Unmanned aerial vehicle route control method and related equipment

Abstract

The invention provides an unmanned aerial vehicle route control method and related equipment. A standby landing point in an inspection route of a target unmanned aerial vehicle can be deployed according to a preset inspection route of the target unmanned aerial vehicle; when the electric quantity of the target unmanned aerial vehicle is not enough to complete all the inspection tasks in the inspection task execution process, safe standby drop is carried out at any time, and the battery remaining electric quantity of the target unmanned aerial vehicle is determined and recorded in real time according to the voltage of the battery of the target unmanned aerial vehicle; analyzing the total quantity of batteries required by the target unmanned aerial vehicle to complete the inspection route to be executed and the flight action; whether the current target unmanned aerial vehicle needs to be subjected to standby landing or not and whether standby landing conditions exist or not can be judged in real time according to the electric quantity consumption model and the remaining electric quantity of the unmanned aerial vehicle patrol inspection action; wherein the model can be dynamically adjusted according to external conditions, and if the model is dynamically adjusted according to external conditions, related alarm parameters of the target unmanned aerial vehicle are reported, so that a standby landing strategy of the target unmanned aerial vehicle can be determined based on the alarm parameters, and safe standby landing of the target unmanned aerial vehicle is realized.

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] The unmanned aerial vehicle route control method comprises the following steps: deploying a backup landing point in a target unmanned aerial vehicle inspection route according to a preset target unmanned aerial vehicle inspection route; determining and recording the remaining battery capacity of the target unmanned aerial vehicle in real time according to the voltage of the battery of the target unmanned aerial vehicle; analyzing the total battery capacity required by the target unmanned aerial vehicle to complete a to-be-executed inspection route and flight action according to a preset unmanned aerial vehicle inspection action battery consumption model, wherein the preset unmanned aerial vehicle inspection action battery consumption model is obtained by training the flight inspection data of the unmanned aerial vehicle, taking the total battery capacity required to complete the training inspection route and flight action included in the flight inspection data of the unmanned aerial vehicle as a sample label; judging in real time whether the target unmanned aerial vehicle needs to make an emergency landing according to the total battery capacity required by the target unmanned aerial vehicle to complete the to-be-executed inspection route and flight action and the remaining battery capacity of the target unmanned aerial vehicle; if it is determined that the target unmanned aerial vehicle needs to make an emergency landing, reporting relevant alarm parameters of the target unmanned aerial vehicle, and making an emergency landing for the target unmanned aerial vehicle according to the backup landing point of the preset target unmanned aerial vehicle inspection route.

[0006] Preferably, the backup landing point in the target unmanned aerial vehicle inspection route is deployed according to the preset target unmanned aerial vehicle inspection route, comprising: determining a backup landing point arrangement scheme of the target unmanned aerial vehicle according to the preset target unmanned aerial vehicle inspection route; and deploying the backup landing point in the target unmanned aerial vehicle inspection route according to the backup landing point arrangement scheme of the target unmanned aerial vehicle.

[0007] Preferably, the backup landing point arrangement scheme of the target unmanned aerial vehicle is determined according to the preset target unmanned aerial vehicle inspection route, comprising: constructing map data corresponding to the target unmanned aerial vehicle inspection route in real time according to the preset target unmanned aerial vehicle inspection route; and labeling the emergency landing point of the target unmanned aerial vehicle in the process of executing the preset inspection route based on the constructed map data and a preset emergency landing point labeling rule; wherein the selection of the emergency landing point is related to the scene corresponding to the preset inspection route, and the safety factor of the scene is used as the first selection element of the emergency landing point; and the interval of each emergency landing point is set based on the on-site route corresponding to each scene.

[0008] Preferably, the remaining battery capacity of the target unmanned aerial vehicle is determined and recorded in real time according to the voltage of the battery of the target unmanned aerial vehicle, comprising: acquiring temperature data of the target unmanned aerial vehicle in the flight environment corresponding to the preset inspection task in real time; determining the corresponding discharge curve relationship between the battery of the target unmanned aerial vehicle and the flight environment temperature according to the temperature data of the target unmanned aerial vehicle in the flight environment corresponding to the preset inspection task and the battery data of the target unmanned aerial vehicle; monitoring the voltage of the battery of the target unmanned aerial vehicle in real time; and determining and recording the remaining battery capacity of the target unmanned aerial vehicle in real time according to the voltage of the battery of the target unmanned aerial vehicle and the corresponding discharge curve of the target unmanned aerial vehicle.

[0009] Preferably, the real-time judgment of whether the target UAV needs to make an emergency landing according to the total amount of battery required by the target UAV to complete the inspection route and flight action to be performed and the remaining battery capacity of the target UAV comprises: determining the total amount of battery of the target UAV; determining the battery capacity required by the target UAV to complete the flight action corresponding to the current unfinished inspection route; judging whether the ratio between the difference between the current remaining battery capacity of the target UAV and the battery capacity required by the target UAV to complete the flight action corresponding to the current unfinished inspection route and the total amount of battery of the target UAV is less than a preset battery alarm threshold according to the current remaining battery capacity of the target UAV; and if so, determining that the target UAV needs to start an emergency landing.

[0010] Preferably, the analysis of the total amount of battery required by the target UAV to complete the inspection route and flight action to be performed according to the inspection route and flight action to be performed by the target UAV comprises: determining the mapping relationship between the power required by the target UAV to complete the inspection route and flight action to be performed and the total capacity of the battery of the target UAV according to the inspection route and flight action to be performed by the target UAV; wherein the mapping relationship between the power required by the target UAV to complete the inspection route and flight action to be performed and the total capacity of the battery of the target UAV is as follows: ; wherein, represents the corresponding unit flight time milliampere-hour energy consumption consumption; represents the duration of the corresponding flight behavior; represents the corresponding weighting coefficient under different temperature conditions and altitude conditions, is less than 1; represents the battery capacity required by the target UAV to complete the inspection route and flight action to be performed.

[0011] Preferably, the emergency landing of the target UAV according to the preset emergency landing point of the target UAV on the inspection route comprises: determining at least one target emergency landing point closest to the current position of the target UAV according to the current position of the target UAV and the corresponding emergency landing point layout scheme of the target UAV; comparing the safety coefficients of the determined target emergency landing points to select the target emergency landing point closest to the target UAV and having the highest safety coefficient for the target UAV to make an emergency landing.

[0012] The unmanned aerial vehicle route control device comprises: a landing point deployment unit configured to deploy a backup landing point in a preset inspection route of a target unmanned aerial vehicle according to the target unmanned aerial vehicle; a power determination unit configured to determine and record a remaining power of a battery of the target unmanned aerial vehicle in real time according to a voltage of the battery of the target unmanned aerial vehicle; an analysis unit configured to preset a power consumption model of an unmanned aerial vehicle inspection action, and analyze a total power required by the target unmanned aerial vehicle to complete a to-be-executed inspection route and a flight action, wherein the power consumption model of the unmanned aerial vehicle inspection action is trained by flight inspection data of the unmanned aerial vehicle, and a total power required by the flight inspection data of the unmanned aerial vehicle to complete a training inspection route and a flight action is taken as a sample label; a judgment unit configured to determine in real time whether the target unmanned aerial vehicle needs to make an emergency landing according to the total power required by the target unmanned aerial vehicle to complete the to-be-executed inspection route and the flight action and the remaining power of the battery of the target unmanned aerial vehicle; and a reporting unit configured to report related alarm parameters of the target unmanned aerial vehicle when the target unmanned aerial vehicle needs to make an emergency landing, and make an emergency landing for the target unmanned aerial vehicle according to the backup landing point of the preset inspection route of the target unmanned aerial vehicle.

[0013] An unmanned aerial vehicle route control device, comprising: one or more processors, and a memory; the memory stores computer readable instructions, when the computer readable instructions are executed by the one or more processors, the steps of the unmanned aerial vehicle route control method as described in any of the foregoing are implemented.

[0014] A readable storage medium, the readable storage medium stores computer readable instructions, when the computer readable instructions are executed by one or more processors, the steps of the unmanned aerial vehicle route control method as described in any of the foregoing are implemented.

[0015] As can be seen from the above introduction, when the unmanned aerial vehicle is used to perform the inspection task, in order to ensure that the unmanned aerial vehicle can safely complete all the inspection tasks, the application can deploy the standby landing point in the inspection route of the target unmanned aerial vehicle according to the preset inspection route of the target unmanned aerial vehicle; so that the target unmanned aerial vehicle can safely make a safety landing at any time when the target unmanned aerial vehicle is insufficient in power to complete all the inspection tasks during the execution of the inspection task. The battery voltage of the target unmanned aerial vehicle reflects the battery capacity. In order to confirm whether the target unmanned aerial vehicle needs to make a safety landing, the residual power of the battery of the target unmanned aerial vehicle can be determined and recorded in real time according to the voltage of the battery of the target unmanned aerial vehicle. The total amount of battery required by the target unmanned aerial vehicle to complete the inspection route to be executed and flight actions is analyzed according to the preset unmanned aerial vehicle inspection action power consumption model. The preset unmanned aerial vehicle inspection action power consumption model can train the flight inspection data of the unmanned aerial vehicle as training samples. The total amount of battery required to complete the training inspection route and flight actions included in the flight inspection data of the unmanned aerial vehicle is used as a sample label to obtain the training. In particular, different flight actions consume different amounts of power. In order to confirm whether the residual power of the target unmanned aerial vehicle can meet the power required by the target unmanned aerial vehicle to complete the inspection route to be executed and flight actions, the current target unmanned aerial vehicle can be further judged whether it needs to make a safety landing in real time according to the total amount of battery required by the target unmanned aerial vehicle to complete the inspection route to be executed and flight actions and the residual power of the battery of the target unmanned aerial vehicle. If it is determined that the target unmanned aerial vehicle needs to make a safety landing, the related alarm parameters of the target unmanned aerial vehicle are reported, and the safety landing strategy of the target unmanned aerial vehicle is determined based on the alarm parameters, so as to realize the safety landing of the target unmanned aerial vehicle.

[0016] From the above introduction, when the unmanned aerial vehicle is used to perform the inspection task, in order to ensure that the unmanned aerial vehicle can safely complete all the inspection tasks, the application realizes effective guarantee for safe flight of the unmanned aerial vehicle through reasonable deployment of the target unmanned aerial vehicle inspection route backup landing point and real-time monitoring and analysis of the battery power, pre-deploys the backup landing point, and when the unmanned aerial vehicle is insufficient in power to complete all the inspection tasks in the process of performing the inspection task, can safely make an emergency landing at any time, avoids unmanned aerial vehicle crash due to power consumption, reduces the risk of equipment damage, and guarantees the safety of the unmanned aerial vehicle itself and the equipment in the inspection area. According to the real-time determination and recording of the battery voltage, the remaining battery power is determined and recorded in real time, and the required power is analyzed in combination with the to-be-executed inspection route and flight action. Considering that different flight actions consume different power, this accurate power analysis method can more accurately judge the power state of the unmanned aerial vehicle, and provide a scientific basis for whether to make an emergency landing. According to the required power and the remaining power, it is judged in real time whether the unmanned aerial vehicle needs to make an emergency landing, and if it needs to make an emergency landing, a warning parameter is reported to the upper layer, so that an emergency landing strategy can be formulated in time. This makes the unmanned aerial vehicle be able to respond to the insufficient power condition in time during flight, make a reasonable decision, and improve the reliability of task execution. Through accurate management of the power and real-time emergency landing decision, the unmanned aerial vehicle can complete the inspection task as much as possible under the condition of power, and can safely make an emergency landing when the power is insufficient, effectively balancing the relationship between task execution and flight safety, and improving the execution efficiency and quality of the whole inspection task. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor. Figure 1 It is a flow chart of a method for controlling the route of an unmanned aerial vehicle; Figure 2 It is a schematic diagram of an indoor unmanned aerial vehicle system architecture; Figure 3 It is a correspondence diagram of unmanned aerial vehicle flight action and flight time; Figure 4 It is a schematic diagram of the structure of a device for controlling the route of an unmanned aerial vehicle; Figure 5 It is a hardware structure block diagram of a device for controlling the route of an unmanned aerial vehicle. DETAILED DESCRIPTION

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

[0019] In view of the fact that most of the current UAV route control schemes are difficult to adapt to complex and variable business needs, the applicant has researched a UAV route control scheme. The UAV route control method can respond to insufficient power in time during the flight of the UAV, make reasonable decisions, and improve the reliability of task execution. Through accurate management of power and real-time emergency landing decision, the UAV can complete the inspection task as much as possible under the condition of power allowance, and can safely make an emergency landing when the power is insufficient, effectively balancing the relationship between task execution and flight safety, and improving the execution efficiency and quality of the overall inspection task.

[0020] The method provided by the embodiments of the present application can be used in many general or special computing device environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or devices, etc. The embodiments of the present application provide a UAV route control method, which can be applied to various UAV inspection management systems, and can also be applied to various computer terminals or intelligent terminals. The execution subject can be the processor or server of the computer terminal or the intelligent terminal.

[0021] The flow of the UAV route control method provided by the embodiments of the present application will be introduced below Figure 1 , as shown in Figure 1 , the flow can include the following steps: Step S101, deploying a standby landing point in the inspection route of the target UAV according to the preset inspection route of the target UAV.

[0022] Specifically, UAV inspection plays an important role in many scenarios due to its flexibility, efficiency, and ability to enter complex environments. UAVs can perform inspection in adverse weather conditions, ensuring that inspection work is not affected by weather, improving the timeliness and accuracy of inspection. As can be seen, UAVs can perform inspection in outdoor environments, and can also perform inspection in indoor environments. In actual application, the UAV may encounter a sudden drop in power during flight in an indoor or outdoor environment, or the battery capacity may decrease after a long period of operation, thereby causing the inability to complete the designed route design task, and the UAV cannot return home, or even the UAV falls, affecting the safety of the inspection scene. For example, in the inspection scene of rail transit vehicles in a vehicle depot, the one-way route is 140 meters, and the two-way route is nearly 300 meters. When the battery capacity of the UAV decreases or the battery is abnormal, the battery energy is 0, and the UAV may land on the pantograph, roof, or other sensitive positions of the vehicle, affecting the safe operation of the vehicle.

[0023] In order to avoid the above situation, the standby landing point in the target unmanned aerial vehicle inspection route can be deployed in the unmanned aerial vehicle management and control system of the indoor unmanned aerial vehicle according to the preset inspection route of the target unmanned aerial vehicle, so as to optimize the reliability of the unmanned aerial vehicle. As shown in Figure 2 As shown in FIG. 6, generally, the indoor unmanned aerial vehicle system is composed of three parts: indoor unmanned aerial vehicle, unmanned aerial vehicle nest control board (indoor unmanned aerial vehicle nest) and indoor unmanned aerial vehicle inspection system (inspection background). Among them, the indoor unmanned aerial vehicle is the main body of the flight function, which realizes the environmental perception of special position through different mounting. It includes machine vision, laser, thermal sensing and so on. Generally, it is a multi-rotor unmanned aerial vehicle, and the basic requirement is 4 rotors, and the enhanced requirement is 8 rotors. The indoor unmanned aerial vehicle is divided into a flight control subsystem (flight control board) and a management subsystem (control board). The flight control board is responsible for realizing the motor control signal PWM of the multi-rotor unmanned aerial vehicle and the flight control algorithm. The real-time performance of the system is the key indicator, and the RTOS operating system is generally used. The control board of the indoor unmanned aerial vehicle is mainly responsible for realizing indoor positioning, indoor flight according to the planned route, and executing various flight actions. The flight control board is controlled by the control board. The control board of the unmanned aerial vehicle is responsible for monitoring the battery capacity and analyzing the completion degree of the flight action, which belongs to the MCU microprocessor module of the unmanned aerial vehicle. Among them, the unmanned aerial vehicle nest control board is mainly responsible for realizing the issuance of control instructions, route issuance and real-time state reporting of the unmanned aerial vehicle, and monitoring whether the unmanned aerial vehicle has landed in the nest on time; the inspection background is mainly designed for multiple routes and their inspection actions, and is issued to the indoor unmanned aerial vehicle nest to realize the management of the unmanned aerial vehicle flight activity.

[0024] Generally, the area where the UAV performs the inspection task can be extensive and the environment is complex. Therefore, in actual application, in order to ensure that the target UAV can successfully perform the inspection task, a backup landing point in the inspection route of the target UAV can be deployed according to the preset inspection route of the target UAV. According to the preset inspection route, the backup landing point of the target UAV can be set, which can ensure that no matter where the target UAV is located on the route, if the target UAV has insufficient power or other emergency landing conditions, a safe landing site can be found within a reasonable distance. For example, when the target UAV is performing inspection in the wild, there may be no suitable flat area for the target UAV to land randomly. By planning the backup landing point closely related to the target UAV inspection route in advance, the target UAV can be prevented from crashing due to the inability to find a suitable landing site. Secondly, generally, the preset inspection route of the target UAV usually covers key areas or equipment, such as substations, power transmission lines, etc. If the target UAV has power problems in these important areas and cannot complete the task, it can land at the backup landing point near the preset inspection route in time, which can prevent the target UAV from causing damage to key 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, the backup landing point matches the preset inspection route, which helps to quickly maintain and take off the target UAV again for subsequent inspection tasks, reduces the task interruption time caused by unexpected situations, improves the efficiency of the inspection work, and ensures the continuity of the task. For example, when the target UAV lands at a backup point due to insufficient power, the staff can quickly replace the battery or perform a simple check to make it start from the current backup landing point as soon as possible to perform the remaining inspection task. In addition, some inspection environments may have complex factors such as strong wind, electromagnetic interference, etc. Even if the normal power consumption is planned, unexpected power consumption may occur. According to the preset inspection route of the target UAV, the backup landing point can be deployed, which can better cope with these unpredictable complex environmental challenges, so that the target UAV still has a safe landing option when facing unexpected situations.

[0025] The preset inspection route of the target UAV is generally set according to the inspection task to be performed by the target UAV and safety, and can determine where the target UAV takes off, how to fly and where to fly. The preset inspection route can include task information related to the inspection task, device identification information, core space path information, flight parameters and safety rules (such as control logic of the inspection route), load coordination control information (such as logic of the route performing the inspection task), environmental adaptation and redundancy information of the inspection route. Among them, the task information related to the inspection task can include the inspection task name, number, and execution time or time period, task responsible person and contact information. The device identification information can include the model of the target UAV bound to perform the inspection task, the body serial number, and the type and parameters of the inspection load carried by the target UAV. The core space path information can include key coordinate point data in the route, waypoint type information and path connection rules, such as information including latitude and longitude, relative height or absolute height, takeoff point or landing point, inspection point or transition point; among them, the path connection rule can define the flight trajectory type between each waypoint to ensure that the path is stable and meets the inspection requirements. The flight parameters can include flight state parameters, such as flight speed, waypoint dwell time and flight mode, wherein the flight speed is generally set according to the type of the inspection target; the waypoint dwell time mainly includes the dwell time at the "inspection point" to ensure that the load completes data collection; the flight mode is mainly to set the control mode of the UAV. The safety rules can include safety constraint rules, such as automatically avoiding the marked no-fly zone and restricted flight zone on the preset route, setting "automatic return" or "detouring turning point" if the route approaches the no-fly zone; setting the maximum and minimum flight heights of the route; other emergency rules include low power emergency (such as automatically interrupting the task and returning to the nearest takeoff point when the remaining power is less than 20%), signal loss emergency, and obstacle avoidance trigger emergency; the load coordination control information can include load trigger conditions, such as setting the timing of starting / stopping the load; such as trigger waypoint conditions can be set to automatically start the camera to take pictures or record when the UAV reaches the "inspection point", and the sensor detects; distance trigger condition can be to start the load when the UAV is less than a set value from the inspection target; time trigger can be set to start the load when the route executes to a specific time period. The load parameter adaptation can adjust the load parameters according to the route height and speed, for example, the higher the flight height, the larger the focal length needs to be (such as 100m height inspection, focal length set to 50mm to ensure clear target); the temperature measurement range of the infrared thermal imager is set to -20℃-200℃ when inspecting electrical equipment, and 0℃-80℃ when inspecting photovoltaic panels, to avoid exceeding the range and causing invalid data; the environmental adaptation and redundancy information of the inspection route can include meteorological adaptation parameters: the preset route needs to be associated with meteorological threshold values, such as setting a wind speed threshold, such as when the wind speed exceeds 8m / s, the route is automatically suspended; setting precipitation / visibility threshold, such as rain, heavy fog, etc., to prohibit the route from starting to prevent load failure or collision risk.The backup waypoint can be set in the key inspection area. If the main waypoint cannot be located due to signal interference, the UAV automatically switches to the backup waypoint to ensure that the inspection is not interrupted.

[0026] Therefore, in the process of deploying the backup landing point in the preset inspection route of the target UAV, the backup landing point distribution scheme of the target UAV can be determined according to the preset inspection route of the target UAV, so that the backup landing point in the inspection route of the target UAV can be deployed according to the backup landing point distribution scheme of the target UAV. A "safety redundancy system" can be constructed for the UAV inspection operation, which not only ensures the survival ability of the UAV under the risk of sudden failure, environmental mutation and the like, but also ensures the continuity and efficiency of the inspection task, reduces the risk of equipment loss, personnel safety and environmental interference, helps to avoid "unintended risks", ensures the continuity of the inspection task, reduces the "task interruption loss", reduces the task restart cost, avoids missing inspection of the key area, optimizes the efficiency of the inspection task, and reduces the labor and management cost.

[0027] In step S102, the remaining battery capacity of the target UAV is determined and recorded in real time according to the voltage of the battery of the target UAV.

[0028] Specifically, in order to ensure that the target UAV safely completes the inspection and smoothly reaches the backup point, and to avoid out-of-control crash, after designing the backup landing point for the target UAV, the remaining battery capacity of the target UAV is determined and recorded in real time according to the voltage of the battery of the target UAV. Designing a backup landing point for the target UAV provides a safety bottom option for the target UAV in case of sudden conditions, but having a backup point does not mean that the target UAV can safely land on the backup point. Whether the target UAV can reach the backup point depends on whether the remaining battery capacity of the target UAV can support the target UAV to fly from the current position to the backup landing point. The battery voltage is the most direct and easiest to monitor indicator that reflects the remaining capacity in real time. In practical applications, the battery of the UAV is mostly a lithium battery, and the voltage and remaining capacity have a clear corresponding relationship. For example, when the single-piece voltage of a lithium polymer battery is 3.7V, the capacity is about 50%, and when the voltage is below 3.2V, the capacity is close to zero. If the voltage of the battery of the UAV is not monitored in real time, and the remaining battery capacity is not calculated, the UAV may suddenly fail during inspection and need to land on the backup landing point. However, the actual remaining battery capacity of the target UAV may not be sufficient to support the flight distance to the backup landing point where the target UAV wants to land, resulting in a mid-air power failure and crash. In practice, battery aging may cause the voltage to be falsely high, and a sudden voltage drop may occur in a low-temperature environment. Therefore, if the voltage of the battery of the target UAV is not tracked in real time, the remaining battery capacity of the target UAV may be misjudged as sufficient, and the inspection task may continue to be performed, and the target UAV may run out of power before reaching the landing point where it wants to land.

[0029] Further, according to the voltage of the battery of the target UAV, the remaining power of the battery of the target UAV is determined and recorded in real time, which can also guide the flight decision in real time and avoid the risk of overdraft. The UAV inspection usually involves fixed routes, flight paths, fixed heights and speeds. In practice, the maximum flight distance / time corresponding to the remaining power of the UAV can be calculated in real time through voltage data, and compared with the distance from the current position to the standby point. If the remaining power of the UAV is greater than or equal to the sum of the flight consumption and the redundant power (usually 10%-15% is reserved to cope with sudden gusts and other additional consumption) of the standby point, the UAV can continue to complete the current inspection section according to the task requirements of the inspection, or smoothly transfer to the standby point in case of abnormality. If the remaining power of the UAV is less than the flight consumption of the UAV to the standby point, the priority decision may need to be triggered immediately: either interrupt the current inspection and return to the nearest standby point (rather than hard to complete the task), or call the ground personnel to assist in troubleshooting the battery problem (such as whether there is a cell failure causing voltage anomaly). For example, a certain power inspection UAV in flight found through voltage monitoring that the remaining power was only enough to support 90% of the distance from the current position to the standby point. At this time, the subsequent inspection route needs to be terminated immediately, and the nearest standby point is turned to for landing, avoiding insufficient power.

[0030] In practical applications, the standby landing point is not absolutely fixed. If the UAV needs to switch to the second standby point due to task adjustment (such as temporary increase of inspection points) or environmental changes (such as the standby point being blocked) during inspection, the real-time power data of the UAV needs to be used to determine whether the distance to the new standby point is within the range that can be supported by the remaining power of the UAV. If the second standby point is switched, does the UAV need to adjust the flight speed (such as reducing the speed to reduce power consumption)? If there is no real-time power data of the UAV, blindly switching the standby point may result in "double risk", deviating from the original plan and being unable to reach the new standby point due to insufficient power. Therefore, real-time recording of the power data of the UAV provides a basis for post-fault tracing and battery management. Real-time recording of the corresponding data of "voltage-remaining power-flight time / position" is a key link in the operation and maintenance of the UAV. If the UAV finally has power-related problems (such as crashing or failing to reach the standby point), the cause can be investigated through historical power data to determine whether it is a battery voltage monitoring error, battery aging (such as uneven cell voltage), or did the standby point design underestimate the flight consumption? By recording the voltage change curve (such as the voltage drop rate during each flight) over a long period of time, the battery degradation can be determined (such as a new battery with a gentle voltage drop and an aged battery with a steep voltage drop), and the aged battery can be replaced in time to avoid power misjudgment due to battery problems in subsequent flights. Based on the historical power data of the UAV, the layout of the standby points of the UAV can be optimized (such as adding standby points near the inspection section with faster power consumption), the inspection route can be adjusted (such as reducing the "long-distance no standby point" route section), and the overall inspection safety can be improved. Secondly, in the battery power monitoring of the UAV, voltage is the most mainstream real-time monitoring parameter, not the direct measurement of the remaining capacity (mAh). Because voltage can be collected in real time by the battery management system (BMS) (the sampling frequency can reach 1 / s), it can quickly reflect the change in power. The remaining capacity needs to be estimated by a multi-parameter algorithm (i.e. the "coulomb counting" principle) using voltage, current, temperature, etc. The calculation process has a certain delay and cannot meet the "second-level decision-making" needs of the UAV. And the flight environment of the UAV is complex (high temperature, vibration, electromagnetic interference), the voltage monitoring circuit has strong anti-interference ability, and the data stability is better than other parameters. When the battery voltage is lower than the "protection voltage" (such as the single piece of lithium polymer battery is lower than 3.0V), over-discharge protection will be triggered, and the power supply will be forcibly cut off. Real-time monitoring of voltage can provide early warning before triggering protection, avoiding permanent damage caused by over-discharge of the battery (over-discharge will shorten the service life of the battery, and even cause the battery to swell).

[0031] Step S103, according to the preset power consumption model of the UAV inspection action, analyzing the total battery capacity required by the target UAV to complete the inspection route and flight action to be executed.

[0032] Specifically, after real-time grasping the remaining battery capacity of the unmanned aerial vehicle, further analyzing the total battery capacity required for the to-be-executed inspection route and flight action is the key to solving whether the remaining battery capacity can match the task consumption. Only through dynamic comparison of the two, can the balance of safely completing the task and avoiding battery overdraft be truly achieved, especially for the complex task characteristics of unmanned aerial vehicle inspection (such as power, photovoltaic, industrial scene), which is the core guarantee to avoid power failure and task failure. Therefore, after real-time recording the battery capacity data of the unmanned aerial vehicle, the total battery capacity required for the target unmanned aerial vehicle to complete the to-be-executed inspection route and flight action can be analyzed according to the preset unmanned aerial vehicle inspection action battery consumption model, wherein the preset unmanned aerial vehicle inspection action battery consumption model can train the flight inspection data of the unmanned aerial vehicle as training samples, and train the total battery capacity required for completing the training inspection route and flight action included in the flight inspection data of the unmanned aerial vehicle as sample labels. In practice, the unmanned aerial vehicle inspection action battery consumption model can also be dynamically optimized according to external conditions, such as temperature and air pressure, to improve its accuracy.

[0033] The total battery capacity required for the to-be-executed inspection route (distance, height, environment) and flight action (climbing, hovering, high-speed cruising) is the lower limit of energy consumption required to complete the task. Among them, different flight actions require different flight times, and the flight time corresponding to each flight action can be as shown in Figure 3 The relationship between the two directly determines whether the task can be advanced. If only the remaining battery capacity is known, but the amount required to complete the task is not known, two typical risks may occur, such as the risk of misjudgment of sufficiency. In fact, the remaining battery capacity seems sufficient (such as 50%), but the to-be-executed route contains a large number of high-power consumption actions (such as continuous climbing, long-time hovering inspection), the actual consumption is far more than expected, which may lead to power consumption in the middle of the way. Like, it may be misjudged as a low-power consumption task due to the lack of knowledge of the actual power consumption of the task, and the inspection is interrupted in advance (such as the remaining battery capacity is sufficient to support the completion of the task and the standby point landing, but it is returned due to misjudgment), resulting in low task efficiency.

[0034] The UAV inspection is usually divided into tasks according to the flight section (such as flight section A: base station one→base station two→backup point one), before starting each to-be-executed flight section, the feasibility decision needs to be made through the comparison of the remaining power of the UAV and the required power of the to-be-executed flight section. Assuming that the total amount of battery required for the to-be-executed task is equal to the sum of the route energy consumption (distance x unit distance power consumption) and the flight action energy consumption (climbing height x unit height power consumption + hovering time x unit time power consumption) and the redundancy energy consumption (10%-15% reserved, to deal with sudden airflow and temporary adjustment), the decision standard can be as follows: if the remaining power is greater than or equal to the total amount of battery required for the to-be-executed task, the flight section can be normally started, and the remaining power after completion is also clear (such as remaining 50%, task requiring 30%, after completion remaining 20%, enough to return to the backup point). If the remaining power is less than the total amount of battery required for the to-be-executed task, it needs to be adjusted immediately, such as reducing the task range (such as reducing 1 inspection point, reducing the required power), or replacing the backup battery, or returning to the nearest backup point in priority, abandoning the flight section. Further, during the UAV inspection process, the actual energy consumption may exceed the initial estimate due to environmental changes or action deviations (such as prolonged hovering time), at which time the real-time remaining power and the remaining required power of the uncompleted task need to be combined for dynamic adjustment. The core premise of this dynamic adjustment is to clearly understand the total energy consumption demand of the to-be-executed task (including the uncompleted part), if not, it cannot be determined whether it can bear additional consumption, and can only be passive waiting for power alarm.

[0035] The core role of the backup landing point is the safety guarantee when the task fails or the power is insufficient, but completing the to-be-executed task and reaching the backup point are two consecutive links, and it needs to be ensured that the total energy consumption (i.e. the energy consumption required for the to-be-executed task and the energy consumption to the backup point) is less than or equal to the remaining power. If only the required power of the to-be-executed task is analyzed, but the energy consumption to the backup point after completion is ignored, there may be a risk that the task is completed, but there is no power to reach the backup point. For example, the UAV to-be-executed task requires 30% power consumption, and the terminal to the required landing backup point requires 15% power consumption, a total of 45% power consumption. If the remaining power is 40%, only looking at the task required 30% will be enough, but after completing the task, only 10% is left, which cannot reach the backup point; only by analyzing the total required 45%, can the task be abandoned in advance, and the backup point is returned in priority. Further, in the circuit related inspection of the UAV, the high power consumption action of the to-be-executed task accounts for a high proportion, if the total amount of battery required for the target UAV to complete the to-be-executed inspection route and flight action is not analyzed, it is easy to lose control of energy consumption.

[0036] In practice, the mapping relationship between the power required for the target UAV to complete the to-be-executed inspection route and flight action and the total capacity of the battery of the target UAV can be determined first. The mapping relationship between the power required for the target UAV to complete the to-be-executed inspection route and flight action and the total capacity of the battery of the target UAV is as follows: ; wherein represents the corresponding unit flight time milliampere-hour energy consumption, such as the milliampere-hour energy consumed by hovering action; represents hovering; represents flying at a speed of 0.5 m / s; represents flying at a speed of 1 m / s; represents homing action; represents rotating action; represents the time duration of the corresponding flight behavior; represents the corresponding weighting coefficient under different temperature conditions, altitude conditions, less than 1; battery capacity required for the target UAV to complete the to-be-executed inspection route and flight action.

[0037] In particular, in practice, when mapping between the two, for the intermediate state parameters, an interpolation method can be used to supplement the data.

[0038] Step S104, according to the total battery capacity required for the target UAV to complete the to-be-executed inspection route and flight action and the remaining battery capacity of the target UAV, it is judged in real time whether the current target UAV needs to be prepared to land.

[0039] Specifically, after analyzing the total amount of battery required for the UAV to complete the to-be-executed inspection task and grasping the real-time remaining battery, it is still necessary to determine whether the UAV needs to be prepared for landing based on the two core data in real time. Therefore, in order to build safety redundancy of the inspection task from the perspective of battery matching, avoid risks such as task failure, UAV disconnection or crash due to insufficient battery, and further determine whether the current target UAV needs to be prepared for landing according to the total amount of battery required for the target UAV to complete the to-be-executed inspection route and flight action and the remaining battery of the target UAV. It is also helpful to verify whether the remaining battery can support the complete task and safe landing, and to eliminate the risk of battery gap. The inspection task of the UAV is not just completing the route, but also needs to meet the two core requirements of "completing the to-be-executed inspection action" and "safely reaching the standby landing point after the task is completed (or in case of an emergency)". Knowing only the remaining battery and the total amount of battery required for the task cannot directly determine the matching relationship between the two, and it is necessary to verify whether there is a battery gap by real-time comparison and calculation. If the remaining battery is greater than or equal to the sum of the total amount of battery required for completing the inspection task and the standby landing redundancy (the standby landing redundancy refers to the flight power consumption from the current task position to the standby landing point, including the additional consumption of take-off, hovering, landing and other actions), it means that the battery is sufficient to support safe landing after the task is completed, and there is no need to trigger the landing, and the inspection can continue. If the remaining battery is less than the sum of the total amount of battery required for completing the inspection task and the standby landing redundancy, it means that even if the standby landing is prioritized, the existing battery may not be able to support the UAV to reach the standby landing point (for example, the battery is depleted halfway), or it will lose the ability to land after forcibly completing part of the task. At this time, the standby landing must be triggered in real time to avoid the UAV out of control.

[0040] In actual inspection, the battery consumption of the UAV is not completely fixed according to the total amount of the preset analysis, but is affected by the real-time environment and flight state changes, resulting in a deviation between the actual power consumption and the predicted value. For example, sudden strong wind or headwind will increase the motor load, resulting in power consumption far exceeding the preset value; if additional hovering or flying around (such as avoiding obstacles) is required, additional power consumption will be increased; in low temperature environment, the battery capacity will temporarily decrease, and the actual remaining power may be lower than the theoretical value converted by voltage. At this time, real-time judgment of whether the UAV needs to be prepared for landing helps to match the real remaining power of the UAV with the dynamically adjusted total power consumption of the inspection task and the prepared landing. For example, the remaining power is just enough to support the UAV to complete the inspection task and the prepared landing, but the actual power consumption is accelerated due to sudden headwind. If real-time rejudgment is not performed, the task may soon run out of power. Through real-time comparison, it can be found that the remaining power is not enough to cover the dynamically increased total power consumption, so the prepared landing is triggered in advance to avoid risks. It helps to ensure the flexibility of task decision-making and balance the task completion degree and equipment safety. For example, in the inspection task, the safety of the UAV needs to be ensured after completing the preset route, but it is not necessary to land immediately when the power is tight. Instead, flexible decisions need to be made based on real-time power comparison. For example, if the remaining power of the UAV is only slightly lower than the total power consumption required to complete the inspection task and the prepared landing, but the current position is close to the inspection end point (only one target point is left), it can be determined that the last target point is completed first and then the landing. At this time, the total power consumption of completing the last inspection target and the prepared landing needs to be recalculated to confirm that the remaining power is sufficient. If the remaining power is much lower than the total power consumption required to complete the inspection task and the prepared landing, and the current position is close to the prepared landing point, the task needs to be interrupted immediately to avoid power depletion due to hesitation. This real-time judgment provides a basis for dynamic decision-making, avoiding frequent task interruption due to excessive conservatism (not fully utilizing power) and eliminating equipment loss due to excessive aggression (ignoring power gap), ultimately achieving the goal of maximizing task completion degree under the premise of safety.

[0041] Therefore, if it is determined that the target UAV needs to be prepared for landing, step S105 can be performed.

[0042] In step S105, the related alarm parameters of the target UAV are reported, and the target UAV is prepared for landing according to the prepared landing point of the preset inspection route of the target UAV.

[0043] Specifically, after determining that the target UAV needs to be landed, in order to ensure that the target UAV landing process is safe, controllable, traceable, and cooperative, the related alarm parameters of the target UAV can be reported, and the target UAV is landed according to the standby landing point of the preset inspection route of the target UAV. Among them, the reported alarm parameters can provide key basis for landing cooperation and risk tracing. UAV landing is not an isolated action, especially in professional inspection scenarios, which usually involves multi-role cooperation such as ground command center, inspection team, airspace management, etc. The reported alarm parameters can clearly convey the reasons, status and risks of the landing to the relevant parties, avoid coordination confusion or secondary risks caused by information gaps, and clearly determine the reasons for the landing to avoid misjudgment and invalid intervention.

[0044] In practice, the alarm parameters can include core data triggering the landing, for example. The alarm parameters can be an array, and the main title can be UAV landing start alarm, including information such as landing point information, flight execution completion degree, remaining power information of the UAV, and flight report abnormal points (which will not trigger landing according to normal flight route planning). For example, the alarm parameters of the target UAV can include its battery remaining power (current voltage conversion value), task remaining power gap, environmental interference factors, flight report abnormal information, etc. Through these parameters, the ground command center can quickly confirm that the UAV landing is due to insufficient power, rather than equipment failure, thereby avoiding misstarting of the equipment failure emergency plan and focusing on the core task of guiding the landing.

[0045] The reported related alarm parameters of the target unmanned aerial vehicle can support ground coordination and ensure the safety of the standby landing environment. If there are temporary obstacles in the standby landing area, the ground team can arrive at the standby landing point in advance to clean up the environment and set warning signs based on the reported current position, estimated standby time, flight trajectory, and other parameters of the unmanned aerial vehicle, to avoid collisions when the unmanned aerial vehicle lands. At the same time, if the standby landing point involves airspace coordination, airspace management personnel can report in advance based on the standby flight path in the alarm parameters to ensure that the unmanned aerial vehicle complies with the airspace during the standby landing process. The reported related alarm parameters of the target unmanned aerial vehicle can also store risk data for subsequent review and optimization. The alarm parameters record the complete state when the standby landing is triggered, which can be reviewed after the event, for example, to determine whether there is a large deviation in the current task power consumption prediction, whether the battery voltage accurately reflects the remaining power, and whether the standby landing point is too far away, and to optimize the subsequent power calculation model, task planning logic, or standby landing site selection to reduce the recurrence of similar standby landing scenarios. Second, based on the standby landing at the pre-selected landing site, the standby landing action can be ensured to be safe, efficient, and predictable. The pre-selected landing site is the optimal landing area selected in advance based on the flight range of the unmanned aerial vehicle, terrain safety, and landing convenience during the planning of the inspection task. The selection of this site for standby landing instead of random landing is mainly to avoid the uncertainty risks of temporary site selection. For example, the pre-selected landing site has been risk-free through pre-survey during planning, such as no soft soil, no high-voltage cables or trees, and the ground flatness meets the standard. If the pre-selected landing site is abandoned and a random area is selected for landing, the unmanned aerial vehicle may be damaged by motor or body collision, or even cause more serious accidents such as battery fire due to factors such as gravel or water accumulation on the ground, hidden obstacles, etc. Second, the coordinates, altitude, and landing radius of the pre-selected landing site have been pre-recorded in the flight control system of the unmanned aerial vehicle. When the standby landing is triggered, the flight control system can directly call the pre-set flight path to quickly plan the optimal flight path from the current position to the standby landing site, reducing the risk of flight path deviation due to temporary path calculation errors. At the same time, the flight control system is more familiar with the landing height and hovering position of the pre-selected site, which can accurately perform the action of "low-altitude hovering, slow descent, and smooth landing" to avoid operation errors due to unfamiliarity with the terrain during random landing. The pre-selected landing site is usually selected in an area that is easy for the ground team to reach and has convenient transportation, so that the ground personnel can quickly find the unmanned aerial vehicle after standby landing to check the equipment status and replace the battery. If the task is not completed, the unmanned aerial vehicle can be re-launched based on this site for subsequent inspection. If a random area is selected for landing, the unmanned aerial vehicle may be difficult to locate, and the ground personnel may be difficult to reach, which not only increases the recovery cost but also may result in equipment loss or damage due to long-term exposure.

[0046] If only the alarm parameter is reported, and not according to the preset point, the ground team knows that the unmanned aerial vehicle needs to be prepared for landing, but cannot predict the landing position of the unmanned aerial vehicle, and it is difficult to support in advance. The unmanned aerial vehicle may be in danger due to temporary selection error, and the ground recovery has no clear target, resulting in loss of control after landing. If only the preset point is landed according to the alarm parameter, the unmanned aerial vehicle can land safely, but the ground command center does not know the "landing reason and current state", which may misjudge that the unmanned aerial vehicle is out of contact, start unnecessary search and rescue process, and also cannot save the landing data for subsequent optimization.

[0047] In the field of civil unmanned aerial vehicle inspection, relevant regulations clearly require that "when the unmanned aerial vehicle needs to be prepared for landing in an emergency, the state should be reported to the ground control station in time, and the preset emergency landing point should be selected preferentially". Reporting the alarm parameter is "fulfilling the obligation of state reporting", and landing according to the preset point is to comply with the emergency operation specification. If the two steps are not performed, not only the equipment may be lost, but also the regulatory requirements may be violated, resulting in regulatory penalties. At the same time, if the third party is damaged during the landing process, legal liability also needs to be borne.

[0048] From the above introduction, when the unmanned aerial vehicle is used to perform the inspection task, in order to ensure that the unmanned aerial vehicle can safely complete all the inspection tasks, the present application realizes effective guarantee for safe flight of the unmanned aerial vehicle through reasonable deployment of the standby landing point of the target unmanned aerial vehicle inspection route, and real-time monitoring and analysis of the battery capacity. When the unmanned aerial vehicle is insufficient in power to complete all the inspection tasks during the execution of the inspection task, the unmanned aerial vehicle can be safely prepared for landing at any time, avoiding the unmanned aerial vehicle crashing due to power consumption, reducing the risk of equipment damage, and protecting the safety of the unmanned aerial vehicle itself and the equipment in the inspection area. The battery capacity is determined and recorded in real time according to the battery voltage, and the required power is analyzed in combination with the to-be-executed inspection route and flight action. Considering that different flight actions consume different power, this accurate power analysis method can more accurately judge the power state of the unmanned aerial vehicle, and provide a scientific basis for whether to prepare for landing. According to the required power and the remaining power, it is judged in real time whether the unmanned aerial vehicle needs to be prepared for landing, and if so, the alarm parameter is reported, so as to timely formulate a landing strategy. This makes the unmanned aerial vehicle be able to respond to the insufficient power condition in time during flight, make a reasonable decision, and improve the reliability of task execution. Through accurate management of the power and real-time landing decision, the unmanned aerial vehicle can complete the inspection task as much as possible under the condition of power, and can safely land when the power is insufficient, effectively balancing the relationship between task execution and flight safety, and improving the execution efficiency and quality of the whole inspection task.

[0049] From the above introduction, the present application can determine the standby landing point distribution scheme of the target unmanned aerial vehicle according to the preset inspection route of the target unmanned aerial vehicle. Next, the process is introduced, which can include the following: Step S201, according to the preset inspection route of the target unmanned aerial vehicle, real-time construction of the target unmanned aerial vehicle inspection route corresponding map data.

[0050] Specifically, in order to break the information limitation of "only relying on preset route coordinates", through dynamic and accurate geographic environment information, the "safety, accessibility and effectiveness" of the backup landing point are provided with decision basis, and the unreasonable distribution caused by the lack of environmental information is avoided. When determining the backup landing point distribution scheme according to the preset inspection route of the target unmanned aerial vehicle, the map data corresponding to the inspection route of the target unmanned aerial vehicle can be constructed in real time according to the preset inspection route of the target unmanned aerial vehicle, which is helpful to solve the problem of "deviation between preset route and actual environment" and ensure the "spatial accuracy" of the distribution. In practice, the preset inspection route of the unmanned aerial vehicle is usually planned based on the preliminary basic map, but the recorded information is mostly "static coordinate information", which cannot cover the dynamic environmental changes that may occur after planning or the details of obstacles that are not identified in the early stage. For example, the area along the preset route may have added temporary buildings after planning, the dense growth of trees may cause the original open area to be blocked, and the ground may have pits / water accumulation, etc.; if the equipment landing point is directly distributed based on the preset route coordinates, it may mistakenly select the area that "shows as open land on coordinates, but actually has obstacles" as the landing point, which will greatly increase the risk of collision when the unmanned aerial vehicle emergency lands. Real-time construction of the map data corresponding to the route can dynamically update the "real space state" along the route, ensure that the distribution coordinates of the backup landing point match the actual geographic environment, and avoid the safety hazard of "disconnection between coordinates and real scene" from the root.

[0051] First, according to the preset inspection route of the target UAV, real-time map data corresponding to the inspection route of the target UAV is constructed, and "terrain and obstacle features" can also be extracted to screen "basic safety areas" that meet the landing conditions. The core requirement of the UAV backup landing point is "no fatal obstacle, gentle terrain, and sufficient buffer space", and these conditions must rely on fine map data feature extraction to determine. Through real-time map data, the "terrain slope", "obstacle type and height", and "ground material" along the route can be accurately identified. If there is a lack of real-time map data, relying only on the "macro path" of the preset route cannot determine which areas along the route meet the above safety conditions, which may lead to the situation that the point seems to be near the route but cannot meet the basic requirements for landing. First, according to the preset inspection route of the target UAV, real-time map data corresponding to the inspection route of the target UAV is constructed, and the "reachability of the UAV and the candidate landing point" can also be calculated to ensure that it can be "quickly reached" in an emergency. The core value of the backup landing point is that the UAV can safely reach the shortest time in the event of a sudden failure, and "reachability" depends on the spatial relationship between the route and the landing point in the real-time map data. It needs to be confirmed through real-time maps whether the "candidate landing point is within the emergency range of the UAV": combined with the current remaining power of the UAV, the flight speed, and the "shortest path distance from the route to the candidate landing point" in the real-time map, it is determined whether the landing point is within the "emergency radius" that can be reached; it needs to be confirmed whether the "relative position of the landing point and the route is convenient for maneuvering": for example, if the candidate landing point is directly below the route and there is no vertical obstacle, the UAV can directly dive and land; if the landing point is on the side of the route, it needs to be determined whether there is enough airspace for the UAV to turn. These calculations all need to be based on the "path topological relationship" and "elevation data" of the real-time map, and linear coordinates of the preset route cannot complete them.

[0052] First, according to the preset inspection route of the target UAV, real-time construction of the map data corresponding to the inspection route of the target UAV helps to plan the redundancy and coverage of multiple backup points and avoid single-point failure without emergency options. The backup landing point distribution needs to follow the redundancy principle, and multiple landing points need to be arranged along the route to ensure that if a landing point cannot be used due to an emergency, there are still other options. The rationality of redundant coverage depends on the spatial distribution analysis of the real-time map. Through the real-time map, the distance between adjacent backup points can be calculated; for example, according to the emergency endurance of the UAV, the distance is controlled within 1-2 kilometers (to ensure that the UAV can fly to the next landing point when the previous landing point fails), to avoid too far distance leading to power failure on the way, or too close distance causing resource waste; the real-time map can also identify the key coverage of the key nodes of the route: for example, when passing through weak signal areas and high-risk areas, backup points need to be arranged near these nodes, and the real-time map can accurately locate the geographical range of these key nodes to ensure that there is no emergency blind area in the key areas. In practice, SLAM technology (Simultaneous Localization and Mapping) can be used to construct indoor 3D point cloud maps or grid maps in real time, including but not limited to laser radar / LiDAR, visual SLAM.

[0053] Step S202, based on the constructed map data and the preset backup point labeling rule, the backup landing point of the target UAV in the process of executing the preset inspection route is labeled.

[0054] Specifically, in order to better solve the key transformation from "having available space to having accurate available landing points", through regular screening, definition and solidification, it is ensured that the marked landing points not only meet the geographical environment requirements, but also match the functional requirements, operation logic and safety standards of the emergency landing of the unmanned aerial vehicle, avoiding the problem of "there are potential areas in the map data, but they cannot be directly used as effective landing points". After the corresponding map data of the inspection route is constructed in real time, the landing points of the target unmanned aerial vehicle during the execution of the preset inspection route can be further marked based on the constructed map data and the preset landing point marking rules. In practice, the map data constructed in real time can only identify potential safe areas with flat terrain and no obvious obstacles, but these areas may not fully meet the quantitative standards for unmanned aerial vehicle landing. The preset landing point marking rules (usually rigid requirements formulated in combination with unmanned aerial vehicle models and task scenarios) are used as the screening basis to convert potential areas into effective landing points, avoiding emergency risks caused by qualified areas but substandard details. For example, the marking rules may specify that the minimum area of the landing point needs to be greater than or equal to 2 times the wingspan of the unmanned aerial vehicle, and there are no low obstacles with a height greater than 0.5 meters within 5 meters around the center point of the landing point. If not marked by rules, relying only on the macro safe areas of the map data may mistakenly consider areas with insufficient area and low obstacles as landing points. By checking each potential area in the map data through rules, areas with substandard details can be accurately excluded, ensuring that the marked landing points fully meet the quantitative safety standards for emergency landing of the unmanned aerial vehicle.

[0055] In addition, when the unmanned aerial vehicle encounters a sudden failure, the operator or the automatic driving system needs to quickly determine which landing point is most suitable for the current situation, which requires the landing point not only to be a geographic coordinate, but also to have functional attribute tags, and these tags need to be generated through marking rules combined with map data. For example, the marking rules may require marking the landing point type based on the ground material of the map data: "cement landing point (priority 1, suitable for all-weather landing, stable ground hardness)" "grass landing point (priority 2, prone to water accumulation on rainy days, note that it is only available on sunny days)" "hardened soil road landing point (priority 3, prone to dust in strong wind weather, note that it is not recommended when the wind speed is greater than 5 m / s)"; for example, combined with the surrounding facilities of the map data, auxiliary information is marked: "the landing point is 1.2 kilometers away from the nearest artificial maintenance point (note that it is suitable for manual recovery after failure)" "the landing point is located within the coverage of the signal tower (note that it is suitable for re-planning the route after signal recovery)". These functional attributes cannot be directly obtained from the map data, and must be "translated" from the map features (such as ground material, surrounding facilities) to decision-making attribute tags through marking rules, so that the landing point selection in emergency can be based on attributes rather than blindly selecting coordinates.

[0056] 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.

[0057] 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.

[0058] In the scene of marking the landing point for unmanned aerial vehicle (UAV) inspection, the marking method needs to be designed in combination with the "map data type (such as vector map and raster map)", "UAV operation logic (automatic / manual)", and "emergency scene demand (quick response / accurate positioning)". In order to make the marked information not only automatically recognized by the system, but also quickly interpreted by the human, the marking can include the manual dotting method to mark the landing point, or can be performed in three ways of "spatial identification dimension", "information presentation dimension", and "interactive subject dimension".

[0059] Among them, the classification marking according to the "spatial identification dimension" can clearly determine the geographical positioning and boundary of the landing point, which is to solve the problem of "where is the landing point and how large is the range". It needs to be based on the coordinate system of the map data (such as WGS84 and UTM coordinate system) to convert the spatial characteristics of the landing point into accurate visual identification, which is the basis for subsequent operations. The core area and safety boundary of the landing point can be clearly determined through "accurate coordinates and geometric figures". The "center point longitude and latitude" or "planar rectangular coordinates" of the landing point are first marked on the map, usually presented in combination with "red dot + coordinate value", as the target positioning point for UAV navigation. Then, based on the actual available range of the landing point, the boundary is marked on the map with "red solid line box (rectangle)" or "polygon (irregular area such as trapezoidal grassland)", filled with semi-transparent red (without blocking the map background), and the "boundary size" and "ground slope (such as less than or equal to 8°)" are also marked to clearly determine the "physical safety range" for UAV landing. In combination with the correlation between the landing point and the surrounding geographical features (such as flight route, obstacle, and landmark facility), the landing point can be quickly found by the UAV to avoid flight deviation. For example, the "blue dashed line" is used to connect the center point of the landing point and the nearest point of the inspection flight route, and the "distance from the flight route (such as 200m)" and "turning angle (such as turning 15° to the right from the flight route)" are marked to clearly determine the "shortest path direction from the current flight route to the landing point". The "red triangular warning symbol" can also be marked near the obstacles (such as trees and power poles) within 50m around the landing point, and the "obstacle height (such as 12m)" and "distance from the landing point (such as 35m)" are marked to remind the UAV to avoid the area during landing. If there is a landmark facility (such as signal tower and water well house) near the landing point, the "green arrow" can be used to point to the landmark, and the "landmark name (such as X signal tower)" and "relative distance (such as 80m northeast of the landing point)" are marked to assist the visual positioning during manual control.

[0060] The annotation according to the "information presentation dimension" can give the decision-making attribute information of the standby point, which solves the problem of "whether this standby point can be used, when it can be used, and how it can be used", and needs to convert "map features (such as ground material, surrounding environment)" into "visual attribute labels" through pre-standby point annotation rules to support emergency decision-making. Based on the annotation priority and application conditions of the "safety level and environmental adaptability" of the standby point, the operator or system can "lock the best option in 1 second" in an emergency. For example, different color circular labels are used to mark the priority (red = 1st (best), orange = 2nd, yellow = 3rd), which are pasted beside the center point of the standby point. For example, the standby point with "cement ground + no shelter + signal coverage" is marked as "red 1st", and the standby point with "grass + water accumulation in rainy days" is marked as "orange 2nd (remark: usable in sunny days)". The "gray small labels (with icons)" can be used to mark the restriction conditions, such as "disabled (with wind level icon) when wind speed > 5m / s", "disabled (with raindrop icon) in rainy days", "only available for manual recovery (with personnel icon)", which are directly pasted outside the bounding box and easy to see. For example, when the unmanned aerial vehicle suddenly has low power (only enough for 1 minute of flight), the system can automatically filter the standby points with "red 1st + no disabled conditions", without the need to analyze the environment one by one, thereby shortening the decision-making time. The "functional attributes (such as whether to support maintenance and signal recovery)" and "operation requirements (such as landing orientation and buffer range)" of the standby point can also be marked to ensure that the unmanned aerial vehicle can "safely land and be used". For example, the "blue function icon" is used to mark the core functions, such as "maintenance support (with wrench icon, remark: 1.2km from maintenance point)" and "signal recovery (with signal tower icon, remark: supports 4G / Beidou positioning)", which are concentrated on the right side of the standby point. The "black text box" can also be used to mark specific operation requirements, such as "recommended landing orientation: 350° (against the wind, with arrow icon)", "safe buffer radius: 8m (prohibit other devices from entering, with circle icon)", and "descent speed limit: ≤2m / s (with speedometer icon)", which are directly associated with the corresponding positions of the bounding box (such as the orientation annotation in front of the bounding box). When the unmanned aerial vehicle needs to land urgently due to sensor failure, the operator can preferentially select the "standby point with wrench icon" (convenient for subsequent maintenance) and control according to the "landing orientation 350°" guidance to avoid side flipping due to incorrect orientation.

[0061] The annotation according to the "interaction subject dimension" classification can adapt to both the "system automatic identification" and the "manual operation" scenarios, and is compatible with the different needs of the "unmanned aerial vehicle automatic driving system" and the "manual operation personnel". The system needs "machine-readable structured data", and the manual operation needs "intuitive and understandable visual identification", and the two need to be coordinated through the annotation method. For example, the machine-readable annotation can support the automatic decision of the automatic driving system. For example, all the information of the emergency landing point is converted into "structured data tags" and embedded in the map data, so that the flight control system of the unmanned aerial vehicle can automatically read, analyze and execute without human intervention. Specifically, structured information can be embedded in the coordinate data of the emergency landing point, and a "machine-recognizable two-dimensional code / AR marker" can be marked on the ground of the emergency landing point (the marker position needs to be planned in advance through map data). The unmanned aerial vehicle scans the marker through the camera before landing to automatically correct the coordinate deviation. For example, when the unmanned aerial vehicle suddenly fails in the "fully automatic driving mode", the flight control system can directly read the metadata tags, automatically select the emergency landing point with the highest priority, and correct the position combined with the machine vision identification to realize "automatic landing without human intervention". Manual readable annotation can also be performed to assist the operator to quickly judge. For example, the emergency landing point information can be presented through "intuitive graphics, text and color" to meet the human visual habit, so that the operator can "quickly understand by scanning once", which is suitable for "semi-automatic operation" or "emergency manual intervention" scenarios. For example, a "semi-transparent white information card" can be suspended beside the emergency landing point, which contains the title, core information and color warning mark, such as "green ring = available", "yellow ring = use with caution (such as current wind speed close to 5m / s)", "red ring = forbidden (such as sudden rain)", and the card can be directly fitted outside the boundary box of the emergency landing point. For example, when the flight control system of the unmanned aerial vehicle prompts "low power", but the automatic landing path is blocked by temporary obstacles, the operator can quickly scan the "green ring + level 1" emergency landing point on the map, select the point "closest to the flight path" in the information card, and manually control the landing.

[0062] No matter which annotation method, it is "to convert the 'original geographic information' of the map data into 'decision and operation information suitable for the emergency needs of the unmanned aerial vehicle' through the pre-emergency landing point annotation rule", so that the machine can automatically identify and execute, and the manual operation can quickly understand and judge, to achieve the goal of "precise positioning, quick screening and safe use of the emergency landing point in an emergency". In practice, multiple annotation methods are usually combined to form an "accurate in space, complete in information and man-machine collaborative" emergency landing point annotation system.

[0063] As can be known from the above introduction, the application can determine the backup landing point distribution scheme of the target unmanned aerial vehicle according to the preset patrol route of the target unmanned aerial vehicle, which helps to improve the safety of the system.

[0064] From the above introduction, the application can determine and record the remaining battery capacity of the target UAV in real time according to the voltage of the battery of the target UAV. Next, the process is introduced, which can include the following: In step S301, the temperature data of the target UAV in the flight environment corresponding to the execution of the preset inspection task is acquired in real time.

[0065] Specifically, the temperature will significantly affect the "voltage-capacity" correspondence of the battery. Therefore, in the process of determining the remaining capacity according to the voltage of the UAV battery, the flight environment temperature data needs to be acquired in real time first, which will directly determine the accuracy of the capacity calculation, and further guarantee the safety of the inspection task and the reliability of the decision of the emergency landing. For example, the lithium battery (such as lithium polymer battery, lithium ion battery) commonly used by the UAV is a temperature-sensitive energy storage device. The internal electrochemical reaction efficiency and internal resistance characteristics will change dramatically with the environment temperature, and these changes will directly break the static relationship of "a fixed voltage corresponding to a certain remaining capacity". For example, low temperature will inhibit the migration rate of lithium ions inside the battery, causing the electrochemical reaction to be blocked. At this time, even if the actual remaining capacity of the battery is high (such as 50%), the voltage output by the battery will also be "falsely reduced", showing a similar voltage value as the low capacity (such as 20%). If the capacity is calculated only according to the voltage, the battery capacity will be misjudged as being exhausted, which may cause the emergency landing to be triggered too early, or even the inspection task to be interrupted when the real capacity is sufficient. High temperature will accelerate the internal side reactions of the battery (such as electrolyte decomposition and electrode material aging), and at the same time, the internal resistance of the battery will be reduced. At this time, the "decreasing curve" of the battery voltage will be "flattened": even if the actual remaining capacity is already low (such as 15%), the voltage may still be maintained at a high level (close to the voltage value of 30% capacity). If the capacity is determined only according to the voltage, it will be misjudged as sufficient, which may cause the UAV to lose control and crash due to the real capacity being exhausted during the inspection.

[0066] Secondly, the temperature affects the "actual available capacity" of the battery. In the actual calculation process, the capacity calculation reference needs to be corrected. The "nominal capacity" of the battery of the UAV is a test value under standard temperature environment, while in actual flight, the temperature will directly change the "actual available capacity" of the battery. For example, at low temperature, the "actual available capacity" of the battery will be significantly reduced; at high temperature, although the short-term available capacity may increase slightly, but in the long term, the capacity will be permanently attenuated due to side reactions, and if the effect of high temperature on the "safety capacity threshold" is not considered in the capacity calculation during flight, the safety risk will be increased.

[0067] The UAV inspection task is usually performed in a complex environment. The remaining battery capacity is the core basis for determining whether the current task segment can be completed and whether emergency landing is needed. If the battery capacity is misjudged as low due to low temperature, the emergency landing is triggered too early, which may cause the interruption of the inspection route, the decrease of the task efficiency, and the increase of the risk of take-off and landing of the UAV in complex terrain due to frequent emergency landing. If the battery capacity is misjudged as sufficient due to high temperature, the emergency landing is not timely, which may directly cause the UAV to lose contact and crash, resulting in equipment loss and even secondary accidents. Real-time acquisition of temperature data and correction of battery capacity calculation are ultimately to provide accurate and reliable battery capacity basis for task endurance judgment and emergency landing timing selection in the inspection process, to balance the task efficiency and flight safety, and to avoid various risks caused by battery capacity misjudgment.

[0068] In step S302, a corresponding discharge curve relationship between the battery of the target UAV and the temperature of the flight environment is determined according to the temperature data of the target UAV in the flight environment corresponding to the preset inspection task and the battery data of the target UAV.

[0069] Specifically, in order to establish a dynamic correlation model of "temperature-power-voltage-flight state", the isolated temperature and voltage data are converted into "battery energy consumption law" which can directly guide flight decision, fundamentally solving the problem that "static parameters cannot match dynamic flight requirements". After obtaining the flight environment temperature data and the unmanned aerial vehicle battery data, the corresponding discharge curve relationship between the battery of the target unmanned aerial vehicle and the flight environment temperature can be determined according to the temperature data in the flight environment corresponding to the preset inspection task of the target unmanned aerial vehicle and the battery data of the target unmanned aerial vehicle. The discharge curve is a "dynamic correlation carrier" of "temperature-power-voltage", solving the limitation of static calculation. The temperature data obtained in advance is to correct the static corresponding relationship of "voltage-power" (for example, low voltage at low temperature does not mean low power); but the unmanned aerial vehicle is not "static power consumption" during inspection, the flight speed (cruise / hover), load (carrying camera / sensor), flight attitude (climbing / descending) will change the discharge current of the battery in real time, and the discharge current will further interact with the temperature, resulting in dynamic changes in the "voltage-power" relationship with the flight state. At this time, the "discharge curve corresponding to the temperature" can convert this "multi-variable interaction" into a visual and calculable law. Among them, the discharge curve mainly takes "remaining power (SOC)" as the horizontal axis and "voltage" as the vertical axis, and labels the voltage change trend under "different temperatures" and "different discharge currents" (for example, at the same temperature, the voltage drop slope of hovering (low current) and climbing (high current) is different). For example, at 25°C and low current (cruise), the battery discharges from 100% power to 20%, and the voltage drops from 4.2V to 3.7V. But at -10°C and high current (climbing), the same discharge from 100% to 20% will make the voltage drop from 4.1V to 3.5V quickly. If only using the static "temperature-voltage" corresponding table, the voltage change caused by "current difference" cannot be distinguished, and the discharge curve can integrate "temperature + current + power" three elements, so that the power calculation is upgraded from "static estimation" to "dynamic accurate calculation".

[0070] The corresponding discharge curve relationship between the battery of the target UAV and the flight environment temperature can also adapt to the "dynamic power consumption scene" of the inspection task, ensuring that the endurance judgment is in line with the actual situation. The power consumption mode of the UAV inspection is not constant, and the difference in discharge current in different scenarios will directly affect the "actual endurance capability" of the battery, and the temperature will amplify this difference. Only through the discharge curve can the influence of "temperature + dynamic power consumption" be converted into quantifiable endurance data; for example, it is known that a certain inspection section needs to last for 30 minutes, of which 10 minutes is hovering (discharge current 5A) and 20 minutes is cruising (discharge current 3A). If the environmental temperature is 25℃, the "5A discharge for 10 minutes + 3A discharge for 20 minutes" will consume 35% of the power, and if the current remaining power is 50%, it is determined that the task can be completed. If the environmental temperature is -5℃, through the discharge curve corresponding to -5℃, it is found that "the voltage drops faster under the same current", and the same power consumption mode will consume 48% of the power, and if the current remaining power is 50%, it is determined that a landing should be made in priority to avoid insufficient power. Without the discharge curve, relying only on the "static power consumption ratio corrected by temperature" cannot predict the endurance in combination with the "dynamic power consumption rhythm" of the inspection task, which may lead to the misjudgment of "theoretical power sufficient but actual power consumption too fast". Secondly, the route often needs to be adjusted temporarily during inspection (such as bypassing obstacles), at which time the discharge current will suddenly increase. By matching the discharge curve of the current temperature in real time, it can be quickly calculated how much the power consumption rate will increase after the current increases, and then the subsequent flight plan can be adjusted (such as shortening the circumnavigation distance or planning a landing point in advance), to avoid the loss of control of the power due to "sudden change in power consumption rate". Further, the "safe discharge threshold" of lithium batteries (such as the minimum safe voltage and the maximum allowable discharge current) is not a fixed value, but varies with temperature. For example, the minimum safe voltage is 3.0V at 25℃, and needs to be increased to 3.3V at -10℃ (otherwise lithium dendrites will be precipitated, damaging the battery), and the maximum allowable discharge current needs to be reduced from 10A to 8A at 45℃ (otherwise the electrolyte will be decomposed, causing bulging). The "discharge curve corresponding to the temperature" is the basis for defining the "safe discharge boundary". The "lower limit of voltage" of the curve marks the "safe cut-off voltage" at different temperatures: for example, on the discharge curve, the voltage of 20% power at -10℃ is 3.3V, at which time the landing should be triggered, rather than following the standard of 3.0V for 20% power at 25℃, to avoid over-discharge at low temperature; the "current tolerance zone" of the curve marks the "maximum safe discharge current" at different temperatures: for example, at 45℃, if the discharge current exceeds 8A, the curve will show a "voltage drop", indicating that the battery has exceeded the safe load, at which time the flight attitude should be automatically limited to avoid over-current at high temperature. Without the discharge curve, relying only on temperature data cannot accurately define the "safe discharge range at the current temperature", which may lead to the risk of "apparently sufficient power but close to the safety threshold", and further cause battery failure or flight accidents.

[0071] Step S303, real-time monitoring of the battery voltage of the target UAV.

[0072] Specifically, the discharge curve is a "regularity-based prediction model", and the real-time voltage is a "dynamic feedback signal reflecting the current real state of the battery". Only through continuous comparison and correction of the real-time voltage and the discharge curve can the problem of "model prediction deviation from the actual state" be solved, ensuring the accuracy of the battery remaining capacity calculation and the timeliness of the flight safety decision. Based on the construction of the "battery-temperature corresponding discharge curve", it is still necessary to monitor the battery voltage of the target UAV in real time, so the battery voltage of the target UAV can be monitored in real time. The real-time voltage is a "dynamic calibration source" for the "discharge curve model", solving the cumulative error of the power calculation. In the actual flight of the UAV, the individual differences of the battery and the fluctuation of the flight working condition will cause the deviation between the "actual discharge process" and the "curve prediction", and the real-time monitoring of the battery voltage of the target UAV helps to correct the deviation. The UAV inspection is a "dynamic process lasting for several hours", if the voltage is not monitored in real time, the prediction error of the discharge curve will accumulate with the flight time (for example, 1% error every 10 minutes, 6% error after 1 hour). For tasks with tight endurance (such as returning when the remaining capacity is only 10%), such cumulative errors may directly lead to "power depletion crash", and the continuous calibration of the real-time voltage can control the error within 1%, ensuring the reliability of the power calculation. Moreover, the real-time voltage of the UAV is the "first early warning signal" to capture battery sudden abnormalities, which helps to avoid safety risks. The discharge curve can only predict the "discharge law under normal working conditions", but sudden battery failures and external abnormal loads may occur during UAV flight. The first "monitorable signal" of this sudden situation is the abnormal fluctuation of the voltage, which often occurs "outside the normal range predicted by the discharge curve" and must be captured in time through real-time voltage monitoring. For example, common "voltage abnormality warning scenarios" include voltage drop and voltage stagnation (cell imbalance), therefore, real-time detection of voltage is an "immediate feedback" for "dynamic adaptation to changes in flight scenarios", ensuring the timeliness of endurance decision-making. In UAV inspection, the flight scenario will change frequently (such as switching from cruising to hovering for photography, and from level flight to climbing for obstacle avoidance), which will cause instantaneous fluctuations in the discharge current. Although the discharge curve can predict the "voltage change law under different currents", the "specific timing and amplitude of current fluctuations" occur in real time, and only through real-time voltage monitoring can the current scene discharge state be quickly matched and endurance decision-making be dynamically adjusted.

[0073] Step S304, determining and recording the remaining capacity of the battery of the target UAV in real time according to the voltage of the battery of the target UAV and comparing the corresponding discharge curve between the battery of the target UAV and the flight environment temperature.

[0074] Specifically, the battery voltage itself is a "isolated numerical signal", which cannot be directly equivalent to the remaining power; and the discharge curve is a "correlation model of voltage, temperature and power", only through the real-time matching of voltage and curve, can the abstract voltage value be converted into concrete and usable "remaining power", providing the core basis for the endurance decision of unmanned aerial vehicle inspection. Therefore, on the basis of real-time monitoring of battery voltage and construction of "battery-temperature corresponding discharge curve", it is still necessary to determine and record the remaining power of the target unmanned aerial vehicle battery in real time according to the voltage of the target unmanned aerial vehicle battery and the corresponding discharge curve between the target unmanned aerial vehicle battery and the flight environment temperature, so as to solve the "multi-value" contradiction of "voltage does not equal power" and avoid power misjudgment. Generally speaking, the voltage and remaining power of lithium battery are not "one-to-one corresponding" relationship, but there is a multi-value contradiction that "the same voltage corresponds to multiple power, and the same power corresponds to multiple voltage". The root cause of this contradiction is the influence of "temperature" and "discharge condition", and the essence of the discharge curve is a "calibration tool to eliminate this contradiction". If only relying on real-time voltage to directly judge the power, it will lead to serious misjudgment, and even cause flight accidents. The electrochemical characteristics of lithium battery are extremely sensitive to temperature: low temperature will cause the activity of electrolyte to decrease, and the voltage will be lower under the same power; high temperature will accelerate the chemical reaction, and the voltage will be higher under the same power, therefore, under the same voltage, lithium battery at different temperatures may correspond to completely different power. The "isolated voltage" without temperature is meaningless, if the voltage is directly used to judge the power without "voltage comparison with discharge curve", it may appear that "misjudgment of sufficient power under low temperature (actually only 20%) leads to crash", or "misjudgment of power shortage under normal temperature (actually 50%) leads to task interruption". Through the calibration of discharge curve, the "voltage-power relationship" under the current temperature can be accurately matched, and the power judgment error can be reduced from ±10% to within ±1%. Furthermore, when the unmanned aerial vehicle performs the inspection task, the flight condition is not fixed, and the discharge current under different conditions differs greatly, and the change of current will directly lead to "voltage fluctuation under the same power", further exacerbating the "voltage-power" correspondence contradiction. At this time, the discharge curve not only contains the "temperature dimension", but also has the "voltage-power law under different currents (conditions)", through the comparison of real-time voltage and curve, the change of condition can be dynamically adapted, and the accuracy of power calculation can be ensured. It can provide "executable quantitative basis" for "endurance decision and safety control". One of the core needs of unmanned aerial vehicle inspection is to "ensure that the task is completed before the power is exhausted and the unmanned aerial vehicle returns safely", and "remaining power" is the only quantitative standard for this decision. If only the voltage is monitored, it cannot give specific judgment on "how long it can fly" and "whether it needs to be landed in advance"; only through "voltage comparison with discharge curve" can "remaining power percentage" be obtained, so that the abstract voltage signal can be converted into "executable operation instruction", supporting the safety control of the entire flight task.

[0075] The real-time voltage of the target UAV is the original signal (input) of the "current state of the battery", but the signal itself has no clear meaning; the discharge curve corresponding to the battery of the target UAV and the flight environment temperature is the correlation model (calibration tool) of "temperature, current, voltage, and power" that is responsible for converting the original signal into effective information; the remaining power of the battery of the target UAV is the "calibrated quantitative result" (output) that directly supports the task decision and safety control of the UAV. The relationship among the three is a complete closed loop of "signal input → model calibration → decision output", if only the voltage is monitored, the power cannot be judged, if only the curve is relied on, the real state of the battery cannot be reflected, only by comparing the real-time voltage with the discharge curve, the accurate and available remaining power can be obtained, and the UAV inspection task can be ensured to be "accurate, stable, and returnable".

[0076] Therefore, the application can determine and record the remaining power of the battery of the target UAV in real time according to the voltage of the battery of the target UAV, which helps to grasp the battery condition of the UAV in time and make decisions in time.

[0077] As can be known from the above introduction, the application can determine whether the current target UAV needs to be prepared for landing in real time according to the total amount of the battery required by the target UAV to complete the to-be-executed inspection route and flight action and the remaining power of the battery of the target UAV. Next, the process will be introduced, which can include the following: Step S401, determining the total amount of the battery of the target UAV.

[0078] Specifically, in the decision of whether the UAV needs to be prepared for landing, determining the "total amount of the battery required to complete the to-be-executed inspection task" is the core premise. Only after knowing "how much power is required to complete the task", can the "current remaining power" be compared, and then it can be judged whether the power is sufficient or whether it needs to be prepared for landing. Determining the total amount of the battery of the target UAV can quantify the "task power consumption demand" and avoid "power judgment without basis". If the "total amount of the battery required to complete the current task" is not determined first, only knowing the "remaining power" is meaningless, and it cannot be judged whether it can support subsequent tasks.

[0079] Step S402, determining the battery capacity required by the target UAV to complete the flight action corresponding to the current unfinished inspection route.

[0080] Specifically, in the decision of whether the UAV needs to make an emergency landing, in order to avoid the misjudgment caused by the confusion between the total task power consumption and the current remaining task power consumption, and to ensure that the emergency landing decision is more accurate and more in line with the real-time task progress, the battery capacity required by the target UAV to complete the flight action corresponding to the current unfinished inspection route can be determined to focus on the accurate power consumption demand of the current remaining task. The inspection task of the UAV is usually "executed in segments", and there is a significant difference between the total task battery capacity and the battery capacity required for the current unfinished task. The total task power consumption is a "global goal", while the current unfinished task power consumption is a "local demand to be solved", and only by focusing on the latter can the misjudgment caused by "measuring local progress with global standards" be avoided, and the decision can be more in line with the real-time task state. The flight action of the UAV inspection is not a "uniform speed power consumption extreme value", and the flight actions of different sections (such as climbing, descending, hovering, and uniform speed flying) have great differences in power consumption, and these differences may be concentrated in the "unfinished route". For example: the completed 6km route is mostly "flat ground uniform speed flying" (10W of power consumption per unit time), while the unfinished 4km route contains "2 times of climbing to 50 meters height + 3 times of hovering detection" (the power consumption per unit time during climbing / hovering reaches 25W), at this time, the battery capacity required by the flight action of the unfinished route will be much higher than the power consumption of the same distance of the completed route (the unfinished 4km may require 25% capacity, while the completed 6km only consumes 20% capacity); if the power consumption of the flight action of the unfinished route is not calculated separately, and the remaining 4km requires 32% capacity according to the "total task average power consumption" (such as 10km consumes 80%, i.e. 1km consumes 8%), there will be a deviation from the actual required 25% capacity, which may lead to "misjudgment of needing to make an emergency landing" or "misjudgment of sufficient power". Therefore, the power consumption of the flight action of the unfinished route is determined separately in order to accurately match the "action characteristics" of this section, avoid the "average estimation" that hides the local power consumption difference, and ensure the accuracy of the power judgment.

[0081] In step S403, whether the ratio between the difference between the current battery remaining capacity of the target UAV and the battery capacity required by the target UAV to complete the flight action corresponding to the current unfinished inspection route and the total battery capacity of the target UAV is less than a preset battery warning threshold is determined according to the current battery remaining capacity of the target UAV.

[0082] Specifically, in the unmanned aerial vehicle emergency landing decision, in order to convert the "power gap / redundancy" into a "standardized, quantifiable safety warning index", avoid misjudgment due to the limitation of "absolute power difference", and at the same time ensure the safety, consistency and flexibility of the decision. According to the current battery remaining capacity of the target unmanned aerial vehicle, it is judged whether the ratio between the difference between the current battery remaining capacity of the target unmanned aerial vehicle and the battery capacity required for the target unmanned aerial vehicle to complete the corresponding flight action of the current unfinished inspection route and the total battery capacity of the target unmanned aerial vehicle is less than the preset battery warning threshold. Introducing the judgment logic of "whether the ratio between the difference between the current remaining power and the power required for the unfinished task and the total battery capacity is less than the preset warning threshold", using "relative ratio" instead of "absolute difference", solving the problem of "unified warning standard under different total battery capacity".

[0083] The total battery capacity of the unmanned aerial vehicle is not a fixed value. If only "absolute power difference" (such as "remaining power - power required for unfinished task") is used as a warning basis, there will be a problem that "the same difference has completely different safety meanings under different total battery capacity".

[0084] For example, example 1: the total battery capacity is 10000mAh (100%), the power required for the unfinished task is 3000mAh (30%), and the current remaining power is 2500mAh (25%). The absolute difference is: 2500mAh-3000mAh=-500mAh (gap 500mAh); the relative ratio is (-500mAh) / 10000mAh=-5% (gap accounts for 5% of the total capacity).

[0085] Example 2: the total battery capacity is 5000mAh (100%), the power required for the unfinished task is 1500mAh (30%), and the current remaining power is 1250mAh (25%). The absolute difference is 1250mAh-1500mAh=-250mAh (gap 250mAh); the relative ratio is (-250mAh) / 5000mAh=-5% (the gap also accounts for 5% of the total capacity).

[0086] If the preset alarm threshold is "-5%", both cases trigger an alarm - which conforms to the actual situation that "although the absolute gap is different, the 'battery gap percentage' is the same, and the safety risk level is consistent". Conversely, if only "the absolute difference is less than or equal to -500mAh" is used as the threshold, -250mAh in example 2 will be misjudged as "no risk", but in fact its "battery gap percentage" has reached -5%, and there is also the risk of being unable to complete the task. Therefore, by converting the absolute difference into a relative percentage through "the ratio of the difference to the total battery capacity Q", the warning standard can be freed from the restriction of "specific battery capacity", and a "unified safety criterion" for different drones and different battery configurations can be achieved. In the unmanned aerial vehicle inspection scene, different tasks have different safety requirements, and the "preset battery alarm threshold" can convert these "scenario-based safety requirements" into "executable quantitative rules". If the "ratio + preset threshold" logic is not used, the "current gap is dangerous" needs to be judged manually every time, which is not only inefficient, but also may lead to inconsistent safety standards due to differences in personnel experience. The "ratio less than preset threshold" judgment simplifies the complex "safety risk assessment" into a clear "yes / no" conclusion, ensuring that different operators and different task scenarios can follow a unified safety rule and reduce the risk of decision-making errors. Therefore, if the ratio of the difference between the current battery capacity of the target unmanned aerial vehicle and the battery capacity required for the target unmanned aerial vehicle to complete the corresponding flight action of the unfinished inspection route to the total battery capacity of the target unmanned aerial vehicle is less than the preset battery alarm threshold, step S405 is executed. Experience shows that the preset battery alarm threshold can be set to [10%-20%].

[0087] Step S405, determine that the target unmanned aerial vehicle needs to start a backup landing.

[0088] Specifically, if the ratio of the difference between the current battery capacity of the target unmanned aerial vehicle and the battery capacity required for the target unmanned aerial vehicle to complete the corresponding flight action of the unfinished inspection route to the total battery capacity of the target unmanned aerial vehicle is less than the preset battery alarm threshold, it means that the target unmanned aerial vehicle is out of power and cannot continue to perform the task, so a backup landing is needed, and it can be determined that the target unmanned aerial vehicle needs to start a backup landing.

[0089] In practice, the "minimum spatial distance" needs to be prioritized to build the "safest and most efficient emergency landing path" for the unmanned aerial vehicle in the alarm state, fundamentally reduce the probability of "risk expansion" such as "battery depletion, device failure, etc.", and ensure the efficiency of subsequent disposal after the interruption of the inspection task. After reporting the relevant alarm parameters of the target unmanned aerial vehicle, the present application can further determine at least one emergency landing point closest to the current position of the target unmanned aerial vehicle according to the current position of the target unmanned aerial vehicle and the corresponding emergency landing point layout scheme of the target unmanned aerial vehicle. One of the core goals of the unmanned aerial vehicle alarm is to "minimize the impact on the inspection task as much as possible under the premise of ensuring safety, while reducing the cost of subsequent rescue and device recovery", and the "closest emergency landing point" can optimize efficiency from "time, manpower, and resources" in three aspects, such as shortening the emergency landing flight time, the closest emergency landing point means "the shortest flight time", the unmanned aerial vehicle can land faster, avoiding the "task interruption window expansion" caused by long-time hovering and long-distance flight; after emergency landing, the operator needs to retrieve the unmanned aerial vehicle and troubleshoot the fault, the closest emergency landing point can minimize the "distance and time of the operator's round trip to the emergency landing point", for example, the closest emergency landing point is 5 kilometers away from the operator, and it takes 1 hour to complete the retrieval; the non-closest emergency landing point is 20 kilometers away, and it takes 3 hours to go back and forth, greatly increasing the labor cost; reducing equipment wear and tear, the unmanned aerial vehicle in the alarm state may have hidden faults (such as battery bulging and slight damage to the fuselage), if the emergency landing point is too far away, the equipment wear and tear may be aggravated due to shaking and vibration during flight; and the short path of the closest emergency landing point can reduce the "equipment wear and tear in an unstable state", reducing the subsequent maintenance cost.

[0090] Furthermore, the emergency landing point layout of the unmanned aerial vehicle is not "a single fixed point", but a "multi-point coverage network" designed according to the inspection scene (which may include temporary emergency landing points, fixed emergency landing points, emergency landing areas, etc.), but some emergency landing points may not be available due to "occupancy, environmental changes" in the actual scene, at this time, "determining the closest 'at least one' emergency landing point" can "filter the 'optimal and available' options in the layout network", avoiding the dilemma of "no emergency landing point available" leading to "no emergency landing point available", effectively dealing with unexpected situations such as "emergency landing point unavailable", for example, the emergency landing point layout of a certain inspection route includes A (closest, 1 km), B (second closest, 3 km), and C (farther, 5 km), after reporting the alarm, it is found that point A cannot be used due to "temporary vehicle parking", if only point A is determined in advance, it will fall into the crisis of "no emergency landing point"; if "at least one" (such as A and B) is determined in advance, it can immediately switch to point B, ensuring the continuity of emergency landing.

[0091] The unmanned aerial vehicle route control device provided by the present application is described below, and the unmanned aerial vehicle route control device described below can be correspondingly referred to the unmanned aerial vehicle route control method described above. Referring to 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.

[0092] 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 unmanned aerial vehicle route control device can 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 the embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3 and the communication bus 4 is at least one, and the processor 1, the communication interface 2 and the memory 3 complete the communication with each other through the communication bus 4. The processor 1 can be a central processing unit CPU, or an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement the embodiments of the present application, etc.; the memory 3 can include a high-speed RAM memory, and can also include a non-volatile memory, etc., for example, at least one disk memory; wherein the memory stores a program, and the processor can call the program stored in the memory, and the program is used to: implement each processing flow in the aforementioned terminal unmanned aerial vehicle route control scheme. The embodiments of the present application also provide a readable storage medium, which can store a program suitable for the processor to execute, and the program is used to: implement each processing flow in the aforementioned terminal unmanned aerial vehicle route control scheme. Finally, it should be noted that, in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the existence of other identical elements in the process, method, article or device including the element. The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other. The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications of the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments can be combined with each other. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to 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, The method comprises the following steps: deploying a backup landing point in the preset inspection route of the target UAV according to the preset inspection route of the target UAV; determining and recording the remaining battery capacity of the target UAV in real time according to the voltage of the battery of the target UAV; analyzing the total battery capacity required by the target UAV to complete the inspection route and flight action to be executed according to the preset UAV inspection action battery consumption model, wherein the preset UAV inspection action battery consumption model is trained by using the flight inspection data of the training UAV as the training sample, and the total battery capacity required by the training UAV to complete the training inspection route and flight action included in the flight inspection data of the training UAV is used as the sample label, and the preset UAV inspection action battery consumption model is obtained by training; determining in real time whether the target UAV needs to make an emergency landing according to the total battery capacity required by the target UAV to complete the inspection route and flight action to be executed and the remaining battery capacity of the target UAV; if it is determined that the target UAV needs to make an emergency landing, reporting the related alarm parameters of the target UAV, and making an emergency landing for the target UAV according to the backup landing point of the preset inspection route of the target UAV.

2. The method of claim 1, wherein, The method of deploying a backup landing point in the inspection route of the target UAV according to the preset inspection route of the target UAV comprises the following steps: determining the backup landing point arrangement scheme of the target UAV according to the preset inspection route of the target UAV; deploying the backup landing point in the inspection route of the target UAV according to the backup landing point arrangement scheme of the target UAV.

3. The method of claim 2, wherein, The method of determining the backup landing point arrangement scheme of the target UAV according to the preset inspection route of the target UAV comprises the following steps: constructing the map data corresponding to the inspection route of the target UAV in real time according to the preset inspection route of the target UAV; annotating the emergency landing point of the target UAV in the process of executing the preset inspection route based on the constructed map data and the preset emergency landing point annotation rule, wherein the selection of the emergency landing point is related to the scene corresponding to the preset inspection route, and the safety factor of the scene is used as the first selection element of the emergency landing point; the interval of each emergency landing point is set based on the on-site route corresponding to each scene.

4. The method of claim 1, wherein, The method of determining and recording the remaining battery capacity of the target UAV in real time according to the voltage of the battery of the target UAV comprises the following steps: obtaining the temperature data of the target UAV in the flight environment corresponding to the preset inspection task in real time; determining the corresponding discharge curve relationship between the battery of the target UAV and the flight environment temperature according to the temperature data of the target UAV in the flight environment corresponding to the preset inspection task and the battery data of the target UAV; monitoring the voltage of the battery of the target UAV in real time; determining and recording the remaining battery capacity of the target UAV in real time according to the voltage of the battery of the target UAV and the corresponding discharge curve between the battery of the target UAV and the flight environment temperature.

5. The method of claim 1, wherein, The battery total amount required by the target UAV to complete the to-be-executed inspection route and flight action and the remaining battery capacity of the target UAV are determined, and it is determined in real time whether the target UAV needs to make an emergency landing according to the battery total amount and the remaining battery capacity. The battery total amount of the target UAV is determined. The battery capacity required by the target UAV to complete the current unfinished inspection route corresponding to the flight action is determined. It is determined whether the ratio between the difference between the current remaining battery capacity of the target UAV and the battery capacity required by the target UAV to complete the current unfinished inspection route corresponding to the flight action and the battery total amount of the target UAV is less than a preset battery alarm threshold according to the current remaining battery capacity of the target UAV. If yes, it is determined that the target UAV needs to start an emergency landing.

6. The method of claim 1, wherein, The battery total amount required by the target UAV to complete the to-be-executed inspection route and flight action is analyzed according to the to-be-executed inspection route and flight action of the target UAV. A mapping relationship between the power required by the target UAV to complete the to-be-executed inspection route and flight action and the total capacity of the battery of the target UAV is determined according to the to-be-executed inspection route and flight action of the target UAV. The mapping relationship between the power required by the target UAV to complete the to-be-executed inspection route and flight action and the total capacity of the battery of the target UAV is as follows. ； wherein, represents the corresponding unit flight time milliampere-hour energy consumption consumption; represents the time duration of the corresponding flight behavior; represents the corresponding different temperature conditions, altitude conditions, weighting factors, less than 1; represents the battery capacity required for the target unmanned aerial vehicle to complete the inspection route to be executed and the flight action.

7. The method of claim 1, wherein, The target UAV is made to make an emergency landing according to the backup landing point of the preset inspection route of the target UAV. At least one target emergency landing point closest to the current position of the target UAV is determined according to the current position of the target UAV and the target emergency landing point layout scheme corresponding to the target UAV. The safety coefficients of the determined target emergency landing points are compared, and the target emergency landing point closest to the target UAV and having the highest safety coefficient is selected for the target UAV to make an emergency landing.

8. An unmanned aerial vehicle route control apparatus, characterized by, It includes: An emergency landing point deployment unit is configured to deploy a backup landing point in an inspection route of a target UAV according to a preset inspection route of the target UAV. An electric quantity determination unit is configured to determine and record a remaining battery capacity of the target UAV in real time according to a voltage of a battery of the target UAV. An analysis unit is configured to analyze a battery total amount required by a target UAV to complete a to-be-executed inspection route and flight action according to a preset UAV inspection action electric quantity consumption model, wherein the preset UAV inspection action electric quantity consumption model is trained by taking flight inspection data of a training UAV as a training sample and taking a battery total amount required by the training UAV to complete a training inspection route and flight action included in the flight inspection data of the training UAV as a sample label. A judgment unit is configured to determine in real time whether the target UAV needs to make an emergency landing according to the battery total amount required by the target UAV to complete the to-be-executed inspection route and flight action and the remaining battery capacity of the target UAV. The reporting unit is configured to report the related alarm parameters of the target UAV when the execution result of the judging unit is that the target UAV needs to make an emergency landing, and to make an emergency landing for the target UAV according to the backup landing point of the preset inspection route of the target UAV. 9.A UAV route control device, characterized by, The method comprises the following steps: One or more processors and a memory; the memory stores computer readable instructions, and the computer readable instructions are executed by the one or more processors to implement the steps of the UAV route control method in any one of claims 1 to 7.

10. A readable storage medium characterized by: The readable storage medium stores computer readable instructions, and the computer readable instructions are executed by one or more processors to make one or more processors implement the steps of the UAV route control method in any one of claims 1 to 7.

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