Image data acquisition method and device based on unattended intelligent airport

Abstract

The invention discloses an image data acquisition method and device based on an unattended intelligent airport, and relates to the technical field of image data processing.The method comprises the steps that forest image data are acquired based on the unattended intelligent airport, the unattended intelligent airport selects a forest area meeting preset mountain forest conditions, and the selected forest area is selected as a forest area; the unattended intelligent airport is used for taking off, landing and parking a predetermined type of unmanned aerial vehicle, and the predetermined mountain forest conditions comprise that the height fall of a mountain land is greater than a preset fall threshold value, the forest vegetation coverage rate is higher than a preset coverage rate threshold value, and the number of roads in a forest is lower than a preset number threshold value; and analyzing the preprocessed forest image data by adopting a pre-trained machine learning model, identifying a forest resource state, and displaying the forest resource state on a terminal screen. The technical problems that image resource analysis is difficult to carry out on an unattended intelligent airport system in a severe environment and the operation efficiency is low in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of image data processing technology or other related fields, and more specifically, to an image data acquisition method and apparatus based on an unmanned intelligent airport. Background Technology

[0002] Forest resource monitoring is a core aspect of forestry management, crucial for assessing forest health, protecting biodiversity, providing early warning of forest fires, monitoring illegal logging, and conducting long-term ecosystem research. Traditional forest patrols and resource monitoring rely primarily on manual ground surveys or satellite remote sensing, but these methods suffer from inefficiency, high costs, difficulty in real-time monitoring, and operational limitations in complex terrain and adverse weather conditions. To overcome these limitations, unmanned aerial vehicle (UAV) technology has been widely applied in forest monitoring in recent years. UAVs are characterized by their maneuverability, rapid response, adaptability to various terrains, and cost-effectiveness, making them particularly suitable for patrol tasks in complex environments such as forests. However, the endurance and ease of remote operation of UAVs remain major bottlenecks in their application.

[0003] Currently, various unmanned intelligent airport and vertical take-off and landing (VTOL) fixed-wing UAV solutions are available in both domestic and international markets, primarily applied in logistics, security monitoring, and environmental monitoring. These solutions typically focus on the automated take-off and landing, remote control, and automatic charging capabilities of UAVs to reduce human intervention and improve operational efficiency. However, their application in forest patrol scenarios is still insufficient, especially in terms of data acquisition accuracy and automation. Existing unmanned intelligent airport systems rarely consider integration with high-precision image data acquisition, particularly in mountainous areas with significant elevation differences, dense vegetation, harsh living conditions, and areas difficult to access by humans or vehicles. These areas are precisely the key and challenging locations for forest patrols, placing higher demands on the performance of UAVs and the functionality of data acquisition systems.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method and apparatus for acquiring image data based on unmanned intelligent airports, which at least solves the technical problems of low operational efficiency in related technologies, such as the difficulty in performing image resource analysis on unmanned intelligent airport systems in harsh environments.

[0006] To achieve the above objectives, according to one aspect of this application, a method for acquiring image data based on an unmanned intelligent airport is provided, comprising: acquiring forest image data based on an unmanned intelligent airport, wherein the unmanned intelligent airport is selected in a forest area that meets predetermined forest conditions, the unmanned intelligent airport is used for take-off, landing and parking of predetermined types of unmanned aerial vehicles (UAVs), and the predetermined forest conditions include at least one of the following: the elevation difference of the mountain is greater than a preset elevation difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold; preprocessing the forest image data and analyzing the preprocessed forest image data using a pre-trained machine learning model to identify the forest resource status, wherein the forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation; and displaying the forest resource status on the display screen of a target terminal.

[0007] Optionally, the steps for collecting forest image data based on an unmanned intelligent airport include: sending a start command and a one-click operation command to the drone through the central control software of the image acquisition system, wherein the one-click operation command is used to instruct the drone to perform operation patrol and forest image acquisition according to the pre-planned airport patrol route; and organizing the forest images returned by each drone according to the drone identification and timeline order through the central control software of the image acquisition system to obtain the forest image data.

[0008] Optionally, the steps of the UAV conducting operational patrols and forest image acquisition according to a pre-planned airport patrol route include: obtaining the required camera parameters for the current flight operation mission, mounting a corresponding visible light camera based on the required camera parameters; controlling the visible light camera mounted on the UAV to take pictures at predetermined time intervals and designated airport shooting locations to acquire forest images.

[0009] Optionally, after equipping the corresponding visible light camera based on the required camera parameters, the method further includes: checking the status and bandwidth of the solid-state drive and storage transceiver line paired with the visible light camera through the central control software of the image acquisition system, and obtaining the status check result; if the visible light camera passes the status check, setting the shooting interval, exposure time and focal length of the visible light camera based on the required camera parameters of the current flight operation task.

[0010] Optionally, the steps for collecting forest image data based on an unmanned intelligent airport include: initializing and checking the status of the integrated navigation system through the central control software of the image acquisition system; and after the status check is passed, using the integrated navigation system to provide positioning signals and navigation information to the UAV.

[0011] Optionally, the steps of initializing and checking the status of the integrated navigation system through the central control software of the image acquisition system include: initializing the inertial measurement unit in the integrated navigation system through the central control software of the image acquisition system, wherein the inertial measurement unit includes a three-axis gyroscope and an accelerometer; checking the search status and signal quality value of the satellite positioning system based on satellite broadcast ephemeris to complete the status check.

[0012] Optionally, after controlling the visible light camera mounted on the UAV to take pictures at predetermined time intervals and designated airport shooting locations to acquire forest images, the method further includes: after the central control software of the image acquisition system receives the status information of the UAV completing the flight route, it sends a return command to the UAV, wherein the return command is used to control the UAV to return to the unmanned smart airport and land.

[0013] According to another aspect of the present invention, an image data acquisition device based on an unmanned intelligent airport is also provided, comprising: a forest image acquisition unit, used to acquire forest image data based on the unmanned intelligent airport, wherein the unmanned intelligent airport is selected in a forest area that meets predetermined mountain and forest conditions, the unmanned intelligent airport is used for take-off, landing and parking of predetermined types of drones, the predetermined mountain and forest conditions include at least one of the following: the height difference of the mountain is greater than a preset height difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold; an image analysis unit, used to preprocess the forest image data and analyze the preprocessed forest image data using a pre-trained machine learning model to identify the forest resource status, wherein the forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation; and a forest resource display unit, used to display the forest resource status on the display screen of a target terminal.

[0014] Optionally, the forest image acquisition unit includes: an instruction sending module, used to send start instructions and one-click operation instructions to the UAV through the central control software of the image acquisition system, wherein the one-click operation instruction is used to instruct the UAV to perform operation patrol and forest image acquisition according to a pre-planned airport patrol route; and an image processing module, used to process the forest images returned by each UAV according to the UAV identifier and timeline order through the central control software of the image acquisition system to obtain the forest image data.

[0015] Optionally, the image data acquisition device based on the unmanned intelligent airport, when controlling the drone to perform operational patrols and forest image acquisition according to a pre-planned airport patrol route, includes: a camera selection unit, used to acquire the required camera parameters of the drone for the current flight operation task, and to mount a corresponding visible light camera based on the required camera parameters; and a forest image shooting unit, used to control the visible light camera mounted on the drone to shoot at predetermined time intervals and designated airport shooting locations to acquire forest images.

[0016] Optionally, the image data acquisition device based on the unmanned intelligent airport further includes: a camera status check unit, used to check the status and bandwidth of the solid-state drive and storage transceiver line paired with the visible light camera through the central control software of the image acquisition system after the corresponding visible light camera is equipped based on the required camera parameters, and obtain the status check result; and a camera setting unit, used to set the shooting interval, exposure time and focal length of the visible light camera based on the required camera parameters of the current flight operation task when the visible light camera passes the status check.

[0017] Optionally, the forest image acquisition unit includes: a navigation system check unit, used to initialize and check the status of the integrated navigation system through the central control software of the image acquisition system; and a navigation unit, used to provide positioning signals and navigation information to the UAV using the integrated navigation system after the status check is passed.

[0018] Optionally, the navigation system inspection unit includes: an initialization module, used to perform initialization operations on the inertial measurement unit in the integrated navigation system through the central control software of the image acquisition system, wherein the inertial measurement unit includes: a three-axis gyroscope and an accelerometer; and a positioning inspection module, used to check the search status and signal quality value of the satellite positioning system based on satellite broadcast ephemeris, and complete the status inspection.

[0019] Optionally, the image data acquisition device based on the unmanned intelligent airport further includes: a return-to-home command sending unit, which is used to send a return-to-home command to the drone after the visible light camera on the drone takes pictures at a predetermined time interval and a designated airport shooting position, and after the central control software of the image acquisition system receives the status information of the drone completing the flight route, the return-to-home command is used to control the drone to return to the unmanned intelligent airport and land.

[0020] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the image data acquisition method based on an unattended intelligent airport as described above.

[0021] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the image data acquisition method based on an unmanned intelligent airport as described above.

[0022] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the image data acquisition method based on an unmanned intelligent airport as described in any one of the above embodiments.

[0023] In this disclosure, forest image data is collected based on an unmanned intelligent airport. The unmanned intelligent airport is selected in a forest area that meets predetermined forest conditions. The unmanned intelligent airport is used for the take-off, landing, and parking of predetermined types of drones. The predetermined forest conditions include at least one of the following: the elevation difference of the mountain is greater than a preset elevation difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold. The forest image data is preprocessed, and a pre-trained machine learning model is used to analyze the preprocessed forest image data to identify the forest resource status. The forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation. The forest resource status is displayed on the display screen of the target terminal.

[0024] Based on the above disclosure, this invention ensures the stable operation of unmanned aerial vehicles (UAVs) in all weather conditions and around the clock through an unmanned intelligent airport. It can adapt to complex and ever-changing natural environments. By automatically controlling UAVs within the unmanned intelligent airport to collect image data from forests in harsh environments, and using a pre-trained machine learning model to analyze the pre-processed forest image data, it identifies the status of forest resources. This achieves unmanned automated data collection and storage, reduces labor costs, and improves operational efficiency. Thus, it solves the technical problems of low operational efficiency in unmanned intelligent airport systems operating in harsh environments. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0026] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing an image data acquisition method based on an unmanned smart airport is shown.

[0027] Figure 2 This is a flowchart of an optional image data acquisition method based on an unattended smart airport according to an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of an optional image data acquisition and analysis method based on an unmanned intelligent airport according to an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of an optional image data acquisition device based on an unmanned smart airport according to an embodiment of the present invention;

[0030] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] It should be noted that the image data acquisition method and device based on unmanned intelligent airports in this disclosure can be used in the field of image data processing technology for image data acquisition and forest resource analysis based on unmanned equipment, and can also be used in any field other than the field of image data processing technology. In the case of image data acquisition and forest resource analysis based on unmanned equipment, this disclosure does not limit the application field of the image data acquisition method and device based on unmanned intelligent airports.

[0034] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) collected in this public disclosure are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse. For example, this system has interfaces with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained only after receiving consent from the aforementioned user or organization.

[0035] It should be noted that in this disclosure, customer information is collected and analyzed, and users are provided with corresponding operation entry points to choose whether to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0036] The following embodiments of the present invention can be applied to various systems / applications / equipment for image data acquisition based on unmanned intelligent airports. The present invention can be applied to forest resource monitoring and inspection scenarios. By monitoring resources in forest areas located in mountainous environments with large elevation differences, dense vegetation, harsh living conditions, and inaccessible by humans or vehicles, it reduces labor costs in forest inspection processes, improves the quality and quantity of remote sensing image acquisition, and achieves effective monitoring of forest resources. It can also be applied to other fields such as geological disaster early warning and forest environmental protection. For example, the unmanned intelligent airport of the present invention can be deployed. The airport has the functions of automatic charging, remote control, and one-click operation. A vertical take-off and landing fixed-wing UAV (which integrates an industrial-grade combined navigation system and a spectral imaging camera, capable of adapting to complex terrain and acquiring high-precision image data) is used. Through the integrated data acquisition system control software, coordinated control of the UAV, airport, camera, and navigation system is achieved. The control software automatically initializes and sets parameters for the visible light camera and checks the initialization status of the combined navigation system according to task requirements, ensuring data integrity and reliability. It sends the UAV take-off command, acquires and stores data during flight, and finally automatically returns to the airport after the mission is completed.

[0037] The solution provided by this invention reduces reliance on manual intervention, enabling unattended automated data acquisition and storage, thus lowering labor costs. The acquisition of high-precision remote sensing imagery and integrated navigation data allows for more comprehensive and detailed monitoring of forest resources, providing a rich and accurate data foundation for subsequent analysis.

[0038] Moreover, unmanned intelligent airports can adapt to complex and ever-changing natural environments, ensuring stable operation of drones around the clock and in all weather conditions, especially in harsh environments such as forests. Combined with the infrastructure of the tower visual network, unmanned intelligent airports provide reliable hardware support for multi-drone collaborative control, enhancing the flexibility and operational efficiency of drone swarms.

[0039] The present invention will now be described in detail with reference to various embodiments.

[0040] Example 1

[0041] According to an embodiment of the present invention, an embodiment of an image data acquisition method based on an unmanned intelligent airport is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] The image data acquisition method based on unmanned intelligent airport provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal or similar computing device.Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing an image data acquisition method based on an unmanned intelligent airport is shown. Figure 1 As shown, computer terminal 10 (or mobile device) may include one or more ( Figure 1 (Illustrated as 102a, 102b, ..., 102n) Processor 102 (processor 102 may include, but is not limited to, a microprocessor MCU (Microcontroller Unit) or a programmable gate array (FPGA), etc.), memory 104 for storing data, and transmission device 106 for communication functions. In addition, it may include: a display, input / output interface (I / O interface), Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), network interface, power supply, and / or camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0043] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0044] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the image data acquisition method based on an unmanned intelligent airport in this embodiment of the application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned image data acquisition method based on an unmanned intelligent airport. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0045] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0046] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0047] Under the aforementioned operating environment, this application provides the following: Figure 2 The image data acquisition method shown is based on an unmanned intelligent airport. Figure 2 This is a flowchart of an optional image data acquisition method based on an unmanned smart airport according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0048] Step S201: Collect forest image data based on unmanned intelligent airport. The unmanned intelligent airport is selected in a forest area that meets the predetermined mountain and forest conditions. The unmanned intelligent airport is used for take-off, landing and parking of predetermined types of drones. The predetermined mountain and forest conditions include at least one of the following: the height difference of the mountain is greater than a preset height difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold.

[0049] For unmanned intelligent airports, site selection requires areas with dense forest cover, complex terrain, and sparse human activity. These areas often have significant elevation differences, high vegetation coverage, or very few internal roads, indicating that the use of drones is necessary and efficient, as traditional manual or vehicle inspections are extremely difficult in such environments. Unmanned intelligent airports not only provide safe takeoff and landing sites for their drones but also possess automatic charging, data storage, and communication relay capabilities, ensuring that drones can continuously and efficiently perform inspection tasks without frequent human intervention.

[0050] Optionally, the steps for collecting forest image data based on an unmanned intelligent airport include: sending start commands and one-click operation commands to the drones through the central control software of the image acquisition system, wherein the one-click operation command is used to instruct the drones to perform operation patrols and collect forest images according to the pre-planned airport patrol route; and organizing the forest images returned by each drone according to the drone identification and timeline order through the central control software of the image acquisition system to obtain forest image data.

[0051] In this embodiment, the control center of the unmanned intelligent airport can be the central control software of the image acquisition system. This software integrates and controls the UAV platform and the image acquisition system. The UAV platform must be equipped with vertical take-off and landing capabilities to adapt to complex mountainous terrain and ensure safe start-up and recovery within limited space. It first sends a start command to the UAV to activate its various systems; then it sends a one-click operation command, which includes detailed parameters such as the UAV's flight path, mission mode, and data acquisition frequency, instructing the UAV to begin the forest image acquisition mission according to the preset cruise route.

[0052] During flight, the drone's high-precision visible light camera automatically captures images of the forest. These images cover detailed morphology and structure of the vegetation, providing a foundation for subsequent resource status analysis. Furthermore, the central control software performs preliminary processing on the raw images returned by the drone, sorting them by acquisition time and classifying them according to the drone's unique identifier. This ensures that each frame can be accurately traced back to its acquisition time and specific drone platform, facilitating subsequent data analysis and management.

[0053] It should be noted that, in selecting unmanned intelligent airports in this embodiment, GIS (Geographic Information System) tools can be used to assess whether potential forest areas meet the preset mountain and forest conditions, combining terrain data, vegetation coverage, and road distribution information. For example, for mountainous areas with an elevation difference greater than a preset threshold, it is ensured that the drone can overcome the impact of terrain undulations; for areas with vegetation coverage higher than a preset threshold, the drone's penetration and image clarity are assessed; and for cases where the number of roads within the forest is lower than a preset threshold, the necessity and feasibility of using drones to replace human patrols are considered.

[0054] Through the above implementation steps, from the site selection and function of the unmanned intelligent airport to the operation instructions of the UAV platform and the preliminary processing of image data, the entire process is highly automated and intelligent, which can effectively collect forest remote sensing data without human intervention, providing strong support for forest resource monitoring and management.

[0055] Optionally, the steps of the drone conducting operational patrols and forest image acquisition according to a pre-planned airport patrol route include: acquiring the required camera parameters for the current flight operation mission, mounting a corresponding visible light camera based on the required camera parameters, and controlling the visible light camera mounted on the drone to take pictures at predetermined time intervals and designated airport shooting locations to acquire forest images.

[0056] The visible light camera in this embodiment can be a thermal imaging camera, a multispectral camera, etc. Select an appropriate payload according to the mission requirements, optimize the preset parameters of the camera and other sensors to adapt to the changes in light and vegetation obstruction in the mountain forest environment, and debug the data transmission link to ensure that the image data stored on the UAV can be transmitted back to the central control system in a timely and safe manner for subsequent processing and analysis.

[0057] Before each flight mission begins, the central control software retrieves camera parameter templates from the database based on preset forest patrol requirements and makes appropriate adjustments. These parameter templates take into account different forest types (such as coniferous forests, broad-leaved forests, and mixed forests), vegetation density, terrain features (such as mountains and plains), and weather conditions (such as light intensity and wind speed). For example, for forest areas with low light, a longer exposure time is set to improve image sharpness; in areas with high vegetation density, a wide-angle focal length is selected to obtain a wider field of view; and in windy weather, image stabilization is enhanced to ensure continuous shooting.

[0058] The central control software automatically selects and equips the most suitable visible light camera based on the acquired camera parameters. The selection of the visible light camera is based on its resolution, dynamic range, environmental adaptability, storage capacity, and data transmission capabilities. Once the camera is determined, the central control software sends commands via the wireless communication interface to ensure the camera is correctly installed at the designated location on the drone to achieve the optimal shooting angle and coverage.

[0059] Furthermore, before the drone takes off, the central control software sets the shooting parameters of the visible light camera according to requirements, including but not limited to the shooting interval, exposure time, and focal length. The shooting interval setting must take into account the dynamic change frequency of forest resources and the limitations of storage capacity to ensure that enough image information is captured without causing data overload. The exposure time and focal length settings need to be adjusted according to real-time lighting conditions and the distance to the shooting target to ensure image quality.

[0060] After takeoff, the drone uses satellite positioning systems such as BeiDou and inertial navigation systems (IMUs, which measure and report the acceleration and angular velocity of objects, used in navigation and motion tracking systems) for precise positioning, ensuring stable flight along a pre-planned airport cruise route. The central control software, through the drone's onboard visible light camera and integrated navigation system, acquires and analyzes the drone's position information in real time, controlling the visible light camera to take pictures at preset shooting locations. These shooting locations are typically set in key areas of the forest, such as forest edges, sparsely populated areas, and potential disaster sites, to ensure the collection of the most valuable data for forest resource management.

[0061] As the drone flies along its preset route, the central control software continuously monitors the operational status of the visible light camera, ensuring that the camera captures images accurately within predetermined time intervals. The captured forest image data is transmitted in real time to the storage system of the unmanned intelligent airport, where preliminary quality checks are performed to ensure data integrity and accuracy.

[0062] Optionally, after equipping the visible light camera with the corresponding camera based on the required camera parameters, the method further includes: checking the status and bandwidth of the solid-state drive and storage transceiver line paired with the visible light camera through the central control software of the image acquisition system, and obtaining the status check result; if the visible light camera passes the status check, setting the shooting interval, exposure time and focal length of the visible light camera based on the required camera parameters of the current flight operation task.

[0063] During the drone's flight along a preset route and its forest image acquisition mission, the entire system's operation is rigorously monitored and controlled by central control software. The software maintains real-time communication with the drone via a wireless data link, ensuring that the drone's mission status information is promptly fed back to the central control system. The visible light camera onboard the drone operates according to parameters set by the central control software during flight. These parameters include, but are not limited to, image capture interval, exposure time, and focal length, to acquire high-resolution forest ground images. These visible light camera parameter settings enable the camera to capture high-quality images under varying lighting conditions, while ensuring effective data storage and avoiding unnecessary redundant data acquisition, thereby improving data transmission and storage efficiency.

[0064] Before the drone takes off, the central control software checks whether the storage capacity of the solid-state drive (SSD) paired with the visible light camera meets the image data storage requirements of the mission, and also checks the physical condition of the storage transceiver cable to ensure unobstructed data transmission. Furthermore, the software also monitors the data transmission bandwidth of the storage transceiver cable to ensure efficient data transfer from the camera to the storage system, preventing data bottlenecks from impacting operational efficiency.

[0065] Only after all status checks have passed will the central control software perform detailed parameter settings for the visible light camera according to the needs of the current flight operation. Specifically, this includes setting the shooting interval: based on the characteristics of the forest area and the target monitoring accuracy, a reasonable shooting interval time is set to ensure sufficient temporal resolution and data coverage. Exposure time settings are also included: the exposure time needs to be dynamically adjusted according to flight altitude, speed, and lighting conditions to ensure that the captured images are neither overexposed nor underexposed, and have good contrast and detail. Finally, focal length settings are also performed: the focal length selection must consider the distance and coverage of the target area, as well as the image sharpness and resolution, to ensure that every captured image clearly presents the state of the forest resources.

[0066] Through the detailed settings described above, the visible light camera can automatically adjust its parameters to adapt to different flight conditions and mission requirements, thereby acquiring high-quality image data in complex and ever-changing forest environments, providing a solid foundation for subsequent data analysis. At the same time, the automated processes of status checks and parameter settings significantly improve the efficiency and reliability of data acquisition, reducing reliance on manual intervention.

[0067] Optionally, the steps for collecting forest image data based on an unmanned intelligent airport include: initializing and checking the status of the integrated navigation system through the central control software of the image acquisition system; and after the status check is passed, using the integrated navigation system to provide positioning signals and navigation information for the UAV.

[0068] In this embodiment, the integrated navigation system can be initialized through the central control software of the image acquisition system to ensure that the UAV platform can acquire accurate position, speed and attitude data before flight, laying the foundation for flight safety and high-precision acquisition of image data.

[0069] Optionally, the steps of initializing and checking the status of the integrated navigation system through the central control software of the image acquisition system include: initializing the inertial measurement unit in the integrated navigation system through the central control software of the image acquisition system, wherein the inertial measurement unit includes a three-axis gyroscope and an accelerometer; checking the search status and signal quality value of the satellite positioning system based on satellite broadcast ephemeris, and completing the status check.

[0070] During the initialization of the Inertial Measurement Unit (IMU), the central control software first initializes the IMU within the integrated navigation system. This includes calibrating and zero-point adjusting the three-axis gyroscopes and accelerometers mounted on the UAV platform. This step automatically zeroes and compensates for bias in each sensor via the software interface, ensuring that the IMU provides accurate horizontal plane angle and acceleration information when the UAV is stationary or in motion, thus enabling precise measurement of the UAV's attitude changes during flight. Checking the IMU may include verifying that the output fluctuations of the gyroscopes and accelerometers are within acceptable limits when stationary. If any sensor anomalies are detected, such as high-frequency vibration or noise interference, the central control software will prompt a re-initialization to eliminate potential measurement errors.

[0071] The central control software also evaluates the fusion performance of the integrated navigation system, confirming whether the short-term high-precision data from the IMU can be effectively combined with the long-term stability data from the satellite positioning system during flight. This step tests the positioning accuracy and robustness in dynamic environments by simulating flight trajectories, ensuring that the integrated navigation system can still provide reliable navigation even under conditions of poor satellite signal (such as deep forests or canyons).

[0072] The central control software then checks the search status and signal quality of the satellite positioning system (such as BeiDou). This includes searching based on satellite broadcast ephemeris to determine if the system has locked onto a sufficient number of satellites; simultaneously, it assesses the signal quality ratio (SNR) to ensure that the strength and quality of the received satellite signals meet the requirements for flight safety and positioning accuracy.

[0073] After initialization, the central control software further conducts a comprehensive check on the status of the integrated navigation system to ensure that it can provide the UAV with stable and continuous positioning signals and navigation information, avoiding unexpected interruptions or positioning errors during flight.

[0074] Step S202: Preprocess the forest image data and use a pre-trained machine learning model to analyze the preprocessed forest image data to identify the forest resource status, wherein the forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation.

[0075] After the UAV completes the forest image data acquisition, the image data is first transmitted back to the data acquisition system of the unmanned intelligent airport. Then, the image data undergoes preprocessing. It should be noted that the preprocessing strategies used in this embodiment are primarily image-specific, including but not limited to: noise removal, using techniques such as median filtering or Gaussian filtering to remove random noise from the image and improve image clarity; image correction, based on the position and attitude information provided by the integrated navigation system, performing geometric correction on the image to eliminate image distortion caused by flight attitude and ensure the accuracy of image geolocation; radiometric correction, using atmospheric correction models, such as the MODTRAN model (Moderate Resolution Atmospheric Transmission Model), to perform radiometric correction on the image, removing the influence of atmospheric effects and ensuring the objectivity and consistency of image brightness; and format conversion and standardization, converting the raw image data into a common image format, such as TIFF or JPEG, and standardizing its size and aspect ratio to facilitate input for subsequent machine learning models.

[0076] After preprocessing the forest image data, a pre-trained machine learning model can be used to analyze the preprocessed forest image data. It should be noted that the learning model involved in this embodiment is based on a deep learning framework (such as a Convolutional Neural Network (CNN)) and is trained using a large amount of labeled data. During the model analysis process, the model can first load a machine learning model specifically trained for forest resource status identification, including but not limited to vegetation cover identification models, tree species classification models, growth status analysis models, and pest and disease detection models. Then, image feature extraction is performed, such as using the convolutional and pooling layers of CNN to extract useful visual features from the image, such as texture, edges, and patterns. After that, it is necessary to classify and identify the objects involved in the image. The model output may include a distribution probability map of each type of forest resource or a specific category label.

[0077] It should be noted that, if vegetation coverage needs to be quantified, the proportion of pixels in the image that are identified as vegetation is calculated; if tree species are identified, the number of trees of each type is counted; for growth status and pest and disease conditions, the model may output a rating of healthy, good, diseased, or attacked.

[0078] Step S203: Display the status of forest resources on the display screen of the target terminal.

[0079] Optionally, this embodiment can utilize report generation tools to convert the analysis results into a human-readable report format, such as PDF or web page format, containing detailed text descriptions and data analysis charts. Then, on a Geographic Information System (GIS) platform, the identified forest resource status can be overlaid onto location images captured by drones to visually display resource distribution and status. Alternatively, a data dashboard can be developed to display real-time trends in key forest resource indicators, such as seasonal changes in vegetation cover and heat maps showing the geographical distribution of tree species.

[0080] In addition, this embodiment can also provide an interactive interface through the target terminal, allowing users to filter and view specific forest resource status according to their needs, such as zooming in to view the pest and disease situation in a certain area, or querying the distribution details of a certain tree species. For abnormal forest resource status, such as the discovery of serious pests and diseases or drastic changes in vegetation coverage, the system automatically sends a notification or alarm to the target terminal to ensure that managers can respond in a timely manner.

[0081] Through the above steps, the analysis results of forest resource status can not only be presented with high accuracy and real-time performance, but also be quickly understood and responded to by forest managers without technical backgrounds through an intuitive visualization interface, thereby improving the efficiency of forest resource management and protection.

[0082] Through the above steps, forest image data can be collected based on an unmanned intelligent airport. The unmanned intelligent airport is selected in a forest area that meets the predetermined forest conditions. The unmanned intelligent airport is used for the take-off, landing, and parking of predetermined types of drones. The predetermined forest conditions include at least one of the following: the elevation difference of the mountain is greater than a preset elevation difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold. The forest image data is preprocessed, and a pre-trained machine learning model is used to analyze the preprocessed forest image data to identify the forest resource status. The forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation. The forest resource status is displayed on the display screen of the target terminal. In this embodiment, unmanned intelligent airports can ensure the stable operation of drones around the clock and in all weather conditions, adapting to complex and ever-changing natural environments. By automatically controlling drones within the unmanned intelligent airport to collect image data from forests in harsh environments, and using pre-trained machine learning models to analyze the pre-processed forest image data, the status of forest resources is identified. This achieves unmanned automated data collection and storage, reduces labor costs, and improves operational efficiency, thereby solving the technical problems of low operational efficiency in unmanned intelligent airport systems operating in harsh environments.

[0083] Optionally, after the visible light camera on the drone takes pictures at predetermined time intervals and designated airport shooting locations to collect forest images, the system further includes: after the central control software of the image acquisition system receives the status information of the drone completing the flight route, it sends a return command to the drone, wherein the return command is used to control the drone to return to the unmanned smart airport for landing.

[0084] Once the drone completes its predetermined forest imagery acquisition mission—that is, after flying along the pre-set flight path—the drone's sensors and communication modules will automatically send a series of status information to the central control software of the imagery acquisition system. This information includes, but is not limited to, the drone's position, remaining battery power, mission completion signal, and abnormal situation reports. After receiving the drone's completion status information, the central control software will analyze and confirm the information to ensure that the drone's flight mission has been fully completed and that the drone is in a safe state.

[0085] After confirming that the drone mission is completed and the drone is in a safe state, the central control software will generate and send a return-to-home command. The return-to-home command includes the specific path information for the drone to return to the unmanned smart airport, the target airport coordinate data, and parameters such as flight altitude and speed during the return process. These parameters will be set based on the drone's current location information, remaining battery power, and weather conditions, thereby ensuring that the drone can return to the airport safely and efficiently.

[0086] Upon receiving the return-to-home command from the central control software, the drone will immediately adjust its flight attitude and, based on the path information and parameters in the command, begin its return-to-home mission. During the return process, the drone's integrated navigation system (including a positioning system, an inertial navigation system, and possibly a visual positioning system) will work together to ensure that the drone accurately returns to the unmanned smart airport along the path indicated by the return-to-home command. Furthermore, the drone will monitor its battery level and flight status in real time and maintain communication with the central control software via a data link to ensure a safe and smooth return process.

[0087] Upon receiving the drone's return-to-home signal, the unmanned smart airport will automatically initiate landing preparation procedures, including clearing the landing area, adjusting the airport's internal power and charging systems, and ensuring the airport's environmental monitoring modules are functioning properly to provide the drone with accurate weather information. The unmanned smart airport will also activate its automatic landing system, using cameras and radar to track and locate the drone in real time, ensuring the drone lands accurately and safely at the airport.

[0088] After the drone lands at the unmanned smart airport, the airport's automatic charging system will immediately activate, providing the drone with power to prepare for its next flight mission. Simultaneously, the airport's maintenance system will inspect the drone's onboard equipment, including visible light cameras, integrated navigation systems, and battery status, to ensure equipment integrity and identify potential faults. If any problems are found, the maintenance system will generate a maintenance request report and send it to the central control software via wireless communication for remote diagnostics and maintenance scheduling. If both the drone and the airport system are functioning normally, the central control software will update the drone's availability status, making it ready to be scheduled for new missions.

[0089] Through the above steps, this embodiment can not only control the sending and execution of return commands after the drone completes forest image collection, realizing how unmanned smart airports can work in collaboration with drones, but also ensure the safe and efficient operation of drones throughout the entire operation cycle, as well as how to automatically charge and maintain drones after they return to the airport, thereby achieving long-term stable operation of the system.

[0090] The following describes in detail another optional implementation method.

[0091] Most existing unmanned smart airports are used for cargo and logistics, and the location and attitude data acquisition equipment used are low-precision integrated navigation systems that come with the drone platform. At the same time, even in the field of inspection, the cameras used are mostly ordinary shooting lenses, and most unmanned smart airports are deployed in open environments, without applications in mountainous environments with large elevation differences and dense vegetation that are difficult for people and vehicles to reach.

[0092] In response to the above situation, this embodiment proposes a data acquisition system based on unmanned intelligent airports, vertical take-off and landing fixed-wing UAVs, and image data acquisition systems, based on the premise of exploring the coupling relationship among unmanned intelligent airports, and solving the problems of harsh environment and high maintenance costs.

[0093] Figure 3 This is a schematic diagram of an optional image data acquisition and analysis method based on an unmanned intelligent airport according to an embodiment of the present invention, as shown below. Figure 3 As shown, it includes:

[0094] Step 1: Integrated design of the data acquisition system.

[0095] The acquisition system's control software enables integrated power supply and communication control for the UAV platform, unmanned intelligent airport, visible light camera, and integrated navigation system, ensuring that each key payload functions normally as planned during the acquisition system's operation.

[0096] Step 2: Visible light camera initialization and parameter settings.

[0097] The initialization and parameter setting of the visible light camera mainly includes two steps: checking the memory and transmit / receive bandwidth status of the visible light camera and setting the camera parameters in a task-driven manner.

[0098] Step 2.1 Visible light camera memory and transceiver bandwidth status check: This mainly involves checking the status and bandwidth of the solid-state drive and storage transceiver line paired with the visible light camera through the acquisition system control software to ensure that the camera has sufficient memory and storage channels to store flight data.

[0099] Step 2.2 Camera parameter settings mainly involve setting the camera's shooting interval, exposure time, and focal length for the current flight mission.

[0100] Step 3: Initialize and check the status of the integrated navigation system.

[0101] The initialization setup and status check of the integrated navigation system mainly includes two steps: initialization of the inertial measurement unit in the integrated navigation system and status check of satellite navigation data reception.

[0102] Step 3.1 Initialization of the inertial measurement unit of the integrated navigation system mainly refers to the initialization of the three-axis gyroscope and accelerometer in the inertial measurement unit after the acquisition system is switched to power supply by the UAV platform, which usually takes 1 minute.

[0103] Step 3.2 Satellite navigation data reception status check mainly refers to the search status of satellite broadcast ephemeris of satellite positioning systems such as GPS, GLONASS and BeiDou in the acquisition system, to ensure that the acquisition system can observe enough satellite positioning signals in the flight results and effectively locate itself.

[0104] Step four: Send the drone takeoff command.

[0105] Sending the drone takeoff command mainly refers to sending a command to the drone after the data acquisition system's central control software has completed the checks mentioned above, so that the drone can take off and plan its flight path according to the day's mission deployment.

[0106] Step 5: Data acquisition and storage during flight.

[0107] In-flight data acquisition and storage mainly refers to the process by which the acquisition system stores ground remote sensing image data acquired by the visible light camera, combined navigation position and attitude data, and UAV platform status data on the hard drive built into the acquisition system during the flight of the UAV along the predetermined route.

[0108] Step six: Return after the task is completed.

[0109] "Returning after mission completion" means that after receiving the status information of the drone's flight path completion, the central control software sends a return command to the drone, causing it to return to the unmanned smart airport and land.

[0110] Through the above implementation methods, the present invention provides an image data acquisition strategy based on an unmanned intelligent airport, which is aimed at the application of UAV platforms in forest patrol scenarios. Based on the relationship between the unmanned intelligent airport, vertical take-off and landing fixed-wing UAVs and image data acquisition systems, it solves the problems caused by the large elevation difference, dense vegetation, harsh living environment and difficulty in access by manpower or vehicles in mountainous environments.

[0111] The following is a detailed description with reference to another embodiment.

[0112] Example 2

[0113] The image data acquisition device based on an unmanned intelligent airport provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in the above embodiment one. The specific implementation method and beneficial effects can be referred to the aforementioned method embodiment, and will not be repeated here.

[0114] Figure 4 This is a schematic diagram of an optional image data acquisition device based on an unmanned smart airport according to an embodiment of the present invention, such as... Figure 4 As shown, the image data acquisition device based on the unmanned intelligent airport may include: a forest image acquisition unit 41, an image analysis unit 42, and a forest resource display unit 43.

[0115] The forest image acquisition unit 41 is used to acquire forest image data based on the unmanned intelligent airport. The unmanned intelligent airport is selected in a forest area that meets the predetermined mountain and forest conditions. The unmanned intelligent airport is used to take off, land, and park a predetermined type of drone. The predetermined mountain and forest conditions include at least one of the following: the height difference of the mountain is greater than a preset height difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold.

[0116] Image analysis unit 42 is used to preprocess forest image data and analyze the preprocessed forest image data using a pre-trained machine learning model to identify the status of forest resources, wherein the status of forest resources includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation.

[0117] The forest resource display unit 43 is used to display the status of forest resources on the display screen of the target terminal.

[0118] The aforementioned image data acquisition device based on an unmanned intelligent airport can acquire forest image data through the forest image acquisition unit 41 based on the unmanned intelligent airport. The unmanned intelligent airport is selected in a forest area that meets the predetermined mountain and forest conditions. The unmanned intelligent airport is used for take-off, landing and parking of predetermined types of drones. The predetermined mountain and forest conditions include at least one of the following: the height difference of the mountain is greater than a preset height difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold. The image analysis unit 42 preprocesses the forest image data and uses a pre-trained machine learning model to analyze the preprocessed forest image data and identify the forest resource status. The forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation. The forest resource status is displayed on the display screen of the target terminal through the forest resource display unit 43. In this embodiment, unmanned intelligent airports can ensure the stable operation of drones around the clock and in all weather conditions, adapting to complex and ever-changing natural environments. By automatically controlling drones within the unmanned intelligent airport to collect image data from forests in harsh environments, and using pre-trained machine learning models to analyze the pre-processed forest image data, the status of forest resources is identified. This achieves unmanned automated data collection and storage, reduces labor costs, and improves operational efficiency, thereby solving the technical problems of low operational efficiency in unmanned intelligent airport systems operating in harsh environments.

[0119] Optionally, the forest image acquisition unit includes: an instruction sending module, used to send start instructions and one-click operation instructions to the drone through the central control software of the image acquisition system, wherein the one-click operation instruction is used to instruct the drone to perform operation patrol and forest image acquisition according to the pre-planned airport patrol route; and an image processing module, used to process the forest images returned by each drone according to the drone identification and timeline order through the central control software of the image acquisition system to obtain forest image data.

[0120] Optionally, the image data acquisition device based on the unmanned intelligent airport, when controlling the drone to perform operational patrols and forest image acquisition according to a pre-planned airport patrol route, includes: a camera selection unit, used to acquire the required camera parameters of the drone for the current flight operation task, and to mount a corresponding visible light camera based on the required camera parameters; and a forest image shooting unit, used to control the visible light camera mounted on the drone to shoot at predetermined time intervals and designated airport shooting locations to acquire forest images.

[0121] Optionally, the image data acquisition device based on the unmanned intelligent airport also includes: a camera status check unit, used to check the status and bandwidth of the solid-state drive and storage transceiver line paired with the visible light camera through the central control software of the image acquisition system after the corresponding visible light camera is equipped based on the required camera parameters, and obtain the status check result; and a camera setting unit, used to set the shooting interval, exposure time and focal length of the visible light camera based on the required camera parameters of the current flight operation task when the visible light camera passes the status check.

[0122] Optionally, the forest image acquisition unit includes: a navigation system check unit, used to initialize and check the status of the integrated navigation system through the central control software of the image acquisition system; and a navigation unit, used to provide positioning signals and navigation information to the UAV using the integrated navigation system after the status check is passed.

[0123] Optionally, the navigation system inspection unit includes: an initialization module, used to perform initialization operations on the inertial measurement unit in the integrated navigation system through the central control software of the image acquisition system, wherein the inertial measurement unit includes: a three-axis gyroscope and an accelerometer; and a positioning inspection module, used to check the search status and signal quality value of the satellite positioning system based on satellite broadcast ephemeris, and complete the status inspection.

[0124] Optionally, the image data acquisition device based on the unmanned smart airport also includes: a return-to-home command sending unit, which is used to control the visible light camera on the drone to take pictures at a predetermined time interval and a designated airport shooting location after acquiring forest images. After the central control software of the image acquisition system receives the status information of the drone completing the flight route, it sends a return-to-home command to the drone. The return-to-home command is used to control the drone to return to the unmanned smart airport and land.

[0125] The aforementioned image data acquisition device based on unmanned intelligent airport may also include a processor and a memory. The aforementioned forest image acquisition unit 41, image analysis unit 42, forest resource display unit 43, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0126] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters enables the acquisition and analysis of forest imagery data based on unmanned intelligent airports.

[0127] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0128] Example 3

[0129] Embodiments of this application may provide an electronic device. Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 Only one of the components is shown: processor 502, memory 504, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module, and display.

[0130] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the image data acquisition method and device based on unmanned intelligent airports in this application embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned image data acquisition method based on unmanned intelligent airports. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0131] The processor can access information and applications stored in the memory via a transmission device to execute the following steps: Collecting forest image data based on an unmanned intelligent airport, wherein the unmanned intelligent airport is selected in a forest area that meets predetermined forest conditions, and the unmanned intelligent airport is used for take-off, landing, and parking of predetermined types of drones. The predetermined forest conditions include at least one of the following: the elevation difference of the mountain is greater than a preset elevation difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold; preprocessing the forest image data and using a pre-trained machine learning model to analyze the preprocessed forest image data and identify the forest resource status, wherein the forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation; displaying the forest resource status on the display screen of the target terminal.

[0132] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: send start command and one-click operation command to the drone through the central control software of the image acquisition system. The one-click operation command is used to instruct the drone to carry out operation patrol and forest image acquisition according to the pre-planned airport patrol route; and organize the forest images returned by each drone according to the drone identification and timeline order through the central control software of the image acquisition system to obtain forest image data.

[0133] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: obtain the required camera parameters of the UAV for the current flight operation mission, and carry the corresponding visible light camera based on the required camera parameters; control the visible light camera carried by the UAV to take pictures at predetermined time intervals and designated airport shooting locations to collect forest images.

[0134] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: After the corresponding visible light camera is equipped based on the required camera parameters, the status and bandwidth of the solid-state drive and storage transceiver line equipped with the visible light camera are checked by the central control software of the image acquisition system to obtain the status check results; if the visible light camera passes the status check, the shooting interval, exposure time and focal length of the visible light camera are set based on the required camera parameters of the current flight operation mission.

[0135] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: initialize the integrated navigation system and check its status through the central control software of the image acquisition system; after the status check is passed, use the integrated navigation system to provide the UAV with positioning signals and navigation information.

[0136] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: initialize the inertial measurement unit in the integrated navigation system through the central control software of the image acquisition system, wherein the inertial measurement unit includes a three-axis gyroscope and an accelerometer; check the search status and signal quality value of the satellite positioning system based on satellite broadcast ephemeris, and complete the status check.

[0137] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: After controlling the visible light camera on the drone to take pictures at a predetermined time interval and a designated airport shooting location to collect forest images, after the central control software of the image acquisition system receives the status information of the drone completing the flight route, it sends a return command to the drone. The return command is used to control the drone to return to the unmanned smart airport and land.

[0138] Those skilled in the art will understand that Figure 5 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 5 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5 The different configurations shown.

[0139] Those skilled in the art will understand that all or part of the steps in the various image data acquisition methods based on unmanned smart airports in the above embodiments can be implemented by a program instructing the hardware of the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0140] Example 4

[0141] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the image data acquisition method based on an unattended intelligent airport provided in Embodiment 1.

[0142] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute any one of the above embodiments of the image data acquisition method based on an unattended intelligent airport.

[0143] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0144] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the image data acquisition method based on an unmanned intelligent airport as described in various embodiments of this application.

[0145] This application also provides a computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the image data acquisition method based on an unattended intelligent airport described in various embodiments of this application.

[0146] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0147] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0151] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0152] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for acquiring image data based on an unmanned intelligent airport, characterized in that, include: Forest image data is collected based on unmanned intelligent airports, wherein the unmanned intelligent airports are selected in forest areas that meet predetermined mountain and forest conditions, and the unmanned intelligent airports are used for take-off, landing and parking of predetermined types of drones. The predetermined mountain and forest conditions include at least one of the following: the height difference of the mountain is greater than a preset height difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold. The forest image data is preprocessed, and the preprocessed forest image data is analyzed using a pre-trained machine learning model to identify the forest resource status, wherein the forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation. The status of the forest resources is displayed on the target terminal's screen.

2. The method according to claim 1, characterized in that, The steps for collecting forest imagery data based on unmanned smart airports include: The central control software of the image acquisition system sends start commands and one-click operation commands to the drone. The one-click operation command is used to instruct the drone to perform operation patrols and forest image acquisition according to the pre-planned airport patrol route. The forest images returned by each UAV are organized by the central control software of the image acquisition system according to the UAV identifier and timeline order to obtain the forest image data.

3. The method according to claim 2, characterized in that, The steps for drones to conduct operational patrols and collect forest images according to a pre-planned airport patrol route include: Obtain the required camera parameters for the UAV in the current flight operation mission, and equip the corresponding visible light camera based on the required camera parameters; The visible light camera mounted on the drone is controlled to take pictures at predetermined time intervals and designated airport shooting locations to collect forest images.

4. The method according to claim 3, characterized in that, After mounting a corresponding visible light camera based on the required camera parameters, the system further includes: The central control software of the image acquisition system checks the status and bandwidth of the solid-state drive and storage transceiver line paired with the visible light camera, and obtains the status check results. If the visible light camera passes the status check, the camera's shooting interval, exposure time, and focal length are set based on the camera parameters required for the current flight mission.

5. The method according to claim 3, characterized in that, The steps for collecting forest imagery data based on unmanned smart airports include: The central control software of the image acquisition system is used to initialize and check the status of the integrated navigation system. After the status check is passed, the integrated navigation system is used to provide the UAV with positioning signals and navigation information.

6. The method according to claim 5, characterized in that, The steps for initializing and checking the status of the integrated navigation system using the central control software of the image acquisition system include: The central control software of the image acquisition system initializes the inertial measurement unit in the integrated navigation system, wherein the inertial measurement unit includes a three-axis gyroscope and an accelerometer. Check the satellite positioning system's search status and signal quality values ​​based on satellite broadcast ephemeris to complete the status check.

7. The method according to claim 3, characterized in that, After controlling the visible light camera mounted on the drone to take pictures at predetermined time intervals and designated airport shooting locations to acquire forest images, the process also includes: After receiving the status information of the UAV completing its flight route, the central control software of the image acquisition system sends a return-to-home command to the UAV. The return-to-home command is used to control the UAV to return to the unmanned smart airport and land.

8. An image data acquisition device based on an unmanned intelligent airport, characterized in that, include: A forest image acquisition unit is used to acquire forest image data based on an unmanned intelligent airport. The unmanned intelligent airport is selected in a forest area that meets predetermined mountain and forest conditions. The unmanned intelligent airport is used for take-off, landing and parking of predetermined types of drones. The predetermined mountain and forest conditions include at least one of the following: the height difference of the mountain is greater than a preset height difference threshold, the forest vegetation coverage is higher than a preset coverage threshold, and the number of roads in the forest is lower than a preset number threshold. The image analysis unit is used to preprocess the forest image data and analyze the preprocessed forest image data using a pre-trained machine learning model to identify the forest resource status, wherein the forest resource status includes at least one of the following: vegetation coverage, tree species, growth status, and pest and disease situation. The forest resource display unit is used to display the status of the forest resources on the display screen of the target terminal.

9. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the image data acquisition method based on any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the image data acquisition method based on any one of claims 1 to 7.

Images