Geological disaster monitoring and early warning method based on AI

Abstract

The invention provides a geological disaster monitoring and early warning method based on AI, and relates to the field of geological disaster early warning. The invention discloses a geological disaster monitoring and early warning method based on AI. Comprising the following specific steps: step 1, arranging a whole sensor at a position where a geological disaster may occur through arrangement of a plurality of groups of equipment such as an Internet of Things sensor, and sending acquired information to a monitoring and information acquisition module by a multi-source heterogeneous data integration and outdoor monitoring unmanned aerial vehicle; the monitoring and information acquisition module receives the signal and then sends the signal to the system server, and the acquired information processing is completed through AI software. Fastening is achieved through the fastening bolt, two points of the connecting rope are in a straightened state after fastening, when geological cracking occurs at one position, the whole sensor frame can have a corresponding displacement condition, at the moment, the distance between the connecting rope and the whole sensor frame connected with the connecting rope changes, the displacement sensor can achieve the sensing effect on the tiny condition, and the early warning effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster early warning technology, and specifically to an AI-based geological disaster monitoring and early warning method. Background Art

[0002] Geological hazards refer to geological phenomena caused by natural geological processes or human activities that pose a threat to human life, property, and the living environment. Common types include landslides, debris flows, collapses, ground subsidence, and ground fissures. These hazards are often triggered by factors such as geological tectonic structures, rainfall, earthquakes, or engineering activities, and are characterized by sudden onset and destructive power. With 70% of my country's land area comprising mountains and hills, geological hazards are common. In recent years, an integrated "air, space, and ground" early warning system has been established through technologies such as satellite remote sensing, drone inspections, and intelligent monitoring. For example, in 2024, a phased array radar was used in Yibin, Sichuan, to provide 72 hours of advance warning of a landslide, resulting in zero casualties. Disaster prevention and control efforts are shifting from reactive relief to proactive prevention and control, integrating engineering measures with ecological restoration to continuously enhance disaster prevention and mitigation capabilities.

[0003] In the existing technology, geological disaster warnings are carried out by using sensors to sense signals. However, for large mountain areas, more sensors need to be laid, which increases costs and manpower investment. Traditional landslides are caused by cracks. Cracks can provide early warning of geological disasters. When the cracks are too small or are not within the monitoring range of the sensor, the warning effect will be affected.

[0004] When applying for the present invention, the applicant searched and discovered that a Chinese patent disclosed a "geological disaster monitoring and early warning method based on AI vision" with application number "202410954901.8". This patent mainly uses the mutual verification of two methods, namely AI model recognition and rolling comparison algorithm of front and back pictures, to increase the reliability of early warning and reduce the possibility of false alarms and missed alarms. Through the mutual verification of the two results, it is possible to more accurately judge whether a geological disaster has occurred or the precursor of a geological disaster has occurred, and issue early warnings in a timely manner. However, this will affect the judgment in the blind spot of the sensor. Therefore, according to the applicant's invention, an AI-based geological disaster monitoring and early warning method was invented to solve the problem that when the crack is too small or is not within the monitoring range of the sensor, the early warning function will be affected. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides an AI-based geological disaster monitoring and early warning method, which solves the problem that for large mountains, more sensors need to be laid, which increases costs and manpower investment. Traditional landslides are caused by cracks. Cracks can provide early warning of geological disasters. When the cracks are too small or are not within the monitoring range of the sensor, the warning effect will be affected.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a geological disaster monitoring and early warning method based on AI, including the following specific methods:

[0009] Step 1: Use multiple sets of equipment such as IoT sensors to install the entire sensor rack at locations where geological disasters may occur, integrate multi-source heterogeneous data, and use outdoor monitoring drones to send the collected information to the monitoring and information collection module. After receiving the signal, the monitoring and information collection module can send it to the system server and complete the collected information processing through AI software;

[0010] Step 2: After the AI ​​processing software completes the information processing and classifies it, the suspicious images will be transmitted to the technicians for judgment and monitoring, while the normal information will be sent to the normal warning through the processor;

[0011] Step 3: After receiving suspicious images from the AI ​​processing software, the technicians use multiple sets of equipment to determine the possible occurrence of geological disasters. When there is information about the possible occurrence of geological disasters, a signal will be sent to the processor;

[0012] Step 4: Finally, the processor will issue an early warning to predict the occurrence of geological disasters in advance and improve safety protection.

[0013] The IoT sensor arrangement includes: a sensor frame, a pointed cone being fixedly connected to the bottom end of the sensor frame, a threaded block being fixedly connected to the middle of the interior of the sensor frame, a rotating rod being sleeved inside the sensor frame, external threads being provided in the middle and top of the rotating rod, the external threads being threadedly matched with the threaded block, a rain shield being threadedly connected to the external threads at the top, a rope fixing block being fixedly connected to the middle of the top of the rain shield, a connecting rope being sleeved between the rope fixing blocks, and a plurality of sensor fixing plates being fixedly connected to the outer side of the upper portion of the sensor frame.

[0014] Preferably, the bottom end of the rotating rod is rotatably connected to a rotating plate, and the bottom end of the rotating plate is fixedly connected to a plurality of rotating shafts.

[0015] Preferably, the internal rotation connection of the rotating shaft is provided with a limit pin, and the bottom end of the rotating plate is provided with a support plate, and the support plate is fixedly connected to the inner wall of the entire sensor frame.

[0016] Preferably, the support plate and the limiting pin are in abutting engagement.

[0017] Preferably, the inner side of the sensor fixing plate is sleeved with a sensor body, the outer side of the sensor fixing plate is threadedly connected with an interference bolt, and the top end of the rope fixing block is threadedly connected with a fastening bolt.

[0018] Preferably, a cross slot is provided at the top end of the rotating rod.

[0019] An AI-based geological disaster monitoring and early warning system, including system servers and IoT sensor deployment

[0020] The IoT sensor deployment is used to record the induction of inclinometers, rain gauges, real-time displacement monitoring, cracks, and groundwater level sensors, and transmit the information to the monitoring and information collection module. The input end of the monitoring and information collection module also includes multi-source heterogeneous data integration and outdoor monitoring drones;

[0021] The monitoring and information acquisition module transmits the collected signals to the system server, and then transmits them to the AI ​​processing software through the system server, and finally transmits them to the controller through the AI ​​processing software to complete the early warning.

[0022] (3) Beneficial effects

[0023] The present invention provides an AI-based geological disaster monitoring and early warning method.

[0024] Beneficial effects:

[0025] 1. The present invention uses multiple groups of equipment such as Internet of Things sensors to install the entire sensor frame at the location where geological disasters may occur, integrate multi-source heterogeneous data, and conduct outdoor monitoring drones. The collected information is processed by AI processing software and then classified. The suspicious images are transmitted to technical personnel for judgment and monitoring. After receiving the suspicious images from the AI ​​processing software, the technical personnel judge and monitor the possible occurrence of geological disasters through the combination of multiple groups of equipment. When information about the possible occurrence of geological disasters appears, a signal will be sent to the processor, and the processor will finally issue an early warning to predict the occurrence of geological disasters in advance and improve safety protection.

[0026] 2. The present invention is provided with: a sensor body, a sensor frame and a connecting rope. The sensor body is fixed to realize geological condition sensing operation, and the connecting rope needs to pass through the two sensor frames and is fastened by fastening bolts. After fastening, the two points of the connecting rope are in a straight state. When a geological crack appears, the sensor frame will have a corresponding displacement. At this time, the distance between the connecting rope and the connected sensor frame changes, and the displacement sensor can sense this tiny condition, thereby improving the early warning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the AI-based geological disaster monitoring and early warning system proposed by the present invention;

[0028] Figure 2 This is a flow chart of IoT sensor deployment for an AI-based geological disaster monitoring and early warning system proposed by the present invention;

[0029] Figure 3 This is a three-dimensional structural diagram of the sensor frame of the AI-based geological disaster monitoring and early warning method proposed in the present invention;

[0030] Figure 4 This is a cross-sectional front view of the entire sensor frame of the AI-based geological disaster monitoring and early warning method proposed in the present invention;

[0031] Figure 5 This is a three-dimensional structural diagram of the rotating rod of the AI-based geological disaster monitoring and early warning method proposed in the present invention.

[0032] Among them, 1. Sensor frame; 2. Rain shield; 3. Sensor fixing plate; 4. Interference bolt; 5. Cone; 6. Limit pin; 7. Fastening bolt; 8. Connecting rope; 9. Rope fixing block; 10. Threaded block; 11. Sensor body; 12. Rotating rod; 13. External thread; 14. Rotating plate; 15. Rotating shaft; 16. Support plate; 17. Cross slot. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] Example 1:

[0035] like Figure 1-5 As shown, the embodiment of the present invention provides an AI-based geological disaster monitoring and early warning method, including the following specific methods:

[0036] Step 1: Use multiple sets of equipment such as IoT sensors to install the entire sensor rack at locations where geological disasters may occur, integrate multi-source heterogeneous data, and use outdoor monitoring drones to send the collected information to the monitoring and information collection module. After receiving the signal, the monitoring and information collection module can send it to the system server and complete the collected information processing through AI software;

[0037] Step 2: After the AI ​​processing software completes the information processing and classifies it, the suspicious images will be transmitted to the technicians for judgment and monitoring, while the normal information will be sent to the normal warning through the processor;

[0038] Step 3: After receiving suspicious images from the AI ​​processing software, the technicians use multiple sets of equipment to determine the possible occurrence of geological disasters. When there is information about the possible occurrence of geological disasters, a signal will be sent to the processor;

[0039] Step 4: Finally, the processor will issue an early warning to predict the occurrence of geological disasters in advance and improve safety protection.

[0040] The IoT sensor arrangement includes: a sensor frame 1, the bottom end of the sensor frame 1 is fixedly connected with a sharp cone 5, the sensor frame 1 is installed in a location prone to geological disasters, and is inserted into the ground through the sharp cone 5, the interior of the sensor frame 1 is fixedly connected with a threaded block 10, the interior of the sensor frame 1 is sleeved with a rotating rod 12, the middle and top of the rotating rod 12 are provided with external threads 13, the external threads 13 and the threaded block 10 are threadedly matched, the top external threads 13 are threadedly connected with a rain shield 2, and the rain shield 2 is threadedly matched with the top external threads 13 of the rotating rod 12 to achieve the installation effect, the top middle of the rain shield 2 is fixedly connected with a rope fixing block 9, the rope fixing block 9 A connecting rope 8 is sleeved between the two sensor frames 1. The connecting rope 8 needs to pass through the two sensor frames 1 and is tightened by the fastening bolts 7. After tightening, the two points of the connecting rope 8 are in a straight state. When a geological crack appears, the sensor frame 1 will have a corresponding displacement. Several sensor fixing plates 3 are fixedly connected to the upper outer side of the sensor frame 1. The inner side of the sensor fixing plate 3 is sleeved with a sensor body 11. The sensor body 11 is fixed to the inner side of the sensor fixing plate 3, and the resistance bolt 4 is rotated to fix the sensor body 11 to realize the geological condition sensing operation. The outer side of the sensor fixing plate 3 is threadedly connected with the resistance bolt 4, and the top of the rope fixing block 9 is threadedly connected with the fastening bolt 7.

[0041] The bottom end of the rotating rod 12 is rotatably connected to a rotating plate 14, and the rotating plate 14 at the bottom is lowered. Since the limit pin 6 cooperates with the inner wall opening position of the sensor frame 1, the limit effect can be achieved. The bottom end of the rotating plate 14 is fixedly connected to a plurality of rotating shafts 15, and the internal rotation connection of the rotating shaft 15 is limited. When the rotating plate 14 drops, the limit pin 6 can be expanded outward and inserted into the soil during the lowering process, thereby increasing contact with the soil and increasing firmness. The bottom end of the rotating plate 14 is provided with a supporting plate 16, which is fixedly connected to the inner wall of the sensor frame 1. The supporting plate 16 and the limit pin 6 are in conflict with each other. The top end of the rotating rod 12 is provided with a cross groove 17. When inserted to a certain depth, the cross groove 17 can be rotated. After rotation, the external thread 13 is threadedly matched with the thread block 10. A displacement sensor is provided at the connection of the connecting rope 8. If the distance of the sensor frame 1 connected relative to the connecting rope 8 changes, the displacement sensor can sense the tiny situation and improve the warning effect.

[0042] Example 2:

[0043] The difference between this embodiment and the first embodiment is that: a geological disaster monitoring and early warning system based on AI, including a system server and an IoT sensor deployment

[0044] IoT sensors are deployed to record the induction of inclinometers, rain gauges, and real-time displacement, crack, and groundwater level sensors, while transmitting the information to the monitoring and information collection module. The input of the monitoring and information collection module also includes multi-source heterogeneous data integration and outdoor monitoring drones;

[0045] The monitoring and information acquisition module transmits the collected signals to the system server, and then transmits them to the AI ​​processing software through the system server, and finally transmits them to the controller through the AI ​​processing software to complete the early warning.

[0046] Working principle: During use, the sensor frame 1 can be installed in a location prone to geological disasters and inserted into the ground through the pointed cone 5. When inserted to a certain depth, the cross slot 17 can be rotated, and the external thread 13 and the threaded block 10 can be threadedly matched to achieve thread movement, which will lower the rotating plate 14 at the bottom. Since the limit pin 6 and the inner wall opening position of the sensor frame 1 are matched, the limiting effect can be achieved. When the rotating plate 14 descends, the limit pin 6 can be expanded outward and inserted into the soil during the descent process, thereby increasing contact with the soil and increasing firmness. Subsequently, the rain shield 2 is used to connect with the rotating rod. The top external thread 13 of 12 is threadedly matched to realize the installation function, and the sensor body 11 is fixed on the inner side of the sensor fixing plate 3, and the resistance bolt 4 is rotated to fix the sensor body 11 to realize the geological condition sensing operation, and the connecting rope 8 needs to pass through the two sensor frames 1 and be tightened by the fastening bolt 7. After tightening, the two points of the connecting rope 8 are in a straight state. When a geological crack appears, the sensor frame 1 will have a corresponding displacement. At this time, the distance between the connecting rope 8 and the connected sensor frame 1 changes, and the displacement sensor can sense the tiny situation and improve the early warning effect.

[0047] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An AI-based geological disaster monitoring and early warning method, characterized by: The following specific methods are included: Step 1: Use multiple sets of equipment such as IoT sensors to install the entire sensor rack at locations where geological disasters may occur, integrate multi-source heterogeneous data, and use outdoor monitoring drones to send the collected information to the monitoring and information collection module. After receiving the signal, the monitoring and information collection module can send it to the system server and complete the collected information processing through AI software; Step 2: After the AI ​​processing software completes the information processing and classifies it, the suspicious images will be transmitted to the technicians for judgment and monitoring, while the normal information will be sent to the normal warning through the processor; Step 3: After receiving suspicious images from the AI ​​processing software, the technicians use multiple sets of equipment to determine the possible occurrence of geological disasters. When there is information about the possible occurrence of geological disasters, a signal will be sent to the processor; Step 4: Finally, the processor will issue an early warning to predict the occurrence of geological disasters in advance and improve safety protection; The Internet of Things sensor arrangement comprises: a sensor frame (1), the bottom end of the sensor frame (1) is fixedly connected with a pointed cone (5), the middle of the interior of the sensor frame (1) is fixedly connected with a threaded block (10), the interior of the sensor frame (1) is sleeved with a rotating rod (12), the middle and top of the rotating rod (12) are provided with external threads (13), the external threads (13) and the threaded block (10) are threadedly matched, the top of the external threads (13) is threadedly connected with a rain shield (2), the middle of the top of the rain shield (2) is fixedly connected with a rope fixing block (9), a connecting rope (8) is sleeved between the rope fixing blocks (9), and the upper outer side of the sensor frame (1) is fixedly connected with a plurality of sensor fixing plates (3).

2. The AI-based geological disaster monitoring and early warning method according to claim 1, characterized in that: The bottom end of the rotating rod (12) is rotatably connected to a rotating plate (14), and the bottom end of the rotating plate (14) is fixedly connected to a plurality of rotating shafts (15).

3. The AI-based geological disaster monitoring and early warning method according to claim 2 is characterized by: The internal rotation connection of the rotating shaft (15) is connected to a limit pin (6), and the bottom end of the rotating plate (14) is provided with a support plate (16), and the support plate (16) is fixedly connected to the inner wall of the sensor frame (1).

4. The AI-based geological disaster monitoring and early warning method according to claim 3 is characterized by: The support plate (16) and the limiting pin (6) are in abutment with each other.

5. The AI-based geological disaster monitoring and early warning method according to claim 1, characterized in that: The inner side of the sensor fixing plate (3) is sleeved with a sensor body (11), the outer side of the sensor fixing plate (3) is threadedly connected to a resistance bolt (4), and the top end of the rope fixing block (9) is threadedly connected to a fastening bolt (7).

6. The AI-based geological disaster monitoring and early warning method according to claim 1, characterized in that: A cross slot (17) is provided at the top end of the rotating rod (12).

7. An AI-based geological disaster monitoring and early warning system, based on the AI-based geological disaster monitoring and early warning method according to any one of claims 1 to 6, characterized in that: Including system servers and IoT sensor deployment The IoT sensor deployment is used to record the induction of inclinometers, rain gauges, real-time displacement monitoring, cracks, and groundwater level sensors, and transmit the information to the monitoring and information collection module. The input end of the monitoring and information collection module also includes multi-source heterogeneous data integration and outdoor monitoring drones; The monitoring and information acquisition module transmits the collected signals to the system server, and then transmits them to the AI ​​processing software through the system server, and finally transmits them to the controller through the AI ​​processing software to complete the early warning.

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