Real-time data collection and processing method of scenic spots based on drones

Abstract

The present invention discloses a method for real-time data collection and processing in a scenic area based on an unmanned aerial vehicle (UAV), which relates to the technical field of data collection and processing. First, the scenic area is divided into different landscape areas, and signal image data and quality data of each area are collected within a preset monitoring time period. By extracting this data, the image collection quality of each area is analyzed and evaluated, and optimized. Combined with the signal quality data, an environmental interference assessment value and a signal interference assessment value are obtained. By improving the quality of UAV data collection and adjusting the image collection frequency and signal strength of each landscape area, an interference assessment value for each area is finally obtained. The UAV flight parameters are optimized, which is conducive to improving the data collection quality of the landscape area and enhancing the efficiency of resource allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and processing, and in particular to a method for real-time data acquisition and processing of scenic spots based on drones. Background Art

[0002] Drones collect and process data from scenic spots, relying on the rapid development of drone technology, advancements in sensor technology, improvements in data processing and analysis capabilities, and the needs of scenic spot management and services. They can capture detailed image data of scenic spots in real time, providing important data support for intelligent management of scenic spots, tourist behavior analysis, and environmental monitoring.

[0003] For example, the invention patent with announcement number CN109164827B announces a data acquisition system based on a drone, which includes a ground system and a drone with a camera. The drone includes: a main controller, a vibration sensor and a wind speed sensor, which are used to detect the wind speed at the location of the drone and send the wind speed data to the main controller; when the main controller determines based on the vibration data whether the proportion of time that the vibration amplitude of the drone exceeds the preset vibration threshold within a preset time one exceeds the proportional limit one, when the vibration amplitude exceeds the preset vibration threshold, the drone stops shooting and hovers at a preset height. After the drone hovers, the main controller determines whether the proportion of time that the wind speed data exceeds the wind speed threshold within a preset time two exceeds the proportional limit two. If so, the drone lands on the ground. If not, the drone reaches the destination or returns to the starting point along the flight route.

[0004] For example, the invention patent with announcement number CN114167891B announces a ground data acquisition and processing system based on drones, including a path planning module, a video acquisition module, and a data processing module. The path planning module is used to output the drone's travel data, the video acquisition module is used to control the drone's flight and shoot videos, and the data processing module is used to calculate and process the shot video data. First, a high-altitude acquisition is performed, and the height and area of ​​individual areas in the collected video are calculated and analyzed to obtain a directional acquisition area, and then a low-altitude acquisition is performed on the directional acquisition area; low-altitude acquisition can obtain detailed ground data that high-altitude acquisition cannot obtain due to environmental and viewing angle reasons, and low-altitude acquisition is not performed in the entire range, but is targeted and highly efficient.

[0005] Based on the above findings, the existing technical solutions may have problems such as low quality of data collection in the landscape area and poor signal during data collection, which may lead to improper resource allocation in the landscape area, waste of resources, low real-time data collection, wrong decision-making, and reduced tourist experience. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a real-time data collection and processing method for scenic spots based on drones, which solves the problems designed in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time data collection and processing method for a scenic area based on a drone, comprising dividing the scenic area into various landscape areas, collecting signal image data and signal quality data of each landscape area in each preset monitoring time period, and obtaining signal image data and signal quality data of each landscape area in each monitoring time period.

[0008] The signal image data of each landscape area in each monitoring time period are extracted, and the acquisition quality assessment value of each landscape area image in each monitoring time period is obtained by analysis. The contrast of each signal image data of each landscape area in each monitoring time period is optimized and adjusted according to the acquisition quality assessment value of each landscape area image in each monitoring time period.

[0009] The signal quality data of each landscape area in each monitoring time period and the optimized signal image data of each landscape area in each monitoring time period are extracted, and the environmental interference assessment value and signal interference assessment value of each landscape area in each monitoring time period are obtained by processing.

[0010] The image acquisition frequency of each landscape area is adjusted based on the environmental interference assessment value of each landscape area in each monitoring time period, and the signal strength is adjusted based on the signal interference assessment value of each landscape area in each monitoring time period.

[0011] The environmental interference assessment value of each landscape area in each monitoring time period and the signal interference assessment value of each landscape area in each monitoring time period are extracted to adjust the flight trajectory of the UAV and replan the flight path of the UAV.

[0012] Furthermore, the signal quality data includes the average signal strength and average signal-to-noise ratio received by the drone in each landscape area during each monitoring time period.

[0013] The signal image data of each landscape area in each monitoring time period includes each image of each landscape area in each monitoring time period, image quality data of each image of each landscape area in each monitoring time period, and feature data of each image.

[0014] Furthermore, the acquisition quality assessment value of the images of each landscape area in each monitoring time period is analyzed and obtained. The specific process is: the image quality data of each image of each landscape area in each monitoring time period includes the average contrast, average brightness, edge clarity and average saturation of each image.

[0015] The average contrast, average brightness, edge clarity, and average saturation of each image in each landscape area during each monitoring time period were extracted, and the acquisition quality assessment values ​​of the images in each landscape area during each monitoring time period were obtained through processing.

[0016] The acquisition quality assessment value of each landscape area image in each monitoring time period is used to quantitatively assess the quality of each landscape area image in each monitoring time period.

[0017] Furthermore, the contrast of each signal image data of each landscape area in each monitoring time period is optimized and adjusted according to the acquisition quality assessment value of each landscape area image in each monitoring time period. The specific process is: the acquisition quality assessment value of each landscape area image in each monitoring time period is compared with the image quality assessment threshold. If the acquisition quality assessment value of each landscape area image in each monitoring time period is higher than or equal to the image quality assessment threshold, the contrast of the landscape area image collected in the monitoring time period is adjusted. If the acquisition quality assessment value of each landscape area image in each monitoring time period is lower than the image quality assessment threshold, the landscape area image is re-acquired.

[0018] Furthermore, the environmental interference assessment value and signal interference assessment value of each landscape area in each monitoring time period are obtained. The specific process is: extracting the characteristic data of each image in each landscape area in each monitoring time period, and the characteristic data of each image in each landscape area in each monitoring time period include crowd density, normalized vegetation index, and average noise.

[0019] The highest crowd density in each image of each landscape area in each monitoring time period was counted, and the average of the crowd density in each image of each landscape area in each monitoring time period was calculated to obtain the average crowd density in each landscape area in each monitoring time period. The number of monitoring periods in which the crowd density in each image of each landscape area in each monitoring time period was higher than the crowd density threshold of each landscape area stored in the database was counted, and recorded as the number of time periods of personnel gathering in each landscape area in each monitoring time period.

[0020] The normalized vegetation index of each image in each landscape area in each monitoring time period was extracted, and the average value was calculated to obtain the normalized average vegetation index of each landscape area in each monitoring time period.

[0021] The average crowd density and the maximum crowd density in each landscape area during each monitoring time period are counted, and the number of time periods with gathering of people in each landscape area during each monitoring time period and the normalized average vegetation index of each landscape area during each monitoring time period are extracted. The environmental interference assessment value of each landscape area during each monitoring time period is obtained by processing. The environmental interference assessment value of each landscape area during each monitoring time period is used to adjust the collection frequency of the drone.

[0022] The average signal strength and average signal-to-noise ratio of the drone received in each landscape area during each monitoring time period were extracted, and combined with the average noise of the images of each landscape area during each monitoring time period, the signal interference assessment value of each landscape area during each monitoring time period was obtained.

[0023] Furthermore, the image acquisition frequency of each landscape area is adjusted based on the environmental interference assessment value of each landscape area in each monitoring time period. The specific process is: extract the environmental interference assessment value of each landscape area in each monitoring time period and compare it with the environmental interference assessment threshold of the landscape area stored in the database. If the environmental interference assessment value of each landscape area in each monitoring time period is lower than the environmental interference assessment threshold of the landscape area, then continue to collect image data of each monitoring area.

[0024] If the environmental disturbance assessment value of each landscape area in each monitoring time period is higher than or equal to the environmental disturbance assessment threshold of the landscape area, an early warning will be issued and the image acquisition frequency of each landscape area in each monitoring time period will be increased.

[0025] Furthermore, based on the signal interference evaluation value of each landscape area in each monitoring time period, the signal strength is adjusted. The specific process is: comparing the signal interference evaluation value of each landscape area in each monitoring time period with the signal interference evaluation threshold in the database.

[0026] When the signal interference evaluation value of each landscape area in each monitoring time period is higher than or equal to the signal interference evaluation threshold, a signal is sent to increase the number of signal amplifiers turned on in each landscape area.

[0027] When the signal interference assessment value of each landscape area in each monitoring time period is lower than the signal interference assessment threshold, the signal interference data will continue to be monitored.

[0028] Furthermore, the interference assessment value of each landscape area in each monitoring time period is obtained. The specific process is: extracting the environmental interference assessment value of each landscape area in each monitoring time period and the signal interference assessment value of each landscape area in each monitoring time period, and introducing the weight correction factor of the environmental interference assessment value of the landscape area and the weight correction factor of the signal interference assessment value of the landscape area into the interference assessment value of each landscape area in each monitoring time period. The interference assessment value of each landscape area in each monitoring time period is used to adjust the flight trajectory of the UAV.

[0029] Furthermore, the interference assessment value of each landscape area in each monitoring period is calculated as follows:

[0030] ;

[0031] Where, represents the interference assessment value of the jth landscape area in the i-th monitoring period, represents the environmental disturbance assessment value of the jth landscape area in the i-th monitoring period, represents the signal interference assessment value of the jth landscape area in the i-th monitoring period, Indicates the weight factor corresponding to the environmental disturbance assessment value of the landscape area, It represents the weight factor corresponding to the signal interference assessment value of the landscape area, e represents a natural constant, i represents the number of the monitoring time period, , m represents the total number of monitoring time periods, j represents the number of landscape areas, , n represents the total number of landscape areas.

[0032] Furthermore, the flight path of the UAV is adjusted, and the specific process is: extracting the interference assessment value of each landscape area in each monitoring time period, and comparing it with the comprehensive assessment threshold of landscape area interference in the database; if the interference assessment value of each landscape area in each monitoring time period is higher than or equal to the comprehensive assessment threshold of landscape area interference, the flight path of the UAV is adjusted.

[0033] If it is detected that the interference assessment value of each landscape area in each monitoring time period is lower than the landscape area interference assessment value, there is no need to adjust the flight path of the UAV.

[0034] The present invention has the following beneficial effects:

[0035] (1) The present invention divides a scenic area into different landscape areas and collects signal image data and quality data of each area within a preset monitoring time period. By extracting these data, analyzing and evaluating the acquisition quality of the images of each area, and performing optimization processing, the environmental interference evaluation value and the signal interference evaluation value are obtained in combination with the signal quality data. By improving the quality of drone data acquisition and adjusting the image acquisition frequency and signal strength of each landscape area, the interference evaluation value of each area is finally obtained, and the drone flight parameters are optimized, which is conducive to improving the data acquisition quality of the landscape area and improving the efficiency of resource allocation.

[0036] (2) The present invention obtains the acquisition quality assessment value of each landscape area image in each monitoring time period, optimizes the image according to the acquisition quality assessment value of each landscape area image in each monitoring time period, and provides reliable data for the subsequent analysis of the environmental interference assessment value and the signal interference assessment value of the image.

[0037] (3) The present invention obtains the first interference evaluation value of the signal in each landscape area in each monitoring time period, adjusts the opening time of the equipment in the landscape area, and processes the obtained signal interference evaluation value of each landscape area in each monitoring time period. Signal amplifiers are deployed in the landscape area, which can effectively reduce interference and improve signal quality, providing a data basis for subsequent interference evaluation values.

[0038] (4) The present invention can effectively identify potential risk areas by calculating the interference evaluation value to determine the degree of signal interference to the drone, thereby reducing the need for drone data collection in areas with unstable signals or dense crowds.

[0039] (5) The present invention optimizes the image of the landscape area, evaluates and checks the crowd density and signal interference in the scenic area based on the environmental interference evaluation value and the signal interference evaluation value of the landscape area, and plans the flight path of the drone based on the interference evaluation value, which helps to improve the overall management level and safety assurance capability of the scenic area.

[0040] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the process of the present invention.

[0042] Figure 2 This is the interference evaluation value curve of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0045] See also Figure 1 An embodiment of the present invention provides a technical solution: a real-time data collection and processing method for a scenic area based on a drone, comprising dividing the scenic area into various landscape areas, collecting signal image data and signal quality data of each landscape area in each preset monitoring time period, and obtaining signal image data and signal quality data of each landscape area in each monitoring time period.

[0046] The signal image data of each landscape area in each monitoring time period are extracted, and the acquisition quality assessment value of each landscape area image in each monitoring time period is obtained by analysis. The contrast of each signal image data of each landscape area in each monitoring time period is optimized and adjusted according to the acquisition quality assessment value of each landscape area image in each monitoring time period.

[0047] The signal quality data of each landscape area in each monitoring time period and the optimized signal image data of each landscape area in each monitoring time period are extracted, and the environmental interference assessment value and signal interference assessment value of each landscape area in each monitoring time period are obtained by processing.

[0048] The image acquisition frequency of each landscape area is adjusted based on the environmental interference assessment value of each landscape area in each monitoring time period, and the signal strength is adjusted based on the signal interference assessment value of each landscape area in each monitoring time period.

[0049] The interference assessment value of each landscape area in each monitoring time period is obtained. The specific process is: extracting the environmental interference assessment value of each landscape area in each monitoring time period and the signal interference assessment value of each landscape area in each monitoring time period, and introducing the weight correction factor of the environmental interference assessment value of the landscape area and the weight correction factor of the signal interference assessment value of the landscape area to obtain the interference assessment value of each landscape area in each monitoring time period. The interference assessment value of each landscape area in each monitoring time period is used to adjust the flight trajectory of the UAV.

[0050] It should be noted that the classification type is determined according to the natural geographical characteristics of the scenic area. For example, according to the vegetation type of the scenic area, the scenic area is divided into forest landscape area, grassland landscape area, and wetland landscape area.

[0051] It should be noted that the scenic area is divided into various landscape areas, signal quality data is collected within the preset monitoring time period, and the image data is extracted, analyzed and optimized, and finally the crowd density assessment and signal interference assessment of each area are obtained, so as to adjust the equipment opening time and formulate anti-interference measures, and at the same time adjust the drone flight parameters.

[0052] Specifically, the signal quality data includes the average signal strength and average signal-to-noise ratio received by the drone in each landscape area during each monitoring time period.

[0053] The signal image data of each landscape area in each monitoring time period includes each image of each landscape area in each monitoring time period, image quality data of each image of each landscape area in each monitoring time period, and feature data of each image.

[0054] It should be noted that signal detection software (such as Network Signal Info) can be used to monitor signal strength; control software (such as QGroundControl and Mission Planner) can be used to monitor the drone's signal-to-noise ratio in real time.

[0055] It should be noted that the collection time points are set within the monitoring period, and the signal strength and signal-to-noise ratio received by the drone at each collection time point are obtained. The average values ​​are calculated to obtain the average signal strength and average signal-to-noise ratio received by the drone in each landscape area during each monitoring period.

[0056] Specifically, the acquisition quality assessment value of the image of each landscape area in each monitoring time period is analyzed and obtained. The specific process is: the image quality data of each image of each landscape area in each monitoring time period includes the average contrast, average brightness, edge clarity, and average saturation of each image.

[0057] The average contrast, average brightness, edge clarity, and average saturation of each image in each landscape area during each monitoring time period were extracted, and the acquisition quality assessment values ​​of the images in each landscape area during each monitoring time period were obtained through processing.

[0058] The acquisition quality assessment value of each landscape area image in each monitoring time period is used to quantitatively assess the quality of each landscape area image in each monitoring time period.

[0059] Specifically, the contrast of each signal image data of each landscape area in each monitoring time period is optimized and adjusted according to the acquisition quality assessment value of each landscape area image in each monitoring time period. The specific process is: the acquisition quality assessment value of each landscape area image in each monitoring time period is compared with the quality assessment threshold of the image; if the acquisition quality assessment value of each landscape area image in each monitoring time period is higher than or equal to the quality assessment threshold of the image, the contrast of the acquired landscape area image in the monitoring time period is adjusted; if the acquisition quality assessment value of each landscape area image in each monitoring time period is lower than the quality assessment threshold of the image, the landscape area image is re-acquired.

[0060] It should be noted that when the acquisition quality assessment value is above or equal to the threshold, the image quality generally meets requirements (e.g., resolution and clarity meet standards), but contrast may not be optimal. The system then optimizes the contrast, and the adjusted image data is considered usable. If the assessment value is below the threshold, fundamental image quality flaws (e.g., blurring or excessive noise) cannot be corrected by simply adjusting the contrast, and re-acquisition is required. Image data that passes the quality assessment after re-acquisition is considered usable. The degree of adjustment should be based on preserving natural detail. For example, for a landscape photo with blurred mountain details due to backlighting, if the quality assessment value is slightly above the threshold, the system may automatically increase the contrast to a range of +15 to +25, and simultaneously adjust the blackpoint input value to 5-15 and the whitepoint input value to 240-245 through color levels adjustment.

[0061] It should be noted that image processing software (such as Photoshop and GIMP) is used to obtain the contrast, average brightness, and average saturation of the image; the Sobel operator is used to detect the edges in the image and calculate the gradient amplitude of the edge. The Sobel operator is a discrete difference operator used to calculate the approximate value of the gradient of the image brightness function. It is mainly used to obtain the first-order gradient of the digital image, thereby realizing edge detection and obtaining the edge clarity of the image.

[0062] It should be noted that the specific analysis process of the acquisition quality assessment value of each landscape area image in each monitoring time period is as follows:

[0063] ;

[0064] Where, represents the acquisition quality assessment value of the j-th landscape area image in the i-th monitoring period, represents the average contrast of the qth image in the jth landscape area during the i-th monitoring period, represents the average brightness of the qth image of the jth landscape area in the i-th monitoring period, represents the edge clarity of the qth image in the jth landscape area during the i-th monitoring period, represents the average saturation of the qth image in the jth landscape area during the i-th monitoring period, Indicates the reference value of the set image average contrast. Indicates the reference value of the set average brightness of the image. Indicates the reference value of the set image edge clarity. Indicates the reference value of the set average saturation of the image. Indicates the correction factor corresponding to the set image contrast, Indicates the correction factor corresponding to the set average brightness of the image, Indicates the correction factor corresponding to the set image edge clarity, Indicates the correction factor corresponding to the set average saturation of the image. , i represents the number of the monitoring time period, m represents the total number of monitoring time periods, , i represents the number of landscape areas, n represents the total number of landscape areas, , q represents the image number, and r represents the total number of images.

[0065] It should be noted that there is a correlation between the contrast, average brightness, edge clarity, and average saturation of an image. Contrast is positively correlated with edge clarity. When the contrast of an image increases, the difference between light and dark areas in the image becomes more obvious, and edge clarity also improves accordingly. High average brightness can enhance the detail performance of the image and enhance edge clarity. High contrast can enhance average saturation, so as the contrast increases, the average saturation will also increase.

[0066] This is the correction factor for the average image contrast set in the database, representing the degree to which the image average contrast modifies the quality assessment value of the landscape area image. This correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, the image average contrast and the correction factors set in the database form a mapping set. The real-time image average contrast is input into the mapping set to obtain the correction factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0067] This is the correction factor for the average image brightness set in the database, representing the degree to which the image average brightness modifies the quality assessment value of the landscape area image. This correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, the image average brightness and the correction factors set in the database form a mapping set. The real-time image average brightness is input into the mapping set to obtain the correction factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0068] This is the correction factor for image edge clarity set in the database, representing the degree to which the image edge clarity modifies the quality assessment value of the landscape area image. This correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, a mapping set is formed between image edge clarity and the correction factor set in the database. The real-time image edge clarity is input into the mapping set to obtain the correction factor. This mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0069] This is the correction factor for the average image saturation set in the database, representing the degree to which the average image saturation modifies the quality assessment value of the landscape area image. This correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, the average image saturation and the correction factors set in the database form a mapping set. The real-time average image saturation is input into the mapping set to obtain the correction factor. The mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0070] It should be noted that if the acquisition quality assessment value of a landscape area image in a certain monitoring time period is higher than or equal to the image quality assessment threshold, the image contrast is improved by enhancing the image brightness and shadow; if the acquisition quality assessment value of a landscape area image in a certain monitoring time period is lower than the image quality assessment threshold, the drone is arranged to re-collect the landscape area image, which helps to optimize the visual effect of the image and improve the image data quality.

[0071] Specifically, the environmental interference assessment value and signal interference assessment value of each landscape area in each monitoring time period are obtained. The specific process is: extracting the characteristic data of each image in each landscape area in each monitoring time period, and the characteristic data of each image in each landscape area in each monitoring time period include crowd density, normalized vegetation index, and average noise.

[0072] It should be noted that average noise reflects the intensity of random fluctuations in brightness or color information in an image. In this embodiment, average noise can be obtained by calculating the standard deviation of pixel values ​​in a single or multiple image frames. Crowd density is extracted from images using computer vision algorithms. For example, based on photos or videos taken by users of public places, the number of human subjects can be counted and then combined with the area to obtain the number of people per unit area.

[0073] The highest crowd density in each image of each landscape area in each monitoring time period was counted, and the average of the crowd density in each image of each landscape area in each monitoring time period was calculated to obtain the average crowd density in each landscape area in each monitoring time period. The number of monitoring periods in which the crowd density in each image of each landscape area in each monitoring time period was higher than the crowd density threshold of each landscape area stored in the database was counted, and recorded as the number of time periods of personnel gathering in each landscape area in each monitoring time period.

[0074] The normalized vegetation index of each image in each landscape area in each monitoring time period was extracted, and the average value was calculated to obtain the normalized average vegetation index of each landscape area in each monitoring time period.

[0075] The average crowd density and the maximum crowd density in each landscape area during each monitoring time period are counted, and the number of time periods with gathering of people in each landscape area during each monitoring time period and the normalized average vegetation index of each landscape area during each monitoring time period are extracted. The environmental interference assessment value of each landscape area during each monitoring time period is obtained by processing. The environmental interference assessment value of each landscape area during each monitoring time period is used to adjust the collection frequency of the drone.

[0076] The average signal strength and average signal-to-noise ratio of the drone received in each landscape area during each monitoring time period were extracted, and combined with the average noise of the images of each landscape area during each monitoring time period, the signal interference assessment value of each landscape area during each monitoring time period was obtained.

[0077] It should be noted that the YOLO algorithm (You Only Look Once: Unified, Real-Time Object Detection), an object detection system used for computer vision tasks, was used to count the flow of people in the images of each landscape area during each monitoring time period. The area of ​​each landscape area was extracted from the database and divided by the area to obtain the crowd density in each image of each landscape area during each monitoring time period. Image processing software (such as Photoshop and GIMP) was used to analyze and obtain the average noise of the image. Noise refers to random changes or interference in image data, which usually reduces image quality, making the image blurry, grainy, and contrast-degraded.

[0078] It should be noted that the Normalized Difference Vegetation Index (NDVI) is an index widely used in remote sensing and vegetation monitoring to assess vegetation coverage and health. Generally, the higher the NDVI value, the higher the chlorophyll content of the vegetation, the greater the vegetation coverage, and the better the vegetation growth state. By comparing the reflectivity of vegetation in the red and near-infrared light bands, it is possible to effectively distinguish between plant and non-plant surfaces. Image processing software (such as ENVI and QGIS) is used to process the image to obtain the reflectivity of the red and near-infrared bands, and the Normalized Vegetation Index is calculated using the formula , where NDVI represents the normalized difference vegetation index, NIR represents the reflectance of the near-infrared band, and Red represents the reflectance of the red band.

[0079] It should be noted that the specific analysis method for the environmental interference assessment value of each landscape area in each monitoring period is as follows:

[0080] ;

[0081] Where, represents the environmental disturbance assessment value of the jth landscape area in the i-th monitoring period, represents the average population density in the jth landscape area during the i-th monitoring period, represents the highest population density in the jth landscape area during the i-th monitoring period, represents the number of people gathering in the jth landscape area during the i-th monitoring period, represents the normalized vegetation mean index of the jth landscape area in the i-th monitoring period, It represents the average crowd density reference value in the j-th landscape area. Indicates the reference value of the highest population density in the j-th landscape area. Indicates the allowed number of people gathering in the j-th landscape area during the specified time period. Indicates the reference value of the normalized vegetation mean index of the j-th landscape area. Indicates the correction factor corresponding to the set average crowd density, Indicates the correction factor corresponding to the set maximum crowd density, Indicates the correction factor corresponding to the number of people gathering time periods. It represents the correction factor corresponding to the set normalized vegetation mean index, e represents the natural constant, , i represents the number of the monitoring time period, m represents the total number of monitoring time periods, , i represents the number of the landscape area, and n represents the total number of landscape areas.

[0082] It should be noted that there is a correlation between the average crowd density, the maximum crowd density, and the number of time periods when people gather. When the maximum crowd density increases, the average crowd density will increase accordingly. When the number of time periods when people gather increases, the average crowd density will also increase accordingly.

[0083] This is the correction factor for the average crowd density set in the database, representing the degree to which the average crowd density modifies the environmental interference assessment value of the landscape area. This correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, the average crowd density and the correction factor for the average crowd density set in the database form a mapping set. The real-time average crowd density is input into the mapping set to obtain the correction factor for the average crowd density. The mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0084] This is the correction factor for the maximum crowd density set in the database, representing the degree to which the maximum crowd density modifies the environmental interference assessment value of the landscape area. This correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, a mapping set is formed between the maximum crowd density and the correction factor for the maximum crowd density set in the database. The real-time maximum crowd density is input into the mapping set to obtain the correction factor for the maximum crowd density. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0085] This is the correction factor for the number of time periods with a large gathering of people, as set in the database. It represents the degree to which the number of time periods with a large gathering of people affects the environmental interference assessment value of the landscape area. This correction factor can be directly obtained from the database. Its corresponding relationship can be a pre-set mapping relationship. For example, the number of time periods with a large gathering of people and the correction factor for the number of time periods with a large gathering of people set in the database form a mapping set. The real-time number of time periods with a large gathering of people is input into the mapping set to obtain the correction factor for the number of time periods with a large gathering of people. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0086] The correction factor for the NDVI set in the database represents the degree to which the NDVI corrects the environmental interference assessment value of the landscape area. The correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, the NDVI and the correction factor set in the database form a mapping set. The real-time NDVI is input into the mapping set to obtain the correction factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0087] It should be noted that by quantifying crowd density data and vegetation coverage, and timely identifying high-density gathering areas and high-vegetation coverage areas in scenic areas, we can provide early warning of potential safety risks and ensure the safety of tourists.

[0088] It should be noted that the specific analysis method for the signal interference assessment value of each landscape area in each monitoring time period is as follows:

[0089] ;

[0090] Where, represents the signal interference assessment value of the jth landscape area in the i-th monitoring period, represents the average signal strength received by the UAV in the jth landscape area during the i-th monitoring period, represents the average signal-to-noise ratio received by the UAV in the jth landscape area during the i-th monitoring period, represents the average noise of the jth landscape area image in the i-th monitoring period, Indicates the average signal strength reference value received by the set drone. Indicates the average signal-to-noise ratio reference value received by the set drone. Indicates the average noise reference value of the set landscape area image. Indicates the correction factor corresponding to the average signal strength received by the set drone, Indicates the correction factor corresponding to the average signal-to-noise ratio received by the set UAV, Indicates the correction factor corresponding to the average noise of the landscape area image. , i represents the number of the monitoring time period, m represents the total number of monitoring time periods, , i represents the number of the landscape area, and n represents the total number of landscape areas.

[0091] It should be noted that there is a correlation between the average signal strength received by the drone, the average signal-to-noise ratio received by the drone, and the average noise of the image. The average signal strength received by the drone is positively correlated with the average signal-to-noise ratio received by the drone. When the average signal-to-noise ratio received by the drone in the scenic area increases, the average signal strength received by the drone will also increase; when the average noise of the landscape area image increases, the average signal-to-noise ratio received by the drone may increase.

[0092] This is the correction factor for the average signal strength received by the drone, as set in the database. It represents the degree to which the average signal strength received by the drone corrects the signal interference assessment value of the landscape area. The correction factor for the average signal strength received by the drone can be directly obtained from the database. Its corresponding relationship can be a pre-set mapping relationship. For example, the average signal strength received by the drone and the correction factor for the average signal strength received by the drone set in the database form a mapping set. The real-time average signal strength received by the drone is input into the mapping set to obtain the correction factor for the average signal strength received by the drone. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0,1].

[0093] This is the correction factor for the average signal-to-noise ratio (SNR) received by the drone, as specified in the database. It represents the degree to which the average SNR received by the drone corrects the signal interference assessment value of the landscape area. The correction factor for the average SNR received by the drone can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the average SNR received by the drone and the correction factor for the average SNR received by the drone as specified in the database form a mapping set. The real-time average SNR received by the drone is input into the mapping set to obtain the correction factor for the average SNR received by the drone. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0,1].

[0094] This is the correction factor for the average noise of the image set in the database, representing the degree to which the image's average noise corrects the signal interference assessment value of the landscape area. The correction factor can be directly obtained from the database. Its correspondence can be a pre-set mapping relationship. For example, the image's average noise and the correction factors for the average noise of the image set in the database form a mapping set. The real-time image's average noise is input into the mapping set to obtain the correction factor for the image's average noise. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0095] It should be noted that by continuously monitoring signal interference, potential communication failures or equipment problems can be discovered in advance, so that timely measures can be taken to ensure system reliability.

[0096] Specifically, the image acquisition frequency of each landscape area is adjusted based on the environmental interference assessment value of each landscape area in each monitoring time period. The specific process is: extract the environmental interference assessment value of each landscape area in each monitoring time period and compare it with the environmental interference assessment threshold of the landscape area stored in the database. If the environmental interference assessment value of each landscape area in each monitoring time period is lower than the environmental interference assessment threshold of the landscape area, then continue to collect image data of each monitoring area.

[0097] If the environmental disturbance assessment value of each landscape area in each monitoring time period is higher than or equal to the environmental disturbance assessment threshold of the landscape area, an early warning will be issued and the image acquisition frequency of each landscape area in each monitoring time period will be increased.

[0098] It should be noted that the environmental interference assessment value of each landscape area in each monitoring time period is extracted and compared with the environmental interference assessment threshold of the landscape area in the database. If the environmental interference assessment value of each landscape area in each monitoring time period is lower than the environmental interference assessment threshold of the landscape area, the image data of each landscape area is continuously collected; if the environmental interference assessment value of each landscape area in each monitoring time period is higher than or equal to the environmental interference assessment threshold of the landscape area, the drone sends a signal to remind the crowd density in the landscape area, and doubles the number of collection time points within the monitoring time period, thereby increasing the frequency of drone image collection, helping to improve the quality of collected data and provide reliable data for subsequent scenic area management.

[0099] Specifically, the signal interference evaluation value of each landscape area in each monitoring time period is compared with the signal interference evaluation threshold in the database.

[0100] When the signal interference evaluation value of each landscape area in each monitoring time period is higher than or equal to the signal interference evaluation threshold, a signal is sent to increase the number of signal amplifiers turned on in each landscape area.

[0101] When the signal interference assessment value of each landscape area in each monitoring time period is lower than the signal interference assessment threshold, the signal interference data will continue to be monitored.

[0102] It should be noted that when the signal interference assessment value of a certain landscape area in a certain monitoring time period is higher than or equal to the signal interference assessment threshold, the drone sends a signal to prompt the landscape area to increase the number of signal amplifiers turned on, thereby improving the signal quality; when the signal interference assessment value of a certain landscape area in a certain monitoring time period is lower than the signal interference assessment threshold, continuous monitoring of the signal interference in the landscape area can save energy and computing resources.

[0103] Specifically, the environmental interference assessment value of each landscape area in each monitoring time period and the signal interference assessment value of each landscape area in each monitoring time period are extracted, and the weight correction factor of the environmental interference assessment value of the landscape area and the weight correction factor of the signal interference assessment value of the landscape area are introduced to obtain the interference assessment value of each landscape area in each monitoring time period. The interference assessment value of each landscape area in each monitoring time period is used to adjust the flight trajectory of the UAV.

[0104] Specifically, the interference assessment value of each landscape area in each monitoring period is as follows:

[0105] ;

[0106] Where, represents the interference assessment value of the jth landscape area in the i-th monitoring period, represents the environmental disturbance assessment value of the jth landscape area in the i-th monitoring period, represents the signal interference assessment value of the jth landscape area in the i-th monitoring period, Indicates the weight factor corresponding to the environmental disturbance assessment value of the landscape area, represents the weight factor corresponding to the signal interference assessment value of the landscape area, e represents a natural constant, , i represents the number of the monitoring time period, m represents the total number of monitoring time periods, , j represents the number of the landscape area, and n represents the total number of landscape areas.

[0107] This is the correction factor for the environmental interference assessment value of a landscape area set in the database, representing the degree to which the environmental interference assessment value of the landscape area corrects the interference assessment value of the landscape area. The correction factor for the environmental interference assessment value of the landscape area can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the environmental interference assessment value of the landscape area and the correction factor for the environmental interference assessment value of the landscape area set in the database form a mapping set. The real-time environmental interference assessment value of the landscape area is input into the mapping set to obtain the correction factor for the environmental interference assessment value of the landscape area. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0108] This is the correction factor for the signal interference assessment value of the landscape area set in the database, representing the degree to which the signal interference assessment value of the landscape area corrects the interference assessment value of the landscape area. The correction factor for the signal interference assessment value of the landscape area can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the signal interference assessment value of the landscape area and the correction factor for the signal interference assessment value of the landscape area set in the database form a mapping set. The real-time signal interference assessment value of the landscape area is input into the mapping set to obtain the correction factor for the signal interference assessment value of the landscape area. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0,1].

[0109] It should be noted that by extracting the environmental interference assessment values ​​and signal interference assessment values ​​of different landscape areas in each monitoring time period, the interference assessment value can be calculated to evaluate the tourist safety risks in each landscape area. By timely identifying potential risks and optimizing tourist management strategies, the safety experience of tourists can be enhanced.

[0110] like Figure 2 As shown, Figure 2 Represents the interference assessment value, where the horizontal axis represents the signal interference assessment value of the jth landscape area in the i-th monitoring period, and the vertical axis represents the interference assessment value of the jth landscape area in the i-th monitoring period. Three different sets of example parameters are defined in the figure, corresponding to different situations of the three curves, represented by solid lines, dashed lines and dotted lines respectively, and the corresponding curve labels are a, b, and c respectively. It is assumed that the weight factor corresponding to the environmental interference assessment value =0.6, weight factor corresponding to the signal interference evaluation value =0.4, when the environmental interference assessment value of the jth landscape area in the i-th monitoring time period is 0.8, the schematic diagram of the interference assessment value of the jth landscape area in the i-th monitoring time period is shown as curve a, when the environmental interference assessment value of the jth landscape area in the i-th monitoring time period is 1.5, the schematic diagram of the interference assessment value of the jth landscape area in the i-th monitoring time period is shown as curve b, and when the environmental interference assessment value of the jth landscape area in the i-th monitoring time period is 2.0, the schematic diagram of the interference assessment value of the jth landscape area in the i-th monitoring time period is shown as curve c.

[0111] As shown in Table 1, Table 1 is example data of interference evaluation values ​​of landscape areas, which lists the interference evaluation values ​​of landscape areas and signal interference evaluation values ​​of landscape areas.

[0112] Table 1 Example data of disturbance assessment values ​​for landscape areas

[0113]

[0114] As shown in Table 1, in a specific embodiment, it is assumed that the weight factor corresponding to the environmental interference evaluation value is =0.6, weight factor corresponding to the signal interference evaluation value =0.4, and the interference assessment value of the landscape area is determined by the environmental interference assessment value of each landscape area in each monitoring time period and the signal interference assessment value of each landscape area in each monitoring time period.

[0115] Specifically, the flight path of the drone is adjusted, and the specific process is: extracting the interference assessment value of each landscape area in each monitoring time period, and comparing it with the comprehensive assessment threshold of landscape area interference in the database; if the interference assessment value of each landscape area in each monitoring time period is higher than or equal to the comprehensive assessment threshold of landscape area interference, the flight path of the drone is adjusted.

[0116] If it is detected that the interference assessment value of each landscape area in each monitoring time period is lower than the landscape area interference assessment value, there is no need to adjust the flight path of the UAV.

[0117] It should be noted that if it is detected that the interference assessment value of a certain landscape area in a certain monitoring time period is higher than or equal to the comprehensive assessment threshold of the landscape area interference, the landscape area will be imaged and the YOLO algorithm will be used to identify the gathering points of the crowd. In combination with the lidar, the laser beam will be used to measure the distance between the drone and the crowd gathering point to generate high-precision three-dimensional point cloud data to determine the location of the densely populated area. The Dijkstra algorithm (shortest path algorithm) can be used to calculate the feasible path by treating the location of the crowd gathering area as an obstacle, and adjust the flight path of the drone to avoid the densely populated area. If it is detected that the interference assessment value of a certain landscape area in a certain monitoring time period is lower than the comprehensive assessment threshold of the landscape area interference, there is no need to adjust the flight path of the drone, and the crowd density and signal interference level of each landscape area will be continuously monitored, thereby improving the emergency response capability and ensuring that the drone can stably collect high-quality image and signal data in areas with less interference.

[0118] It should be noted that the real-time data collection and processing method for scenic spots based on drones also includes a database for storing and managing image quality data and image feature data. In this embodiment, the database is used to store the image quality assessment threshold, the weight factor corresponding to the set environmental interference assessment value of the landscape area, the weight factor corresponding to the set signal interference assessment value of the landscape area, the set reference value of the image contrast, the set reference value of the image average brightness, the set reference value of the image edge clarity, the set reference value of the image average saturation, the correction factor corresponding to the set image contrast, the correction factor corresponding to the set image average brightness, the correction factor corresponding to the set image edge clarity, the correction factor corresponding to the set image average saturation, the reference value of the average crowd density in the set landscape area, the highest crowd density in the set landscape area Density reference value, allowed number of people gathering in a set time period in a landscape area, set reference value of the normalized average vegetation index of the landscape area, correction factor corresponding to the set normalized average vegetation index, correction factor corresponding to the set average crowd density, correction factor corresponding to the set maximum crowd density, correction factor corresponding to the number of people gathering in a set time period, set reference value of the average signal strength received by a drone, set reference value of the average signal-to-noise ratio received by a drone, set reference value of the average noise of the landscape area image, correction factor corresponding to the set average signal strength received by a drone, correction factor corresponding to the set average signal-to-noise ratio received by a drone, correction factor corresponding to the set average noise of the landscape area image, environmental interference assessment threshold, signal interference assessment threshold, comprehensive interference assessment threshold, and crowd density threshold for each landscape area.

[0119] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0120] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for collecting and processing real-time data of scenic spots based on drones, characterized in that: include: The scenic area is divided into various landscape areas, and the signal image data and signal quality data of each landscape area are collected in each preset monitoring time period to obtain the signal image data and signal quality data of each landscape area in each monitoring time period; Extracting the signal image data of each landscape area in each monitoring time period, analyzing and obtaining the acquisition quality assessment value of the image of each landscape area in each monitoring time period, and optimizing and adjusting the contrast of each signal image data of each landscape area in each monitoring time period according to the acquisition quality assessment value of the image of each landscape area in each monitoring time period; Extract the signal quality data of each landscape area in each monitoring time period and the optimized signal image data of each landscape area in each monitoring time period, and process them to obtain the environmental interference assessment value and signal interference assessment value of each landscape area in each monitoring time period; Adjust the image acquisition frequency of each landscape area based on the environmental interference assessment value of each landscape area in each monitoring time period, and adjust the signal strength based on the signal interference assessment value of each landscape area in each monitoring time period; The image acquisition frequency of each landscape area is adjusted based on the environmental interference assessment value of each landscape area in each monitoring time period. The specific process is as follows: If the environmental disturbance assessment value of each landscape area in each monitoring period is higher than or equal to the environmental disturbance assessment threshold of the landscape area, an early warning will be issued and the frequency of drone image acquisition in each landscape area in each monitoring period will be increased; The drone sends a signal to the landscape area to warn of high population density and doubles the number of acquisition time points during the monitoring period, thereby increasing the frequency of drone image acquisition; Extracting and analyzing the environmental interference assessment value of each landscape area in each monitoring time period and the signal interference assessment value of each landscape area in each monitoring time period to obtain the interference assessment value of each landscape area in each monitoring time period. The interference assessment value of each landscape area in each monitoring time period is used to adjust the flight trajectory of the UAV and replan the flight path of the UAV; The specific process of adjusting the flight path of the drone is as follows: If the interference assessment value of each landscape area in each monitoring time period is higher than or equal to the comprehensive assessment threshold of the landscape area interference, the flight path of the UAV will be adjusted; If the interference assessment value of a certain landscape area in a certain monitoring period is detected to be higher than or equal to the comprehensive assessment threshold of the landscape area interference, the landscape area will be imaged and the YOLO algorithm will be used to identify the gathering points of the crowd. Combined with the lidar, the laser beam will be used to measure the distance between the drone and the crowd gathering point to generate high-precision three-dimensional point cloud data to determine the location of the densely populated area. The Dijkstra algorithm will be used to calculate the feasible path by treating the location of the crowd gathering area as an obstacle, and the flight path of the drone will be adjusted.

2. The method for real-time scenic area data collection and processing based on drones according to claim 1 is characterized by: The signal quality data includes the average signal strength and average signal-to-noise ratio received by the drone in each landscape area during each monitoring period; The signal image data of each landscape area in each monitoring time period includes each image of each landscape area in each monitoring time period, image quality data of each image of each landscape area in each monitoring time period, and feature data of each image.

3. The method for real-time scenic area data collection and processing based on drones according to claim 2 is characterized by: The analysis obtains the acquisition quality assessment value of each landscape area image in each monitoring time period. The specific process is as follows: Image quality data of each image in each landscape area during each monitoring time period, including average contrast, average brightness, edge definition, and average saturation of each image; The average contrast, average brightness, edge clarity, and average saturation of each image in each landscape area during each monitoring period were extracted, and the acquisition quality assessment values ​​of the images in each landscape area during each monitoring period were obtained by processing. The acquisition quality assessment value of each landscape area image in each monitoring time period is used to quantitatively assess the quality of each landscape area image in each monitoring time period.

4. The method for real-time scenic area data collection and processing based on drones according to claim 3 is characterized by: The contrast of each signal image data of each landscape area in each monitoring time period is optimized and adjusted according to the acquisition quality evaluation value of each landscape area image in each monitoring time period, and the specific process is: The acquisition quality assessment value of each landscape area image in each monitoring time period is compared with the image quality assessment threshold. If the acquisition quality assessment value of each landscape area image in each monitoring time period is higher than or equal to the image quality assessment threshold, the contrast of the landscape area image acquired in the monitoring time period is adjusted. If the acquisition quality assessment value of each landscape area image in each monitoring time period is lower than the image quality assessment threshold, the landscape area image is re-acquired.

5. The method for real-time scenic area data collection and processing based on drones according to claim 1 is characterized by: The specific process of obtaining the environmental interference assessment value and the signal interference assessment value of each landscape area in each monitoring time period is as follows: Extracting characteristic data of each image of each landscape area in each monitoring time period, wherein the characteristic data of each image of each landscape area in each monitoring time period includes crowd density, normalized vegetation index, and average noise; Count the highest crowd density in each image of each landscape area in each monitoring time period, calculate the average of the crowd density in each image of each landscape area in each monitoring time period, and calculate the number of monitoring periods in which the crowd density in each image of each landscape area in each monitoring time period is higher than the crowd density threshold of each landscape area stored in the database, and record it as the number of time periods of personnel gathering in each landscape area in each monitoring time period; Extract the normalized vegetation index of each image of each landscape area in each monitoring time period, and calculate the average value to obtain the normalized vegetation average index of each landscape area in each monitoring time period; The average crowd density and the maximum crowd density in each landscape area during each monitoring period are calculated, and the number of people gathered in each landscape area during each monitoring period and the normalized average vegetation index of each landscape area during each monitoring period are extracted. The environmental interference assessment value of each landscape area during each monitoring period is obtained. The environmental interference assessment value of each landscape area during each monitoring period is used to adjust the collection frequency of the drone. The average signal strength and average signal-to-noise ratio of the drone received in each landscape area during each monitoring time period were extracted, and combined with the average noise of the images of each landscape area during each monitoring time period, the signal interference assessment value of each landscape area during each monitoring time period was obtained.

6. The method for real-time scenic area data collection and processing based on drones according to claim 1, characterized in that: The image acquisition frequency of each landscape area is adjusted based on the environmental interference assessment value of each landscape area in each monitoring time period, and the specific process also includes: The environmental interference assessment value of each landscape area in each monitoring time period is extracted and compared with the environmental interference assessment threshold of the landscape area stored in the database. If the environmental interference assessment value of each landscape area in each monitoring time period is lower than the environmental interference assessment threshold of the landscape area, the image data of each monitoring area is continuously collected.

7. The method for real-time scenic area data collection and processing based on drones according to claim 1, characterized in that: The signal strength is adjusted based on the signal interference assessment value of each landscape area in each monitoring time period. The specific process is as follows: Compare the signal interference assessment value of each landscape area in each monitoring time period with the signal interference assessment threshold in the database; When the signal interference assessment value of each landscape area in each monitoring time period is higher than or equal to the signal interference assessment threshold, a signal is issued to increase the number of signal amplifiers in each landscape area; When the signal interference assessment value of each landscape area in each monitoring time period is lower than the signal interference assessment threshold, the signal interference data will continue to be monitored.

8. The method for real-time scenic area data collection and processing based on drones according to claim 1, characterized in that: The specific process of obtaining the interference assessment value of each landscape area in each monitoring time period is as follows: The environmental interference assessment value of each landscape area in each monitoring time period and the signal interference assessment value of each landscape area in each monitoring time period are extracted, and the weight correction factor of the environmental interference assessment value of the landscape area and the weight correction factor of the signal interference assessment value of the landscape area are introduced to obtain the interference assessment value of each landscape area in each monitoring time period. The interference assessment value of each landscape area in each monitoring time period is used to adjust the flight trajectory of the UAV.

9. The method for real-time scenic area data collection and processing based on drones according to claim 8, characterized in that: The specific process of the interference assessment value of each landscape area in each monitoring time period is as follows: ； Where, represents the interference assessment value of the jth landscape area in the i-th monitoring period, represents the environmental disturbance assessment value of the jth landscape area in the i-th monitoring period, represents the signal interference assessment value of the jth landscape area in the i-th monitoring period, Indicates the weight factor corresponding to the environmental disturbance assessment value of the landscape area, It represents the weight factor corresponding to the signal interference assessment value of the landscape area, e represents a natural constant, i represents the number of the monitoring time period, , m represents the total number of monitoring time periods, j represents the number of landscape areas, , n represents the total number of landscape areas.

10. The method for real-time scenic area data collection and processing based on drones according to claim 8, characterized in that: The specific process of adjusting the flight path of the drone also includes: Extract the disturbance assessment value of each landscape area in each monitoring period and compare it with the comprehensive disturbance assessment threshold of the landscape area in the database; If it is detected that the interference assessment value of each landscape area in each monitoring time period is lower than the landscape area interference assessment value, there is no need to adjust the flight path of the UAV.

Images