An intelligent perception and prediction method and system for a basin disaster chain

Abstract

This invention discloses an intelligent perception and prediction method and system for watershed disaster chains, relating to the field of disaster chain prediction technology. During intelligent perception and prediction, the control module generates destination location information based on earthquake disaster information. Then, based on the current location information of the takeoff platform module, map information, and destination location information, it drives the UAV module to fly at a first altitude and along a first trajectory. Meanwhile, the inertial navigation system can calculate the real-time location, speed, and direction information of the UAV module by measuring its acceleration and angular velocity to ensure that the UAV module flies at the first altitude and along the first trajectory. Simultaneously, the vision system acquires visual image information to achieve intelligent perception and prediction.

Description

Technical Field

[0001] This invention relates to the field of disaster chain prediction technology, specifically to an intelligent sensing and prediction method and system for watershed disaster chains. Background Technology

[0002] With global climate change, rising living standards, and accelerated urbanization, natural disasters are having an increasingly severe impact on human society. Earthquakes, as a common natural disaster, are characterized by their high destructive power and wide coverage, posing a significant threat to people's lives and property. Therefore, how to effectively prevent and control earthquake disasters using modern technology has become a very important and urgent issue.

[0003] In response to earthquakes within a river basin, the use of unmanned aerial vehicles (UAVs) for rapid detection allows for precise assessment of slope stability on both sides of the river. Traditional methods require significant manpower and time, while UAVs can accomplish this task much faster and more accurately. Furthermore, the data collected by UAVs provides more detailed and accurate topographic information, enabling the determination of stability performance and effectively preventing the formation of landslide dams, thus achieving situational awareness of the potential disaster chain in the river basin. However, current technologies, even when using UAV detection, often still require full-time operator involvement, lacking true intelligence and relying primarily on visual detection, raising concerns about the accuracy of their perception and prediction.

[0004] Therefore, it is necessary to provide an intelligent sensing and prediction method and system for watershed disaster chains to solve the problems mentioned in the background. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent sensing and prediction system for watershed disaster chains, comprising:

[0006] A takeoff platform module, wherein a first GPS is provided in the takeoff platform module for obtaining the current location information of the takeoff platform module;

[0007] The unmanned aerial vehicle (UAV) module, in its initial stage, is placed on the launch platform module. The UAV module integrates an inertial navigation system and a vision system.

[0008] The control module is used to control the takeoff platform module and the UAV module. The control module has pre-installed map information and earthquake disaster information.

[0009] During intelligent perception and prediction, the control module generates destination location information based on earthquake disaster information. Then, based on the current location information of the takeoff platform module, map information, and destination location information, it drives the UAV module to fly at a first altitude and on a first trajectory. The inertial navigation system can calculate the real-time location information, real-time speed information, and real-time direction information of the UAV module by measuring the acceleration and angular velocity of the UAV module to ensure that the UAV module flies at a first altitude and on a first trajectory. At the same time, the vision system acquires visual image information to achieve intelligent perception and prediction.

[0010] Furthermore, as a preferred embodiment, the drone module also integrates a second GPS to assist the drone module in flying at a first altitude and along a first trajectory.

[0011] Furthermore, as a preferred embodiment, the control module can generate a first maximum altitude based on the current location information of the takeoff platform module, map information, and destination location information, wherein the first altitude is greater than the first maximum altitude.

[0012] Furthermore, as a preferred embodiment, the drone module further includes:

[0013] body;

[0014] Four first arms are arranged in a rectangular pattern and fixed to the side of the fuselage.

[0015] A first power component is fixed to the first arm, which is used to provide flight power to the fuselage and adjust the attitude of the fuselage;

[0016] Two symmetrically arranged second arms, fixed to the side of the fuselage and located between two adjacent first arms; and

[0017] The second power component is installed on the second arm.

[0018] Furthermore, as a preferred embodiment, the vision system includes a 3D gimbal mounted below the camera body and a camera mounted on the 3D adjustment end of the 3D gimbal.

[0019] Furthermore, as a preferred embodiment, a bearing ring is fixed to the bottom of the second power component to provide support for the fuselage, and a first pressure sensor is embedded in the bearing ring;

[0020] A detection rod is fixed at the bottom of the machine body;

[0021] When the UAV module flies to a point where the real-time position information fed back by the inertial navigation system corresponds to the destination position information, the first power component adjusts the height of the fuselage to a second height. At this time, the first power component stops, the second power component starts, and the fuselage can impact the ground with a first momentum.

[0022] When the first pressure information detected by the first pressure sensor is greater than the threshold, it indicates that the soil stability is poor; when the first pressure information detected by the first pressure sensor is less than or equal to the threshold, it indicates that the soil stability is good.

[0023] Furthermore, as a preferred embodiment, a third power component is also provided at both ends of the second power component. When the detection rod just touches the ground, the second power component stops, and the second power margin generated by the second power component is eliminated by the third power component.

[0024] Furthermore, as a preferred embodiment, the detection rod is a telescopic rod, wherein when the detection rod is retracted, its bottom is higher than the bearing ring, and when the detection rod is extended, its bottom is lower than the bearing ring.

[0025] Furthermore, as a preferred embodiment, a plurality of second pressure sensors are fixed on the telescopic end of the telescopic rod, which are spaced apart along its length. The second pressure information detected by the second pressure sensors can assist in the stability classification of soils with poor stability.

[0026] A smart sensing and prediction method for watershed disaster chains includes the following steps:

[0027] S1. Lay out the takeoff platform module and use the first GPS to obtain the current location information of the takeoff platform module;

[0028] S2. Place the drone module on the takeoff platform module;

[0029] S3. The control module generates destination location information based on earthquake disaster information;

[0030] S4. The control module drives the UAV module to fly at the first altitude and along the first trajectory based on the current position information, map information, and destination position information of the takeoff platform module;

[0031] S5. The inertial navigation system can calculate the real-time position, speed and orientation information of the UAV module by measuring its acceleration and angular velocity to ensure that the UAV module flies at the first altitude and on the first trajectory. At the same time, the vision system acquires visual image information to achieve perception and prediction.

[0032] Compared with existing technologies, this invention provides an intelligent sensing and prediction method and system for watershed disaster chains, which has the following beneficial effects:

[0033] In this embodiment of the invention, during intelligent perception and prediction, the control module generates destination location information based on earthquake disaster information. Then, based on the current location information of the takeoff platform module, map information, and destination location information, it drives the UAV module to fly at a first altitude and along a first trajectory. The inertial navigation system can calculate the real-time location information, real-time speed information, and real-time direction information of the UAV module by measuring its acceleration and angular velocity to ensure that the UAV module flies at the first altitude and along the first trajectory. At the same time, the vision system acquires visual image information to achieve intelligent perception and prediction. This ensures that the UAV module can fly to its destination relatively accurately, so that the vision system can acquire visual image information to achieve intelligent perception and prediction.

[0034] In this embodiment of the invention, when the UAV module flies to a point where the real-time position information fed back by the inertial navigation system corresponds to the destination position information, the first power component adjusts the height of the fuselage to a second height. At this time, the first power component stops, the second power component starts, and the fuselage can impact the ground with a first momentum. When the first pressure information detected by the first pressure sensor is greater than a threshold, it indicates that the soil stability is poor. When the first pressure information detected by the first pressure sensor is less than or equal to the threshold, it indicates that the soil stability is good, thereby achieving accurate perception and prediction. Attached Figure Description

[0035] Figure 1 A schematic diagram of a module for an intelligent sensing and prediction system for watershed disaster chains;

[0036] Figure 2 A three-dimensional structural diagram of an unmanned aerial vehicle (UAV) module in an intelligent sensing and prediction system for watershed disaster chains;

[0037] Figure 3 A schematic diagram of the planar structure of an unmanned aerial vehicle (UAV) module in an intelligent sensing and prediction system for watershed disaster chains;

[0038] In the diagram: 1. Body; 2. 3D gimbal; 3. Camera; 4. First arm; 5. First power component; 6. Second arm; 7. Second power component; 8. Bearing ring; 9. Detection rod; 10. Third power component. Detailed Implementation

[0039] Please refer to Figure 1-3This invention provides an intelligent sensing and prediction system for watershed disaster chains, including a takeoff platform module, a drone module, and a control module. The takeoff platform module can be a flat plate structure, a rectangular cargo box structure, a disc structure, etc., and can be used to position and support the drone module. The takeoff platform module is equipped with a first GPS for acquiring the current position information of the takeoff platform module. It should be noted that the takeoff platform module is preferably placed on a plane, and this plane is far from the earthquake center to prevent the first GPS from being affected. The first GPS is a GPS sensor. In the initial stage, the drone module is placed on the takeoff platform module. The drone module integrates an inertial navigation system and a vision system. The inertial navigation system can calculate the real-time position information, real-time speed information, and real-time direction information of the drone module by measuring its acceleration and angular velocity. The control module controls the takeoff platform module and the drone module, and the control module is pre-loaded with map information and earthquake disaster information.

[0040] During intelligent perception and prediction, the control module generates destination location information based on earthquake disaster information. Then, based on the current location information of the takeoff platform module, map information, and destination location information, it drives the UAV module to fly at a first altitude and along a first trajectory. The inertial navigation system can calculate the real-time location information, real-time speed information, and real-time direction information of the UAV module by measuring its acceleration and angular velocity to ensure that the UAV module flies at the first altitude and along the first trajectory. At the same time, the vision system acquires visual image information to achieve intelligent perception and prediction. This ensures that the UAV module can fly to the destination relatively accurately, so that the vision system can acquire visual image information to achieve intelligent perception and prediction.

[0041] In this embodiment, the drone module also integrates a second GPS to assist the drone module in flying at a first altitude and along a first trajectory.

[0042] In fact, when the UAV module integrates a second GPS, the takeoff platform module does not need to be equipped with a first GPS.

[0043] In this embodiment, the control module can generate a first maximum altitude based on the current position information of the takeoff platform module, map information, and destination position information. The first maximum altitude is the maximum altitude on the first trajectory. The first altitude is greater than the first maximum altitude. Since the UAV module of this system mainly uses an inertial navigation system for navigation, and in order to reduce human intervention, the UAV module flies at a relatively low altitude as much as possible while ensuring that it is not affected by trees or other factors during flight.

[0044] In this embodiment, the drone module further includes:

[0045] Fuselage 1;

[0046] Four first arm bodies 4 are arranged in a rectangular shape and are fixed to the side of the fuselage 1;

[0047] The first power component 5 is fixed to the first arm 4. The first power component 5 is used to provide the fuselage 1 with flight power and to adjust the attitude of the fuselage 1.

[0048] Two symmetrically arranged second arms 6, fixed to the side of the fuselage and located between two adjacent first arms 4; and

[0049] The second power component 7 is installed on the second arm body 6.

[0050] The bottom of the second power component 7 is fixed with a bearing ring 8 to provide support for the fuselage 1, and a first pressure sensor is embedded in the bearing ring 8.

[0051] A detection rod 9 is fixed at the bottom of the machine body 1;

[0052] When the UAV module flies to a point where the real-time position information fed back by the inertial navigation system corresponds to the destination position information, the first power component 5 adjusts the height of the fuselage 1 to a second height. At this time, the first power component 5 stops, the second power component 7 starts, and the fuselage 1 can impact the ground with a first momentum.

[0053] When the first pressure information detected by the first pressure sensor is greater than the threshold, it indicates that the soil stability is poor; when the first pressure information detected by the first pressure sensor is less than or equal to the threshold, it indicates that the soil stability is good.

[0054] It should be explained that the second altitude is to ensure that the power provided by the second power unit 7 can enable the UAV module to impact the ground with the first momentum; in other words, the second altitude is a fixed altitude.

[0055] At this point, two scenarios are possible:

[0056] In the first scenario: the detection rod 9 is not fully inserted into the soil. In this case, the pressure detected by the first pressure sensor is zero, which means that the first pressure information is less than the threshold, indicating that the soil stability is good.

[0057] The second scenario: the detection rod 9 is fully inserted into the soil, and the pressure detected by the first pressure sensor is relatively small. In other words, when the first pressure information is less than or equal to the threshold, it indicates that the soil stability is good.

[0058] The third scenario: the detection rod 9 is fully inserted into the soil, and the pressure detected by the first pressure sensor is relatively large at this time. In other words, the first pressure information is greater than the threshold, indicating that the soil stability is poor.

[0059] In response to the first scenario, in this embodiment, a plurality of second pressure sensors are fixed on the telescopic end of the telescopic rod, which are spaced apart along its length. The second pressure information detected by the second pressure sensors can assist in the stability classification of soil with poor stability.

[0060] In a preferred embodiment, a third power component 10 is also provided at both ends of the second power component 7. When the detection rod 9 just touches the ground, the second power component 7 stops, and the second power margin generated by the second power component 7 is eliminated by the third power component 10.

[0061] In a preferred embodiment, the detection rod 9 is a telescopic rod. When the detection rod 9 is retracted, its bottom is higher than the bearing ring 8, and when the detection rod 9 is extended, its bottom is lower than the bearing ring 8.

[0062] In a preferred embodiment, the vision system includes a 3D gimbal 2 disposed below the body 1 and a camera 3 disposed at the 3D adjustment end of the 3D gimbal 2.

[0063] A smart sensing and prediction method for watershed disaster chains includes the following steps:

[0064] S1. Lay out the takeoff platform module and use the first GPS to obtain the current location information of the takeoff platform module;

[0065] S2. Place the drone module on the takeoff platform module;

[0066] S3. The control module generates destination location information based on earthquake disaster information;

[0067] S4. The control module drives the UAV module to fly at the first altitude and along the first trajectory based on the current position information, map information, and destination position information of the takeoff platform module;

[0068] S5. The inertial navigation system can calculate the real-time position, speed and orientation information of the UAV module by measuring its acceleration and angular velocity to ensure that the UAV module flies at the first altitude and on the first trajectory. At the same time, the vision system acquires visual image information to achieve perception and prediction.

[0069] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An intelligent perception and prediction system for a basin disaster chain, characterized in that: include: A takeoff platform module, wherein a first GPS is provided in the takeoff platform module for obtaining the current location information of the takeoff platform module; In the initial stage, the drone module is placed on the takeoff platform module, and the drone module integrates an inertial navigation system and a vision system. The control module is used to control the takeoff platform module and the UAV module. The control module has pre-installed map information and earthquake disaster information. During intelligent perception and prediction, the control module generates destination location information based on earthquake disaster information. Then, based on the current location information of the takeoff platform module, map information, and destination location information, it drives the UAV module to fly at a first altitude and on a first trajectory. The inertial navigation system can calculate the real-time location information, real-time speed information, and real-time direction information of the UAV module by measuring the acceleration and angular velocity of the UAV module to ensure that the UAV module flies at a first altitude and on a first trajectory. At the same time, the vision system acquires visual image information to achieve intelligent perception and prediction. The drone module also includes: Fuselage (1); Four first arm bodies (4) are arranged in a rectangular shape and are fixed to the side of the fuselage (1); The first power component (5) is fixed on the first arm (4). The first power component (5) is used to provide the fuselage (1) with flight power and to adjust the attitude of the fuselage (1). Two symmetrically arranged second arms (6) are fixed to the side of the fuselage and located between two adjacent first arms (4); The second power component (7) is installed on the second arm body (6); The bottom of the second power component (7) is fixed with a bearing ring (8) for providing support for the fuselage (1), and a first pressure sensor is embedded in the bearing ring (8); A detection rod (9) is fixed below the body (1); When the UAV module flies to a point where the real-time position information fed back by the inertial navigation system corresponds to the destination position information, the first power unit (5) adjusts the height of the fuselage (1) to a second height. At this time, the first power unit (5) stops, the second power unit (7) starts, and the fuselage (1) can impact the ground with a first momentum. When the first pressure information detected by the first pressure sensor is greater than the threshold, it indicates that the soil stability is poor; when the first pressure information detected by the first pressure sensor is less than or equal to the threshold, it indicates that the soil stability is good. 2.The intelligent perception and prediction system of a basin disaster chain according to claim 1, characterized in that: The drone module also integrates a second GPS to assist the drone module in flying at a first altitude and on a first trajectory. 3.The intelligent perception and prediction system of a basin disaster chain according to claim 1, characterized in that: The control module can generate a first maximum altitude based on the current location information of the takeoff platform module, map information, and destination location information, and the first altitude is greater than the first maximum altitude. 4.The intelligent perception and prediction system of a basin disaster chain according to claim 1, characterized in that: The vision system includes a three-dimensional gimbal (2) located below the body (1) and a camera (3) located at the three-dimensional adjustment end of the three-dimensional gimbal (2). 5.The intelligent perception and prediction system of a basin disaster chain according to claim 1, characterized in that: The second power component (7) is also provided with a third power component (10) at both ends. When the detection rod (9) just touches the ground, the second power component (7) stops and the second power margin generated by the second power component (7) is eliminated by the third power component (10). 6.The intelligent perception and prediction system of a basin disaster chain according to claim 1, characterized in that: The detection rod (9) is a telescopic rod. When the detection rod (9) is retracted, its bottom is higher than the bearing ring (8). When the detection rod (9) is extended, its bottom is lower than the bearing ring (8).

7. The intelligent sensing and prediction system for watershed disaster chains according to claim 6, characterized in that: Multiple second pressure sensors are fixed on the telescopic end of the telescopic rod, which are spaced apart along its length. The second pressure information detected by the second pressure sensors can be used to assist in the stability classification of soil with poor stability.

8. An intelligent perception and prediction method of a basin disaster chain, which adopts the intelligent perception and prediction system of the basin disaster chain according to any one of claims 1-7. Includes the following steps: S1. Lay out the takeoff platform module and use the first GPS to obtain the current location information of the takeoff platform module; S2. Place the drone module on the takeoff platform module; S3. The control module generates destination location information based on earthquake disaster information; S4. The control module drives the UAV module to fly at the first altitude and along the first trajectory based on the current position information, map information, and destination position information of the takeoff platform module; S5. The inertial navigation system can calculate the real-time position, speed and orientation information of the UAV module by measuring its acceleration and angular velocity to ensure that the UAV module flies at the first altitude and on the first trajectory. At the same time, the vision system acquires visual image information to achieve perception and prediction.

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