Ruins monitoring system and method based on intelligent measurement and borer network

Abstract

The invention discloses a ruins monitoring system and method based on an intelligent measuring and drilling network, and relates to the technical field of ruins protection, the system comprises an intelligent measuring and drilling unit, a data processing platform and a communication module; the intelligent drill rod measuring unit comprises a GNSS (Global Navigation Satellite System) positioning module (1), an elevation sensor (2), a composite shell structure (3), an environment sensor (4), a soil humidity sensor (5), a turbidity sensor (6), a tilt angle sensor (7) and a spiral anchoring claw (8); the data processing platform comprises a data receiving and storage module, a data processing module, a visualization and early warning module, an application decision and support module and a feedback and optimization module; the communication module is connected with the intelligent drill rod measuring unit and the data processing platform. According to the invention, the problems of poor timeliness, low precision and incapability of underwater measurement in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of heritage site protection, and in particular to a heritage site monitoring system and method based on an intelligent drilling network. Background Art

[0002] As precious legacies of human history and culture, cultural sites are non-renewable. Their destruction would be a tremendous loss to human civilization. With growing global awareness of the importance of protecting world cultural heritage, monitoring has become a crucial method and tool for heritage protection.

[0003] The Three Gorges Reservoir, one of the world's largest hydropower stations, faces numerous challenges surrounding its surrounding areas. Due to the combined effects of reservoir water storage and precipitation, the area surrounding the reservoir has experienced land subsidence, forming a land subsidence zone known as a drawdown zone. This phenomenon not only severely impacts the ecological environment and economic development surrounding the reservoir, but also places tremendous pressure on cultural heritage preservation. The Three Gorges Reservoir area boasts a rich heritage landscape, including river channels, water conservancy and navigation facilities, historical and cultural districts, and ancient architectural complexes, all of which carry profound historical and cultural value. Effective monitoring of these sites is crucial for protecting humanity's shared heritage.

[0004] In this context, it is particularly important to conduct real-time and accurate terrain monitoring of the drawdown zone of the Three Gorges Reservoir. Existing monitoring technologies mainly rely on remote sensing means (such as satellite images, drone aerial photography) and traditional ground measurement methods. However, these technologies have many limitations. Optical remote sensing is easily affected by clouds and vegetation, making it difficult to capture centimeter-level terrain changes. It is also affected by atmospheric interference and the image quality is unstable. In addition, the acquisition cycle of remote sensing images is long and cannot meet the needs of real-time monitoring of instantaneous erosion caused by floods or tides. At the same time, the model is highly dependent and needs to rely on inversion algorithms to infer surface changes. It is easily affected by parameter errors. Although traditional ground measurements are highly accurate, they are inefficient and difficult to cover large areas. In addition, the data islanding problem is serious and cannot adapt to changes in dynamic terrain. Summary of the Invention

[0005] In response to the above-mentioned deficiencies in the prior art, the present invention provides a site monitoring system and method based on an intelligent measuring network, which solves the problems of the prior art in terms of poor timeliness, low precision, and inability to conduct underwater measurements.

[0006] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a site monitoring system based on an intelligent borehole detection network, including an intelligent borehole detection unit, a data processing platform and a communication module;

[0007] The intelligent drilling unit includes a GNSS positioning module, an elevation sensor, a composite housing structure, an environmental sensor, a soil moisture sensor, a turbidity sensor, a tilt sensor, and a spiral anchoring claw. The GNSS positioning module is located at the top of the composite housing structure. The elevation sensor, the environmental sensor, the soil moisture sensor, the turbidity sensor, and the tilt sensor are located on the side wall of the composite housing structure from top to bottom. The spiral anchoring claw is located at the bottom of the composite housing structure.

[0008] The data processing platform includes a data receiving and storage module, a data processing module, a visualization and early warning module, an application decision and support module, and a feedback and optimization module connected in sequence;

[0009] The communication module is connected to the intelligent drilling unit and the data processing platform.

[0010] Furthermore, the GNSS positioning module adopts multi-constellation RTK differential positioning technology, with a plane accuracy of ±5mm and an elevation accuracy of ±8mm.

[0011] Furthermore, the composite shell structure is made of alloy steel and the surface is treated for corrosion resistance.

[0012] Furthermore, the intelligent drilling unit further includes a power supply module for supplying power to each sensor and a control module for controlling the operation of the sensors.

[0013] Furthermore, the power module adopts a solar power supply system, equipped with a 10W solar panel and a 5000mAh lithium battery.

[0014] The present invention also adopts a technical solution: a site monitoring method based on an intelligent drilling network, comprising the following steps:

[0015] S1: Use the intelligent drilling unit to collect real-time terrain data of the research area, including GNSS positioning information, elevation information, site microenvironment, atmospheric pollutants, soil moisture information, turbidity information and inclination information;

[0016] S2: Use the communication module to transmit the collected terrain data to the data processing platform in real time;

[0017] S3: Use the data processing platform to store and process the received terrain data, generate a three-dimensional terrain model based on the spatiotemporal interpolation algorithm, and analyze the three-dimensional terrain model based on the machine learning algorithm to complete the site monitoring based on the intelligent drilling network.

[0018] Furthermore, the step S3 includes the following sub-steps:

[0019] S31: using the data receiving and storage module to receive terrain data from multiple intelligent drilling units in real time and store it in a cloud database;

[0020] S32: pre-processing the received terrain data using a data processing module, and generating a three-dimensional terrain model based on a spatiotemporal interpolation algorithm;

[0021] S33: Use the visualization and early warning module to visualize the 3D terrain model and generate a change heat map. Simultaneously, based on a machine learning algorithm, it intelligently analyzes terrain changes, automatically identifies terrain changes, and issues early warning information.

[0022] S34: Use the application decision and support module to provide decision support and combine hydrological data for integrated analysis to optimize the treatment plan;

[0023] S35: Use the feedback and optimization module to optimize algorithms and models based on actual application results and user feedback.

[0024] Furthermore, the warning information in S33 is sent to relevant users via SMS, email or APP push.

[0025] The beneficial effects of the present invention are:

[0026] (1) Real-time dynamic monitoring: Through the intelligent measuring network, terrain data including GNSS positioning, elevation, site microenvironment, atmospheric pollutants, soil moisture, turbidity and inclination are collected in real time. Data is uploaded every second, which significantly improves the timeliness of monitoring and achieves real-time capture of terrain changes. Multimodal data fusion integrates multiple sources of data such as GNSS, acoustic and pressure sensors, eliminates single sensor errors, builds a comprehensive and accurate terrain model, and enhances system adaptability and data reliability.

[0027] (2) High-precision measurement, using a GNSS module with multi-constellation RTK differential positioning technology, with a plane accuracy of ±5mm and an elevation accuracy of ±8mm. Combined with high-precision sensors, it ensures accurate and reliable data, providing a basis for detailed terrain analysis.

[0028] (3) Intelligent analysis and early warning: using machine learning algorithms to intelligently analyze terrain data, automatically identify changes such as collapse and siltation, and issue early warnings, thereby discovering potential geological disasters in advance, providing a scientific basis for decision-making, and ensuring regional safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a diagram of a site monitoring system based on an intelligent drilling network according to the present invention.

[0030] Figure 2 This is a flow chart of a site monitoring system based on an intelligent drilling network according to the present invention.

[0031] Among them: 1. GNSS positioning module; 2. Elevation sensor; 3. Composite shell structure; 4. Environmental sensor; 5. Soil moisture sensor; 6. Turbidity sensor; 7. Inclination sensor; 8. Spiral anchor claw 8. DETAILED DESCRIPTION

[0032] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0033] Example 1, as Figure 1 As shown, a site monitoring system based on an intelligent borehole detection network includes an intelligent borehole detection unit, a data processing platform and a communication module;

[0034] The intelligent drilling unit includes a GNSS positioning module 1, an elevation sensor 2, a composite shell structure 3, an environmental sensor 4, a soil moisture sensor 5, a turbidity sensor 6, a tilt sensor 7 and a spiral anchor claw 8. The GNSS positioning module 1 is located at the top of the composite shell structure 3. The elevation sensor 2, the environmental sensor 4, the soil moisture sensor 5, the turbidity sensor 6 and the tilt sensor 7 are located on the side wall of the composite shell structure 3 from top to bottom. The spiral anchor claw 8 is located at the bottom of the composite shell structure 3.

[0035] The GNSS positioning module 1 adopts multi-constellation (BDS-3, GPS, Galileo) RTK differential positioning technology, with a plane accuracy of ±5mm and an elevation accuracy of ±8mm. It can capture tiny surface changes in real time and provide accurate positioning data for the entire monitoring system.

[0036] The composite shell structure 3 is made of alloy steel and its surface is treated with anti-corrosion, so it can adapt to the complex river environment and ensure that the equipment can work stably for a long time under harsh conditions.

[0037] The measurement range of the elevation sensor 2 is 0-100 meters, with an accuracy of ±1mm, and is used to monitor small changes in the ground in real time.

[0038] Environmental sensor 4 is used to monitor the site microenvironment and atmospheric pollutants.

[0039] Soil moisture sensor 5, with a range of 0-100% and an accuracy of ±2%, is used to monitor changes in soil moisture content and assist in analyzing sediment deposition or erosion trends.

[0040] The turbidity sensor 6 is close to the ground and is based on the principle of laser scattering, with a wavelength of 650nm and a resolution of 1NTU. It is used to monitor changes in sand content and distinguish between erosion (increase in sand content) and deposition (decrease in sand content).

[0041] Inclination sensor 7, with a measuring range of ±30° and an accuracy of ±0.05°, is used to monitor the vertical state of the measuring rod and the stability of the slope.

[0042] The bottom is equipped with an adjustable spiral anchor claw 8, and the anchoring depth can be adjusted within the range of 0.5-1 meter to adapt to different soil conditions and ensure that the probe is firmly fixed.

[0043] The intelligent drilling unit uses various sensors to collect real-time terrain data, including GNSS positioning, elevation, soil moisture, turbidity, and inclination. This data is transmitted to the data processing platform via a communication module. The unit's collection frequency can be adjusted according to actual needs to ensure real-time and continuous data transmission.

[0044] The intelligent drilling unit further comprises a power supply module for supplying power to each sensor and a control module for controlling the operation of the sensors.

[0045] The power module uses a solar power supply system equipped with a 10W solar panel and a 5000mAh lithium battery, which can support continuous operation for 7 days even on rainy days. The control module is responsible for coordinating the working status of each sensor to ensure the synchronization and accuracy of data collection.

[0046] The data processing platform includes a data receiving and storage module, a data processing module, a visualization and early warning module, an application decision and support module, and a feedback and optimization module connected in sequence;

[0047] The communication module is connected to the intelligent drilling unit and the data processing platform.

[0048] The communication module bridges the gap between the intelligent borehole measurement unit and the data processing platform, ensuring stable data transmission. It supports multiple communication methods, such as NB-IoT and LoRa, ensuring stable data transmission in complex environments. The module features low power consumption and long-distance transmission, including support for ad hoc network transmission. With a communication range of up to 10 km, it can meet the monitoring needs of complex terrain such as river islands.

[0049] Example 2, as Figure 2 As shown, a site monitoring method based on an intelligent drilling network includes the following steps:

[0050] S1: Use the intelligent drilling unit to collect real-time terrain data of the research area, including GNSS positioning information, elevation information, site microenvironment, atmospheric pollutants, soil moisture information, turbidity information and inclination information;

[0051] S2: Use the communication module to transmit the collected terrain data to the data processing platform in real time;

[0052] S3: Use the data processing platform to store and process the received terrain data, generate a three-dimensional terrain model based on the spatiotemporal interpolation algorithm, and analyze the three-dimensional terrain model based on the machine learning algorithm to complete the site monitoring based on the intelligent drilling network.

[0053] The S1 collects terrain data in real time through an intelligent measuring network, including GNSS positioning, elevation, soil moisture, turbidity and inclination, and uploads data every second, significantly improving monitoring timeliness and achieving real-time capture of terrain changes. The GNSS module adopts multi-constellation RTK differential positioning technology, with a plane accuracy of ±5mm and an elevation accuracy of ±8mm. Combined with high-precision sensors, it ensures data accuracy and reliability, providing a basis for fine terrain analysis. It integrates multi-source data such as GNSS, acoustic and pressure sensors to eliminate single sensor errors, build a comprehensive and accurate terrain model, and enhance system adaptability and data reliability.

[0054] The deployment density of the intelligent measuring rod units is optimized according to the complexity of the terrain and monitoring needs. A measuring rod is deployed every 50 meters along the edge of the island, and the density is increased to one every 10 meters at key landform mutation points.

[0055] The S3 includes the following sub-steps:

[0056] S31: Use the data receiving and storage module to receive terrain data from multiple intelligent drilling units in real time and store it in the cloud database, supporting TB-level data volume;

[0057] S32: Preprocessing the received terrain data using a data processing module, including removing outliers and noise, and generating a three-dimensional terrain model based on a spatiotemporal interpolation algorithm (such as Kriging interpolation);

[0058] S33: The visualization and early warning module visualizes the 3D terrain model and generates a heat map of changes. Users can view terrain changes directly through the browser. Simultaneously, machine learning algorithms (such as random forests) are used to intelligently analyze terrain changes, automatically identifying terrain changes (such as collapse and siltation) and issuing early warning information.

[0059] S34: Use the application decision-making and support module to provide decision support for applications such as ecological restoration, waterway management, and disaster warning, and combine hydrological data for integrated analysis and optimization of management plans;

[0060] S35: Use the feedback and optimization module to optimize algorithms and models based on actual application results and user feedback to improve system performance.

[0061] The warning information in S33 is sent to relevant users via SMS, email, or APP push. Users can visually view the terrain changes through the browser. When the terrain changes exceed the set threshold, the system will issue a warning via SMS, email, or APP push, notifying relevant personnel to take measures.

[0062] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the invention.

Claims

1. A site monitoring system based on an intelligent drilling network, characterized in that: It includes intelligent drilling unit, data processing platform and communication module; The intelligent drilling unit comprises a GNSS positioning module (1), an elevation sensor (2), a composite shell structure (3), an environmental sensor (4), a soil moisture sensor (5), a turbidity sensor (6), an inclination sensor (7) and a spiral anchoring claw (8); the GNSS positioning module (1) is located at the top of the composite shell structure (3); the elevation sensor (2), the environmental sensor (4), the soil moisture sensor (5), the turbidity sensor (6) and the inclination sensor (7) are located on the side wall of the composite shell structure (3) in order from top to bottom; and the spiral anchoring claw (8) is located at the bottom of the composite shell structure (3); The data processing platform includes a data receiving and storage module, a data processing module, a visualization and early warning module, an application decision and support module, and a feedback and optimization module connected in sequence; The communication module is connected to the intelligent drilling unit and the data processing platform.

2. The site monitoring system based on the intelligent drilling network according to claim 1 is characterized in that: The GNSS positioning module (1) adopts multi-constellation RTK differential positioning technology, with a plane accuracy of ±5mm and an elevation accuracy of ±8mm.

3. The site monitoring system based on the intelligent drilling network according to claim 1 is characterized in that: The composite shell structure (3) is made of alloy steel, and its surface has been treated for corrosion resistance.

4. The site monitoring system based on the intelligent drilling network according to claim 1 is characterized in that: The intelligent drilling unit further comprises a power supply module for supplying power to each sensor and a control module for controlling the operation of the sensors.

5. The site monitoring system based on the intelligent drilling network according to claim 4 is characterized in that: The power module adopts a solar power supply system, equipped with a 10W solar panel and a 5000mAh lithium battery.

6. A site monitoring method based on an intelligent drilling network, characterized in that: The following steps are involved: S1: Use the intelligent drilling unit to collect real-time terrain data of the research area, including GNSS positioning information, elevation information, site microenvironment, atmospheric pollutants, soil moisture information, turbidity information and inclination information; S2: Use the communication module to transmit the collected terrain data to the data processing platform in real time; S3: Use the data processing platform to store and process the received terrain data, generate a three-dimensional terrain model based on the spatiotemporal interpolation algorithm, and analyze the three-dimensional terrain model based on the machine learning algorithm to complete the site monitoring based on the intelligent drilling network.

7. The site monitoring method based on the intelligent drilling network according to claim 6 is characterized in that: The S3 includes the following sub-steps: S31: using the data receiving and storage module to receive terrain data from multiple intelligent drilling units in real time and store it in a cloud database; S32: pre-processing the received terrain data using a data processing module, and generating a three-dimensional terrain model based on a spatiotemporal interpolation algorithm; S33: Use the visualization and early warning module to visualize the 3D terrain model and generate a change heat map. Simultaneously, based on a machine learning algorithm, it intelligently analyzes terrain changes, automatically identifies terrain changes, and issues early warning information. S34: Use the application decision and support module to provide decision support and combine hydrological data for integrated analysis to optimize the treatment plan; S35: Use the feedback and optimization module to optimize algorithms and models based on actual application results and user feedback.

8. The site monitoring method based on the intelligent drilling network according to claim 7 is characterized in that: The warning information in S33 is sent to relevant users via SMS, email or APP push.

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