An environment-adaptive unmanned aerial vehicle computing task scheduling system and method

Abstract

This invention discloses an environment-adaptive unmanned aerial vehicle (UAV) computing task scheduling system and method. The system includes a perception module, an environmental difference detection module, a planning module, a collision detection module, a control module, an environmental complexity calculation module, a system resource monitor, and a frequency calculator. Through the collaborative operation of these modules, the invention constructs a closed-loop control system that enables autonomous UAV flight in complex environments. The system can adaptively adjust map resolution and optimize the detection frequency of corresponding modules in response to environmental changes, ultimately achieving efficient and safe completion of flight missions.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) scheduling, and in particular to an environment-adaptive UAV computing task scheduling system and method thereof. Background Art

[0002] With the rapid development of drone technology, a variety of drone dispatch systems have emerged on the market, each varying in functionality and scope of application. Generally speaking, the basic process flow of existing drone dispatch systems can be summarized as follows: First, the sensor module transmits environmental data to the computing processing unit. Second, the computing processing unit processes the received data using a preset control algorithm, generates an assessment of the current environmental status, and constructs a map. Then, based on the established map and environmental information, the system performs path planning to determine the drone's flight trajectory. Finally, the planning results are transmitted to the drone's flight control system via the communication module, which implements actual flight operations, continuously monitors the drone's status, and updates the path plan based on new sensor data.

[0003] In drone scheduling systems, perception frequency refers to the number of times a drone senses and processes information about its environment per unit time. A high frequency enables rapid response to changes in the surrounding environment, which is particularly important for obstacle avoidance. However, an excessively high frequency can increase processing burden and energy consumption, requiring a reasonable balance based on the application scenario. Planning frequency refers to how often a drone updates its flight path or action plan, directly impacting the system's adaptability and flexibility. A higher planning frequency can improve mission efficiency, but it can also lead to excessive consumption of computing resources. The Environmental Complexity Index (ECI) is a key parameter that measures the density of obstacles in a drone's operating environment. It assesses the number, size, and distribution of obstacles near the drone's flight path, as well as their potential impact on flight safety. Higher ECI values ​​indicate a more complex environment and place greater demands on the drone's obstacle avoidance, path planning, and autonomous decision-making capabilities. Therefore, when designing a drone scheduling system, these three parameters must be comprehensively considered to ensure flight safety and operational effectiveness.

[0004] Currently, different drone models on the market may be equipped with different development boards, such as the TX2 and Raspberry Pi, and the chip computing power of these boards varies. However, given the significant differences in computing power between development boards, most drone scheduling systems on the market use fixed perception and planning frequencies. When applied to different drones, this static scheduling system is clearly unable to adapt to the differences in computing power between development boards, and is unable to dynamically adjust the perception and planning frequencies during operation. In many cases, when drones equipped with existing scheduling systems perform autonomous flight missions, this inability to dynamically adjust the perception and planning frequencies based on the actual computing power of the development board leads to a series of operational problems and safety hazards.

[0005] For example, if the computing power of a drone's development board is relatively low, if certain scheduling systems ignore the board's current computing power during perception and planning and forcibly use its fixed, higher frequency, the board may be unable to withstand such high-intensity computing tasks due to insufficient computing power, causing the entire system to freeze or even crash, severely impacting flight safety and the expected completion of the flight mission. Conversely, if the drone's development board has sufficient computing power, some scheduling systems may use its fixed, lower frequency for perception and planning operations, resulting in a waste of computing power. Furthermore, the insufficiently detailed environmental information collected by these systems makes it impossible for the drone to build a complete and usable map, and thus unable to plan an ideal path that can effectively avoid obstacles and reach the target point. As a result, the drone is very likely to collide with surrounding obstacles during flight, causing serious flight accidents.

[0006] In addition to using fixed perception and planning rates when running on different development boards, drone dispatch systems currently on the market also generally employ fixed perception and planning frequencies when facing environments of varying complexity. This static setting fails to account for the impact of real-time environmental changes on drone operations, resulting in an inability to adjust flight strategies in a dynamic environment. Specifically, when drones are in highly complex or rapidly changing environments, such as densely populated urban areas or at the site of a natural disaster, existing systems are often unable to dynamically adjust their perception and planning frequencies based on changes in environmental complexity. This lack of flexibility makes drones sluggish in responding to rapidly changing environments, making efficient obstacle avoidance and path correction impossible.

[0007] For example, when a drone is flying in an area with dense obstacles, if the system cannot increase the perception frequency according to the complexity of the environment, it may miss important obstacle information, resulting in inaccurate path planning and increased collision risk. At the same time, due to the inability to increase the planning frequency, the system may not be able to update the flight path in a timely manner based on new environmental information, causing the drone to face more dangerous situations during flight. This not only significantly reduces the safety of flight, but also affects the efficiency of mission execution, resulting in potential economic losses and safety hazards. Therefore, the present invention aims to dynamically adjust the perception and planning frequency according to the real-time environmental complexity through an intelligent scheduling mechanism, so as to fundamentally solve the problem of insufficient adaptability of existing scheduling systems in complex environments.

[0008] In summary, existing drone scheduling systems on the market generally adopt fixed perception and planning frequencies when equipped with development boards of different computing powers and facing environments of different complexities. This static strategy with obvious defects significantly reduces the universality and security of these scheduling systems. The present invention aims to fundamentally solve the above key problems by dynamically adjusting the perception and planning frequencies according to the real-time environmental complexity and the computing power of the development board through an intelligent scheduling mechanism.

[0009] The Chinese invention patent application with application number 202410903819.2 discloses a "Path Planning Method for Intelligent UAV Inspections at Construction Sites." Its technical solution uses 3D reconstruction technology to construct a dynamic 3D model of the construction site using images and data collected by the drone's onboard camera. Secondly, based on a custom ant colony optimization algorithm and a combination of deep reinforcement learning algorithms, it generates strategic inspection paths and dynamically adjusts inspection tasks and paths based on real-time data feedback. Next, it integrates an event-driven dynamic scheduling system to respond to critical events at the construction site, including equipment failures or the emergence of new structures, and replans the drone's flight path. Finally, a machine learning method is used to train a predictive obstacle avoidance model to predict potential risks and adjust the flight path in real time. The above solution aims to solve the path planning problem of drones in complex construction sites. Summary of the Invention

[0010] To address the technical problem of low universality and security in existing drone scheduling systems due to the inability to dynamically adjust perception and planning frequencies, the present invention provides an environment-adaptive drone computing task scheduling system and method thereof. The technical solution adopted by the present invention is:

[0011] A first aspect of the present invention provides an environment-adaptive UAV computing task scheduling system, the system comprising a perception module, an environment difference detection module, a planning module, a collision detection module, a control module, an environment complexity calculation module, a system resource monitor, and a frequency calculator;

[0012] The perception module is used to detect obstacle information in the environment where the drone is located, obtain continuous perception data frames, and transmit the continuous perception data frames to the environment difference detection module, the planning module, and the environment complexity calculation module respectively;

[0013] The environmental difference detection module is used to detect the difference between the current perception data frame and the previous perception data frame based on the continuous perception data frames, obtain the environmental change difference, and transmit the environmental change difference to the planning module;

[0014] The planning module is used to adjust the resolution of a preset map according to the difference in the environmental change, generate a flight path in the preset map according to a preset target position and the continuous perception data frames, and finally transmit the flight path to the control module;

[0015] The collision detection module is used to detect the distance between the drone and surrounding obstacles based on the continuous perception data frames, and when the distance is less than a preset safety threshold, send an emergency obstacle avoidance instruction to the control module;

[0016] The control module is used to fly according to the flight path generated by the planning module and perform corresponding obstacle avoidance actions according to emergency obstacle avoidance instructions;

[0017] The environment complexity calculation module is used to calculate the environment complexity according to the continuous perception data frames, and transmit the obtained environment complexity to the frequency calculator;

[0018] The system resource monitor is used to monitor the status information of the drone hardware and transmit the status information of the hardware to the frequency calculator;

[0019] The frequency calculator is used to calculate the perception frequency, the environment difference detection frequency and the collision detection frequency according to the environment complexity and the status information of the hardware and transmit them to the perception module, the environment difference detection module and the collision detection module respectively.

[0020] As a preferred solution, the continuous sensing data frames include:

[0021] Obstacle location, obstacle size, obstacle speed, and obstacle distance.

[0022] As a preferred solution, the planning module includes a judgment module, a resolution adjustment module and a path generation module;

[0023] The judgment module is used to judge whether the difference of the environmental change exceeds a preset threshold;

[0024] The resolution adjustment module is used to adjust the resolution of the preset map according to the judgment result of the judgment module;

[0025] The path generation module is used to generate a flight path in a preset map according to a preset target position and the continuous perception data frames.

[0026] As a preferred solution, the environment complexity calculation module includes a field of view partitioning module, a weight distribution module, an obstacle detection module, and a complexity calculation module;

[0027] The visual field partitioning module is used to partition the perception visual field of the perception module;

[0028] The weight allocation module is used to allocate the complexity weight of each partition view;

[0029] The obstacle detection module is used to perform obstacle detection on each partition according to the visual field complexity weight of each partition, and finally obtain the environmental complexity of each partition;

[0030] The complexity calculation module is used to perform weighted summation on the environmental complexity of each partition to obtain a final environmental complexity.

[0031] As a preferred solution, the hardware status information includes:

[0032] Core processor usage, memory usage.

[0033] A second aspect of the present invention provides an environment-adaptive UAV computing task scheduling method, which is implemented based on the aforementioned environment-adaptive UAV computing task scheduling system, and includes:

[0034] The perception module detects obstacle information in the environment where the drone is located to obtain continuous perception data frames;

[0035] Detecting the difference between the current perception data frame and the previous perception data frame based on the continuous perception data frames by the environment difference detection module to obtain the degree of difference in environment change;

[0036] Adjusting the resolution of a preset map according to the difference in the environmental changes by the planning module, and generating a flight path in the preset map according to a preset target position and the continuous perception data frames;

[0037] The collision detection module detects the distance between the drone and surrounding obstacles based on the continuous perception data frames, and sends an emergency obstacle avoidance instruction to the control module when the distance is less than a preset safety threshold;

[0038] The control module flies according to the flight path generated by the planning module and performs corresponding obstacle avoidance actions according to emergency obstacle avoidance instructions.

[0039] As a preferred solution, the method further comprises:

[0040] Calculating the environmental complexity according to the continuous perception data frames by an environmental complexity calculation module;

[0041] Monitor the status of drone hardware through the system resource monitor;

[0042] The frequency calculator calculates the perception frequency, environmental difference detection frequency and collision detection frequency according to the environmental complexity and the status information of the hardware, and enables the perception module, the environmental difference detection module and the collision detection module to perform detection according to the calculated perception frequency, environmental difference detection frequency and collision detection frequency respectively.

[0043] As a preferred solution, the method of adjusting the resolution of a preset map according to the difference in the environmental change by a planning module and generating a flight path in the preset map according to a preset target position and the continuous perception data frames includes:

[0044] Determine whether the difference in environmental changes exceeds a preset threshold by a judgment module;

[0045] If the difference in the environmental change exceeds a preset threshold, the resolution of the preset map is adjusted by the resolution adjustment module;

[0046] If the difference in the environmental change does not exceed a preset threshold, the original map resolution is maintained;

[0047] The flight path is generated in a preset map according to the preset target position and the continuous perception data frames by the path generation module.

[0048] As a preferred solution, the method for calculating the environmental complexity according to the continuous perception data frames by the environmental complexity calculation module includes:

[0049] Partitioning the perception field of view of the perception module;

[0050] Assign each partition's view complexity weight;

[0051] Obstacle detection is performed on each partition according to the complexity weight of the field of view of each partition, and finally the environmental complexity of each partition is obtained;

[0052] The weighted sum of the environmental complexity of each partition is performed to obtain the final environmental complexity.

[0053] The third aspect of the present invention provides a computer device, including a storage medium, a processor, and a computer program stored in the storage medium and executable by the processor. When the computer program is executed by the processor, the steps of the aforementioned environment-adaptive drone computing task scheduling method are implemented.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention uses an adaptive frequency adjustment strategy to monitor CPU and memory usage in real time and automatically adjust the frequency according to the complexity of the current environment and the requirements of the flight mission, thereby reducing unnecessary energy consumption and extending the flight time of the UAV, enabling it to maintain efficient operation during long-term flight missions.

[0056] By dynamically adjusting map resolution, this invention enables precise obstacle perception, enabling drones to obtain high-resolution environmental data in a timely manner and react quickly in complex environments and unexpected situations, effectively reducing collision risks and ensuring flight safety. Dynamically adjusting map resolution also rationally allocates computing resources, dynamically adjusting resolution based on real-time conditions, reducing unnecessary data processing, improving overall drone performance, and enhancing the system's adaptability to diverse application scenarios.

[0057] The present invention realizes real-time monitoring of the hardware performance of the UAV (such as CPU and memory occupancy) through a system resource monitor. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of the structure of an environment-adaptive UAV computing task scheduling system provided in this embodiment;

[0059] Figure 2 A flow chart of an environment-adaptive UAV computing task scheduling method provided in this embodiment;

[0060] Figure 3 The low-resolution drone perception map provided in this embodiment;

[0061] Figure 4 This is the high-resolution drone perception map provided by this embodiment. DETAILED DESCRIPTION

[0062] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting the present invention;

[0063] It should be clear that the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the embodiments of the present application.

[0064] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of the present application. The singular forms "a," "the," and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0065] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0066] In addition, in the description of this application, unless otherwise specified, "plurality" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship. The present invention is further described below with reference to the accompanying drawings and examples.

[0067] The present invention is further described below with reference to the accompanying drawings and embodiments.

[0068] Example 1

[0069] Please refer to Figure 1 ,This embodiment provides an environment-adaptive UAV computing task scheduling system, the system includes a perception module, an environment difference detection module, a planning module, a collision detection module, a control module, an environment complexity calculation module, a system resource monitor, and a frequency calculator;

[0070] The perception module is used to detect obstacle information in the environment where the drone is located, obtain continuous perception data frames, and transmit the continuous perception data frames to the environment difference detection module, the planning module, and the environment complexity calculation module respectively;

[0071] Specifically, the perception module includes an integrated radar and a camera.

[0072] The environmental difference detection module is used to detect the difference between the current perception data frame and the previous perception data frame based on the continuous perception data frames, obtain the environmental change difference, and transmit the environmental change difference to the planning module;

[0073] Specifically, the environmental difference detection module builds a model of the environmental state at the previous moment and compares it with the current perception data to determine whether there are significant changes in the environment. If the difference in environmental change exceeds a threshold, the resolution adjustment module adjusts the map resolution to the highest possible level, dividing it into several smaller grids for more precise obstacle avoidance path planning.

[0074] The planning module is used to adjust the resolution of a preset map according to the difference in the environmental change, generate a flight path in the preset map according to a preset target position and the continuous perception data frames, and finally transmit the flight path to the control module;

[0075] The collision detection module is used to detect the distance between the drone and surrounding obstacles based on the continuous perception data frames, and when the distance is less than a preset safety threshold, send an emergency obstacle avoidance instruction to the control module;

[0076] The control module is used to fly according to the flight path generated by the planning module and perform corresponding obstacle avoidance actions according to emergency obstacle avoidance instructions;

[0077] The environment complexity calculation module is used to calculate the environment complexity according to the continuous perception data frames, and transmit the obtained environment complexity to the frequency calculator;

[0078] The system resource monitor is used to monitor the status information of the drone hardware and transmit the status information of the hardware to the frequency calculator;

[0079] The frequency calculator is used to calculate the perception frequency, the environment difference detection frequency and the collision detection frequency according to the environment complexity and the status information of the hardware, and transmit the calculations to the perception module, the environment difference detection module and the collision detection module respectively;

[0080] Specifically, the frequency calculator considers environmental complexity and system status information provided by the system resource monitor. Using a reinforcement learning network, it dynamically calculates the perception frequency, collision detection frequency, and environmental difference detection frequency. It then adjusts the operating states of the corresponding modules based on these calculation results. By intelligently adjusting these frequencies based on changes in the environment and system load, the system optimizes resource utilization while ensuring safety and performance, avoiding resource waste or system overload.

[0081] In a specific embodiment, the continuous sensing data frames include:

[0082] Obstacle location, obstacle size, obstacle speed, and obstacle distance.

[0083] In a specific embodiment, the planning module includes a judgment module, a resolution adjustment module, and a path generation module;

[0084] The judgment module is used to judge whether the difference of the environmental change exceeds a preset threshold;

[0085] The resolution adjustment module is used to adjust the resolution of the preset map according to the judgment result of the judgment module;

[0086] The path generation module is used to generate a flight path in a preset map according to a preset target position and the continuous perception data frames.

[0087] In a specific embodiment, the environment complexity calculation module includes a field of view partitioning module, a weight distribution module, an obstacle detection module, and a complexity calculation module;

[0088] The visual field partitioning module is used to partition the perception visual field of the perception module;

[0089] The weight allocation module is used to allocate the complexity weight of each partition view;

[0090] The obstacle detection module is used to perform obstacle detection on each partition according to the visual field complexity weight of each partition, and finally obtain the environmental complexity of each partition;

[0091] The complexity calculation module is used to perform weighted summation on the environmental complexity of each partition to obtain a final environmental complexity.

[0092] In a specific embodiment, the hardware status information includes:

[0093] Core processor usage, memory usage.

[0094] By integrating these modules, the present invention constructs a closed-loop control system that enables autonomous flight in complex environments. The system can quickly respond to environmental changes and optimize resource utilization, ultimately completing flight missions efficiently and safely.

[0095] Example 2

[0096] Please refer to Figure 2 This embodiment provides an environment-adaptive UAV computing task scheduling method, which is based on the environment-adaptive UAV computing task scheduling system described in Example 1 and includes:

[0097] S1: Detecting obstacle information in the environment of the drone through the perception module to obtain continuous perception data frames;

[0098] S2: Detecting, by the environmental difference detection module, a difference between a current perception data frame and a previous perception data frame based on the continuous perception data frames to obtain an environmental change difference degree;

[0099] Specifically, the system builds a model of the environment's state at the previous moment and compares it with the current perception data to determine whether there are significant changes in the environment. Once the difference in environmental changes exceeds a threshold, the system adjusts the map resolution to the highest possible level, dividing it into several smaller grids for more precise obstacle avoidance path planning.

[0100] More specifically, during the flight of the drone, as the environment continues to change, the system can dynamically adjust the map resolution based on the distance between the flight trajectory and obstacles. Figure 3 and Figure 4 When the drone approaches a significant obstacle or enters a complex area, the system increases the map's resolution to obtain more detailed environmental information. This process is controlled by the resolution adjustment module, which determines when to increase or decrease the resolution based on real-time monitoring data. In this way, the drone can effectively reduce unnecessary computational burden and resource consumption while maintaining efficient perception of its surroundings. The increased resolution allows the system to accurately depict the location and characteristics of obstacles, providing effective support for subsequent path planning.

[0101] S3: adjusting the resolution of a preset map according to the difference in the environmental change by the planning module, and generating a flight path in the preset map according to a preset target position and the continuous perception data frames;

[0102] S4: Detecting the distance between the UAV and surrounding obstacles through the collision detection module based on the continuous perception data frames, and sending an emergency obstacle avoidance instruction to the control module when the distance is less than a preset safety threshold;

[0103] S5: Flying according to the flight path generated by the planning module through the control module, and executing corresponding obstacle avoidance actions according to the emergency obstacle avoidance instructions.

[0104] In a specific embodiment, the method further comprises:

[0105] Calculating the environmental complexity according to the continuous perception data frames by an environmental complexity calculation module;

[0106] Monitor the status of drone hardware through the system resource monitor;

[0107] The frequency calculator calculates the perception frequency, environmental difference detection frequency and collision detection frequency according to the environmental complexity and the status information of the hardware, and enables the perception module, the environmental difference detection module and the collision detection module to perform detection according to the calculated perception frequency, environmental difference detection frequency and collision detection frequency respectively.

[0108] Specifically, after obtaining the Environment Complexity Index (ECI), the system comprehensively evaluates this information through the policy controller. During this process, the system uses the ECI value as a basis, combined with the current computing power of the development board, for intelligent scheduling. The frequency calculator dynamically adjusts the sensing and planning frequencies based on ECI changes. A high ECI value indicates a more complex environment, and the system automatically increases the sensing frequency to collect environmental data more quickly. Conversely, in simpler environments, the frequency can be reduced to conserve resources.

[0109] This invention uses an adaptive frequency adjustment strategy to monitor CPU and memory usage in real time and automatically adjust frequencies based on the complexity of the current environment and the requirements of the flight mission. This optimization not only reduces unnecessary energy consumption but also extends the drone's flight time, enabling it to maintain efficient operation during long-duration missions. By effectively reducing resource consumption, this invention saves users operating costs while improving the drone's overall operating efficiency, giving it a significant advantage in the market.

[0110] In a specific embodiment, the method of adjusting the resolution of a preset map according to the difference in the environmental change by a planning module and generating a flight path in the preset map according to a preset target position and the continuous perception data frames includes:

[0111] Determine whether the difference in environmental changes exceeds a preset threshold by a judgment module;

[0112] If the difference in the environmental change exceeds a preset threshold, the resolution of the preset map is adjusted by the resolution adjustment module;

[0113] If the difference in the environmental change does not exceed a preset threshold, the original map resolution is maintained;

[0114] The flight path is generated in a preset map according to the preset target position and the continuous perception data frames by the path generation module.

[0115] In a specific embodiment, the method for calculating the environmental complexity according to the continuous perception data frames by the environmental complexity calculation module includes:

[0116] Partitioning the perception field of view of the perception module;

[0117] Assign each partition's view complexity weight;

[0118] Obstacle detection is performed on each partition according to the complexity weight of the field of view of each partition, and finally the environmental complexity of each partition is obtained;

[0119] The weighted sum of the environmental complexity of each partition is performed to obtain the final environmental complexity.

[0120] In summary, the key technical innovations of this invention give this scheduling system significant advantages over existing drone scheduling systems in terms of dynamic adaptability, resource optimization, security, and user-friendliness. By incorporating advanced technologies such as real-time monitoring, dynamic adjustment, and intelligent decision-making, our solution not only enhances drones' autonomous flight capabilities in complex environments but also lays a solid foundation for their widespread application. These improvements enable this invention to stand out among today's numerous drone scheduling and control systems, meet the growing demand for drone applications, and provide new direction and impetus for the future development of drone technology.

[0121] Example 3

[0122] This embodiment provides a computer device, including a storage medium, a processor, and a computer program stored in the storage medium and executable by the processor. When the computer program is executed by the processor, the steps of the environment-adaptive drone computing task scheduling method described in Example 2 are implemented.

[0123] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. An environment-adaptive UAV computing task scheduling system, characterized in that: The system includes a perception module, an environmental difference detection module, a planning module, a collision detection module, a control module, an environmental complexity calculation module, a system resource monitor, and a frequency calculator; The perception module is used to detect obstacle information in the environment where the drone is located, obtain continuous perception data frames, and transmit the continuous perception data frames to the environment difference detection module, the planning module, and the environment complexity calculation module respectively; The environmental difference detection module is used to detect the difference between the current perception data frame and the previous perception data frame based on the continuous perception data frames, obtain the environmental change difference, and transmit the environmental change difference to the planning module; The planning module is used to adjust the resolution of a preset map according to the difference in the environmental change, generate a flight path in the preset map according to a preset target position and the continuous perception data frames, and finally transmit the flight path to the control module; The collision detection module is used to detect the distance between the drone and surrounding obstacles based on the continuous perception data frames, and when the distance is less than a preset safety threshold, send an emergency obstacle avoidance instruction to the control module; The control module is used to fly according to the flight path generated by the planning module and perform corresponding obstacle avoidance actions according to emergency obstacle avoidance instructions; The environment complexity calculation module is used to calculate the environment complexity according to the continuous perception data frames, and transmit the obtained environment complexity to the frequency calculator; The system resource monitor is used to monitor the status information of the drone hardware and transmit the status information of the hardware to the frequency calculator; The frequency calculator is used to calculate the perception frequency, the environment difference detection frequency and the collision detection frequency according to the environment complexity and the status information of the hardware and transmit them to the perception module, the environment difference detection module and the collision detection module respectively.

2. The environment-adaptive UAV computing task scheduling system according to claim 1, characterized in that: The continuous sensing data frames include: Obstacle location, obstacle size, obstacle speed, and obstacle distance.

3. The environment-adaptive UAV computing task scheduling system according to claim 1, characterized in that: The planning module includes a judgment module, a resolution adjustment module and a path generation module; The judgment module is used to judge whether the difference of the environmental change exceeds a preset threshold; The resolution adjustment module is used to adjust the resolution of the preset map according to the judgment result of the judgment module; The path generation module is used to generate a flight path in a preset map according to a preset target position and the continuous perception data frames.

4. The environment-adaptive UAV computing task scheduling system according to claim 1, characterized in that: The environment complexity calculation module includes a field of view partition module, a weight distribution module, an obstacle detection module, and a complexity calculation module; The visual field partitioning module is used to partition the perception visual field of the perception module; The weight allocation module is used to allocate the complexity weight of each partition view; The obstacle detection module is used to perform obstacle detection on each partition according to the visual field complexity weight of each partition, and finally obtain the environmental complexity of each partition; The complexity calculation module is used to perform weighted summation on the environmental complexity of each partition to obtain a final environmental complexity.

5. The environment-adaptive UAV computing task scheduling system according to claim 1, characterized in that: The hardware status information includes: Core processor usage, memory usage.

6. An environment-adaptive UAV computing task scheduling method, the method being implemented based on the environment-adaptive UAV computing task scheduling system according to any one of claims 1 to 5, characterized in that: The method comprises: The perception module detects obstacle information in the environment where the drone is located to obtain continuous perception data frames; Detecting the difference between the current perception data frame and the previous perception data frame based on the continuous perception data frames by the environment difference detection module to obtain the degree of difference in environment change; Adjusting the resolution of a preset map according to the difference in the environmental changes by the planning module, and generating a flight path in the preset map according to a preset target position and the continuous perception data frames; The collision detection module detects the distance between the drone and surrounding obstacles based on the continuous perception data frames, and sends an emergency obstacle avoidance instruction to the control module when the distance is less than a preset safety threshold; The control module flies according to the flight path generated by the planning module and performs corresponding obstacle avoidance actions according to emergency obstacle avoidance instructions.

7. The method for scheduling computing tasks of an environment-adaptive UAV according to claim 6, characterized in that: The method further comprises: Calculating the environmental complexity according to the continuous perception data frames by an environmental complexity calculation module; Monitor the status of drone hardware through the system resource monitor; The frequency calculator calculates the perception frequency, environmental difference detection frequency and collision detection frequency according to the environmental complexity and the status information of the hardware, and enables the perception module, the environmental difference detection module and the collision detection module to perform detection according to the calculated perception frequency, environmental difference detection frequency and collision detection frequency respectively.

8. The method for scheduling computing tasks of an environment-adaptive UAV according to claim 6, characterized in that: The method of adjusting the resolution of a preset map according to the difference in the environmental change by a planning module and generating a flight path in the preset map according to a preset target position and the continuous perception data frames includes: Determine whether the difference in environmental changes exceeds a preset threshold by a judgment module; If the difference in the environmental change exceeds a preset threshold, the resolution of the preset map is adjusted by a resolution adjustment module; If the difference in the environmental change does not exceed a preset threshold, the original map resolution is maintained; A flight path is generated in a preset map according to a preset target position and the continuous perception data frames by a path generation module.

9. The method for scheduling computing tasks of an environment-adaptive UAV according to claim 7, characterized in that: The method for calculating the environmental complexity according to the continuous perception data frames by the environmental complexity calculation module includes: Partitioning the perception field of view of the perception module; Assign each partition's view complexity weight; Obstacle detection is performed on each partition according to the complexity weight of the field of view of each partition, and finally the environmental complexity of each partition is obtained; The weighted sum of the environmental complexity of each partition is performed to obtain the final environmental complexity.

10. A computer device, characterized in that: The method comprises a storage medium, a processor, and a computer program stored in the storage medium and executable by the processor, wherein when the computer program is executed by the processor, the steps of the method for scheduling computing tasks of an environment-adaptive drone as described in any one of claims 6 to 9 are implemented.

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