Intelligent early warning method for slope instability risk of high-voltage overhead line in mountainous and hilly areas based on unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle-based intelligent early warning method for the slope instability risk of a high-voltage overhead line in mountain and hill areas, and relates to the technical field of power system safety monitoring, and the method comprises the steps: controlling an unmanned aerial vehicle inspection platform to fly in a slope region in a line corridor in an overhead line planning stage, optical image data, three-dimensional point cloud data, thermal infrared image data and meteorological data are obtained by using a sensor carried by the system; identifying multiple geological features in the optical image data, the three-dimensional point cloud data and the thermal infrared image data; inputting the identified geological features into a pre-constructed risk assessment model, assessing the slope instability risk in combination with a regional geological database and meteorological data, and outputting a risk assessment result; and generating and issuing risk early warning information according to a risk assessment result.

Description

Technical Field

[0001] This invention relates to the field of power system safety monitoring technology, and more specifically, to an intelligent early warning method for slope instability risk of high-voltage overhead lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs). Background Technology

[0002] The region is characterized by mountains and hills with complex geological conditions, and 500kV overhead transmission lines often traverse areas with challenging terrain. During the rainy season, landslides and other geological disasters can easily occur on the slopes of transmission line towers, seriously affecting the safe operation of overhead lines. Current technologies for assessing slope instability risks primarily rely on manual inspections and traditional geological survey methods, which have the following drawbacks:

[0003] 1. Manual inspections are inefficient, costly, and difficult to cover all areas;

[0004] 2. Traditional geological exploration methods have limited effectiveness in areas with thick surface overburden and uneven weathering of rock strata;

[0005] 3. Inability to efficiently identify key slope features such as slope structure and groundwater distribution;

[0006] 4. Risk warnings are delayed, making it difficult to identify potential risk points in advance during the planning stage.

[0007] The development of drone technology has made it possible to solve the above problems, but there is currently a lack of dedicated drone intelligent inspection and early warning systems for the characteristics of high-voltage overhead lines in mountainous and hilly areas, especially in the identification of slope instability risks under complex geological conditions. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides an intelligent early warning method for slope instability risk of high-voltage overhead lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs). This method can effectively solve the problem of slope instability risk identification during the planning stage of 500kV overhead lines. It utilizes UAV technology to achieve efficient and accurate geological surveys, providing a scientific basis for line planning and ensuring the safe operation of overhead lines.

[0009] According to one aspect of the present invention, an intelligent early warning method for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs) is provided, comprising:

[0010] During the planning phase of overhead power lines, the unmanned aerial vehicle (UAV) inspection platform is controlled to fly over the slope areas within the line corridor and use its onboard sensors to acquire optical image data, 3D point cloud data, thermal infrared image data, and meteorological data.

[0011] Identify multiple geological features in optical image data, 3D point cloud data, and thermal infrared image data;

[0012] The identified geological features are input into a pre-built risk assessment model, and combined with regional geological databases and meteorological data, the slope instability risk is assessed, and the risk assessment results are output.

[0013] Based on the risk assessment results, risk warning information is generated and released.

[0014] Optionally, multiple geological features include the thickness of the surface overburden; the steps for identifying the thickness of the surface overburden include:

[0015] Generate digital elevation models based on 3D point cloud data;

[0016] Analyze the texture features of optical image data;

[0017] By inputting the digital elevation model and texture features into a pre-trained overburden thickness estimation model based on machine learning algorithms, a distribution map of the surface overburden thickness is predicted.

[0018] Optionally, multiple geological features include the degree of rock weathering; the steps for identifying the degree of rock weathering include:

[0019] Extract reflectance and color texture features of rock areas from optical image data;

[0020] By inputting reflectance features and color texture features into a pre-built weathering degree classification model, the classification results of the weathering degree of rock strata are obtained.

[0021] Optionally, multiple geological features include groundwater distribution; the steps for identifying groundwater distribution include:

[0022] Analyze thermal infrared image data to identify low-temperature areas with abnormal temperatures;

[0023] Based on topographic features and rainfall data, areas prone to water accumulation were identified.

[0024] A groundwater distribution prediction map is generated based on low-temperature areas and areas prone to water accumulation.

[0025] Optionally, multiple geological features include slope structure, slope gradient, and slope aspect; the steps for identifying slope structure, slope gradient, and slope aspect include:

[0026] The slope structure, slope, and aspect are derived from the 3D point cloud data.

[0027] Optionally, multiple geological features identified, including slope structure, slope, aspect, thickness of surface overburden, degree of rock weathering, and groundwater distribution, can be input into a pre-built risk assessment model.

[0028] Optionally, it also includes:

[0029] After the line is completed, multiple geological features and risk assessment results will be updated regularly, and risk warning information will be updated dynamically.

[0030] Optionally, based on the risk assessment results, risk warning information is generated and issued, including:

[0031] Based on the risk assessment results, slopes are classified into different risk levels;

[0032] Early warning information containing differentiated decision-making suggestions is generated based on different risk levels.

[0033] According to another aspect of the present invention, an intelligent early warning device for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas based on unmanned aerial vehicles is provided, comprising:

[0034] The acquisition module is used to control the UAV inspection platform to fly over the slope area within the line corridor during the overhead line planning stage, and to use its onboard sensors to acquire optical image data, 3D point cloud data, thermal infrared image data and meteorological data.

[0035] The identification module is used to identify multiple geological features in optical image data, 3D point cloud data, and thermal infrared image data.

[0036] The assessment module is used to input multiple identified geological features into a pre-built risk assessment model, and combine regional geological databases and meteorological data to assess the risk of slope instability and output the risk assessment results.

[0037] The generation module is used to generate and publish risk warning information based on the risk assessment results.

[0038] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0039] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0040] Therefore, this invention provides an intelligent early warning system and method for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs). This system can identify potential slope instability risk points during the planning stage of 500kV overhead power lines through refined geological survey technology. It effectively solves the problem of slope instability risk identification during the planning stage of 500kV overhead power lines, utilizing UAV technology to achieve efficient and accurate geological surveys, providing a scientific basis for line planning, and ensuring the safe operation of overhead power lines. Attached Figure Description

[0041] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0042] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention regarding an intelligent early warning method for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs).

[0043] Figure 2 This is a schematic diagram of the structure of an intelligent early warning device for slope instability risk of high-voltage overhead lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs) provided in an exemplary embodiment of the present invention.

[0044] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0045] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0046] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0047] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0048] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0049] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0050] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0051] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0052] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0053] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0054] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0055] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0056] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0057] Exemplary methods

[0058] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention regarding an intelligent early warning method for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs). This embodiment can be applied to electronic devices, such as… Figure 1 As shown, the intelligent early warning method 100 for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas based on UAVs includes the following steps:

[0059] Step 101: During the overhead line planning stage, control the UAV inspection platform to fly over the slope area within the line corridor and use its onboard sensors to acquire optical image data, 3D point cloud data, thermal infrared image data and meteorological data.

[0060] Step 102: Identify multiple geological features in optical image data, 3D point cloud data, and thermal infrared image data;

[0061] Step 103: Input the identified geological features into the pre-built risk assessment model, and combine the regional geological database and meteorological data to assess the slope instability risk and output the risk assessment results;

[0062] Step 104: Generate and issue risk warning information based on the risk assessment results.

[0063] Specifically, the purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent early warning system and method for slope instability risk of high-voltage overhead lines in mountainous and hilly areas based on UAVs. This system can identify potential slope instability risk points through refined geological survey technology during the planning stage of 500kV overhead lines.

[0064] To achieve the above objectives, the present invention adopts the following technical solution:

[0065] This invention also provides an intelligent early warning method for slope instability risk of high-voltage overhead lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs), comprising the following steps:

[0066] 1. Data collection during the planning phase: During the planning phase of the 500kV overhead line, a drone inspection platform was used to collect comprehensive data on the slope areas within the line corridor, obtaining high-resolution images, 3D point cloud data, thermal infrared images, and meteorological data.

[0067] 2. Data preprocessing: Preprocess the collected data, including image stitching, point cloud denoising, and data calibration.

[0068] 3. Geological feature identification:

[0069] Based on image processing results, features such as slope structure, slope gradient, and slope aspect are identified.

[0070] Analysis of surface cover layer thickness distribution based on 3D model;

[0071] Identifying groundwater distribution areas based on thermal infrared images;

[0072] Analyze the degree of weathering and distribution characteristics of the rock strata.

[0073] 4. Risk assessment model construction: Based on the regional geological database, a slope instability risk assessment model is constructed. The input parameters of the model include: slope structure, surface overburden thickness, rock weathering degree, groundwater distribution, slope, aspect and historical disaster data.

[0074] 5. Risk Level Classification: Based on the risk assessment results, the slope area is divided into different risk levels. The slope area is classified into four levels: extremely high risk, high risk, medium risk, and low risk, and corresponding decision-making recommendations are generated for each level.

[0075] 6. Early Warning Information Generation: Generates early warning information for high-risk areas to provide decision-making basis for route planning.

[0076] 7. Dynamic monitoring during operation: After the line is completed, drone inspections will be carried out regularly to update data, dynamically assess risks, and update early warning information in a timely manner.

[0077] Furthermore, in step 3, areas with thicker surface covering layers are identified using the following method:

[0078] Using lidar data to obtain surface elevation information;

[0079] Combined with texture feature analysis of high-resolution images;

[0080] A model for estimating overburden thickness was established, and the distribution of overburden thickness was predicted based on machine learning algorithms.

[0081] Furthermore, in step 3, the degree of weathering of the rock strata is assessed using the following methods:

[0082] Analyze the reflectance characteristics of rocks in high-resolution images;

[0083] Identify color and texture variations in exposed rock areas;

[0084] A weathering degree classification model was established by combining prior geological knowledge.

[0085] Furthermore, in step 3, groundwater distribution is identified using the following methods:

[0086] Analyze temperature anomaly regions in thermal infrared images;

[0087] By combining rainfall data and topographic features, areas prone to water accumulation can be identified.

[0088] Establish a groundwater distribution prediction model.

[0089] The intelligent early warning system for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas, built using the above method and based on unmanned aerial vehicles (UAVs), includes:

[0090] 1. Unmanned Aerial Vehicle (UAV) Inspection Platform: Equipped with a multispectral camera, lidar, thermal infrared imager, and meteorological sensors, it is used to acquire high-resolution images, 3D point cloud data, thermal infrared images, and meteorological data of the slope area;

[0091] 2. Ground control station: Communicates with the UAV inspection platform to plan inspection routes, monitor the inspection process in real time, and receive data transmitted by the UAV;

[0092] 3. Data Processing Center: Includes:

[0093] The image processing module is used to process high-resolution images and identify slope structure features;

[0094] The 3D modeling module is used to build 3D models of slopes based on LiDAR data.

[0095] The thermal infrared analysis module is used to analyze thermal infrared images and identify groundwater distribution areas;

[0096] The geological feature extraction module is used to extract geological parameters such as the thickness of the surface overburden and the degree of rock weathering.

[0097] 4. Risk Assessment System: Based on the output of the data processing center, combined with regional geological databases and rainfall forecast data, the risk of slope instability is assessed;

[0098] 5. Early warning system: Based on the risk assessment results, generate risk warning information and push it to relevant personnel.

[0099] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0100] 1. This invention is specifically designed for 500kV overhead lines in mountainous and hilly areas with complex geology. It can identify potential slope instability risk points in advance during the planning stage, thereby improving the scientific nature and safety of line planning.

[0101] 2. Using drone technology for detailed geological surveys is more efficient and less costly than traditional methods, and can cover areas that are difficult for humans to reach.

[0102] 3. By combining multispectral imaging, lidar, and thermal infrared imaging technologies, key features such as slope structure, surface overburden thickness, rock weathering degree, and groundwater distribution can be comprehensively acquired, improving the accuracy of risk identification.

[0103] 4. A specialized risk assessment model has been developed, which can comprehensively consider various geological and meteorological factors to achieve accurate assessment of slope instability risk.

[0104] 5. It is not only applicable to the planning stage, but also to dynamic monitoring and risk assessment during the line operation stage, realizing safety management throughout the entire life cycle.

[0105] Exemplary device

[0106] Figure 2 This is a schematic diagram of the structure of an intelligent early warning device for slope instability risk of high-voltage overhead power lines in mountainous and hilly areas based on unmanned aerial vehicles (UAVs), provided in an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0107] The acquisition module 210 is used to control the UAV inspection platform to fly over the slope area in the line corridor during the overhead line planning stage, and to use its onboard sensors to acquire optical image data, three-dimensional point cloud data, thermal infrared image data and meteorological data.

[0108] The identification module 220 is used to identify multiple geological features in optical image data, three-dimensional point cloud data and thermal infrared image data;

[0109] The assessment module 230 is used to input the identified multiple geological features into a pre-built risk assessment model, and combine regional geological databases and meteorological data to assess the slope instability risk and output the risk assessment results.

[0110] The generation module 240 is used to generate and publish risk warning information based on the risk assessment results.

[0111] Exemplary electronic devices

[0112] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 3 As shown, the electronic device 30 includes one or more processors 31 and memory 32.

[0113] The processor 31 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0114] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 33 and an output device 34, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0115] In addition, the input device 33 may also include, for example, a keyboard, a mouse, etc.

[0116] The output device 34 can output various information to the outside. The output device 34 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0117] Of course, for the sake of simplicity, Figure 3 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0118] Exemplary computer program products and computer-readable storage media

[0119] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0120] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0121] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0122] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0123] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0124] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0125] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0126] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but is to be implemented within the widest scope consistent with the principles and novel features disclosed herein.

[0127] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An unmanned aerial vehicle-based intelligent early warning method for slope instability risk of high-voltage overhead lines in mountain and hilly areas, characterized in that, The method comprises the following steps: In the overhead line planning stage, the unmanned aerial vehicle inspection platform is controlled to fly over the slope area in the line corridor, and sensors carried thereby are used to acquire optical image data, three-dimensional point cloud data, thermal infrared image data and meteorological data; Identify multiple geological features in the optical image data, three-dimensional point cloud data and thermal infrared image data; Input the identified multiple geological features into a pre-constructed risk assessment model, and combine the regional geological database and the meteorological data to assess the slope instability risk and output the risk assessment result; According to the risk assessment result, generate and publish risk warning information.

2. The method of claim 1, wherein, The multiple geological features include the thickness of the surface cover layer; the identification step of the thickness of the surface cover layer comprises: Generate a digital elevation model based on the three-dimensional point cloud data; Analyze the texture characteristics of the optical image data; Input the digital elevation model and the texture characteristics into a pre-trained cover layer thickness estimation model based on a machine learning algorithm to predict the distribution map of the thickness of the surface cover layer.

3. The method of claim 2, wherein, The multiple geological features include the weathering degree of the rock stratum; the identification step of the weathering degree of the rock stratum comprises: Extract the reflectance characteristics and color texture characteristics of the rock area from the optical image data; Input the reflectance characteristics and color texture characteristics into a pre-constructed weathering degree classification model to obtain the classification result of the weathering degree of the rock stratum.

4. The method of claim 2, wherein, The multiple geological features include the distribution of underground water; the identification step of the distribution of underground water comprises: Analyze the thermal infrared image data to identify low-temperature areas with temperature anomalies; Combine the terrain characteristics and rainfall data to filter out areas prone to water accumulation; Based on the low-temperature areas and the areas prone to water accumulation, generate a prediction map of the distribution of underground water.

5. The method of claim 1, wherein, The multiple geological features include the slope body structure, slope gradient and slope direction; the identification step of the slope body structure, slope gradient and slope direction comprises: Derive the slope body structure, slope gradient and slope direction from the three-dimensional point cloud data.

6. The method of claim 1, wherein, Input the identified multiple geological features including the slope body structure, slope gradient, slope direction, thickness of the surface cover layer, weathering degree of the rock stratum and distribution of underground water into a pre-constructed risk assessment model.

7. The method of claim 1, wherein, Further comprising: After the line is built, periodically update the multiple geological features and the risk assessment result, and dynamically update the risk warning information.

8. The method of claim 1, wherein, According to the risk assessment result, generate and publish risk warning information, comprising: According to the risk assessment result, divide the slope into different risk levels; Generate warning information containing differentiated decision suggestions for different risk levels.

9. An unmanned aerial vehicle-based intelligent early warning device for slope instability risk of high-voltage overhead lines in mountainous and hilly areas, characterized in that, The method comprises the following steps: An acquisition module is used to control the unmanned aerial vehicle inspection platform to fly over the slope area in the line corridor in the overhead line planning stage, and sensors carried thereby are used to acquire optical image data, three-dimensional point cloud data, thermal infrared image data and meteorological data; An identification module is used to identify multiple geological features in the optical image data, three-dimensional point cloud data and thermal infrared image data; An evaluation module is used to input the identified multiple geological features into a pre-constructed risk assessment model, and combine the regional geological database and the meteorological data to assess the slope instability risk and output the risk assessment result; A generating module is configured to generate and release risk warning information according to the risk assessment result.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is used for executing the method in any one of claims 1-8.

11. An electronic device, comprising: The electronic device includes: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method in any one of claims 1-8.

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