Geological disaster risk assessment system based on unmanned aerial vehicle and high-resolution remote sensing image

Abstract

The invention discloses a geological disaster risk assessment system based on an unmanned aerial vehicle and a high-resolution remote sensing image, and relates to the technical field of geological disaster risk assessment, the assessment system comprises a geological risk data storage module, a data acquisition module, a data analysis processing module and a risk assessment module, the geological risk data storage module is used for storing different geological risk data, and classifying the geological risk data according to different geological types. The compressed geological image data are transmitted to the data analysis processing module for image recognition, geological type data are obtained, geological feature data are analyzed according to the geological type data, and then the geological feature data obtained through analysis are compared with images collected by the unmanned aerial vehicle; and the risk characteristic data and the coordinate system data of the risk characteristic data are obtained, so that the geological disaster risk assessment speed is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster risk assessment, and in particular to a geological disaster risk assessment system based on unmanned aerial vehicles and high-resolution remote sensing images. Background Art

[0002] Geological disasters refer to disasters related to geological action, such as landslides, mudslides, debris flows, ground subsidence, ground fissures, and ground subsidence, which are caused by natural factors or human activities and endanger people's lives and property safety. With the intensification of global climate change and human activities, the frequency and degree of geological disasters are increasing, posing a huge threat to human society. Therefore, geological disaster risk assessment has become a vital task, which can provide scientific basis and decision-making support for disaster prevention and mitigation.

[0003] Traditional geological hazard risk assessment mainly relies on ground surveys and monitoring data. However, in recent years, with the rapid development of UAV technology and remote sensing technology, they have been widely used in geological hazard monitoring and risk assessment. UAVs have the advantages of mobility, wide coverage, and low monitoring cost. They can quickly obtain high-resolution image data of geological disaster sites. However, the image data collected by UAVs is large. If these uncompressed image data are directly transmitted to the corresponding analysis module for analysis, there are the following problems: due to the large amount of data, the transmission speed will be seriously affected, resulting in slow image recognition and processing. Since the image data has not been optimized, direct use for analysis will increase the complexity and time cost of the analysis, affect the speed of obtaining geological risk type data, and thus affect the speed of geological hazard risk assessment. For this reason, we propose a geological hazard risk assessment system based on UAVs and high-resolution remote sensing images. Summary of the invention

[0004] The purpose of the present invention is to provide a geological disaster risk assessment system based on unmanned aerial vehicles and high-resolution remote sensing images.

[0005] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a geological disaster risk assessment system based on unmanned aerial vehicles and high-resolution remote sensing images, the assessment system includes a geological risk data storage module, a data acquisition module, a map construction module, a data analysis and processing module and a risk assessment module:

[0006] The geological risk data storage module is used to store different geological risk data and classify the geological risk data according to different geological types;

[0007] The data acquisition module includes a drone camera and a comparison unit, which uses the drone camera to collect geological image data, then compresses the collected geological image data, and transmits the compressed geological image data to the data analysis and processing module;

[0008] The data analysis and processing module includes an image recognition unit and a feature processing unit. After receiving the geological image data transmitted by the data acquisition module, the image recognition unit identifies the geological type data in the geological image data and transmits the geological type data to the feature processing unit. The feature processing unit retrieves the corresponding geological risk data in the geological risk data storage module according to the geological type data. Then the feature processing unit analyzes the geological feature data according to the geological risk data and transmits the analyzed geological feature data to the data acquisition module. After the data acquisition module receives the geological feature data, the comparison unit is used to compare the geological feature data with the geological image data collected by the drone camera. When the comparison is successful, the data analysis and processing module analyzes the coordinate system data of the risk feature data, obtains the risk feature data in the geological image data and the coordinate system data of the risk feature data in the image, and transmits the risk feature data and the coordinate system data of the risk feature data in the image to the map construction module.

[0009] The map construction module is used to construct a virtual three-dimensional map, import the risk feature data and the coordinate system data of the risk feature data in the image into the virtual three-dimensional map, and update the virtual three-dimensional map;

[0010] After receiving the update data of the virtual three-dimensional map, the risk assessment module evaluates the virtual three-dimensional map in the map construction module, and the risk assessment module evaluates the risk level of the geology in the virtual three-dimensional map, and divides the geological risk levels into low risk, medium risk and high risk. When the geological risk level is at low risk, it is marked in the virtual three-dimensional map. When the geological risk level is at medium risk, it is marked in the virtual three-dimensional map, and the assessment system generates a corresponding treatment plan. When the geological risk level is at high risk, the assessment system uses a drone to warn the surrounding scene, and the medium risk includes medium risk of geological structure and vegetation composite type, medium risk of vegetation and micro-topography composite type, and medium risk of geological structure and micro-topography composite type. The high risk includes high risk of geological structure and vegetation composite type, high risk of geological structure and micro-topography composite type, high risk of vegetation and micro-topography composite type, and comprehensive extremely high risk.

[0011] According to the above technical scheme, the following technical effects can be achieved: the data acquisition module and the geological risk data storage module cooperate with each other to quickly obtain geological risk type data, the data analysis and processing module cooperates with the data acquisition module to quickly obtain the image data captured by the data acquisition module according to the geological feature data in the geological risk type data, the image data is transmitted to the map construction module to construct a virtual three-dimensional map, and the constructed virtual three-dimensional map is evaluated by the evaluation module;

[0012] By constructing the take-off point of the drone as the initial coordinate system, and then after the comparison unit compares the image feature data, the height of the drone, the initial coordinate system distance and the direction of the drone are analyzed, and then the image feature data coordinate system is obtained according to the image feature data and the drone position.

[0013] As a further solution of the present invention: the data acquisition module includes a high-resolution remote sensing image acquisition unit, a terrain analysis unit and a flight mission planning unit, the high-resolution remote sensing image acquisition unit is used to collect high-resolution remote sensing image data of the mission area, the terrain analysis unit is used to analyze the terrain of the mission area based on the collected high-resolution remote sensing image data of the mission area, and the flight mission planning unit is used to determine the flight path and flight altitude of the UAV based on the terrain of the mission area analyzed by the terrain analysis unit.

[0014] According to the above technical scheme, the following technical effects can be achieved: through the high-resolution remote sensing image acquisition unit, the terrain analysis unit and the flight mission planning unit, the UAV can better collect data during the flight process, thereby obtaining the UAV's altitude flight data and flight path data, and then obtaining the distance data from the initial coordinate system in the flight path data.

[0015] As a further solution of the present invention: the geological risk data storage module includes a data sorting unit, and the data sorting unit is used to automatically retrieve common geological risk data associated with the geographical location of the mission area according to the geographical location of the mission area.

[0016] According to the above technical solution, the following technical effects can be achieved: geological types are sorted through a data sorting unit, thereby quickly acquiring geological risk data.

[0017] As a further solution of the present invention: the data acquisition module includes an environment perception unit, a camera parameter adjustment unit and an auxiliary equipment control unit. The environment perception unit is used to obtain the temperature, humidity and light intensity data of the environment. The camera parameter adjustment unit is used to adjust the aperture and white balance parameters of the camera according to the data provided by the environment perception unit. When a dense fog environment is identified, the camera will automatically reduce the aperture to reduce depth of field blur, and adjust the white balance to compensate for color deviation. In a dark environment, the camera will increase the aperture and adjust the white balance to maintain color authenticity. The auxiliary equipment control unit is used to control the auxiliary equipment on the drone. The auxiliary equipment includes fog lights, fill lights, camera lens heating plates and micro cooling fans. In a dense fog environment, the fog lights and fill lights will automatically turn on, and the camera lens heating plate will automatically start to prevent the lens from condensing and blurring. In a dark environment, the fill lights will provide additional lighting, and the micro cooling fan will automatically adjust the speed according to the operating temperature of the camera.

[0018] According to the above technical scheme, the following technical effects can be achieved: through the environmental perception unit, the camera parameter adjustment unit and the auxiliary equipment control unit, when the drone camera takes high-resolution remote sensing images, the shooting state of the high-resolution remote sensing images can be adjusted, thereby facilitating the data acquisition module to obtain the feature data in the image more accurately.

[0019] As a further solution of the present invention: in the data acquisition module, when the size of the image data collected by the drone is greater than or equal to 100Mb, the image data is compressed to 5Mb-10Mb; when the size of the image data collected by the drone is in the range of 100Mb-10Mb, the image data size is compressed to 1Mb-5Mb.

[0020] According to the above technical solution, the following technical effects can be achieved: by compressing the image data, the image data can be quickly transmitted to the data analysis module for analysis and processing.

[0021] As a further solution of the present invention: in the data analysis and processing module, after obtaining the actual geological characteristic data, the geological structure stability data, vegetation coverage data and micro-geomorphological change data in the geological risk characteristics are analyzed. The specific analysis formula is as follows:

[0022]

[0023] Among them, S is the geological structure stability data collected by the drone, D is the vegetation coverage data collected by the drone, V is the micro-geomorphic change data collected by the drone, A is the geological structure change index data obtained from the analysis of the images collected by the drone, with a value range of 0 - 100, is the vegetation coverage change index data obtained from the analysis of the images collected by the drone, with a value range of 0 - 100, C is the micro-geomorphic change index data obtained from the analysis of the images collected by the drone, with a value range of 0 - 100. In the above value ranges, 0 indicates no obvious change, and 100 indicates a huge change.

[0024] As a further solution of the present invention: in the map construction module, the map projection method uses the Gauss-Kruger projection, the coordinate system uses the plane rectangular coordinate system. After the map is constructed, the geographical coordinates are calibrated and corrected using the collected high-resolution remote sensing image data. The map construction module includes a user interface, and the user interface is used for the user to view and modify the risk markings on the map.

[0025] As a further solution of the present invention: in the risk assessment module, the formula is used to evaluate the geological disaster risk, and the specific formula is as follows:

[0026]

[0027] R represents the risk level, with a value range of 0 - 1. a, b, and c are the weight coefficients of the geological structure stability data, vegetation coverage data, and micro-geomorphic change data respectively, and a + b + c = 1. For mountainous areas, a:b:c = 7:2:1; for plain areas, a:b:c = 3:5:2; for river areas, a:b:c = 4:3:3.

[0028] As a further solution of the present invention: in the risk assessment module, the risk level is divided as follows: when R ≤ 0.3, if S < 3 and D < 3, it is determined as a high risk of the geological structure and vegetation complex type; if S < 3 and V < 3, it is determined as a high risk of the geological structure and micro-geomorphic complex type; if D < 3 and V < 3, it is determined as a high risk of the vegetation and micro-geomorphic complex type; if D < 3, V < 3, and S < 3 are all satisfied simultaneously, it is determined as an extremely high risk of the comprehensive type. When 0.3 < R ≤ 0.6, if S < 5 and D < 5, it is determined as a medium risk of the geological structure and vegetation complex type; if S < 5 and V < 5, it is determined as a medium risk of the geological structure and micro-geomorphic complex type; if D < 5 and V < 5, it is determined as a medium risk of the vegetation and micro-geomorphic complex type; if D < 5, V < 5, and S < 5 are all satisfied simultaneously, it is determined as an extremely medium risk of the comprehensive type. When R > 0.6, it is determined as a low risk.

[0029] Adopting the above technical solutions, compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] 1. The present invention compresses the collected geological image data through the data acquisition module, and transmits the compressed geological image data to the data analysis and processing module for image recognition, obtains geological type data, and then analyzes geological characteristic data according to the geological type data, and then compares the analyzed geological characteristic data with the image collected by the drone to obtain risk characteristic data and coordinate system data of the risk characteristic data, and transmits this key data to the risk assessment module in real time. Through the above process, the speed of geological disaster risk assessment is improved;

[0031] 2. The present invention sets an environment perception unit, a camera parameter adjustment unit and an auxiliary device control unit in the data acquisition module. The environment perception unit can monitor the temperature, humidity and light intensity of the environment in real time, and provide accurate data support for the adjustment of camera parameters. The camera parameter adjustment unit automatically adjusts the aperture and white balance parameters of the camera according to these environmental data to ensure that clear and real images can be taken under different environmental conditions. In addition, the auxiliary device control unit can also control the fog lights, fill lights, camera lens heating plates and micro cooling fans on the drone to further ensure the quality and stability of image acquisition, avoid image quality problems caused by environmental factors during the image acquisition process of the drone, and thus improve the accuracy of geological disaster risk assessment;

[0032] 3. The present invention comprehensively considers multiple factors such as geological structure stability, vegetation coverage and micro-topography changes, and conducts quantitative assessment of geological disaster risks. At the same time, it flexibly adjusts the weight coefficient according to different mission areas to ensure the accuracy and pertinence of the assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the process of the geological disaster risk assessment system in an embodiment of the present invention;

[0034] Figure 2 1 is a flowchart of data analysis and processing in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1:

[0037] Please refer to the attached Figure 1The present invention provides a geological disaster risk assessment system based on unmanned aerial vehicles and high-resolution remote sensing images. The assessment system includes a geological risk data storage module, a data acquisition module, a map construction module, a data analysis and processing module, and a risk assessment module:

[0038] The geological risk data storage module is used to store different geological risk data and classify the geological risk data according to different geological types;

[0039] The data acquisition module includes a drone camera and a comparison unit, which uses the drone camera to collect geological image data, then compresses the collected geological image data, and transmits the compressed geological image data to the data analysis and processing module;

[0040] The data analysis and processing module includes an image recognition unit and a feature processing unit. After receiving the geological image data transmitted by the data acquisition module, the image recognition unit identifies the geological type data in the geological image data and transmits the geological type data to the feature processing unit. The feature processing unit retrieves the corresponding geological risk data in the geological risk data storage module according to the geological type data. Then the feature processing unit analyzes the geological feature data according to the geological risk data and transmits the analyzed geological feature data to the data acquisition module. After the data acquisition module receives the geological feature data, the comparison unit is used to compare the geological feature data with the geological image data collected by the drone camera. When the comparison is successful, the data analysis and processing module analyzes the coordinate system data of the risk feature data, obtains the risk feature data in the geological image data and the coordinate system data of the risk feature data in the image, and transmits the risk feature data and the coordinate system data of the risk feature data in the image to the map construction module.

[0041] The map construction module is used to construct a virtual three-dimensional map, import the risk feature data and the coordinate system data of the risk feature data in the image into the virtual three-dimensional map, and update the virtual three-dimensional map;

[0042] After receiving the update data of the virtual three-dimensional map, the risk assessment module evaluates the virtual three-dimensional map in the map construction module, and the risk assessment module evaluates the risk level of the geology in the virtual three-dimensional map, and divides the geological risk levels into low risk, medium risk and high risk. When the geological risk level is at low risk, it is marked in the virtual three-dimensional map. When the geological risk level is at medium risk, it is marked in the virtual three-dimensional map, and the assessment system generates a corresponding treatment plan. When the geological risk level is at high risk, the assessment system uses a drone to warn the surrounding scene, and the medium risk includes medium risk of geological structure and vegetation composite type, medium risk of vegetation and micro-topography composite type, and medium risk of geological structure and micro-topography composite type. The high risk includes high risk of geological structure and vegetation composite type, high risk of geological structure and micro-topography composite type, high risk of vegetation and micro-topography composite type, and comprehensive extremely high risk.

[0043] Specific workflow: When conducting a geological hazard risk assessment in a certain mountainous area, the data acquisition module obtains high-resolution remote sensing image data of the area, plans the UAV flight mission, and the UAV takes off. The data acquisition module continues to collect geological image data. For example, the UAV collects about 200Mb of geological image data in a flight mission. The data acquisition module quickly compresses it to 8Mb and transmits it to the data analysis and processing module. The image recognition unit of the data analysis and processing module quickly identifies the geological type data in the image. If it is determined to be a sandstone geological type, the feature processing unit quickly retrieves the corresponding geological risk data in the geological risk data storage module and compares it with the feature data in the image collected by the UAV. It only takes 3 hours to obtain the actual geological feature data and transmit it to the risk assessment module. Traditional assessment methods often take 1-2 days to collect data and conduct preliminary analysis in similar areas. It can be seen that the present invention greatly improves the speed of geological hazard risk assessment through the optimization and integration of data collection, transmission and analysis and processing processes.

[0044] Furthermore, the collected geological image data is compressed by the data acquisition module, and the compressed geological image data is transmitted to the data analysis and processing module for image recognition to obtain geological type data, and then the geological risk data is analyzed based on the geological type data. Subsequently, the analyzed geological risk data is compared with the characteristic data in the image collected by the drone to obtain the actual geological characteristic data, and this key data is transmitted to the risk assessment module in real time. Through the above process, the speed and accuracy of geological hazard risk assessment are improved.

[0045] Embodiment 2:

[0046] Based on the first embodiment, the data acquisition module includes an environment perception unit, a camera parameter adjustment unit and an auxiliary equipment control unit. The environment perception unit is used to obtain the temperature, humidity and light intensity data of the environment. The camera parameter adjustment unit is used to adjust the aperture and white balance parameters of the camera according to the data provided by the environment perception unit. When a dense fog environment is identified, the camera will automatically reduce the aperture to reduce the depth of field blur and adjust the white balance to compensate for the color shift. In a dark environment, the camera will increase the aperture and adjust the white balance to maintain color authenticity. The auxiliary equipment control unit is used to control the auxiliary equipment on the drone. The auxiliary equipment includes fog lights, fill lights, camera lens heating plates and micro cooling fans. In a dense fog environment, the fog lights and fill lights will automatically turn on, and the camera lens heating plate will automatically start to prevent the lens from condensing and blurring. In a dark environment, the fill lights will provide additional lighting, and the micro cooling fan will automatically adjust the speed according to the operating temperature of the camera.

[0047] In the data acquisition module, when the size of the image data collected by the drone is greater than or equal to 100Mb, the image data is compressed to 5Mb-10Mb. When the size of the image data collected by the drone is in the range of 100Mb-10Mb, the image data size is compressed to 1Mb-5Mb.

[0048] Specific workflow: Geological hazard risk assessment is carried out in the area around a river. The environmental perception unit in the data acquisition module monitors the environmental conditions at all times. When the drone flies to a riverside area with high humidity and weak light, the environmental perception unit detects that the ambient temperature is 20°C, the humidity is 80%, and the light intensity is low. Based on these data, the camera parameter adjustment unit increases the camera aperture from f / 5.6 to f / 2.8, and adjusts the white balance to adapt to the light changes. At the same time, the auxiliary equipment control unit turns on the fill light and the camera lens heater to prevent the lens from condensing and blurring. The geological image data collected by the drone clearly and accurately reflects the micro-geomorphological changes and vegetation coverage in the area. Subsequent risk assessment results show that there is a medium risk of geological disasters in the area due to micro-geomorphological changes and changes in vegetation coverage. In the subsequent months of observation, small-scale geological changes caused by local riverbank erosion and vegetation reduction did occur in the area. This shows that the present invention ensures image quality through environmental perception and equipment control, and effectively improves the accuracy of geological hazard risk assessment.

[0049] Furthermore, by setting an environmental perception unit, a camera parameter adjustment unit and an auxiliary equipment control unit in the data acquisition module, the environmental perception unit can monitor the temperature, humidity and light intensity of the environment in real time, providing accurate data support for the camera parameter adjustment. The camera parameter adjustment unit automatically adjusts the camera's aperture and white balance parameters according to these environmental data to ensure that clear and true images can be captured under different environmental conditions. In addition, the auxiliary equipment control unit can also control the fog lights, fill lights, camera lens heaters and micro cooling fans on the drone to further ensure the quality and stability of image acquisition, avoid image quality problems caused by environmental factors during the image acquisition process of the drone, and thus further improve the accuracy of geological hazard risk assessment.

[0050] Embodiment three:

[0051] Based on the second embodiment, in the data analysis and processing module, after obtaining the actual geological feature data, the geological structure stability data, vegetation coverage data and micro-geomorphological change data in the geological risk characteristics are analyzed. The specific analysis formula is as follows:

[0052]

[0053] Among them, S is the geological structure stability data collected by the drone, D is the vegetation coverage data collected by the drone, V is the micro-relief change data collected by the drone, A is the geological structure change index data obtained by analyzing the images collected by the drone, and the value range is 0-100, is the vegetation coverage change index data obtained by analyzing the images collected by the drone, and the value range is 0-100, C is the micro-relief change index data obtained by analyzing the images collected by the drone, and the value range is 0-100. In the above value range, 0 means no obvious change, and 100 means a huge change;

[0054] In the risk assessment module, a formula is used to assess the risk of geological disasters. The specific formula is as follows:

[0055]

[0056] R represents the risk level, with a value range of 0-1, a, b, c are the weight coefficients of geological structure stability data, vegetation coverage data and micro-geomorphological change data, and a+b+c=1. For mountainous areas, a:b:c=7:2:1, for plain areas, a:b:c=3:5:2, and for river areas, a:b:c=4:3:3;

[0057] In the risk assessment module, the risk levels are classified as follows: when R ≤ 0.3, if S < 3 and D < 3, it is determined as a high risk of the composite type of geological structure and vegetation; if S < 3 and V < 3, it is determined as a high risk of the composite type of geological structure and microtopography; if D < 3 and V < 3, it is determined as a high risk of the composite type of vegetation and microtopography; if D < 3, V < 3, and S < 3 are all satisfied simultaneously, it is determined as an extremely high risk of the comprehensive type. When 0.3 < R ≤ 0.6, if S < 5 and D < 5, it is determined as a medium risk of the composite type of geological structure and vegetation; if S < 5 and V < 5, it is determined as a medium risk of the composite type of geological structure and microtopography; if D < 5 and V < 5, it is determined as a medium risk of the composite type of vegetation and microtopography; if D < 5, V < 5, and S < 5 are all satisfied simultaneously, it is determined as an extremely medium risk of the comprehensive type. When R > 0.6, it is determined as a low risk;

[0058] In the map construction module, the map projection method adopts the Gauss-Krüger projection, and the coordinate system adopts the plane rectangular coordinate system. After the map construction is completed, the collected high-resolution remote sensing image data is used to calibrate and correct the geographical coordinates. The map construction module includes a user interface, which is used for users to view and modify the risk markings on the map.

[0059] Specific workflow: Conduct a geological disaster risk assessment task in a vast plain area. The data analysis and processing module calculates the geological structure stability data S = 6 (geological structure change index data A = 40), vegetation coverage data D = 4 (vegetation coverage change index data B = 40), and microtopography change data V = 2 (microtopography change index data C = 20) of the images collected by the UAV. Since it is a plain area, the risk assessment module adopts the weight coefficients a:b:c = 3:5:2 for the plain area and substitutes them into the risk assessment formula to calculate the risk level R:

[0060]

[0061] According to the risk level classification, it is determined as a medium risk of the composite type of vegetation and geological structure. Subsequently, during the rainy season in this plain area, some small ground collapses and soil erosion phenomena occurred due to insufficient vegetation coverage and local geological structure instability, which is consistent with the assessment results. The present invention comprehensively evaluates multiple factors quantitatively and adjusts the weight coefficients according to the region, ensuring the accuracy and pertinence of the assessment results.

[0062] Furthermore, by comprehensively considering multiple factors such as geological structure stability, vegetation coverage, and microtopography change, quantitatively evaluating the geological disaster risk, and flexibly adjusting the weight coefficients according to different task regions, the accuracy and pertinence of the assessment results are ensured.

[0063] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A geological disaster risk assessment system based on UAV and high-resolution remote sensing images, characterized by: The assessment system includes geological risk data storage module, data acquisition module, map construction module, data analysis and processing module and risk assessment module: The geological risk data storage module is used to store different geological risk data and classify the geological risk data according to different geological types; The data acquisition module includes a drone camera and a comparison unit. The drone camera acquires geological image data, compresses the acquired geological image data, and transmits the geological image data to the data analysis and processing module after compression; The data analysis and processing module includes an image recognition unit and a feature processing unit. After receiving the geological image data transmitted by the data acquisition module, the image recognition unit identifies the geological type data in the geological image data and transmits the geological type data to the feature processing unit. The feature processing unit retrieves the corresponding geological risk data in the geological risk data storage module according to the geological type data. The feature processing unit analyzes the geological feature data according to the geological risk data and transmits the analyzed geological feature data to the data acquisition module. After the data acquisition module receives the geological feature data, the comparison unit compares the geological feature data with the geological image data collected by the drone camera. When the comparison is successful, the data analysis and processing module analyzes the coordinate system data of the risk feature data, obtains the risk feature data in the geological image data and the coordinate system data of the risk feature data in the image, and transmits the risk feature data and the coordinate system data of the risk feature data in the image to the map construction module. The map construction module is used to construct a virtual three-dimensional map, import the risk feature data and the coordinate system data of the risk feature data in the image into the virtual three-dimensional map, and update the virtual three-dimensional map; After receiving the update data of the virtual three-dimensional map, the risk assessment module evaluates the virtual three-dimensional map in the map construction module, and the risk assessment module evaluates the risk level of the geology in the virtual three-dimensional map, and divides the geological risk levels into low risk, medium risk and high risk. When the geological risk level is at low risk, it is marked in the virtual three-dimensional map. When the geological risk level is at medium risk, it is marked in the virtual three-dimensional map, and the assessment system generates a corresponding treatment plan. When the geological risk level is at high risk, the assessment system uses a drone to warn the surrounding scene, and the medium risk includes medium risk of geological structure and vegetation composite type, medium risk of vegetation and micro-topography composite type, and medium risk of geological structure and micro-topography composite type. The high risk includes high risk of geological structure and vegetation composite type, high risk of geological structure and micro-topography composite type, high risk of vegetation and micro-topography composite type, and comprehensive extremely high risk.

2. The geological disaster risk assessment system based on unmanned aerial vehicle and high-resolution remote sensing images according to claim 1 is characterized in that: The data acquisition module includes a high-resolution remote sensing image acquisition unit, a terrain analysis unit and a flight mission planning unit. The high-resolution remote sensing image acquisition unit is used to collect high-resolution remote sensing image data of the mission area. The terrain analysis unit is used to analyze the terrain of the mission area based on the collected high-resolution remote sensing image data of the mission area. The flight mission planning unit is used to determine the flight path and flight altitude of the UAV based on the terrain of the mission area analyzed by the terrain analysis unit.

3. The geological disaster risk assessment system based on unmanned aerial vehicle and high-resolution remote sensing images according to claim 2 is characterized in that: The geological risk data storage module includes a data sorting unit, which is used to automatically retrieve common geological risk data associated with the geographical location of the mission area according to the geographical location of the mission area.

4. The geological disaster risk assessment system based on unmanned aerial vehicle and high-resolution remote sensing images according to claim 1 is characterized in that: The data acquisition module includes an environment perception unit, a camera parameter adjustment unit and an auxiliary equipment control unit. The environment perception unit is used to obtain the temperature, humidity and light intensity data of the environment. The camera parameter adjustment unit is used to adjust the aperture and white balance parameters of the camera according to the data provided by the environment perception unit. When a dense fog environment is identified, the camera automatically reduces the aperture to reduce depth of field blur and adjusts the white balance to compensate for color deviation. In a dark environment, the camera increases the aperture and adjusts the white balance to maintain color authenticity. The auxiliary equipment control unit is used to control the auxiliary equipment on the drone. The auxiliary equipment includes fog lights, fill lights, camera lens heating plates and micro cooling fans. In a dense fog environment, the fog lights and fill lights are automatically turned on, and the camera lens heating plate is automatically started to prevent the lens from condensation and blurring. In a dark environment, the fill lights provide additional lighting, and the micro cooling fan automatically adjusts the speed according to the operating temperature of the camera.

5. The geological disaster risk assessment system based on unmanned aerial vehicle and high-resolution remote sensing images according to claim 1 is characterized in that: In the data acquisition module, when the size of the image data collected by the drone is greater than or equal to 100Mb, the image data is compressed to 5Mb-10Mb. When the size of the image data collected by the drone is in the range of 100Mb-10Mb, the image data size is compressed to 1Mb-5Mb.

6. The geological disaster risk assessment system based on unmanned aerial vehicle and high-resolution remote sensing images according to claim 1 is characterized by: In the data analysis and processing module, after obtaining the actual geological characteristic data, the geological structure stability data, vegetation coverage data and micro-geomorphological change data in the geological risk characteristics are analyzed. The specific analysis formula is as follows: Among them, S is the geological structure stability data collected by the UAV, D is the vegetation coverage data collected by the UAV, V is the micro-relief change data collected by the UAV, A is the geological structure change index data obtained by analyzing the images collected by the UAV, and the value range is 0-100, is the vegetation coverage change index data obtained by analyzing the images collected by the UAV, and the value range is 0-100, C is the micro-relief change index data obtained by analyzing the images collected by the UAV, and the value range is 0-100. In the above value range, 0 means no obvious change, and 100 means a huge change.

7. The geological disaster risk assessment system based on unmanned aerial vehicle and high-resolution remote sensing images according to claim 1 is characterized by: In the map construction module, the map projection method uses the Gauss-Krüger projection, and the coordinate system uses the plane rectangular coordinate system. After the map is constructed, the high-resolution remote sensing image data collected is used to calibrate and correct the geographic coordinates. The map construction module includes a user interface, and the user interface is used for users to view and modify the risk annotations on the map.

8. The geological disaster risk assessment system based on unmanned aerial vehicles and high-resolution remote sensing images according to claim 7 is characterized in that: In the risk assessment module, a formula is used to assess the geological disaster risk. The specific formula is as follows: R represents the risk level, and the value range is 0-1. a, b, and c are the weight coefficients of the geological structure stability data, vegetation coverage data, and micro-geomorphic change data respectively, and a + b + c = 1. For mountainous areas, a:b:c = 7:2:1; for plain areas, a:b:c = 3:5:2; for river areas, a:b:c = 4:3:

3.

9. The geological disaster risk assessment system based on unmanned aerial vehicles and high-resolution remote sensing images according to claim 8 is characterized by: In the risk assessment module, the risk level is divided as follows: when R ≤ 0.3, when S < 3 and D < 3, it is determined as a high risk of the geological structure and vegetation complex type; when S < 3 and V < 3, it is determined as a high risk of the geological structure and micro-geomorphic complex type; when D < 3 and V < 3, it is determined as a high risk of the vegetation and micro-geomorphic complex type; when D < 3, V < 3, and S < 3 are satisfied, it is determined as an extremely high risk of the comprehensive type; when 0.3 < R ≤ 0.6, S < 5 and D < 5, it is determined as a medium risk of the geological structure and vegetation complex type; when S < 5 and V < 5, it is determined as a medium risk of the geological structure and micro-geomorphic complex type; when D < 5 and V < 5, it is determined as a medium risk of the vegetation and micro-geomorphic complex type; when D < 5, V < 5, and S < 5, it is determined as an extremely medium risk of the comprehensive type; when R > 0.6, it is determined as a low risk.

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