Unmanned aerial vehicle inspection route determination method, device, equipment and medium

Abstract

The invention provides an unmanned aerial vehicle inspection route determination method and device, equipment and a medium. The method for determining the inspection route of the unmanned aerial vehicle comprises the steps that a, basic geographic information, environment state data and task requirements of a target area are obtained, the geographic information of the target area is obtained through remote sensing data, the environment state data is collected in real time through a sensor, and the task requirements comprise a starting point and an ending point of an inspection route and a specific inspection task; according to the unmanned aerial vehicle inspection route determination method and device, the equipment and the medium, by introducing the environmental factor correction model and the adaptive optimization algorithm, the problem that dynamic environmental changes are not fully considered in traditional route planning is solved. The environment factor correction model can pre-process the path in real time according to external factors such as wind speed, air temperature, atmospheric pressure and the like, so that a more accurate environment assessment map is generated, and more comprehensive reference data is provided for path planning.

Description

Technical Field

[0001] The present invention relates to the field of drone inspection technology, and in particular to a method, device, equipment and medium for determining a drone inspection route. Background Art

[0002] The UAV inspection route determination system consists of a path planning module, a UAV control module, a data acquisition and processing module, a task management module, and a communication module. The path planning module generates the optimal inspection path through an algorithm based on geographic information, obstacles, weather and other data; the UAV control module is responsible for navigating the UAV according to the planned path and adjusting the flight speed and direction; the data acquisition module uses various sensors to collect the status data of the target area in real time and transmits it to the ground station for analysis; the task management module is responsible for real-time adjustments based on task priority and resource scheduling; the communication module ensures real-time communication between the UAV and the ground control station, and guarantees the stability of task coordination and data transmission.

[0003] The system also has some defects in practical applications. In complex environments, the existing path planning algorithm cannot fully consider all environmental factors, resulting in insufficient path optimization and affecting flight safety. The system has weak response capabilities to emergencies and lacks an effective adaptive adjustment mechanism, and may not be able to respond in a timely manner in a dynamically changing environment. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method, device, equipment and medium for determining a drone inspection route, which solves the problem that the path planning algorithm cannot fully consider all environmental factors, resulting in insufficient path optimization; the ability to respond to emergencies is weak and there is a lack of an effective adaptive adjustment mechanism.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for determining an unmanned aerial vehicle inspection route, comprising:

[0006] a. Obtain basic geographic information, environmental status data and task requirements of the target area. The geographic information of the target area is obtained through remote sensing data, and the environmental status data is collected in real time by sensors. The task requirements include the starting point and end point of the inspection route and the specific tasks of the inspection;

[0007] b. Use the environmental factor correction model to pre-process the target area and generate a preliminary environmental assessment map. The environmental factor correction model takes into account wind speed, temperature, and atmospheric pressure factors. The correction process is performed using the following formula:

[0008] C env (x)=α1·wind_speed(x)+α2·temperature(x)+α3·pressure(x)

[0009] Among them, C env (x) is the correction factor, windspeed(x), temperature(x), and pressure(x) are the wind speed, temperature, and air pressure of the target area at position x, respectively, and α1, α2, and α3 are the weight coefficients of environmental factors;

[0010] c. A path planning module based on an adaptive optimization algorithm is used to generate a preliminary inspection path. The path planning is optimized based on the preprocessed environmental assessment map and correction factors, using the following path optimization formula:

[0011]

[0012] Among them, P final is the final optimized inspection path, P i is the i-th path point in the path, Cost(P i ) is the cost function of the path point, reflecting the flight distance, time consumption and energy consumption factors, C env (P i ) is the path point P i Environmental correction factor at the location;

[0013] d. Dynamically adjust the path based on the drone’s real-time flight status data, including the drone’s current position, speed, flight altitude, and battery power, and correct the path through the following adaptive adjustment mechanisms:

[0014] P adj =P final +δP

[0015] Among them, P adj is the inspection path after adjustment, δP is the path adjustment amount generated by real-time status data, and the specific adjustment is based on battery power and power consumption factors;

[0016] e. During the inspection process, the drone sensor data is monitored and acquired in real time. The current path is dynamically corrected through the real-time collected environmental data. The following data feedback model is used:

[0017] P feedback =P adj ±δP feedback

[0018] Among them, δP feedback To adjust the path based on obstacle detection and flight status information, and feedback the path to ensure the real-time and safety of the inspection process;

[0019] f. Dynamically adjust the inspection route according to the type of emergency, such as obstacles, low battery, weather changes, etc., and adjust the route planning through the following event response mechanisms:

[0020]

[0021] Among them, Risk(P) is the risk assessment value at path P, which takes into account obstacles and weather changes to ensure the smooth execution of inspection tasks under emergencies;

[0022] g. Dynamically select task priorities and path optimization targets based on task requirements. Adjust the task priorities of the paths under the influence of battery power and task urgency, optimize the execution order of inspection tasks, and use the following priority adjustment model:

[0023] Priority(P)=α4·Urgency(P)+α5·Remaining_Power(P)

[0024] Among them, Urgency(P) is the urgency of the waypoint P to the task, RemainingPower(P) is the remaining power of the drone, and α4 and α5 are adjustment coefficients;

[0025] h. During the inspection process, adjust the path planning and scheduling based on real-time data, and optimize task allocation and path selection according to new environmental changes and drone status;

[0026] i. During the mission execution, generate mission reports and update the system in real time, feed back inspection results, path planning and environmental status to the ground control station, and generate real-time reports for subsequent analysis and mission adjustment;

[0027] j. Optimize the path of the remaining inspection tasks according to the remaining power of the drone, the inspection progress and the task requirements, use the power prediction model to predict the battery life, and adjust the subsequent path planning based on the prediction results to ensure the completion of the task;

[0028] k. Combine task status and environmental changes to generate a long-term task optimization model, and predict and optimize the path and resource scheduling in future inspection tasks by learning historical task data;

[0029] I. Globally optimize the system, combine machine learning and deep learning methods to optimize path planning and task scheduling, and automatically adjust path planning and task priorities through training with historical data to adapt to the changing inspection environment.

[0030] Preferably, the adaptive adjustment mechanism adopts the following steps: during the execution of the inspection task, the flight status of the UAV and the environmental changes in the target area are continuously monitored. Once an environmental change or system failure is detected, a new inspection path is quickly calculated through an adaptive adjustment algorithm. If the battery power is lower than the set threshold, the nearest charging point or return path is calculated, and the ground control station is notified for scheduling.

[0031] Preferably, the environmental status data is collected by the following sensors:

[0032] Wind speed sensor, used to obtain wind speed information in the flight area;

[0033] Temperature sensor, used to obtain temperature information of the flight area;

[0034] Air pressure sensor, used to obtain atmospheric pressure information of the flight area;

[0035] GPS and obstacle sensors to provide flight path and obstacle information.

[0036] Preferably, data collection during the execution of the inspection task includes the following steps: the camera and infrared sensor carried by the drone collect image data of the target area in real time, transmit the collected image data to the ground control station for real-time analysis, and generate an equipment health report.

[0037] Preferably, the objective function in the path optimization process is set in the following manner:

[0038] ObjectiveFunction=λ1·Safety+λ2·Efficiency+λ3·EnvironmentalAdaptation

[0039] Among them, λ1, λ2, and λ3 are the weight coefficients of safety, efficiency, and environmental adaptability, respectively.

[0040] Preferably, the environmental status data is updated in real time and reflected in the path planning and adjustment process, ensuring that the path has high dynamic adaptability during flight.

[0041] Preferably, the path planning algorithm performs two-way communication with a ground control station through the control system of the UAV, optimizes the inspection path in real time, and makes adjustments according to actual flight status and environmental conditions.

[0042] Preferably, the adaptive optimization algorithm adopts a combination of local search and global search to flexibly adjust the path in a dynamic environment to ensure that the path planning is always optimal during long-term flight.

[0043] A drone inspection device for a waste incineration power plant, comprising a memory and a processor; the memory is used to store a program;

[0044] The processor is used to execute the program to implement each step of the method for determining the inspection route of the drone

[0045] A storage medium stores a computer program, which, when executed by a processor, implements the various steps of the drone inspection method for a waste incineration power plant.

[0046] The present invention provides a method, device, equipment and medium for determining a drone inspection route. It has the following beneficial effects:

[0047] The method, device, equipment and medium for determining the inspection route of the UAV solve the problem that the traditional path planning fails to fully consider the dynamic environmental changes by introducing the environmental factor correction model and the adaptive optimization algorithm. The environmental factor correction model can pre-process the path according to external factors such as wind speed, temperature, and atmospheric pressure in real time, thereby generating a more accurate environmental assessment map, providing more comprehensive reference data for path planning. This method significantly improves the adaptability and reliability of the UAV inspection path, ensuring the smooth execution of the inspection task in complex environments.

[0048] The adaptive optimization algorithm adopted by the present invention is combined with real-time flight status data to dynamically adjust the path, so that the drone can respond to emergencies such as low battery, abnormal flight altitude, etc. By adjusting the path according to real-time information such as the drone's current position, speed, and battery level, mission interruption or reduced efficiency due to emergencies is effectively avoided. This path planning method with an adaptive adjustment mechanism greatly improves the flexibility and adaptability of the drone inspection system, and ensures the continuity and stability of long-term, high-load tasks. Therefore, this technical solution has strong innovation and practicality, and provides effective technical support for the development of drone inspection technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of the inspection route of the drone of the present invention;

[0050] Figure 2 This is a schematic diagram of the impact of environmental factors on path optimization in the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Embodiment 1

[0053] like Figure 1-2 As shown, an embodiment of the present invention provides a method, device, equipment and medium for determining a drone inspection route, including: a. obtaining basic geographic information, environmental status data and task requirements of a target area, wherein the geographic information of the target area is obtained through remote sensing data, and the environmental status data is collected in real time by sensors, and the task requirements include the starting point, end point and specific tasks of the inspection path.

[0054] b. Use the environmental factor correction model to pre-process the target area and generate a preliminary environmental assessment map. The environmental factor correction model takes into account wind speed, temperature, and atmospheric pressure factors. The correction process is performed using the following formula:

[0055] C env (x)=α1·wind_speed(x)+α2·temperature(x)+α3·pressure(x)

[0056] Among them, C env (x) is the correction factor, windspeed(x), temperature(x), and pressure(x) are the wind speed, temperature, and air pressure of the target area at position x, respectively, and α1, α2, and α3 are the weight coefficients of environmental factors.

[0057] c. A path planning module based on an adaptive optimization algorithm is used to generate a preliminary inspection path. The path planning is optimized based on the preprocessed environmental assessment map and correction factors, using the following path optimization formula:

[0058]

[0059] Among them, P final is the final optimized inspection path, P i is the i-th path point in the path, Cost(P i ) is the cost function of the path point, reflecting the flight distance, time consumption and energy consumption factors, C env (P i ) is the path point P i The environmental correction factor at the location where the path optimization objective function is set in the following way:

[0060] ObjectiveFunction=λ1·Safety+λ2·Efficiency+λ3·EnvironmentalAdaptation

[0061] Among them, λ1, λ2, and λ3 are the weight coefficients of safety, efficiency, and environmental adaptability, respectively. The adaptive optimization algorithm adopts a combination of local search and global search to flexibly adjust the path in a dynamic environment to ensure that the path planning is always optimal during long-term flight.

[0062] d. Dynamically adjust the path based on the drone’s real-time flight status data, including the drone’s current position, speed, flight altitude, and battery power, and correct the path through the following adaptive adjustment mechanisms:

[0063] P adj =P final +δP

[0064] Among them, P adj is the adjusted inspection path, δP is the path adjustment amount generated by real-time status data, and the specific adjustment is based on the battery power and power consumption factors. The adaptive adjustment mechanism adopts the following steps: During the execution of the inspection task, the flight status of the UAV and the environmental changes in the target area are continuously monitored. Once environmental changes or system failures are detected, the new inspection path is quickly calculated through the adaptive adjustment algorithm. If the battery power is lower than the set threshold, the nearest charging point or return path is calculated, and the ground control station is notified for scheduling.

[0065] e. During the inspection process, the drone sensor data is monitored and acquired in real time. The current path is dynamically corrected through the real-time collected environmental data. The following data feedback model is used:

[0066] P feedback =P adj ±δP feedback

[0067] Among them, δP feedback The path adjustment amount based on obstacle detection and flight status information and the feedback path ensure the real-time and safety of the inspection process.

[0068] f. Dynamically adjust the inspection route according to the type of emergency, such as obstacles, low battery, weather changes, etc., and adjust the route planning through the following event response mechanisms:

[0069]

[0070] Among them, Risk(P) is the risk assessment value at path P, which takes into account obstacles and weather changes to ensure the smooth execution of inspection tasks under emergencies.

[0071] g. Dynamically select task priorities and path optimization targets based on task requirements. Adjust the task priorities of the paths under the influence of battery power and task urgency, optimize the execution order of inspection tasks, and use the following priority adjustment model:

[0072] Priority(P)=α4·Urgency(P)+α5·Remaining_Power(P)

[0073] Among them, Urgency(P) is the urgency of the waypoint P to the task, RemainingPower(P) is the remaining power of the drone, and α4 and α5 are adjustment coefficients.

[0074] h. During the inspection process, adjust the path planning and scheduling based on real-time data, and optimize task allocation and path selection according to new environmental changes and drone status.

[0075] i. During the mission execution, a mission report is generated and the system is updated in real time. The inspection results, path planning and environmental status are fed back to the ground control station, and a real-time report is generated for subsequent analysis and mission adjustment. The environmental status data is updated in real time and reflected in the path planning and adjustment process to ensure that the path has high dynamic adaptability during the flight.

[0076] j. Optimize the path of the remaining inspection tasks based on the remaining battery power of the drone, inspection progress and task requirements, use the power prediction model to predict the battery life, and adjust the subsequent path planning based on the prediction results to ensure the completion of the task. The path planning algorithm communicates bidirectionally with the ground control station through the drone's control system, optimizes the inspection path in real time, and makes adjustments based on the actual flight status and environmental conditions.

[0077] k. Combine task status and environmental changes to generate a long-term task optimization model, and predict and optimize the path and resource scheduling in future inspection tasks by learning historical task data.

[0078] I. Globally optimize the system, combine machine learning and deep learning methods to optimize path planning and task scheduling, and automatically adjust path planning and task priorities through training with historical data to adapt to the changing inspection environment.

[0079] A UAV inspection device for a waste incineration power plant includes a memory and a processor. The memory is used to store programs.

[0080] A processor is used to execute the program to implement each step of the method for determining the inspection route of the drone

[0081] A storage medium stores a computer program, which, when executed by a processor, implements various steps of a method for unmanned aerial vehicle inspection of a waste incineration power plant.

[0082] Embodiment 2

[0083] The method for determining the inspection route of a drone provided by the present invention is applied to the inspection of power equipment. Assuming that the target area is a power transmission line, the task requirement is to go from the starting point A to the ending point B, and the inspection equipment is a drone equipped with a camera, a temperature sensor, an air pressure sensor, and a wind speed sensor.

[0084] Step a: Obtain basic geographic information, environmental status data and mission requirements of the target area

[0085] Geographic information of target area: The geographic information of power lines is obtained through remote sensing satellite image data to generate a digital map of the target area. The latitude and longitude coordinates of the starting point A and the ending point B are:

[0086] Starting point A: longitude 120.5384°, latitude 30.3755°

[0087] End point B: longitude 120.5508°, latitude 30.3807°

[0088] Environmental status data: The environmental data is collected in real time through the sensors on the drone. Assume that during the inspection process, the environmental data of the drone’s current location is as follows:

[0089] Wind speed: 6m / s

[0090] Temperature: 28℃

[0091] Air pressure: 1012hPa

[0092] Task requirements: The inspection task requirements include inspection from starting point A to ending point B. The inspection target is the equipment condition on the power transmission line. Ensure that the inspection path includes the maintenance points of each equipment and optimize the flight distance and energy consumption of the path.

[0093] Step b: Use the environmental factor correction model to pre-process the target area and generate a preliminary environmental assessment map

[0094] Environmental correction factor C env The calculation formula is as follows:

[0095] C env =w wind WindSpeed+w temp Temperature+w pressure Pressure

[0096] Assume that the weight coefficient of environmental factors is:

[0097] w wind =0.4

[0098] w temp =0.3

[0099] w pressure =0.3

[0100] Substitute the current environmental data to calculate the correction factor:

[0101] C env =0.4·6+0.3·28+0.3·1012=2.4+8.4+303.6=314.4

[0102] The correction factor C env =314.4 represents the degree of influence of the current environment on the path planning. Each location point of the environmental assessment map will generate a similar correction factor for the environmental adaptability assessment of the target area.

[0103] Step c: Generate a preliminary inspection path using a path planning module based on an adaptive optimization algorithm

[0104] Based on the environmental assessment map and correction factors generated above, an adaptive optimization algorithm (such as particle swarm optimization algorithm) is used for path planning. Assume that the objective function in the path optimization process is:

[0105] ObjectiveFunction=w safety Safety+w efficiency Efficiency+w env ·Env

[0106] ironmentalAdaptation where:

[0107] Safety reflects the safety of the flight path and avoids obstacles;

[0108] Efficiency reflects the time consumption and flight distance of the path;

[0109] EnvironmentalAdaptation reflects the adaptability of the path to the environmental correction factor

[0110] Assume that the weight coefficients are:

[0111] w safety =0.5

[0112] w efficiency =0.3

[0113] w env =0.2

[0114] The inspection path optimized by this objective function can reduce unnecessary flight distance, avoid high wind speed areas, and ensure efficient completion of inspection tasks. The final generated preliminary inspection path is as follows: starting point A (120.5384°, 30.3755°) to the first inspection point (120.5400°, 30.3765°) to the second inspection point (120.5440°, 30.3790°), and so on, and finally arrive at the end point B (120.5508°, 30.3807°)

[0115] Step d: Dynamically adjust the path based on the real-time flight status data of the drone

[0116] During the flight of the drone, the flight status data of the drone is monitored in real time. For example, when the drone flies to inspection point 2, the battery power is 30%. According to the battery power and flight status, the path adjustment amount is automatically calculated.

[0117] Assume that the path adjustment amount is calculated as follows:

[0118]

[0119] Among them, RemainingBattery=30 represents the remaining battery power, and InitialPathLength=10km is the initially calculated flight path length.

[0120]

[0121] Based on the adjustment amount, the route was shortened by 3 kilometers, and it was ensured that the route could still meet the task requirements after adjustment. The final optimized inspection route length was 7 kilometers.

[0122] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for determining an unmanned aerial vehicle inspection route, characterized in that: include: a. Obtain basic geographic information, environmental status data and task requirements of the target area, where the geographic information of the target area is obtained through remote sensing data, and the environmental status data is collected in real time through sensors. The task requirements include the starting point and end point of the inspection path and the specific tasks of the inspection. The environmental status data is collected through the following sensors: Wind speed sensor, used to obtain wind speed information in the flight area; Temperature sensor, used to obtain temperature information of the flight area; Air pressure sensor, used to obtain atmospheric pressure information of the flight area; GPS and obstacle sensors to provide flight path and obstacle information; b. Use the environmental factor correction model to pre-process the target area and generate a preliminary environmental assessment map. The environmental factor correction model takes into account wind speed, temperature, and atmospheric pressure factors. The correction process is performed using the following formula: C env (x)=α1·wind_speed(x)+α2·temperature(x)+α3·pressure (x) Among them, C env (x) is the correction factor, wind_speed(x), temperature(x), and pressure(x) are the wind speed, temperature, and air pressure of the target area at position x, respectively, and α1, α2, and α3 are the weight coefficients of environmental factors; c. A path planning module based on an adaptive optimization algorithm is used to generate a preliminary inspection path. The path planning is optimized based on the preprocessed environmental assessment map and correction factors, using the following path optimization formula: Among them, P final is the final optimized inspection path, P i is the i-th path point in the path, Cost(P i ) is the cost function of the path point, reflecting the flight distance, time consumption and energy consumption factors, C env (P i ) is the path point P i Environmental correction factor at the location; d. Dynamically adjust the path based on the drone’s real-time flight status data, including the drone’s current position, speed, flight altitude, and battery power, and correct the path through the following adaptive adjustment mechanisms: P adj =P final +δP Among them, P adj is the inspection path after adjustment, δP is the path adjustment amount generated by real-time status data, and the specific adjustment is based on battery power and power consumption factors; e. During the inspection process, the drone sensor data is monitored and acquired in real time. The current path is dynamically corrected through the real-time collected environmental data. The following data feedback model is used: P feedback =P adj ±δP feedback Among them, δP feedback To adjust the path based on obstacle detection and flight status information, and feedback the path to ensure the real-time and safety of the inspection process; f. Dynamically adjust the inspection route according to the type of emergency, such as obstacles, low battery, weather changes, etc., and adjust the route planning through the following event response mechanisms: Among them, Risk (P) is the risk assessment value at the path P, taking into account obstacles and weather changes to ensure the smooth execution of the inspection task under emergencies. The data collection during the execution of the inspection task includes the following steps: the camera and infrared sensor carried by the drone collect image data of the target area in real time, transmit the collected image data to the ground control station for real-time analysis, and generate an equipment health report; g. Dynamically select task priorities and path optimization targets based on task requirements. Adjust the task priorities of the paths under the influence of battery power and task urgency, optimize the execution order of inspection tasks, and use the following priority adjustment model: Priority(P)=α4·Urgency(P)+α5·Remaining_Power(P) Among them, Urgency(P) is the urgency of the waypoint P to the task, RemainingPower(P) is the remaining power of the drone, and α4 and α5 are adjustment coefficients; h. During the inspection process, adjust the path planning and scheduling based on real-time data, and optimize task allocation and path selection according to new environmental changes and drone status; i. During the mission execution, generate mission reports and update the system in real time, feed back inspection results, path planning and environmental status to the ground control station, and generate real-time reports for subsequent analysis and mission adjustment; j. Optimize the path of the remaining inspection tasks according to the remaining power of the drone, the inspection progress and the task requirements, use the power prediction model to predict the battery life, and adjust the subsequent path planning based on the prediction results to ensure the completion of the task; k. Combine task status and environmental changes to generate a long-term task optimization model, and predict and optimize the path and resource scheduling in future inspection tasks by learning historical task data; I. Globally optimize the system, combine machine learning and deep learning methods to optimize path planning and task scheduling, and automatically adjust path planning and task priorities through training with historical data to adapt to the changing inspection environment.

2. A method for determining an unmanned aerial vehicle inspection route according to claim 1, characterized in that: The adaptive adjustment mechanism adopts the following steps: during the execution of the inspection task, the flight status of the UAV and the environmental changes in the target area are continuously monitored. Once an environmental change or system failure is detected, a new inspection path is quickly calculated through an adaptive adjustment algorithm. If the battery power is lower than the set threshold, the nearest charging point or return path is calculated, and the ground control station is notified for scheduling.

3. The method for determining an unmanned aerial vehicle inspection route according to claim 1, characterized in that: The environmental status data is collected by the following sensors: Wind speed sensor, used to obtain wind speed information in the flight area; Temperature sensor, used to obtain temperature information of the flight area; Air pressure sensor, used to obtain atmospheric pressure information of the flight area; GPS and obstacle sensors to provide flight path and obstacle information.

4. The method for determining an unmanned aerial vehicle inspection route according to claim 1, characterized in that: The data collection during the execution of the inspection task includes the following steps: the camera and infrared sensor carried by the drone collect image data of the target area in real time, transmit the collected image data to the ground control station for real-time analysis, and generate an equipment health report.

5. The method for determining an unmanned aerial vehicle inspection route according to claim 1, characterized in that: The objective function in the path optimization process is set in the following way: ObjectiveFunction=λ1·Safety+λ2·Efficiency+λ3·Environmenta Adaptation Among them, λ1, λ2, and λ3 are the weight coefficients of safety, efficiency, and environmental adaptability, respectively.

6. The method for determining an unmanned aerial vehicle inspection route according to claim 1, characterized in that: The environmental status data is updated in real time and reflected in the path planning and adjustment process, ensuring that the path has high dynamic adaptability during flight.

7. The method for determining an unmanned aerial vehicle inspection route according to claim 1, characterized in that: The path planning algorithm conducts two-way communication between the drone's control system and the ground control station, optimizes the inspection path in real time, and makes adjustments based on actual flight status and environmental conditions.

8. The method for determining an unmanned aerial vehicle inspection route according to claim 1, characterized in that: The adaptive optimization algorithm adopts a combination of local search and global search to flexibly adjust the path in a dynamic environment to ensure that the path planning is always optimal during long-term flight.

9. A drone inspection device for a waste incineration power plant, characterized in that: It includes a memory and a processor; the memory is used to store programs; The processor is used to execute the program to implement the various steps of the method for determining the drone inspection route as described in any one of claims 1 to 8.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, each step of the drone inspection method for a waste incineration power plant as described in any one of claims 1 to 8 is implemented.

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