the safety threshold," thereby causing battery failure or flight accidents.

[0074] Step S303: Monitor the battery voltage of the target drone in real time.

[0075] Specifically, the discharge curve is a "predictive model based on patterns," while the real-time voltage is a "dynamic feedback signal reflecting the current true state of the battery." Only through continuous comparison and correction between the real-time voltage and the discharge curve can the problem of "deviation between model prediction and actual state" be solved, ensuring the accuracy of battery remaining capacity calculation and the timeliness of flight safety decisions. Even after constructing a "battery-temperature corresponding discharge curve," it is still necessary to monitor the target drone's battery voltage in real time. Therefore, real-time monitoring of the target drone's battery voltage is crucial. The real-time voltage serves as a "dynamic calibration source" for the "discharge curve model," resolving the cumulative error in capacity calculation. During actual drone flight, individual battery differences and fluctuations in flight conditions can lead to deviations between the "actual discharge process" and the "curve prediction." Real-time monitoring of the target drone's battery voltage helps correct these deviations. Drone inspection is a "dynamic process lasting several hours." Without real-time voltage monitoring, the prediction error of the discharge curve will accumulate over flight time (e.g., 1% error every 10 minutes, reaching 6% after 1 hour). For missions with limited range (such as needing to return when only 10% battery remains), such accumulated errors could directly lead to a crash due to battery depletion. Continuous real-time voltage calibration can control the error to within 1%, ensuring the reliability of battery calculations. Furthermore, the drone's real-time voltage is the "first warning signal" for "detecting sudden battery anomalies," helping to avoid safety risks. Discharge curves can only predict "discharge patterns under normal operating conditions," but during drone flight, sudden battery failures or abnormal external loads may occur. The first "monitorable signal" of such emergencies is abnormal voltage fluctuations, which often occur outside the "normal range" predicted by the discharge curve. Real-time voltage monitoring is essential for timely detection. Common "voltage anomaly warning scenarios" include sudden voltage drops and voltage stagnation (cell imbalance). Therefore, real-time voltage detection is an "instant feedback" for "dynamically adapting to changes in flight scenarios," ensuring timely decision-making regarding flight endurance. During drone inspections, flight scenarios change frequently (such as switching from cruising to hovering for photography, or from level flight to climbing and overcoming obstacles). These changes cause instantaneous fluctuations in discharge current. Although the discharge curve can predict the "voltage change pattern under different currents," the "specific timing and amplitude of current fluctuations" occur in real time. Only through real-time voltage monitoring can the current discharge state be quickly matched, and flight endurance decisions be dynamically adjusted.

[0076] Step S304: Based on the battery voltage of the target drone and by comparing the discharge curves corresponding to the battery temperature of the target drone with the ambient temperature during flight, determine and record the remaining battery power of the target drone in real time.

[0077] Specifically, battery voltage itself is an "isolated numerical signal" and cannot be directly equated with remaining power. The discharge curve, however, is a "correlation model of voltage, temperature, and power." Only through real-time matching of voltage and curve can the abstract voltage value be transformed into a concrete and usable "remaining power," providing a core basis for drone inspection endurance decisions. Therefore, in addition to real-time monitoring of battery voltage and constructing a "battery-temperature corresponding discharge curve," it is still necessary to determine and record the remaining power of the target drone's battery in real time by comparing the target drone's battery voltage with the corresponding discharge curve between the target drone's battery and the flight environment temperature. This resolves the "multi-valued" contradiction of "voltage not equaling power" and avoids misjudgment of power. Generally, the voltage and remaining power of lithium batteries are not in a "one-to-one" correspondence, but rather exhibit a multi-valued contradiction where "the same voltage corresponds to multiple powers, and the same power corresponds to multiple voltages." The root of this contradiction lies in the influence of "temperature" and "discharge conditions," and the discharge curve is essentially a "calibration tool to eliminate this contradiction." Relying solely on real-time voltage to directly determine power can lead to serious misjudgments and even flight accidents. The electrochemical characteristics of lithium batteries are extremely sensitive to temperature: low temperatures reduce electrolyte activity, resulting in lower voltage for the same charge; high temperatures accelerate chemical reactions, leading to higher voltage for the same charge. Therefore, a lithium battery at the same voltage may correspond to completely different charges at different temperatures. "Isolated voltage" detached from temperature is meaningless. If the charge is judged directly by voltage without using a "voltage-to-charge curve comparison," problems such as "misjudging sufficient charge at low temperatures (when it's actually only 20%) leading to a crash" or "misjudging low charge at room temperature (when it's actually 50%) leading to mission interruption" may occur. However, by calibrating the discharge curve, the "voltage-charge relationship" at the current temperature can be accurately matched, reducing the charge judgment error from ±10% to within ±1%. Furthermore, when drones perform inspection missions, flight conditions are not fixed, and the discharge current varies greatly under different conditions. These current changes directly lead to "voltage fluctuations for the same charge," further exacerbating the contradiction in the "voltage-charge" correspondence. At this point, the discharge curve not only includes the "temperature dimension" but also incorporates the "voltage-charge pattern under different current (operating conditions)." By comparing the real-time voltage with the curve, it can dynamically adapt to changes in operating conditions, ensuring the accuracy of charge calculation. This provides an "executable quantitative basis" for "endurance decisions and safety management." One of the core requirements of UAV inspection is "ensuring the mission is completed and the UAV returns safely before the battery is depleted." The "remaining charge" is the only quantitative standard for this decision. If only the voltage is monitored, it is impossible to give a specific judgment on "how much longer can it fly" or "whether an emergency landing is needed." Only by obtaining the "remaining charge percentage" through "voltage comparison with the discharge curve" can the abstract voltage signal be transformed into "executable operational instructions," supporting the safety management of the entire flight mission.

[0078] The real-time voltage of the target drone is the raw signal (input) of the "current battery state," but the signal itself has no explicit meaning. The discharge curve corresponding to the target drone's battery and the flight environment temperature is a correlation model (calibration tool) of "temperature, current, voltage, and charge," responsible for converting the raw signal into effective information. The remaining charge of the target drone is the "calibrated quantitative result" (output), directly supporting the drone's mission decision-making and safety management. The relationship between the three is a complete closed loop of "signal input → model calibration → decision output." If only voltage is monitored, the charge cannot be determined; if only the curve is relied upon, the true state of the battery cannot be reflected. Only by "comparing the real-time voltage with the discharge curve" can an accurate and usable remaining charge be obtained, ensuring that the drone inspection mission "flies accurately, flies stably, and returns safely."

[0079] Therefore, this application can determine and record the remaining battery power of the target drone in real time based on the battery voltage, which helps to grasp the battery status of the drone in a timely manner and make timely decisions.

[0080] As described above, this application can determine in real time whether the target drone needs to make an emergency landing based on the total battery capacity required for the target drone to complete the inspection route and flight maneuvers, as well as the remaining battery power of the target drone. The process will be described below, and may include the following:

[0081] Step S401: Determine the total battery capacity of the target drone.

[0082] Specifically, in deciding whether a drone needs to make an emergency landing, determining the "total battery capacity required to complete the inspection task" is a crucial prerequisite. Only by knowing "how much power is actually needed to complete the task" can we compare it with "how much power is currently available" to determine whether the battery is sufficient and whether an emergency landing is necessary. Determining the total battery capacity of the target drone quantifies the "task power consumption requirement," avoiding "battery capacity judgments without a basis." Knowing only "remaining power" is meaningless without first determining the "total battery capacity required to complete the current task," and it's impossible to determine whether it can support subsequent tasks.

[0083] Step S402: Determine the battery capacity required for the target drone to complete the flight maneuvers corresponding to the currently incomplete inspection route.

[0084] Specifically, in deciding whether a drone needs to make an emergency landing, to avoid misjudgments caused by confusing "total mission power consumption" with "current remaining mission power consumption," and to ensure that the emergency landing decision is more accurate and more in line with the real-time mission progress, the battery capacity required for the target drone to complete the flight maneuvers corresponding to the currently unfinished inspection route can be determined. This allows focusing on the "precise power consumption requirements of the current remaining mission." Drone inspection missions are usually "executed in segments," and there is a significant difference between the "total mission battery capacity required" and the "battery capacity required for the currently unfinished mission." The "total mission power consumption" is the "global objective," while the "current unfinished mission power consumption" is the "local requirement that needs to be addressed." Only by focusing on the latter can we avoid misjudgments caused by "measuring local progress with global standards," making the decision more in line with the real-time mission status. The flight maneuvers of drone inspections are not "uniform speed power consumption values." The power consumption of flight maneuvers on different routes (such as climb, descent, hovering, and uniform speed level flight) varies greatly, and these differences may be concentrated in the "unfinished routes." For example, the completed 6km route mainly involves "uniform speed flight on flat ground" (consuming 10W of power per unit time), while the uncompleted 4km route includes "two climbs to 50 meters altitude + three hovering checks" (consuming up to 25W of power per unit time during climbs / hovering). In this case, the battery capacity required for the "flight maneuvers of the uncompleted route" will be much higher than the "power consumption of the same distance on the completed route" (perhaps 4km requires 25% of the capacity, while the completed 6km only requires 20%). If the "power consumption of the uncompleted route's flight maneuvers" is not calculated separately, and only the "average power consumption of the total mission" (e.g., 80% for 10km, i.e., 8% for 1km) is used to estimate the remaining 4km requiring 32% of the capacity, this will deviate from the actual requirement of 25%, potentially leading to "misjudgment of needing an emergency landing" or "misjudgment of sufficient battery power." Therefore, separately determining the "power consumption of the uncompleted route's flight maneuvers" is crucial for accurately matching the "maneuver characteristics" of that segment, avoiding the "average estimation" masking local power consumption differences, and ensuring the accuracy of battery power assessment.

[0085] Step S403: Based on the current remaining battery capacity of the target drone, determine whether the ratio between the difference between the current remaining battery capacity of the target drone and the battery capacity required for the target drone to complete the flight action corresponding to the currently incomplete inspection route and the total battery capacity of the target drone is less than a preset battery alarm threshold.

[0086] Specifically, in drone emergency landing decisions, to transform "battery shortage / redundancy" into "standardized, quantifiable safety warning indicators" and avoid misjudgments due to the limitations of "absolute battery difference," while ensuring the safety, consistency, and flexibility of the decision-making process, the following logic is introduced: Based on the target drone's current remaining battery capacity, the ratio between the difference between the target drone's current remaining battery capacity and the battery capacity required to complete the flight maneuvers corresponding to the currently incomplete inspection route, and the total battery capacity of the target drone, is determined to be less than a preset battery alarm threshold. This logic replaces the "absolute difference" with a "relative ratio," resolving the issue of "uniform warning standards under different battery capacities."

[0087] The total battery capacity of a drone is not a fixed value. If only the "absolute power difference" (such as "remaining power - power consumed by unfinished tasks") is used as the basis for early warning, the problem will arise that "the same difference has completely different safety implications under different battery capacities".

[0088] For example, Example 1: The total battery capacity is 10000mAh (100%), 3000mAh (30%) is needed to complete the task, and the current remaining capacity is 2500mAh (25%). The absolute difference is: 2500mAh - 3000mAh = -500mAh (shortage of 500mAh); the relative ratio is (-500mAh) / 10000mAh = -5% (shortage accounts for 5% of the total capacity).

[0089] Example 2: The total battery capacity is 5000mAh (100%), 1500mAh (30%) is needed to complete the task, and the current remaining capacity is 1250mAh (25%). The absolute difference is 1250mAh - 1500mAh = -250mAh (the gap is 250mAh); the relative ratio is (-250mAh) / 5000mAh = -5% (the gap also accounts for 5% of the total capacity).

[0090] If the preset alarm threshold is "-5%", both cases will trigger alarms – this aligns with the reality that "although the absolute differences are different, the 'power shortage percentage' is the same, and the safety risk level is consistent." Conversely, if only "absolute difference less than or equal to -500mAh" is used as the threshold, -250mAh in Example 2 would be mistakenly judged as "no risk," but its actual "power shortage percentage" has reached -5%, still posing a risk of failing to complete the task. Therefore, by converting the absolute difference into a relative percentage through the "ratio to the total battery capacity Q," the warning standard can be freed from the limitation of "specific battery capacity," achieving a "unified safety criterion" for different drones and different battery configurations. In drone inspection scenarios, the safety requirements for different tasks vary, and the "preset battery alarm threshold" can transform these "scenario-based safety requirements" into "executable quantitative rules." Without the logic of "ratio + preset threshold," each decision requires manual judgment of "whether the current shortage is dangerous," which is not only inefficient but may also lead to inconsistent safety standards due to differences in personnel experience. The judgment of "whether the ratio is less than the preset threshold" simplifies the complex "safety risk assessment" into a clear "yes / no" conclusion, ensuring that different operators and different task scenarios can follow uniform safety rules and reduce the risk of decision-making errors. Therefore, if the ratio between the difference between the target drone's current remaining battery capacity and the battery capacity required for the target drone to complete the flight action corresponding to the currently incomplete inspection route and the total battery capacity of the target drone is less than the preset battery alarm threshold, then step S405 is executed. Experience shows that the preset battery alarm threshold can be set to [10%-20%].

[0091] Step S405: Determine that the target drone needs to initiate an alternate landing.

[0092] Specifically, if the ratio between the difference between the current remaining battery capacity of the target drone and the battery capacity required for the target drone to complete the flight action corresponding to the currently incomplete inspection route and the total battery capacity of the target drone is less than the preset battery alarm threshold, it indicates that the target drone has insufficient power and cannot continue to perform the mission. Therefore, it needs to make an emergency landing. It can be determined that the target drone needs to initiate an emergency landing.

[0093] In practice, minimizing spatial distance should be the top priority to construct the safest and most efficient emergency landing path for drones in alarm states. This fundamentally reduces the probability of risks such as power depletion and equipment failure escalating, while ensuring efficient follow-up handling after inspection mission interruptions. This application can also, after reporting the relevant alarm parameters of the target drone, further determine at least one alternative landing point closest to the target drone's current location based on the drone's current position and the corresponding alternate landing point layout scheme. One of the core objectives after a drone alarm is to "minimize the impact on inspection tasks while ensuring safety, and reduce the cost of subsequent rescue and equipment recovery." The "nearest alternate landing point" can optimize efficiency in terms of "time, manpower, and resources." For example, it can shorten the alternate landing flight time. The nearest alternate landing point means the "shortest flight time," allowing the drone to land faster and avoiding the "expanded mission interruption window" caused by prolonged hovering and long-distance flights. After an alternate landing, manual retrieval and troubleshooting of the drone are required. The nearest alternate landing point minimizes the "distance and time for manual round trips to the alternate landing point." For example, if the nearest alternate landing point is 5 kilometers away from the operator, retrieval can be completed in 1 hour; if it is 20 kilometers away from a non-nearest alternate landing point, it requires 3 hours for a round trip, significantly increasing manpower costs. It can reduce secondary equipment wear and tear. Drones in alarm states may have hidden faults (such as battery bulging or minor fuselage damage). If the alternate landing point is too far away, turbulence and vibration during flight may exacerbate equipment wear and tear. The short path to the nearest alternate landing point reduces "wear and tear on equipment in unstable conditions," lowering subsequent maintenance costs.

[0094] Furthermore, the layout of alternate landing points for drones is not a "single fixed point," but a "multi-point coverage network" designed according to the inspection scenario (which may include temporary alternate landing points, fixed alternate landing points, emergency landing zones, etc.). However, in actual scenarios, some alternate landing points may become unusable due to "occupancy or environmental changes." In this case, "determining the nearest 'at least one' alternate landing point" allows "filtering the 'optimal and available' options in the layout network," avoiding the dilemma of "no alternate landing point available due to the unavailability of a single alternate landing point." This effectively addresses the sudden situation of "alternate landing point unavailability." For example, the alternate landing point layout of a certain inspection route includes three points: A (closest, 1 km), B (second closest, 3 km), and C (farthest, 5 km). After reporting the alarm, it is found that point A is unusable due to "a vehicle temporarily parked." If only point A had been determined beforehand, it would lead to a crisis of "no alternate landing point." However, by determining "at least one" (such as A and B) in advance, it is possible to immediately switch to point B, ensuring the continuity of alternate landings.

[0095] The UAV flight path control device provided in this application is described below. The UAV flight path control device described below corresponds to the UAV flight path control method described above. See also... Figure 4 , Figure 4This is a schematic diagram of a drone flight path control device. Figure 4 As shown, the UAV route control device may include: a backup landing point deployment unit 101, used to deploy backup landing points in the target UAV's pre-set inspection route according to the target UAV's preset inspection route; a battery power determination unit 102, used to determine and record the remaining battery power of the target UAV in real time based on the battery voltage; and an analysis unit 103, used to analyze the total battery power required for the target UAV to complete the inspection route and flight actions based on a pre-set UAV inspection action power consumption model, wherein the pre-set UAV inspection action power consumption model uses the flight inspection data of training UAVs as training samples. The training process uses the total battery capacity required to complete the training inspection route and flight maneuvers, as included in the flight inspection data of the training UAV, as sample labels. The judgment unit 104 is used to determine in real time whether the target UAV needs to make an emergency landing based on the total battery capacity required to complete the inspection route and flight maneuvers and the remaining battery power of the target UAV. The reporting unit 105, when the judgment unit 104 determines that the target UAV needs to make an emergency landing, reports the relevant alarm parameters of the target UAV and performs an emergency landing for the target UAV based on the preset alternative landing point of the inspection route. The specific processing flow of each unit included in the above UAV route control device can be found in the previous section on UAV route control methods, and will not be repeated here.

[0096] The UAV flight path control device provided in this application embodiment can be applied to UAV flight path control equipment, such as terminals: mobile phones, computers, etc. Optionally, Figure 5 The hardware structure block diagram of the UAV flight path control device is shown. Figure 5The hardware structure of the UAV flight path control device may include at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4. In this embodiment, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and the processor 1, communication interface 2, and memory 3 communicate with each other through the communication bus 4. The processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application; the memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device; wherein, the memory stores a program, and the processor can call the program stored in the memory, the program being used to implement various processing flows in the aforementioned terminal UAV flight path control scheme. This embodiment also provides a readable storage medium, which can store a program suitable for processor execution, the program being used to implement various processing flows in the aforementioned terminal UAV flight path control scheme. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The various embodiments described in this specification are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the flight path of an unmanned aerial vehicle (UAV), characterized in that, include: Deploy backup landing points in the preset inspection route of the target UAV according to the preset inspection route of the target UAV. Real-time acquisition of temperature data in the flight environment of the target UAV during the execution of the preset inspection task; Based on the temperature data of the flight environment corresponding to the target UAV performing the preset inspection task and the battery data of the target UAV, the corresponding discharge curve relationship between the battery of the target UAV and the flight environment temperature is determined. Monitor the battery voltage of the target drone in real time; Based on the battery voltage of the target drone and by comparing the discharge curves corresponding to the battery temperature of the target drone with the flight environment temperature, the remaining battery power of the target drone is determined and recorded in real time. Based on a preset power consumption model for drone inspection actions, the total battery consumption required for the target drone to complete the inspection route and flight actions is analyzed. The preset power consumption model for drone inspection actions is trained using flight inspection data of a training drone as training samples and the total battery consumption required to complete the training inspection route and flight actions included in the flight inspection data of the training drone as sample labels. The flight inspection data of the training drone includes different flight action types of the inspection drone in the inspection route to be executed, the expected duration of each flight action, and the environmental parameters corresponding to the current inspection route. The preset power consumption model for drone inspection actions is dynamically optimized according to external conditions. Determine the total battery capacity of the target drone; Determine the battery capacity required for the target drone to complete the flight maneuvers corresponding to the currently incomplete inspection route; Based on the current remaining battery capacity of the target drone, determine whether the ratio between the difference between the current remaining battery capacity of the target drone and the battery capacity required for the target drone to complete the flight action corresponding to the currently incomplete inspection route and the total battery capacity of the target drone is less than a preset battery alarm threshold; if so, determine that the target drone needs to initiate an emergency landing. If it is determined that the target drone needs to make an alternate landing, the relevant alarm parameters of the target drone are reported, and at least one target alternate landing point that is closest to the current location of the target drone is determined based on the current location of the target drone and the layout scheme of the alternate landing point corresponding to the target drone. Compare the safety factors of each of the determined target alternate landing points, and select the target alternate landing point that is closest to the target UAV and has the highest safety factor for the target UAV to make an alternate landing.

2. The method according to claim 1, characterized in that, The step of deploying backup landing points along the pre-defined inspection route of the target UAV includes: The alternative landing point layout scheme for the target UAV is determined based on the preset inspection route of the target UAV. Based on the backup landing point deployment plan for the target UAV, deploy backup landing points along the inspection route of the target UAV.

3. The method according to claim 2, characterized in that, The step of determining the alternative landing point layout scheme for the target UAV based on the preset inspection route of the target UAV includes: Based on the preset inspection route of the target UAV, map data corresponding to the inspection route of the target UAV is constructed in real time. Based on the constructed map data and the preset alternate landing point marking rules, the alternate landing points of the target UAV during the execution of the preset inspection route are marked; wherein, the selection of alternate landing points is related to the scenario corresponding to the preset inspection route, and the safety factors of the scenario are used as the first selection factor for alternate landing points; the interval of each alternate landing point is set based on the on-site route corresponding to each scenario.

4. The method according to claim 1, characterized in that, The step of analyzing the total battery capacity required for the target drone to complete the inspection route and flight maneuvers, based on the target drone's scheduled inspection route and flight maneuvers, includes: Based on the inspection route and flight maneuvers to be performed by the target UAV, determine the mapping relationship between the power required by the target UAV to complete the inspection route and flight maneuvers and the total battery capacity of the target UAV. The mapping relationship between the power required for the target UAV to complete the inspection route and flight maneuvers and the total battery capacity of the target UAV is as follows: ； in, This indicates the energy consumption per milliampere-hour per unit flight time. Indicates the duration of the corresponding flight behavior; This represents the weighting coefficients under different temperature and altitude conditions. Less than 1; This indicates the battery capacity required for the target drone to complete the inspection route and flight maneuvers to be performed.

5. A drone flight path control device, characterized in that, include: Alternate landing point deployment unit, used to deploy alternative landing points in the inspection route of the target UAV according to the preset inspection route of the target UAV; The power determination unit is used to acquire temperature data in the flight environment corresponding to the target UAV performing the preset inspection task in real time. Based on the temperature data of the flight environment corresponding to the target UAV performing the preset inspection task and the battery data of the target UAV, the corresponding discharge curve relationship between the battery of the target UAV and the flight environment temperature is determined; the battery voltage of the target UAV is monitored in real time; based on the battery voltage of the target UAV and comparing the corresponding discharge curve between the battery of the target UAV and the flight environment temperature, the remaining battery power of the target UAV is determined and recorded in real time. The analysis unit is used to analyze the total battery consumption required by the target drone to complete the inspection route and flight actions based on a preset drone inspection action power consumption model. The preset drone inspection action power consumption model is trained using the flight inspection data of the training drone as training samples and the total battery consumption required to complete the training inspection route and flight actions included in the flight inspection data of the training drone as sample labels. The flight inspection data of the training drone includes different flight action types of the inspection drone in the inspection route to be executed, the expected duration of each flight action, and the environmental parameters corresponding to the current inspection route. The preset drone inspection action power consumption model is dynamically optimized according to external conditions. The judgment unit is used to determine the total battery capacity of the target drone; determine the battery capacity required for the target drone to complete the flight maneuvers corresponding to the currently incomplete inspection route; and, based on the current remaining battery capacity of the target drone, determine whether the ratio between the difference between the current remaining battery capacity of the target drone and the battery capacity required for the target drone to complete the flight maneuvers corresponding to the currently incomplete inspection route and the total battery capacity of the target drone is less than a preset battery alarm threshold; if so, determine that the target drone needs to initiate an emergency landing. The reporting unit is used to report the relevant alarm parameters of the target drone when the execution result of the judgment unit determines that the target drone needs to make an alternate landing, and to determine at least one target alternate landing point that is closest to the current location of the target drone based on the current location of the target drone and the layout scheme of the alternate landing point corresponding to the target drone; compare the safety coefficients of each of the determined target alternate landing points, and select the target alternate landing point that is closest to the target drone and has the highest safety coefficient for the target drone to make an alternate landing.

6. A drone flight path control device, characterized in that, include: One or more processors, and a memory; the memory stores computer-readable instructions that, when executed by the one or more processors, implement the steps of the unmanned aerial vehicle (UAV) flight path control method as described in any one of claims 1 to 4.

7. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the UAV flight path control method as described in any one of claims 1 to 4.

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