Unmanned aerial vehicle-based scenic spot real-time data acquisition and processing method

Abstract

The invention discloses a scenic spot real-time data acquisition and processing method based on an unmanned aerial vehicle, and relates to the technical field of data acquisition and processing. Firstly, a scenic area is divided into different landscape areas, signal image data and quality data of all the areas are collected in a preset monitoring time period, the data are extracted, the collection quality of images of all the areas is analyzed and evaluated, optimization processing is carried out, and in combination with the signal quality data, an environment interference evaluation value and a signal interference evaluation value are obtained; by improving the data acquisition quality of the unmanned aerial vehicle and adjusting the image acquisition frequency and signal intensity of each landscape area, the interference evaluation value of each area is finally obtained, the flight parameters of the unmanned aerial vehicle are optimized, the data acquisition quality of the landscape area is improved, and the resource allocation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and processing, and specifically to a method for real-time data acquisition and processing of scenic spots based on unmanned aerial vehicles (UAVs). Background Art

[0002] The data acquisition and processing of scenic spots by UAVs rely on the rapid development of UAV technology, the progress of sensor technology, the improvement of data processing and analysis capabilities, and the needs of scenic spot management and services. It can capture detailed image data of scenic spots in real time, providing important data support for the intelligent management of scenic spots, tourist behavior analysis, environmental monitoring, etc.

[0003] For example, the invention patent with the publication number CN109164827B discloses a data acquisition system based on UAVs. The data acquisition system based on UAVs includes a ground system and a UAV with a camera. The UAV 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 UAV and send the wind speed data to the main controller. When the main controller determines whether the time ratio of the vibration amplitude of the UAV exceeding the preset vibration threshold within a preset time one exceeds a ratio limit one according to the vibration data, when the vibration amplitude exceeds the preset vibration threshold, the UAV stops shooting and hovers at a preset height. After the UAV hovers, the main controller determines whether the time ratio of the wind speed data exceeding the wind speed threshold within a preset time two exceeds a ratio limit two. If so, the UAV lands on the ground. If not, the UAV reaches the destination or returns to the starting point along the flight route.

[0004] For example, the invention patent with the publication number CN114167891B discloses a ground data acquisition and processing system based on UAVs, including a path planning module, a video acquisition module, and a data processing module. The path planning module is used to output the travel data of the UAV. The video acquisition module is used to control the flight of the UAV and shoot videos. The data processing module is used to calculate and process the captured video data. First, an aerial acquisition is performed, and after calculating the height and area of individual regions in the acquired video and analyzing to obtain a targeted acquisition region, a low-altitude acquisition is performed on the targeted acquisition region. The low-altitude acquisition can obtain detailed ground data that cannot be obtained by the aerial acquisition due to environmental and perspective reasons, and the low-altitude acquisition is not carried out in the whole range but is targeted, which has high efficiency.

[0005] Based on the above findings, in the existing technical solutions, there may be problems such as low quality of data acquisition in landscape areas and poor signals during data acquisition, resulting in improper allocation of landscape area resources, resource waste, low real-time performance of data acquisition, decision-making errors, and a decline in the tourist experience. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a method for real-time data collection and processing of 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 realized through the following technical solutions: A method for real-time data collection and processing of scenic spots based on drones, including dividing the scenic spot into each landscape area, collecting signal image data and signal quality data of each landscape area in each preset monitoring time period, and obtaining each signal image data and signal quality data of each landscape area in each monitoring time period.

[0008] Extract the signal image data of each landscape area in each monitoring time period, analyze to obtain the acquisition quality evaluation value of the images of each landscape area in each monitoring time period, and optimize and adjust the contrast of the signal image data of each landscape area in each monitoring time period according to the acquisition quality evaluation value of the images of each landscape area in each monitoring time period.

[0009] 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 to obtain the environmental interference evaluation value and signal interference evaluation value of each landscape area in each monitoring time period.

[0010] Adjust the image acquisition frequency of each landscape area based on the environmental interference evaluation value of each landscape area in each monitoring time period, and adjust the signal intensity based on the signal interference evaluation value of each landscape area in each monitoring time period.

[0011] Extract the environmental interference evaluation value of each landscape area in each monitoring time period and the signal interference evaluation value of each landscape area in each monitoring time period, which are used to adjust the flight trajectory of the drone and re-plan the flight path of the drone.

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

[0014] Further, the process of analyzing to obtain the acquisition quality evaluation value of the images of each landscape area in each monitoring time period is as follows: The image quality data of each image of each landscape area in each monitoring time period includes the average contrast, average brightness, edge sharpness, and average saturation of each image.

[0015] Extract the average contrast, average brightness, edge sharpness, and average saturation of each image of each landscape area in each monitoring time period, and process to obtain the acquisition quality evaluation value of the images of each landscape area in each monitoring time period.

[0016] The acquisition quality evaluation values of the images of each landscape area in each monitoring time period are used to quantitatively evaluate the quality of the images of each landscape area in each monitoring time period.

[0017] Furthermore, the contrast of the signal image data of each landscape area in each monitoring time period is optimized and adjusted according to the acquisition quality evaluation values of the images of each landscape area in each monitoring time period. The specific process is as follows: compare the acquisition quality evaluation values of the images of each landscape area in each monitoring time period with the image quality evaluation threshold. If the acquisition quality evaluation value of the image of a certain landscape area in a certain monitoring time period is higher than or equal to the image quality evaluation threshold, adjust the contrast of the image of this landscape area collected in this monitoring time period. If the acquisition quality evaluation value of the image of a certain landscape area in a certain monitoring time period is lower than the image quality evaluation threshold, re-collect the image of this landscape area.

[0018] Furthermore, the environmental interference evaluation value and the signal interference evaluation value of each landscape area in each monitoring time period are obtained. The specific process is as follows: extract the feature data of each image of each landscape area in each monitoring time period. The feature data of each image of each landscape area in each monitoring time period includes crowd density, normalized vegetation index, and average noise.

[0019] Statistically analyze the highest crowd density in each image of each landscape area in each monitoring time period, calculate the average value of the crowd density in each image of each landscape area in each monitoring time period to obtain the average crowd density in each landscape area in each monitoring time period, and count the number of monitoring cycles 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, which is recorded as the number of personnel gathering time periods of each landscape area in each monitoring time period.

[0020] 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.

[0021] Statistically analyze the average crowd density and the highest crowd density in each landscape area in each monitoring time period, extract the number of personnel gathering time periods of each landscape area in each monitoring time period, and the normalized vegetation average index of each landscape area in each monitoring time period, and process to obtain the environmental interference evaluation value of each landscape area in each monitoring time period. The environmental interference evaluation value of each landscape area in each monitoring time period is used to adjust the acquisition frequency of the drone.

[0022] Extract the average signal strength and average signal-to-noise ratio received by the drone in each landscape area in each monitoring time period, and combine with the average noise of the images of each landscape area in each monitoring time period to process and obtain the signal interference evaluation value of each landscape area in each monitoring time period.

[0023] Further, based on the environmental interference evaluation values of each landscape area in each monitoring time period, the image acquisition frequency of each landscape area is adjusted. The specific process is as follows: Extract the environmental interference evaluation values of each landscape area in each monitoring time period and compare them with the environmental interference evaluation thresholds of the landscape areas stored in the database. If the environmental interference evaluation values of each landscape area in each monitoring time period are lower than the environmental interference evaluation thresholds of the landscape areas, continuously collect the image data of each monitoring area.

[0024] If the environmental interference evaluation values of each landscape area in each monitoring time period are higher than or equal to the environmental interference evaluation thresholds of the landscape areas, a warning prompt is issued, and the image acquisition frequency of the drones in each landscape area in each monitoring time period is increased.

[0025] Further, based on the signal interference evaluation values of each landscape area in each monitoring time period, the signal intensity is adjusted. The specific process is as follows: Compare the signal interference evaluation values of each landscape area in each monitoring time period with the signal interference evaluation thresholds in the database.

[0026] When the signal interference evaluation values of each landscape area in each monitoring time period are higher than or equal to the signal interference evaluation thresholds, a signal prompt is issued to increase the number of signal amplifiers turned on in each landscape area.

[0027] When the signal interference evaluation values of each landscape area in each monitoring time period are lower than the signal interference evaluation thresholds, continue to monitor the signal interference data.

[0028] Further, the interference evaluation values of each landscape area in each monitoring time period are obtained. The specific process is as follows: Extract the environmental interference evaluation values and the signal interference evaluation values of each landscape area in each monitoring time period, and introduce the weight correction factor of the environmental interference evaluation value of the landscape area and the weight correction factor of the signal interference evaluation value of the landscape area into the interference evaluation values of each landscape area in each monitoring time period. The interference evaluation values of each landscape area in each monitoring time period are used to adjust the flight trajectory of the drones.

[0029] Further, the interference evaluation values of each landscape area in each monitoring time period are as follows: ; where represents the interference evaluation value of the jth landscape area in the ith monitoring time period, represents the environmental interference evaluation value of the jth landscape area in the ith monitoring time period, represents the signal interference evaluation value of the jth landscape area in the ith monitoring time period, represents the weight factor corresponding to the environmental interference evaluation value of the landscape area, The weight factor corresponding to the signal interference evaluation value of the landscape area, e represents the 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, , n represents the total number of landscape areas.

[0030] Furthermore, the adjustment of the flight path of the drone is specifically as follows: Extract the interference evaluation values of each landscape area in each monitoring time period, and compare them with the comprehensive interference evaluation threshold of the landscape area in the database. If the interference evaluation values of each landscape area in each monitoring time period are higher than or equal to the comprehensive interference evaluation threshold of the landscape area, adjust the flight path of the drone.

[0031] If it is detected that the interference evaluation values of each landscape area in each monitoring time period are lower than the interference evaluation value of the landscape area, there is no need to adjust the flight path of the drone.

[0032] The present invention has the following beneficial effects: (1) The present invention divides the scenic area into different landscape areas, and collects the signal image data and its quality data of each area within the preset monitoring time period. By extracting these data, analyzing and evaluating the acquisition quality of the images of each area, and performing optimization processing, combined with the signal quality data, the environmental interference evaluation value and the signal interference evaluation value are obtained. By improving the data acquisition quality of the drone and adjusting the image acquisition frequency and signal intensity of each landscape area, the interference evaluation value of each area is finally obtained, and the flight parameters of the drone are optimized, which is beneficial to improving the data acquisition quality of the landscape area and enhancing the resource allocation efficiency.

[0033] (2) The present invention obtains the acquisition quality evaluation value of the images of each landscape area in each monitoring time period, and optimizes the images according to the acquisition quality evaluation value of the images of each landscape area in each monitoring time period, providing reliable data for subsequent analysis of the environmental interference evaluation value and the signal interference evaluation value of the images.

[0034] (3) The present invention obtains the first signal interference evaluation value of each landscape area in each monitoring time period, adjusts the opening time of the equipment in the landscape area, and processes to obtain the signal interference evaluation value of each landscape area in each monitoring time period. By arranging signal amplifiers in the landscape area, the interference can be effectively reduced and the signal quality can be improved, providing a data basis for subsequent obtaining of the interference evaluation value.

[0035] (4) The present invention calculates the interference evaluation value to judge the degree of signal interference of the drone, can effectively identify potential risk areas, and thus reduce the data acquisition of the drone in places with unstable signals or crowded people.

[0036] (5) By optimizing the images of the landscape areas, the present invention evaluates and checks the crowd density and signal interference in the scenic area according to the environmental interference evaluation value and the signal interference evaluation value of the landscape areas, and plans the flight path of the drone through the interference evaluation value, which helps to improve the overall management level and safety guarantee ability of the scenic area.

[0037] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flow chart of the method of the present invention.

[0039] Figure 2 It is the interference evaluation value curve of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inner", "periphery", etc. indicating the orientation or position relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0042] Please refer to Figure 1 , the embodiments of the present invention provide a technical solution: a method for real-time data collection and processing of a scenic area based on a drone, including dividing the scenic area into each landscape area, collecting the signal image data and signal quality data of each landscape area in each preset monitoring time period, and obtaining the signal image data and signal quality data of each landscape area in each monitoring time period.

[0043] Extract the signal image data of each landscape area in each monitoring time period, analyze to obtain the acquisition quality evaluation value of the images of each landscape area in each monitoring time period, and optimize and adjust the contrast of the signal image data of each landscape area in each monitoring time period according to the acquisition quality evaluation value of the images of each landscape area in each monitoring time period.

[0044] Extract the signal quality data of each landscape area for each monitoring time period and the optimized signal image data of each landscape area for each monitoring time period, and process them to obtain the environmental interference evaluation value and signal interference evaluation value of each landscape area for each monitoring time period.

[0045] Adjust the image acquisition frequency of each landscape area based on the environmental interference evaluation value of each landscape area for each monitoring time period, and adjust the signal intensity based on the signal interference evaluation value of each landscape area for each monitoring time period.

[0046] Obtain the interference evaluation value of each landscape area for each monitoring time period. The specific process is as follows: Extract the environmental interference evaluation value of each landscape area for each monitoring time period and the signal interference evaluation value of each landscape area for each monitoring time period, and introduce the weight correction factor of the environmental interference evaluation value of the landscape area and the weight correction factor of the signal interference evaluation value of the landscape area to obtain the interference evaluation value of each landscape area for each monitoring time period. The interference evaluation value of each landscape area for each monitoring time period is used to adjust the flight trajectory of the drone.

[0047] It should be noted that the classification types are 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 areas, grassland landscape areas, and wetland landscape areas.

[0048] It should be noted that each landscape area of the scenic area is divided, the signal quality data is collected within the preset monitoring time period, and the image data is extracted, analyzed and optimized. Finally, the crowd density evaluation and signal interference evaluation 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.

[0049] Specifically, the signal quality data includes the average signal strength and average signal-to-noise ratio received by the drone in each landscape area for each monitoring time period.

[0050] The signal image data of each landscape area for each monitoring time period includes each image of each landscape area for each monitoring time period, the image quality data of each image of each landscape area for each monitoring time period, and the feature data of each image.

[0051] It should be noted that using signal detection software (such as Network Signal Info) can monitor the signal strength; using control software (such as QGroundControl, Mission Planner) can monitor the signal-to-noise ratio of the drone in real time.

[0052] It should be noted that the acquisition time points are set within the monitoring time period, and the signal strength and signal-to-noise ratio received by the drone at each acquisition time point are obtained, and the mean values are respectively calculated to obtain the average signal strength and average signal-to-noise ratio received by the drone in each landscape area for each monitoring time period.

[0053] Specifically, the acquisition quality evaluation values of the images in each landscape area for each monitoring time period are obtained through analysis. The specific process is as follows: The image quality data of each image in each landscape area for each monitoring time period includes the average contrast, average brightness, edge sharpness, and average saturation of each image.

[0054] Extract the average contrast, average brightness, edge sharpness, and average saturation of each image in each landscape area for each monitoring time period, and process them to obtain the acquisition quality evaluation values of the images in each landscape area for each monitoring time period.

[0055] The acquisition quality evaluation values of the images in each landscape area for each monitoring time period are used to quantitatively evaluate the quality of the images in each landscape area for each monitoring time period.

[0056] Specifically, the contrast of the signal image data in each landscape area for each monitoring time period is optimized and adjusted according to the acquisition quality evaluation values of the images in each landscape area for each monitoring time period. The specific process is as follows: Compare the acquisition quality evaluation values of the images in each landscape area for each monitoring time period with the image quality evaluation threshold. If the acquisition quality evaluation value of the image in each landscape area for each monitoring time period is higher than or equal to the image quality evaluation threshold, then adjust the contrast of the image in this landscape area for this monitoring time period that has been acquired. If the acquisition quality evaluation value of the image in each landscape area for each monitoring time period is lower than the image quality evaluation threshold, then re-acquire the image of this landscape area.

[0057] It should be noted that when the acquisition quality evaluation value is higher than or equal to the threshold, it indicates that the image quality basically meets the requirements (such as the resolution and sharpness meet the standards), but the contrast may not be optimal. At this time, the system optimizes and adjusts the contrast, and the adjusted image data is regarded as available. If the evaluation value is lower than the threshold, it indicates that there are basic defects in the image quality (such as blurring or excessive noise). At this time, directly adjusting the contrast cannot repair it, and re-acquisition is required. The image data that passes the quality evaluation after re-acquisition is available data. The degree of adjustment should be based on retaining natural details. For example, for a landscape photo with blurred mountain details due to backlighting, when the quality evaluation value is slightly higher than the threshold, the system can automatically increase the contrast to the range of +15 to +25, and at the same time, set the black point input value to 5 - 15 and the white point input value to 240 - 245 through level adjustment.

[0058] It should be noted that image processing software (such as Photoshop, 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 edges. The Sobel operator is a discrete difference operator used to approximate the gradient of the image brightness function, mainly used to obtain the first-order gradient of a digital image, thereby achieving edge detection and obtaining the edge sharpness of the image.

[0059] It should be noted that the specific analysis process of the acquisition quality evaluation value of the images in each landscape area for each monitoring time period is as follows: ; In the formula, represents the acquisition quality evaluation value of the image of the j-th landscape area in the i-th monitoring time period, represents the average contrast of the q-th image of the j-th landscape area in the i-th monitoring time period, represents the average brightness of the q-th image of the j-th landscape area in the i-th monitoring time period, represents the edge sharpness of the q-th image of the j-th landscape area in the i-th monitoring time period, represents the average saturation of the q-th image of the j-th landscape area in the i-th monitoring time period, represents the reference value of the average contrast of the set image, represents the reference value of the average brightness of the set image, represents the reference value of the edge sharpness of the set image, represents the reference value of the average saturation of the set image, represents the correction factor corresponding to the contrast of the set image, represents the correction factor corresponding to the average brightness of the set image, represents the correction factor corresponding to the edge sharpness of the set image, represents the correction factor corresponding to the average saturation of the set image, , i represents the number of the monitoring time period, m represents the total number of the monitoring time periods, , j represents the number of the landscape area, n represents the total number of the landscape areas, , q represents the number of the image, r represents the total number of the images.

[0060] It should be noted that there is a correlation among the contrast, average brightness, edge sharpness, and average saturation of the image. The contrast is positively correlated with the edge sharpness. When the contrast of the image rises, the difference between the bright and dark areas in the image becomes more obvious, and the edge sharpness will also increase accordingly; a high average brightness can enhance the detail performance of the image, making the edge sharpness stronger; a high contrast can enhance the average saturation, so when the contrast increases, the average saturation will also increase.

[0061] It is the correction factor of the average image contrast set in the database, which represents the numerical value of the correction degree of the average image contrast to the quality evaluation value of the landscape area image. When used, the correction factor of the average image contrast can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average image contrast and the correction factor of the average image contrast set in the database form a mapping set, and the real-time average image contrast is input into the mapping set to obtain the correction factor of the average image contrast, where the mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0062] It is the correction factor of the average image brightness set in the database, which represents the numerical value of the correction degree of the average image brightness to the quality evaluation value of the landscape area image. When used, the correction factor of the average image brightness can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average image brightness and the correction factor of the average image brightness set in the database form a mapping set, and the real-time average image brightness is input into the mapping set to obtain the correction factor of the average image brightness, where the mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0063] It is the correction factor of the image edge sharpness set in the database, which represents the numerical value of the correction degree of the image edge sharpness to the quality evaluation value of the landscape area image. When used, the correction factor of the image edge sharpness can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the image edge sharpness and the correction factor of the image edge sharpness set in the database form a mapping set, and the real-time image edge sharpness is input into the mapping set to obtain the correction factor of the image edge sharpness, where the mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0064] It is the correction factor of the average image saturation set in the database, which represents the numerical value of the correction degree of the average image saturation to the quality evaluation value of the landscape area image. When used, the correction factor of the average image saturation can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average image saturation and the correction factor of the average image saturation set in the database form a mapping set, and the real-time average image saturation is input into the mapping set to obtain the correction factor of the average image saturation, where the mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0065] It should be noted that if the acquisition quality evaluation value of an image in a certain landscape area during a certain monitoring period is higher than or equal to the image quality evaluation threshold, the contrast of the image is improved by enhancing the brightness and shadow of the image; if the acquisition quality evaluation value of an image in a certain landscape area during a certain monitoring period is lower than the image quality evaluation threshold, the drone is arranged to re-acquire the image of the landscape area, which helps to optimize the visual effect of the image and improve the data quality of the image.

[0066] Specifically, the environmental interference evaluation value and the signal interference evaluation value of each landscape area in each monitoring period are obtained. The specific process is as follows: Extract the feature data of each image in each landscape area in each monitoring period. The feature data of each image in each landscape area in each monitoring period includes population density, normalized vegetation index, and average noise.

[0067] It should be noted that the average noise reflects the random fluctuation intensity of the brightness or color information in the image. In this embodiment, the average noise is obtained by statistically calculating the standard deviation of the pixel values of a single image or multiple frames of images. The population density is extracted from the image through a computer vision algorithm. For example, based on the photos or videos taken by users in public places, the number of human targets is counted, and then combined with the area of the region to obtain the number of people per unit area.

[0068] Statistically calculate the highest population density in each image in each landscape area in each monitoring period, calculate the average value of the population density in each image in each landscape area in each monitoring period to obtain the average population density in each landscape area in each monitoring period, and count the number of monitoring cycles in which the population density in each image in each landscape area in each monitoring period is higher than the population density threshold of each landscape area stored in the database, which is recorded as the number of personnel gathering time periods in each landscape area in each monitoring period.

[0069] Extract the normalized vegetation index of each image in each landscape area in each monitoring period, and calculate the average value to obtain the average normalized vegetation index of each landscape area in each monitoring period.

[0070] Statistically calculate the average population density and the highest population density in each landscape area in each monitoring period, extract the number of personnel gathering time periods in each landscape area in each monitoring period, and the average normalized vegetation index of each landscape area in each monitoring period, and process to obtain the environmental interference evaluation value of each landscape area in each monitoring period. The environmental interference evaluation value of each landscape area in each monitoring period is used to adjust the acquisition frequency of the drone.

[0071] Extract the average signal strength and average signal-to-noise ratio received by the drone in each landscape area in each monitoring period, and combine with the average noise of the images in each landscape area in each monitoring period to process and obtain the signal interference evaluation value of each landscape area in each monitoring period.

[0072] It should be noted that the YOLO algorithm (You Only Look Once: Unified, Real-Time Object Detection) is an object detection system for computer vision tasks. It is used to count the number of people in the images of each landscape area during each monitoring period, and then divide by the area of each landscape area extracted from the database to obtain the population density in each image of each landscape area during each monitoring period. Using image processing software (such as Photoshop, GIMP), the average noise of the image is analyzed. Noise refers to the random changes or interference in the image data, which usually reduces the image quality, making the image blurred, with increased graininess, and decreased contrast, etc.

[0073] It should be noted that the Normalized Difference Vegetation Index (NDVI) is an index widely used in remote sensing and vegetation monitoring for evaluating vegetation cover and health status. Generally, the higher the NDVI value, the higher the chlorophyll content of the vegetation, the greater the vegetation coverage, and the better the growth state of the vegetation. By comparing the reflectance of vegetation in the red and near-infrared light bands, it is possible to effectively distinguish plant and non-plant surfaces. Using image processing software (such as ENVI, QGIS) to process the image, the reflectance of the red band and the near-infrared band is obtained, and the normalized vegetation index is calculated through a formula , where NDVI represents the normalized vegetation index, NIR represents the reflectance of the near-infrared band, and Red represents the reflectance of the red band.

[0074] It should be noted that the specific analysis method for the environmental interference evaluation value of each landscape area during each monitoring period is as follows: ; In the formula, represents the environmental interference evaluation value of the jth landscape area during the ith monitoring period, represents the average population density in the jth landscape area during the ith monitoring period, represents the highest population density in the jth landscape area during the ith monitoring period, represents the number of periods of personnel aggregation in the jth landscape area during the ith monitoring period, represents the average normalized vegetation index of the jth landscape area during the ith monitoring period, represents the reference value of the average population density set within the jth landscape area, represents the reference value of the highest population density set within the jth landscape area, represents the allowable value of the number of periods of personnel aggregation in the jth landscape area, represents the reference value of the normalized average vegetation index for the j-th set landscape area, represents the correction factor corresponding to the set average population density, represents the correction factor corresponding to the set maximum population density, represents the correction factor corresponding to the number of set periods of people gathering, represents the correction factor corresponding to the set normalized average vegetation index, where e is the natural constant, , i represents the number of the monitoring period, and m represents the total number of monitoring periods, , i represents the number of the landscape area, and n represents the total number of landscape areas.

[0075] It should be noted that there is a correlation among the average population density, the maximum population density, and the number of periods of people gathering. When the maximum population density increases, the average population density will increase accordingly. When the number of periods of people gathering increases, the average population density will also increase accordingly.

[0076] is the correction factor of the average population density set in the database, which represents the value of the degree of correction of the average population density to the environmental interference assessment value of the landscape area. When using it, the correction factor of the average population density can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average population density and the correction factor of the average population density set in the database form a mapping set, and the real-time average population density is input into the mapping set to obtain the correction factor of the average population density, where the mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0077] is the correction factor of the maximum population density set in the database, which represents the value of the degree of correction of the maximum population density to the environmental interference assessment value of the landscape area. When using it, the correction factor of the maximum population density can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the maximum population density and the correction factor of the maximum population density set in the database form a mapping set, and the real-time maximum population density is input into the mapping set to obtain the correction factor of the maximum population density, where the mapping relationship can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0078] The correction factor for the number of periods of people gathering set in the database, which represents the degree of correction of the number of periods of people gathering to the environmental interference evaluation value of the landscape area. When in use, the correction factor for the number of periods of people gathering can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the number of periods of people gathering and the correction factor for the number of periods of people gathering set in the database form a mapping set. Inputting the real-time number of periods of people gathering into the mapping set to obtain the correction factor for the number of periods of people gathering, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0079] The correction factor for the number of normalized vegetation average indices set in the database, which represents the degree of correction of the number of normalized vegetation average indices to the environmental interference evaluation value of the landscape area. When in use, the correction factor for the number of normalized vegetation average indices can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the number of normalized vegetation average indices and the correction factor for the number of normalized vegetation average indices set in the database form a mapping set. Inputting the real-time number of normalized vegetation average indices into the mapping set to obtain the correction factor for the number of normalized vegetation average indices, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0080] It should be noted that by quantifying the crowd density data and the vegetation coverage, identifying the high-density gathering areas and the areas with high vegetation coverage in the scenic area in a timely manner can early warn of potential safety risks and ensure the safety of tourists.

[0081] It should be noted that the specific analysis method for the signal interference evaluation value of each landscape area in each monitoring period is as follows: ; In the formula, represents the signal interference evaluation value of the j-th landscape area in the i-th monitoring period, represents the average signal strength received by the drone in the j-th landscape area in the i-th monitoring period, represents the average signal-to-noise ratio received by the drone in the j-th landscape area in the i-th monitoring period, represents the average noise of the image in the j-th landscape area in the i-th monitoring period, represents the reference value of the average signal strength received by the set drone, represents the reference value of the average signal-to-noise ratio received by the set drone, represents the reference value of the average noise of the image in the set landscape area, represents the correction factor corresponding to the average signal strength received by the set drone, represents the correction factor corresponding to the average signal-to-noise ratio received by the set drone, represents the correction factor corresponding to the average noise of the set 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, n represents the total number of landscape areas.

[0082] It should be noted that there is a correlation among the average signal intensity received by the drone, the average signal-to-noise ratio received by the drone, and the average noise of the image. The average signal intensity 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 intensity 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.

[0083] is the correction factor of the average signal intensity received by the set drone in the database, which represents the value of the correction degree of the average signal intensity received by the drone to the signal interference evaluation value of the landscape area. When using it, the correction factor of the average signal intensity received by the drone can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average signal intensity received by the drone and the correction factor of the average signal intensity received by the set drone in the database form a mapping set. Input the real-time average signal intensity received by the drone into the mapping set to obtain the correction factor of the average signal intensity received by the drone, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0084] is the correction factor of the average signal-to-noise ratio received by the set drone in the database, which represents the value of the correction degree of the average signal-to-noise ratio received by the drone to the signal interference evaluation value of the landscape area. When using it, the correction factor of the average signal-to-noise ratio received by the drone can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average signal-to-noise ratio received by the drone and the correction factor of the average signal-to-noise ratio received by the set drone in the database form a mapping set. Input the real-time average signal-to-noise ratio received by the drone into the mapping set to obtain the correction factor of the average signal-to-noise ratio received by the drone, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0085] The correction factor for the average noise of the images set in the database, which represents the degree of correction of the evaluation value of the signal interference in the landscape area by the average noise of the images. When in use, the correction factor for the average noise of the images can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average noise of the images and the correction factor for the average noise of the images set in the database form a mapping set, and the average noise of the real-time images is input into the mapping set to obtain the correction factor for the average noise of the images. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0086] It should be noted that by continuously monitoring the signal interference, potential communication failures or equipment problems can be detected in advance, so as to take timely measures to ensure the reliability of the system.

[0087] Specifically, the image acquisition frequency of each landscape area is adjusted based on the environmental interference evaluation value of each landscape area in each monitoring time period. The specific process is as follows: extract the environmental interference evaluation value of each landscape area in each monitoring time period and compare it with the environmental interference evaluation threshold of the landscape area stored in the database. If the environmental interference evaluation value of each landscape area in each monitoring time period is lower than the environmental interference evaluation threshold of the landscape area, continuously collect the image data of each monitoring area.

[0088] If the environmental interference evaluation value of each landscape area in each monitoring time period is higher than or equal to the environmental interference evaluation threshold of the landscape area, issue a warning prompt and increase the image acquisition frequency of the drones in each landscape area in each monitoring time period.

[0089] It should be noted that extract the environmental interference evaluation value of each landscape area in each monitoring time period and compare it with the environmental interference evaluation threshold of the landscape area in the database. If the environmental interference evaluation value of each landscape area in each monitoring time period is lower than the environmental interference evaluation threshold of the landscape area, continuously collect the image data of each landscape area; if the environmental interference evaluation value of each landscape area in each monitoring time period is higher than or equal to the environmental interference evaluation threshold of the landscape area, the drone sends a signal to remind that the population density in the landscape area is relatively large, and doubles the number of acquisition time points within the monitoring time period, so as to increase the image acquisition frequency of the drone, which helps to improve the quality of the collected data and provide reliable data for subsequent scenic area management.

[0090] Specifically, compare the signal interference evaluation value of each landscape area in each monitoring time period with the signal interference evaluation threshold in the database.

[0091] 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, send a signal prompt to increase the number of signal amplifiers turned on in each landscape area.

[0092] When the signal interference evaluation value of each landscape area in each monitoring time period is lower than the signal interference evaluation threshold, continue to monitor the signal interference data.

[0093] It should be noted that when the signal interference evaluation value of a certain landscape area in a certain monitoring time period is higher than or equal to the signal interference evaluation threshold, the drone emits 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 evaluation value of a certain landscape area in a certain monitoring time period is lower than the signal interference evaluation threshold, continuously monitor the signal interference in the landscape area, which can save energy and computing resources.

[0094] Specifically, extract the environmental interference evaluation value of each landscape area in each monitoring time period and the signal interference evaluation value of each landscape area in each monitoring time period, and introduce the weight correction factor of the environmental interference evaluation value of the landscape area and the weight correction factor of the signal interference evaluation value of the landscape area to obtain the interference evaluation value of each landscape area in each monitoring time period. The interference evaluation value of each landscape area in each monitoring time period is used to adjust the flight trajectory of the drone.

[0095] Specifically, the interference evaluation value of each landscape area in each monitoring time period is as follows: ; In the formula, represents the interference evaluation value of the jth landscape area in the ith monitoring time period, represents the environmental interference evaluation value of the jth landscape area in the ith monitoring time period, represents the signal interference evaluation value of the jth landscape area in the ith monitoring time period, represents the weight factor corresponding to the environmental interference evaluation value of the landscape area, represents the weight factor corresponding to the signal interference evaluation value of the landscape area, e represents the 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, n represents the total number of landscape areas.

[0096] It is the correction factor for the environmental interference evaluation value of the landscape area set in the database, which represents the numerical value of the correction degree of the environmental interference evaluation value of the landscape area to the interference evaluation value of the landscape area. When used, the correction factor of the environmental interference evaluation value of the landscape area can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the environmental interference evaluation value of the landscape area and the correction factor of the environmental interference evaluation value of the landscape area set in the database form a mapping set. Inputting the real-time environmental interference evaluation value of the landscape area into the mapping set can obtain the correction factor of the environmental interference evaluation value of the landscape area, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0097] It is the correction factor for the signal interference evaluation value of the landscape area set in the database, which represents the numerical value of the correction degree of the signal interference evaluation value of the landscape area to the interference evaluation value of the landscape area. When used, the correction factor of the signal interference evaluation value of the landscape area can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the signal interference evaluation value of the landscape area and the correction factor of the signal interference evaluation value of the landscape area set in the database form a mapping set. Inputting the real-time signal interference evaluation value of the landscape area into the mapping set can obtain the correction factor of the signal interference evaluation value of the landscape area, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0,1].

[0098] It should be noted that by extracting the environmental interference evaluation values and signal interference evaluation values of different landscape areas in each monitoring time period, the interference evaluation value can be calculated to evaluate the tourist safety risks of each landscape area. By timely identifying potential risks and optimizing the tourist management strategy, the safety experience of tourists can be enhanced.

[0099] As Figure 2 shown Figure 2 represents the interference evaluation value, where the horizontal axis represents the signal interference evaluation value of the j-th landscape area in the i-th monitoring time period, and the vertical axis represents the interference evaluation value of the j-th landscape area in the i-th monitoring time period. Three different example parameters are defined in the figure, corresponding to different situations of three curves, which are represented by solid lines, dashed lines and dotted lines respectively, and the corresponding curve labels are a, b, c. Assuming that the weight factor corresponding to the environmental interference evaluation value = 0.6, and the weight factor corresponding to the signal interference evaluation value = 0.4. When the environmental interference evaluation value of the j-th landscape area in the i-th monitoring time period is 0.8, the schematic diagram of the interference evaluation value of the j-th landscape area in the i-th monitoring time period is as shown by curve a. When the environmental interference evaluation value of the j-th landscape area in the i-th monitoring time period is 1.5, the schematic diagram of the interference evaluation value of the j-th landscape area in the i-th monitoring time period is as shown by curve b. When the environmental interference evaluation value of the j-th landscape area in the i-th monitoring time period is 2.0, the schematic diagram of the interference evaluation value of the j-th landscape area in the i-th monitoring time period is as shown by curve c.

[0100] As shown in Table 1, Table 1 is an example data of the interference evaluation value of the landscape area, which lists the interference evaluation value of the landscape area and the signal interference evaluation value of the landscape area.

[0101] Table 1 Example data of the interference evaluation value of the landscape area

[0102] As shown in Table 1, in a specific embodiment, assume that the weight factor corresponding to the environmental interference evaluation value = 0.6, and the weight factor corresponding to the signal interference evaluation value = 0.4. The interference evaluation value of the landscape area is jointly determined by the environmental interference evaluation value of each landscape area in each monitoring time period and the signal interference evaluation value of each landscape area in each monitoring time period.

[0103] Specifically, the process of adjusting the flight path of the drone is as follows: Extract the interference evaluation value of each landscape area in each monitoring time period, and compare it with the comprehensive interference evaluation threshold of the landscape area in the database. If the interference evaluation value of each landscape area in each monitoring time period is higher than or equal to the comprehensive interference evaluation threshold of the landscape area, adjust the flight path of the drone.

[0104] If it is detected that the interference evaluation value of each landscape area in each monitoring time period is lower than the interference evaluation value of the landscape area, there is no need to adjust the flight path of the drone.

[0105] It should be noted that if the interference evaluation value of a certain landscape area during a certain monitoring period is higher than or equal to the comprehensive interference evaluation threshold of the landscape area, image acquisition is performed on this landscape area. The YOLO algorithm is used to identify the gathering points of the crowd. In combination with lidar, the distance between the drone and the crowd gathering points is measured using laser beams to generate high-precision three-dimensional point cloud data, determine the location of the crowded area. The Dijkstra algorithm (shortest path algorithm) can calculate the feasible path by treating the location of the crowd gathering area as an obstacle, adjust the flight path of the drone, and avoid the crowded area. If the interference evaluation value of a certain landscape area during a certain monitoring period is lower than the comprehensive interference evaluation threshold of the landscape area, there is no need to adjust the flight path of the drone, and the crowd density and signal interference degree of each landscape area will be continuously monitored, so as to improve the emergency response ability and ensure that the drone can stably collect high-quality image and signal data in areas with less interference.

[0106] It should be noted that the method for real-time data acquisition and processing of scenic spots based on drones also includes a database for storing and managing the image quality data and the feature data of the images. In this embodiment, the database is used to store the quality evaluation threshold of the images, the weight factors corresponding to the environmental interference evaluation values of the set landscape areas, the weight factors corresponding to the signal interference evaluation values of the set landscape areas, the reference values of the image contrast, the reference values of the average image brightness, the reference values of the image edge sharpness, the reference values of the average image saturation, the correction factors corresponding to the image contrast, the correction factors corresponding to the average image brightness, the correction factors corresponding to the image edge sharpness, the correction factors corresponding to the average image saturation, the reference value of the average crowd density in the set landscape area, the reference value of the highest crowd density in the set landscape area, the allowable number of personnel gathering time periods in the set landscape area, the reference value of the normalized vegetation average index in the set landscape area, the correction factor corresponding to the normalized vegetation average index, the correction factor corresponding to the average crowd density, the correction factor corresponding to the highest crowd density, the correction factor corresponding to the number of personnel gathering time periods, the reference value of the average signal strength received by the drone, the reference value of the average signal-to-noise ratio received by the drone, the reference value of the average noise of the landscape area images, the correction factor corresponding to the average signal strength received by the drone, the correction factor corresponding to the average signal-to-noise ratio received by the drone, the correction factor corresponding to the average noise of the landscape area images, the environmental interference evaluation threshold, the signal interference evaluation threshold, the comprehensive interference evaluation threshold, and the crowd density thresholds of each landscape area.

[0107] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0108] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A method for real-time data collection and processing in 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 each landscape area image 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 each landscape area image 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; 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; 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 analyzed 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 re-plan the flight path of the UAV.

2. The method for real-time data collection and processing of scenic spots 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 data collection and processing of scenic spots based on drones according to claim 2 is characterized in that: The analysis obtains the acquisition quality assessment value of each landscape area image in each monitoring time period, and the specific process is as follows: The image quality data of each image in each landscape area in each monitoring time period, including the average contrast, average brightness, edge clarity, and average saturation of each image; The average contrast, average brightness, edge clarity, and average saturation of each image in 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 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 data collection and processing of scenic spots 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 data collection and processing of scenic spots 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 feature data of each image of each landscape area in each monitoring time period, wherein the feature data of each image of each landscape area in each monitoring time period includes crowd density, normalized vegetation index, and average noise; The highest crowd density in each image of each landscape area in each monitoring time period is counted, and the average of the crowd density in each image of each landscape area in each monitoring time period is 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 is higher than the crowd density threshold of each landscape area stored in the database is counted, and recorded 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 in each monitoring time period are counted, and the number of people gathering time periods in each landscape area in each monitoring time period and the normalized vegetation average index of each landscape area in each monitoring time period are extracted, and the environmental interference assessment value of each landscape area in each monitoring time period is obtained by processing. The environmental interference assessment value of each landscape area in each monitoring time 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 signals in each landscape area during each monitoring period were extracted, and combined with the average noise of the images of each landscape area during each monitoring period, the signal interference assessment value of each landscape area during each monitoring period was obtained.

6. The method for real-time data collection and processing of scenic spots based on drones according to claim 1 is characterized by: 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: Extract the environmental disturbance assessment value of each landscape area in each monitoring time period and compare it with the environmental disturbance assessment threshold of the landscape area stored in the database; if the environmental disturbance assessment value of each landscape area in each monitoring time period is lower than the environmental disturbance assessment threshold of the landscape area, then continue to collect image data of each monitoring area; 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 the drone in each landscape area in each monitoring time period will be increased.

7. The method for real-time data collection and processing of scenic spots based on drones according to claim 1 is characterized by: The signal strength is adjusted based on the signal interference evaluation 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 spot data collection and processing based on drones according to claim 1 is characterized by: 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 data collection and processing of scenic spots based on drones according to claim 8 is characterized by: The interference assessment value of each landscape area in each monitoring time period is specifically as follows: ； In the formula, represents the disturbance assessment value of the jth landscape area in the ith 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, Represents 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 the 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 data collection and processing of scenic spots based on drones according to claim 8 is characterized by: The specific process of adjusting the flight path of the drone is as follows: Extract the interference assessment value of each landscape area in each monitoring time period and compare 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, adjust the flight path of the UAV. 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.

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