Ground disaster inspection digital twin system based on 5G + unmanned aerial vehicle and use method thereof

Abstract

The invention relates to the technical field of geological disaster inspection, in particular to a geological disaster inspection digital twin system based on a 5G + unmanned aerial vehicle and a use method of the geological disaster inspection digital twin system. The system comprises an Internet of Things sensing layer, an infrastructure layer, a data resource layer, an application service layer and a user layer, the internet-of-things sensing layer comprises an unmanned aerial vehicle, a camera and a sensor module and is used for acquiring geological disaster information of a research area; the infrastructure layer comprises the Internet, a cloud platform, a 5G network, a server, a display and storage device, and a network environment and hardware environment layer used for supporting the whole system; the data resource layer is used for storing data and managing data resources, receiving the data acquired by the Internet of Things sensing layer and performing processing and storage management; the application service layer is used for providing a statistical analysis calculation service, a visualization service and a unified authority management service; and the user layer is used for providing interactive services and providing remote monitoring and consultation services for users. According to the invention, the inspection path and strategy are optimized through an intelligent algorithm, and the inspection efficiency and effect are improved.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster inspection technology, and in particular to a geological disaster inspection digital twin system based on 5G+unmanned aerial vehicle and a method of using the system. Background Art

[0002] With the rapid development of social economy and the intensification of human activities, there are risks of high and frequent geological disasters in the process of accelerating the construction of water conservancy projects such as hydropower stations. How to quickly and accurately identify and check the hidden dangers of geological disasters in the reservoir area of ​​hydropower stations is a major problem in the safety management of hydropower stations. However, traditional geological disaster inspection methods such as manual inspections or simple mechanical inspections have problems such as low efficiency, high cost, and high safety risks, which cannot meet the needs of modern society for geological disaster warning and management.

[0003] At present, in addition to traditional manual inspections and automatic monitoring stations, drones and other monitoring methods are becoming more and more popular. With the advancement of drones and 5G technology, drone performance continues to improve, signal transmission methods are further enriched, and wireless network performance has leapt forward, making high-definition image transmission, precision flight, and remote real-time consultation more feasible. In particular, the advancement of 5G technology has greatly improved the channels and efficiency of remote signal transmission, solving problems such as incomplete state perception and delayed data transmission.

[0004] In recent years, digital twins, as a new technology that solves the two-way mapping, dynamic interaction, and real-time connection between virtual and real, have attracted widespread attention from all walks of life. Drone technology, 5G communication technology, and digital twin technology have developed rapidly, providing new solutions for geological disaster inspections. Summary of the invention

[0005] In order to quickly identify and troubleshoot potential disaster hazards, the present invention provides a digital twin system for geological disaster inspection based on 5G+UAV and its use method. The UAV collects high-definition images and videos of the hydropower station, and combines 5G technology to transmit the inspection site images and videos in real time to build a digital twin system for geological disaster inspection of the hydropower station. The system can provide intelligent inspection planning, real-time information acquisition, dynamic analysis of changes, remote monitoring and consultation in the geological disaster inspection scenario of the hydropower station, and can realize digital twin management of geological disaster inspection of the hydropower station to meet the needs of intelligent safety management of the hydropower station.

[0006] A digital twin system for geological disaster inspection based on 5G+ drones, including from bottom to top: Internet of Things perception layer, infrastructure layer, data resource layer, application service layer, and user layer; the Internet of Things perception layer includes drones, cameras, and sensor modules, which are used to obtain geological disaster information in the study area; the infrastructure layer includes the Internet, cloud platform, 5G network, server, display and storage devices, which are used to support the network environment and hardware environment layer of the entire system; the data resource layer is used to store data and manage data resources, receive data obtained by the Internet of Things perception layer and perform processing and storage management; the application service layer is used to provide statistical analysis and computing services, visualization services, and unified authority management services; the user layer is used to provide interactive services and provide users with remote monitoring and consultation services.

[0007] A method for using a digital twin for geological disaster inspection based on 5G+UAV is implemented based on the above system and includes the following steps:

[0008] Step 1: Preliminary preparation: Deploy the IoT perception layer, infrastructure layer, data resource layer, application service layer, and user layer. These five layers form a complete closed-loop system to ensure effective connection and communication between the layers. Before the system is used, the digital twin model is initialized, including the import of basic geographic information and the configuration of model parameters.

[0009] Step 2: UAV inspection: According to the inspection requirements, select the UAV platform and equip it with cameras and sensor modules to prepare for the inspection; control the flight of the UAV and set the inspection parameters, including the inspection area, flight altitude, and flight speed, to ensure that the UAV flies along the preset inspection path; collect images, videos, and sensor data of the geological disaster area in real time through the camera and sensor module;

[0010] Step 3: Data transmission and processing: The drone transmits the collected data to the data resource layer in real time through the infrastructure layer, and processes and stores the data in the data resource layer. According to the different data acquisition methods and storage databases, it is divided into geological disaster remote sensing monitoring, geological disaster inspection business, historical geological disaster information, and unmanned aerial photography real-time application data. These data and the processed results data constitute the geological disaster inspection database, which provides basic data services for the application service layer;

[0011] Step 4: Normalize the input and sorted data: Use the Min-Max method to normalize the original image;

[0012] Step 5: In the application service layer, the data processing and analysis module is used to process and analyze the data received from the data resource layer in real time to extract key information, including the location, scope, and development trend of geological disasters;

[0013] Step 6: Object-oriented multi-scale segmentation: Use feature information to perform multi-scale segmentation on the image, and input the segmented object into the convolutional neural network model to further improve the accuracy of information acquisition; the feature information includes the spectrum and shape of the picture and image;

[0014] Step 7: Construction and update of digital twin model: Based on the data collected by drones, the digital twin model construction module is used to build a digital twin model of geological disasters. The digital twin model of geological disasters is used to simulate the development process of geological disasters and provide virtual scenes and visual displays. With the progress of drone inspections and the continuous accumulation of data, the digital twin model of geological disasters needs to be updated in real time to maintain accurate simulation and prediction of geological disasters.

[0015] Step 8: Prediction, warning and decision support: Based on the analysis results of the digital twin model of geological disasters, prediction and warning of geological disasters are realized to detect potential risks in advance; the system displays the prediction and warning information to users through the user layer, and provides decision support functions to help users make scientific and accurate decisions.

[0016] A storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0017] The beneficial effects brought about by the technical solution provided by the present invention are: based on an in-depth analysis of object-oriented multi-scale segmentation technology, the present invention comprehensively considers terrain, spectrum, space and other information, and segments the image data of the study area at different segmentation scales, thereby improving the accuracy of information acquisition. Efficiency and real-time performance: UAVs can be quickly deployed to geological disaster inspection areas, and the inspection data can be transmitted in real time through 5G communication technology, which greatly improves the efficiency and real-time performance of inspections; the digital twin model can be updated in real time to reflect the latest status of geological disasters and provide timely and accurate information support for decision-making. The present invention realizes intelligence and automation. The system combines technologies such as big data analysis and artificial intelligence, and can automatically analyze inspection data, identify potential risks, and issue early warnings; optimize inspection paths and strategies through intelligent algorithms to improve the efficiency and effectiveness of inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0019] Figure 1 It is a schematic diagram of the architecture of the digital twin system for geological disaster inspection of the present invention.

[0020] Figure 2 It is a schematic diagram of the overall design concept of the present invention.

[0021] Figure 3It is a schematic diagram of the close inspection principle of the unmanned aerial vehicle of the present invention.

[0022] In the figure: IoT perception layer 1, infrastructure layer 2, data resource layer 3, application service layer 4, user layer 5, drone 6, camera 7, sensor module 8, Internet 9, cloud platform 10, 5G network 11, server 12, display and storage device 13, geological disaster management department 14, command decision-makers 15, other service personnel 16, and various business application systems 17. DETAILED DESCRIPTION

[0023] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0024] Example 1

[0025] See also Figure 1-3 , a digital twin system for geological disaster inspection based on 5G+UAV, such as Figure 1 As shown, the overall architecture includes from bottom to top: IoT perception layer 1, infrastructure layer 2, data resource layer 3, application service layer 4, and user layer 5, forming a complete closed-loop system. Figure 2 As shown, the present invention transmits the data collected by the drone, professional image transmission and data obtained from the on-site investigation to the cloud platform using the 5G network, and then transmits it to the command center and the remote terminal through the switch.

[0026] The IoT perception layer 1 is a layer for acquiring geological disaster information in the study area, and mainly uses drones 6 for close inspections. The IoT perception layer 1 includes drones 6, high-definition cameras 7, and sensor modules 8. The sensor module 8 includes: a high-resolution camera, a high-power optical zoom lens, a positioning terminal, and an inspection terminal. The positioning terminal uses a positioning sensor to accurately locate the aircraft, and the inspection terminal uses a displacement measurement sensor to monitor flight displacement. The camera 7 in this embodiment is a high-definition camera. As a means of transportation driven by clean energy, drones reduce the impact on the environment; by optimizing inspection paths and strategies, unnecessary energy consumption and waste are reduced. The drone in the present invention can automatically adjust the aircraft's flight altitude according to the terrain undulations, so that the fuselage always remains at an equal height relative to the ground, thereby achieving close inspections of the entire area, such as Figure 3As shown, the route of the actual flight environment consists of nodes A, B, C, D, and E, and the flight route of the drone consists of nodes a, b, c, d, and e. The distance between the two routes is h. This system is equipped with a high-power optical zoom lens to achieve multiple optical zoom and fixed-point magnification during video monitoring, which can clearly observe the geological disaster hidden danger targets at a long distance and obtain high-definition images of the targets. The drone inspection in the present invention replaces the traditional manual inspection, reducing the safety risk of the inspectors; by combining the drone with 5G communication technology, the drone can be remotely controlled for inspection, further improving the safety of the inspection process. The present invention can achieve the comprehensiveness and accuracy of the inspection, because the drone can cover a wider area, so it can achieve comprehensive inspection and reduce the inspection blind area; and the drone can intelligently plan the optimal route under the corresponding inspection task requirements and actively avoid obstacles according to the actual terrain simulated inside the system. During the investigation process, sensitive locations or important points can be locally fixed or surrounded for shooting, and suspected dangerous areas can be enlarged for shooting, increasing the precision and accuracy of the inspection results.

[0027] The infrastructure layer 2 is the network environment and hardware environment layer that supports the entire system, including the Internet 9, cloud platform 10, 5G network 11, server 12, display and storage devices 13 and other hardware devices.

[0028] The data resource layer 3 can be used to store data and manage data resources, receive data obtained by the IoT perception layer 1, and perform processing and storage management.

[0029] The application service layer 4 is located above the data resource layer 3, providing basic support for various application systems and information services, and can perform statistical analysis and calculation, visualization services and unified authority management services.

[0030] The user layer 5 is the top layer of the entire architecture, including the disaster management department 14, command decision-makers 15 and other personnel 16 and various business application systems 17. It is the interactive layer for the system to provide services to users, providing remote monitoring and consultation.

[0031] The drone 6 is equipped with a high-definition camera 7 and a sensor module 8 to perform local fixed-point and close-up photography, automatically perform optical zoom, magnify suspicious targets, and provide basic data resources for the infrastructure layer 2.

[0032] Example 2

[0033] Please refer to Figure 1 , Figure 1 It is a flow chart of a method for using a digital twin system for geological disaster inspection based on 5G+UAV in an embodiment of the present invention, which specifically includes:

[0034] Step 1: Preliminary preparation: Deploy the IoT perception layer 1, infrastructure layer 2, data resource layer 3, application service layer 4, and user layer 5 to form a complete closed-loop system to ensure effective connection and communication between the layers. Before the system is used, initialize the digital twin model, including the import of basic geographic information and the configuration of model parameters.

[0035] Step 2: UAV inspection: According to the inspection requirements, select an appropriate UAV 6 platform, and equip it with a high-definition camera 7 and a variety of sensors to carry out preparations before the inspection; control the flight of the UAV 6 through the system, set parameters such as the inspection area, flight altitude, speed, etc., and ensure that the UAV 6 flies according to the preset inspection path; use the high-definition camera 7 and sensor module 8 to collect images and videos of the geological disaster area in real time, as well as the positioning and inspection data obtained by sensor monitoring.

[0036] Step 3: Data transmission and processing: The drone 6 transmits the collected data to the data resource layer 3 in real time through the infrastructure layer 2. In the data resource layer 3, the data is processed and stored. According to the different data acquisition methods and storage databases, the data is divided into geological disaster remote sensing monitoring, geological disaster inspection business, historical geological disaster information, unmanned aerial photography real-time application and other data. These data and the processed results data constitute the geological disaster inspection database, which provides basic data services for the application service layer 4.

[0037] Step 4: Normalize the input and sorted data: Normalization can speed up the network learning speed and improve the classification accuracy without disrupting the data distribution law, especially when the data dimension is large. The present invention uses the Min-Max method to normalize the original image, and the normalization formula is: DN is the grayscale value of a pixel in a certain band in the original image. norm is the normalized value, DN max DN min They are the maximum and minimum values ​​​​statisticed on the corresponding bands of the original image. Through data normalization processing, the original data can be made to accelerate the learning speed of the network and improve the classification accuracy without disrupting the data distribution law, especially when the data dimension is large.

[0038] Step 5: In the application service layer 4, the data processing and analysis module is used to process and analyze the data received from the data resource layer 3 in real time to extract key information, such as the location, scope, and development trend of geological disasters.

[0039] Step 6: Object-oriented multi-scale segmentation: Make full use of the spectrum, shape and other features of pictures and images to perform multi-scale segmentation on the images, and input the segmented objects into the convolutional neural network model to further improve the accuracy of information acquisition.

[0040] Step 7: Digital twin model construction and update: Based on the data collected by UAV 6, the digital twin model construction module is used to build a digital twin model of geological disasters. The model can simulate the development process of geological disasters and provide virtual scenes and visual displays. As the inspection of UAV 6 progresses and data continues to accumulate, the digital twin model needs to be updated in real time to maintain accurate simulation and prediction of geological disasters. The digital twin model of geological disasters displays the status and changes of geological disasters in a visual way, which is convenient for users to understand intuitively. Users can interact with the system through the interactive interface to obtain the required information, make decisions and operate.

[0041] Step 8: Prediction, warning and decision support: Based on the analysis results of the digital twin model, the system can predict and warn of geological disasters and discover potential risks in advance; the system will display the prediction and warning information to users through user layer 5, and provide decision support functions to help users make scientific and accurate decisions.

[0042] Step 9: System optimization and upgrade: Regularly evaluate the performance of the system, including data processing speed, model accuracy, user experience, etc. According to the evaluation results and user needs, upgrade and optimize the system to improve system performance and stability.

[0043] This system is also scalable and customizable. It can be customized and expanded according to the inspection needs of different geological disasters to meet the application needs in different scenarios; it can be integrated with other systems (such as emergency management systems, geographic information systems, etc.) to achieve a wider range of functions and applications.

[0044] Example 3

[0045] A storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital twin system for geological disaster inspection based on 5G+UAV, characterized in that: The overall architecture of the system includes from bottom to top: IoT perception layer (1), infrastructure layer (2), data resource layer (3), application service layer (4), and user layer (5); the IoT perception layer (1) includes drones (6), cameras (7), and sensor modules (8), which are used to obtain geological disaster information in the study area; the infrastructure layer (2) includes the Internet (9), cloud platform (10), 5G network (11), server (12), display and storage devices (13), which are used to support the network environment and hardware environment layer of the entire system; the data resource layer (3) is used to store data and manage data resources, receive data obtained by the IoT perception layer (1) and perform processing and storage management; the application service layer (4) is used to provide statistical analysis and computing services, visualization services, and unified authority management services; the user layer (5) is used to provide interactive services and provide users with remote monitoring and consultation services.

2. According to claim 1, a digital twin system for geological disaster inspection based on 5G+UAV is characterized in that: The application service layer (4) is located above the data resource layer (3) and provides basic support for various application systems and information services.

3. According to claim 1, a digital twin system for geological disaster inspection based on 5G+UAV is characterized in that: The user layer (5) is the top layer of the entire system, including the geological disaster management department (14), command decision-makers (15) and other service personnel (16) and various business application systems (17).

4. According to claim 1, a digital twin system for geological disaster inspection based on 5G+UAV is characterized in that: The drone (6) is equipped with a high-definition camera (7) and a sensor module (8) for local fixed-point and close-up photography, automatic optical zooming, and magnification of suspicious targets, for ground inspections, and to provide basic data resources for the infrastructure layer (2).

5. A method for using a digital twin system for geological disaster inspection based on 5G+UAV, implemented based on the system described in any one of claims 1 to 4 above, characterized in that: The steps include: Step 1: Preliminary preparation: Deploy the IoT perception layer (1), infrastructure layer (2), data resource layer (3), application service layer (4), and user layer (5). These five layers form a complete closed-loop system to ensure effective connection and communication between the layers. Before the system is used, the digital twin model is initialized, including the import of basic geographic information and the configuration of model parameters. Step 2: UAV inspection: According to the inspection requirements, select the UAV (6) platform, and equip it with a camera (7) and a sensor module (8) to carry out preparatory work before the inspection; control the flight of the UAV (6), set the inspection parameters, including the inspection area, flight altitude, and flight speed, to ensure that the UAV (6) flies according to the preset inspection path; collect images, videos, and sensor data of the geological disaster area in real time through the camera (7) and the sensor module (8); Step 3: Data transmission and processing: The drone (6) transmits the collected data to the data resource layer (3) in real time through the infrastructure layer (2). The data is processed and stored in the data resource layer (3). According to the different data acquisition methods and storage databases, the data is divided into geological disaster remote sensing monitoring, geological disaster inspection business, historical geological disaster information, and unmanned aerial photography real-time application data. These data and the processed results data constitute the geological disaster inspection database, which provides basic data services for the application service layer (4); Step 4: Normalize the input and sorted data: Use the Min-Max method to normalize the original image; Step 5: In the application service layer (4), the data processing and analysis module is used to process and analyze the data received by the data resource layer (3) in real time to extract key information, including the location, scope, and development trend of geological disasters; Step 6: Object-oriented multi-scale segmentation: Use feature information to perform multi-scale segmentation on the image, and input the segmented object into the convolutional neural network model to further improve the accuracy of information acquisition; the feature information includes the spectrum and shape of the picture and image; Step 7: Construction and update of digital twin model: Based on the data collected by the drone (6), a digital twin model of geological disasters is constructed using the digital twin model construction module. The digital twin model of geological disasters is used to simulate the development process of geological disasters and provide virtual scenes and visual displays. As the drone (6) inspection progresses and data continues to accumulate, the digital twin model of geological disasters needs to be updated in real time to maintain accurate simulation and prediction of geological disasters. Step 8: Prediction, warning and decision support: Based on the analysis results of the digital twin model of geological disasters, prediction and warning of geological disasters are achieved to detect potential risks in advance; the system displays the prediction and warning information to users through the user layer (5) and provides decision support functions to help users make scientific and accurate decisions.

6. The method for using the digital twin system for geological disaster inspection based on 5G+UAV as claimed in claim 5, characterized in that: In Step 4, the normalization formula is: Among them, DN is the pixel gray value of a certain band in the original image, DN norm is the normalized value, DN max DN min They are respectively the maximum and minimum values ​​counted on the corresponding bands of the original image.

7. The method for using the digital twin system for geological disaster inspection based on 5G+UAV as claimed in claim 5, characterized in that: The method of use also includes step Step 9, which is used to optimize and upgrade the system. The specific process is: regularly evaluate the performance of the system, including data processing speed, model accuracy, and user experience, and upgrade and optimize the system according to the evaluation results and user needs to improve the performance and stability of the system.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 5 to 7 are implemented.

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