An Internet of Things (IoT) slope signal acquisition device

Abstract

This invention belongs to the field of slope signal acquisition technology, specifically an IoT-based slope signal acquisition device. It includes a multi-source sensing module, an edge acquisition control module, an IoT wireless transmission module, a solar-powered self-powered module, and a protective mounting housing. The signal output of the multi-source sensing module is electrically connected to the signal input of the edge acquisition control module, and the edge acquisition control module and the IoT wireless transmission module have a bidirectional communication connection. This invention adopts an integrated hardware structure of multi-source sensing and edge computing, integrating five types of sensors: tilt angle, displacement, pore water pressure, water content, and micro-vibration. Combined with IP67 protection and solar self-powered operation, it can operate stably for extended periods in complex field environments. The device achieves synchronous sensor signal acquisition, high-precision ADC conversion, and dual-mode wireless communication, solving problems such as cumbersome deployment, power supply difficulties, and signal loss associated with traditional monitoring equipment. Hardware reliability and environmental adaptability are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of slope signal acquisition technology, and in particular to an Internet of Things (IoT) slope signal acquisition device. Background Technology

[0002] Slope instability is a common geological hazard in highway, railway, mining and water conservancy projects. Traditional slope monitoring methods mainly rely on manual inspection, single-point displacement meter monitoring and periodic data collection, which have shortcomings such as discontinuous data collection, single monitoring dimension, delayed signal transmission, crude data processing, high false alarm and missed alarm rates, and inability to achieve early creep identification and early warning.

[0003] While existing IoT-based slope monitoring devices have achieved wireless data transmission, they suffer from the following technical shortcomings: First, it only collects signals and makes simple threshold judgments without designing a dedicated detection algorithm for the temporal characteristics and multi-parameter coupling characteristics of slope deformation, making it difficult to distinguish between normal environmental fluctuations and signs of instability. Second, the data processing does not include noise removal and spatiotemporal benchmark alignment, and signal drift and interference noise in complex field environments directly affect the accuracy of judgments. Third, the detection method is static and fixed, using only fixed thresholds to trigger early warnings, which is difficult to adapt to the deformation patterns of different slope structures and different hydrological conditions, resulting in significant warning lag. Fourth, it is difficult to establish a risk assessment model with multi-parameter weighted fusion; anomalies in a single parameter are prone to false alarms, and anomalies in multiple parameters cannot be identified in a timely manner. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention provides an Internet of Things (IoT) slope signal acquisition device, which overcomes the shortcomings of the prior art and effectively solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An IoT-based slope signal acquisition device includes a multi-source sensing module, an edge acquisition main control module, an IoT wireless transmission module, a solar self-powered module, and a protective mounting housing. The signal output terminal of the multi-source sensing module is electrically connected to the signal input terminal of the edge acquisition main control module. The edge acquisition main control module and the IoT wireless transmission module are bidirectionally connected for communication. The power output terminal of the solar self-powered module is connected to the power supply terminals of the multi-source sensing module, the edge acquisition main control module, and the IoT wireless transmission module, respectively. All three components—multi-source sensing module, edge acquisition main control module, IoT wireless transmission module, and solar self-powered module—are sealed within the protective mounting housing.

[0006] Preferably, the slope signal acquisition and dynamic stability detection method based on this device includes the following steps: Step 1: Multi-source synchronous signal acquisition. According to the preset fixed acquisition cycle, the multi-source sensing module 2 synchronously acquires five types of time-series monitoring signals: slope inclination angle, surface and deep displacement, slope pore water pressure, soil moisture content, and slope micro-vibration. The timestamp and spatial location information corresponding to each group of signals are recorded synchronously. Step 2: Time-series noise reduction preprocessing of acquired signals. The layered wavelet threshold denoising algorithm is used to filter the original acquired signals, remove invalid signals caused by high-frequency random noise, temperature drift, and electromagnetic interference, retain the effective characteristic signals of real slope deformation and pressure change, and correct signal drift and abnormal jump points. Step 3: Unify the spatiotemporal reference of multi-source signals. Unify the data of sensors with different acquisition frequencies, different deployment locations and different types to the same time reference and slope spatial coordinate reference, remove data time difference and point deviation, and construct a standardized multi-dimensional time series data matrix. Step 4: Extraction of sensitive characteristic parameters of slope instability. Based on the preprocessed time series data, the core characteristic parameters characterizing slope stability are dynamically extracted, including cumulative deformation, deformation rate, deformation acceleration, amplitude of change of inclination angle, rate of change of pore water pressure, soil moisture saturation, and amplitude of micro-vibration energy. Step 5: Adaptive dynamic correction of early warning thresholds. Using historical data from a continuous and stable monitoring period as the initial benchmark, combined with real-time meteorological parameters, slope soil and rock types, and monitoring stages, the early warning thresholds at all levels are corrected in real time, and the normal fluctuation range is automatically updated to distinguish between environmental disturbances and unstable abnormal deformation. Step 6: Multi-parameter coupled weighted fusion calculation to construct a slope safety risk index calculation model. The weights of each feature parameter are adaptively allocated according to the slope type and working conditions. The real-time risk index that uniquely represents the overall safety status of the slope is obtained through weighted fusion calculation. Step 7: Slope condition classification and instability precursor determination. Based on the real-time risk index, the slope is classified into four levels: stable state, slightly abnormal state, warning state, and emergency avoidance state. Dual-feature precursor triggering rules are set to identify early creep instability signals of the slope. Step 8: Tiered early warning output and data closed-loop management. The edge acquisition main control module independently triggers the corresponding level of early warning locally, and at the same time uploads the monitoring data, risk assessment results and early warning information to the cloud platform through the Internet of Things wireless transmission module to realize data storage, traceability and remote supervision.

[0007] Preferably, the multi-source sensing module specifically integrates a MEMS tilt sensor, a drawstring displacement sensor, a pore water pressure sensor, a soil moisture sensor, and a vibration acceleration sensor. All sensors are IP67 waterproof and dustproof industrial-grade packaged and are electrically connected to the high-precision ADC analog-to-digital conversion unit built into the edge acquisition main control module.

[0008] Preferably, the IoT wireless transmission module adopts a dual-mode parallel structure of 4G / 5G remote communication unit and LoRa local area networking unit, and has the functions of monitoring data interruption resume transmission, offline local caching, multi-monitoring node synchronous networking, and low-power sleep wake-up transmission.

[0009] Preferably, the layered wavelet threshold denoising algorithm adopts a three-layer wavelet decomposition structure and sets differentiated denoising thresholds for signals of different frequency bands, so as to completely retain the early deformation signals of the small creep of the slope while removing noise.

[0010] Preferably, the spatiotemporal references are uniformly aligned, with the system clock of the edge acquisition main control module as the unified time reference and the main monitoring point of the slope as the spatial reference, to complete the time difference correction and point matching of all data.

[0011] Preferably, the adaptive dynamic threshold correction automatically relaxes the normal fluctuation threshold under special meteorological conditions such as rainfall and sudden temperature changes, thereby reducing the probability of false alarms caused by environmental factors.

[0012] Preferably, the weights of each characteristic parameter are as follows: for soil slopes, the weights for increasing pore water pressure and soil moisture content are adjusted; for rock slopes, the weights for increasing dip angle and deformation acceleration are adjusted.

[0013] Preferably, the dual-feature precursor triggering rule specifically means that when both conditions are met simultaneously—a continuous positive increase in slope deformation acceleration and a synchronous abrupt increase in pore water pressure—the warning level is directly raised to achieve early warning of slope instability.

[0014] Preferably, the local independent early warning system allows the edge acquisition main control module to autonomously complete all detection calculations and trigger audible and visual early warnings without cloud network communication, ensuring uninterrupted monitoring and early warning.

[0015] The beneficial effects of this invention are as follows: The IoT slope signal acquisition device of the present invention adopts an integrated hardware structure of multi-source sensing and edge computing, integrating five types of sensors: tilt angle, displacement, pore water pressure, water content, and micro-vibration. With IP67 protection and solar self-powered power supply, it can operate stably for a long time in complex field environments. The device realizes synchronous acquisition of sensor signals, high-precision ADC conversion and dual-mode wireless communication, solving the problems of cumbersome deployment, power supply difficulties and easy signal loss of traditional monitoring equipment. The hardware reliability and environmental adaptability are significantly improved. The IoT slope signal acquisition device of the present invention achieves intelligent processing of the entire chain from raw signal to risk index through layered wavelet denoising, spatiotemporal reference alignment, dynamic threshold correction and multi-parameter weighted fusion. The method can effectively distinguish between environmental disturbances and instability precursors, greatly reduce false alarms and missed alarms, identify millimeter-level early creep, and the early warning and judgment accuracy are far superior to the traditional fixed threshold judgment mode. The IoT slope signal acquisition device of the present invention supports local independent computing and early warning at the edge. It can still complete the whole process detection and trigger audible and visual alarms even when the network is disconnected. After the network is restored, it automatically resumes data transmission from the breakpoint, forming a complete monitoring closed loop. The device can adaptively adjust the parameter weights and early warning thresholds according to the soil and rock slopes. It is suitable for various slope scenarios such as highways, railways, water conservancy, and mines. It has strong versatility, is easy to deploy, and has significant engineering application value. Attached Figure Description

[0016] Figure 1 This is a three-dimensional schematic diagram of the overall structure of an IoT slope signal acquisition device proposed in this invention; Figure 2 This is a schematic diagram of the overall process of an IoT slope signal acquisition device proposed in this invention; Figure 3 This is a schematic diagram of the multi-source signal acquisition and noise reduction process of an IoT slope signal acquisition device proposed in this invention; Figure 4 This is a schematic diagram illustrating the spatiotemporal reference alignment and feature extraction of an IoT slope signal acquisition device proposed in this invention; Figure 5 This is a schematic diagram illustrating the dynamic threshold and weighted fusion calculation of an IoT slope signal acquisition device proposed in this invention; Figure 6 This is a schematic diagram of the precursor judgment and early warning output of an Internet of Things slope signal acquisition device proposed in this invention.

[0017] In the diagram: 1. Protective mounting housing; 2. Multi-source sensing module; 3. Edge acquisition main control module; 4. Internet of Things wireless transmission module; 5. Solar self-powered module. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Reference Figures 1-6Example 1: An IoT slope signal acquisition device includes a multi-source sensing module 2, an edge acquisition main control module 3, an IoT wireless transmission module 4, a solar self-powered module 5, and a protective mounting housing 1. The signal output terminal of the multi-source sensing module 2 is electrically connected to the signal input terminal of the edge acquisition main control module 3. The edge acquisition main control module 3 and the IoT wireless transmission module 4 are bidirectionally connected. The power output terminal of the solar self-powered module 5 is connected to the power supply terminals of the multi-source sensing module 2, the edge acquisition main control module 3, and the IoT wireless transmission module 4, respectively. The multi-source sensing module 2, the edge acquisition main control module 3, the IoT wireless transmission module 4, and the solar self-powered module 5 are all sealed and encapsulated within the protective mounting housing 1.

[0020] The multi-source sensing module 2 specifically integrates a MEMS tilt sensor, a drawstring displacement sensor, a pore water pressure sensor, a soil moisture sensor, and a vibration acceleration sensor. All sensors are IP67 waterproof and dustproof industrial-grade packaged and are electrically connected to the high-precision ADC analog-to-digital conversion unit built into the edge acquisition main control module 3.

[0021] The IoT wireless transmission module 4 adopts a dual-mode parallel structure of 4G / 5G remote communication unit and LoRa local networking unit, and has the functions of monitoring data interruption resume transmission, offline local caching, multi-monitoring node synchronous networking, and low-power sleep wake-up transmission.

[0022] The hierarchical wavelet threshold denoising algorithm adopts a three-level wavelet decomposition structure and sets differentiated denoising thresholds for signals of different frequency bands. While removing noise, it completely preserves the early deformation signals of the small creep of the slope.

[0023] The time and space references are uniformly aligned, with the system clock of the edge acquisition main control module 3 as the unified time reference and the main monitoring point of the slope as the spatial reference, to complete the time difference correction and point matching of all data.

[0024] Adaptive dynamic threshold correction automatically relaxes the normal fluctuation threshold under special meteorological conditions such as rainfall and sudden temperature changes, reducing the probability of false alarms caused by environmental factors.

[0025] The weights of each characteristic parameter are as follows: for soil slopes, the weights of increasing pore water pressure and soil moisture content are adjusted; for rock slopes, the weights of increasing dip angle and deformation acceleration are adjusted.

[0026] The dual-feature precursor triggering rule is that when both conditions are met simultaneously—a continuous positive increase in slope deformation acceleration and a synchronous abrupt increase in pore water pressure—the warning level is directly raised to achieve early warning of slope instability.

[0027] Local independent early warning: In the absence of cloud network communication, the edge acquisition main control module 3 can autonomously complete all detection calculations and trigger audible and visual early warnings, ensuring uninterrupted monitoring and early warning.

[0028] The slope signal acquisition and dynamic stability detection method based on this device includes the following steps: Step 1: Multi-source synchronous signal acquisition. According to the preset fixed acquisition cycle, the multi-source sensing module 2 synchronously acquires five types of time-series monitoring signals: slope inclination angle, surface and deep displacement, slope pore water pressure, soil moisture content, and slope micro-vibration. The timestamp and spatial location information corresponding to each group of signals are recorded synchronously. Step 2: Time-series noise reduction preprocessing of acquired signals. The layered wavelet threshold denoising algorithm is used to filter the original acquired signals, remove invalid signals caused by high-frequency random noise, temperature drift, and electromagnetic interference, retain the effective characteristic signals of real slope deformation and pressure change, and correct signal drift and abnormal jump points. Step 3: Unify the spatiotemporal reference of multi-source signals. Unify the data of sensors with different acquisition frequencies, different deployment locations and different types to the same time reference and slope spatial coordinate reference, remove data time difference and point deviation, and construct a standardized multi-dimensional time series data matrix. Step 4: Extraction of sensitive characteristic parameters of slope instability. Based on the preprocessed time series data, the core characteristic parameters characterizing slope stability are dynamically extracted, including cumulative deformation, deformation rate, deformation acceleration, amplitude of change of inclination angle, rate of change of pore water pressure, soil moisture saturation, and amplitude of micro-vibration energy. Step 5: Adaptive dynamic correction of early warning thresholds. Using historical data from a continuous and stable monitoring period as the initial benchmark, combined with real-time meteorological parameters, slope soil and rock types, and monitoring stages, the early warning thresholds at all levels are corrected in real time, and the normal fluctuation range is automatically updated to distinguish between environmental disturbances and unstable abnormal deformation. Step 6: Multi-parameter coupled weighted fusion calculation to construct a slope safety risk index calculation model. The weights of each feature parameter are adaptively allocated according to the slope type and working conditions. The real-time risk index that uniquely represents the overall safety status of the slope is obtained through weighted fusion calculation. Step 7: Slope condition classification and instability precursor determination. Based on the real-time risk index, the slope is classified into four levels: stable state, slightly abnormal state, warning state, and emergency avoidance state. Dual-feature precursor triggering rules are set to identify early creep instability signals of the slope. Step 8: Tiered early warning output and data closed-loop management. The edge acquisition main control module 3 independently triggers the corresponding level of early warning locally, and at the same time uploads the monitoring data, risk judgment results and early warning information to the cloud platform through the Internet of Things wireless transmission module 4 to realize data storage, traceability and remote supervision.

[0029] This embodiment is applied to a soil slope along a highway, deploying three sets of monitoring devices with a data acquisition cycle of 1 minute per acquisition. The method for slope signal acquisition and dynamic stability detection is implemented: simultaneously acquiring five types of signals—tilt angle, displacement, pore water pressure, moisture content, and micro-vibration; employing three-layer wavelet decomposition for temporal noise reduction to eliminate rainfall and electromagnetic interference while retaining minute creep signals; using the system clock of the edge acquisition main control module 3 as a reference for spatiotemporal alignment to construct a multi-dimensional temporal data matrix; extracting core features such as cumulative deformation, deformation acceleration, and pore water pressure change rate; dynamically adjusting thresholds based on 30 days of stable data, automatically widening the normal fluctuation range during rainfall; increasing the weight of moisture content and pore water pressure for soil slopes to calculate a risk index; triggering an early warning upgrade when deformation acceleration continuously increases and pore water pressure simultaneously surges; independently completing audible and visual warnings locally at the edge end, while simultaneously uploading data to the cloud via 4G / 5G to achieve uninterrupted monitoring. Example 2 is basically the same as Example 1, except that: the IoT wireless transmission module 4 adopts a LoRa local area networking + 5G remote upload dual-mode structure, with multiple nodes forming a local monitoring network, supporting breakpoint resume and offline caching; the edge acquisition main control module 3 has a built-in large-capacity Flash, which can store more than 7 days of data locally in the absence of network, and automatically retransmits after the network is restored.

[0030] This embodiment is applied to rock slopes along railways in mountainous areas. In the detection method, the weighting of dip angle and deformation acceleration is increased for rock slopes, with the main monitoring point at the top of the slope serving as the spatial reference. An early warning is immediately triggered when the rock mass dip angle changes abruptly or the deformation acceleration continues to rise. Even without network connectivity, the entire calculation and early warning process can be completed at the edge device; once the network is restored, the data is fully uploaded to the cloud, making it suitable for remote mountainous areas with poor communication conditions.

[0031] Example 3 is basically the same as Example 1, except that: the multi-source sensing module 2 adds a deep displacement monitoring probe, which is deployed at a depth of 0.5m-8m to realize synchronous acquisition of surface and deep deformation. The edge acquisition main control module 3 supports remote parameter distribution and can adjust the acquisition cycle, early warning threshold and weight configuration online.

[0032] This embodiment is applied to high slopes in reservoir areas of water conservancy projects. The detection method combines dynamic threshold correction with water level changes to identify early signs of instability caused by water level fluctuations. Parameters can be remotely adjusted via a cloud platform, eliminating the need for on-site operation. This method is suitable for slope projects such as reservoirs and dams where frequent inspections are difficult, enabling intelligent monitoring throughout the entire lifecycle.

[0033] Working Principle: This device uses the protective housing 1 as a carrier, and is continuously powered by the solar self-powered module 5 to the multi-source sensing module 2, the edge acquisition main control module 3, and the IoT wireless transmission module 4, enabling long-term unattended operation in the field without external power supply. During operation, the MEMS tilt sensor, rope displacement sensor, pore water pressure sensor, soil moisture sensor, and vibration acceleration sensor in the multi-source sensing module 2 synchronously collect slope tilt angle, displacement, pore water pressure, soil moisture content, and micro-vibration signals according to a preset cycle, and transmit the analog signals to the high-precision ADC unit of the edge acquisition main control module 3 to convert them into digital signals. The edge acquisition main control module 3 performs layered wavelet threshold denoising on the original signal to remove high-frequency noise, temperature drift, and electromagnetic interference, while retaining the true deformation characteristics. Then, using the system clock and the main monitoring point as a reference, it completes the unified temporal and spatial alignment of multi-source data, constructing a standardized multi-dimensional time-series data matrix. The system extracts key features such as cumulative deformation, deformation rate, deformation acceleration, tilt amplitude, and pressure change rate from this matrix, and performs adaptive dynamic threshold correction based on historical stable data and meteorological conditions, automatically distinguishing between normal fluctuations and abnormal deformation. During the risk assessment phase, the device adaptively allocates feature parameter weights according to the slope type, calculates the real-time risk index through a multi-parameter coupled weighted fusion model, and determines the slope status according to a four-level standard: stable, slightly abnormal, critical warning, and emergency avoidance. When the deformation acceleration continues to increase and the pore water pressure rises simultaneously, the dual-feature precursor rule is triggered and the warning level is upgraded. The edge acquisition main control module 3 can independently complete local audible and visual warnings, and simultaneously upload monitoring data, risk results, and warning information to the cloud platform through 4G / 5G and LoRa dual-mode communication, realizing data storage, traceability, and remote monitoring, forming a fully automated working mechanism of "acquisition-processing-calculation-judgment-warning-cloud upload".

[0034] The above description is only 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 Internet of Things (IoT) slope signal acquisition device, comprising a multi-source sensing module (2), an edge acquisition main control module (3), an IoT wireless transmission module (4), a solar self-powered module (5), and a protective mounting shell (1), characterized in that, The signal output terminal of the multi-source sensing module (2) is electrically connected to the signal input terminal of the edge acquisition main control module (3). The edge acquisition main control module (3) is bidirectionally connected to the Internet of Things wireless transmission module (4). The power output terminal of the solar self-powered module (5) is connected to the power supply terminals of the multi-source sensing module (2), the edge acquisition main control module (3), and the Internet of Things wireless transmission module (4). The multi-source sensing module (2), the edge acquisition main control module (3), the Internet of Things wireless transmission module (4), and the solar self-powered module (5) are all sealed and encapsulated in the protective installation housing (1).

2. The IoT slope signal acquisition device according to claim 1, characterized in that, The slope signal acquisition and dynamic stability detection method based on this device includes the following steps: Step 1: Multi-source synchronous signal acquisition. According to the preset fixed acquisition cycle, the multi-source sensing module (2) synchronously acquires five types of time-series monitoring signals: slope inclination angle, surface and deep displacement, slope pore water pressure, soil moisture content, and slope micro-vibration. The timestamp and spatial location information corresponding to each group of signals are recorded synchronously. Step 2: Time-series noise reduction preprocessing of acquired signals. The layered wavelet threshold denoising algorithm is used to filter the original acquired signals, remove invalid signals caused by high-frequency random noise, temperature drift, and electromagnetic interference, retain the effective characteristic signals of real slope deformation and pressure change, and correct signal drift and abnormal jump points. Step 3: Unify the spatiotemporal reference of multi-source signals. Unify the data of sensors with different acquisition frequencies, different deployment locations and different types to the same time reference and slope spatial coordinate reference, remove data time difference and point deviation, and construct a standardized multi-dimensional time series data matrix. Step 4: Extraction of sensitive characteristic parameters of slope instability. Based on the preprocessed time series data, the core characteristic parameters characterizing slope stability are dynamically extracted, including cumulative deformation, deformation rate, deformation acceleration, amplitude of change of inclination angle, rate of change of pore water pressure, soil moisture saturation, and amplitude of micro-vibration energy. Step 5: Adaptive dynamic correction of early warning thresholds. Using historical data from a continuous and stable monitoring period as the initial benchmark, combined with real-time meteorological parameters, slope soil and rock types, and monitoring stages, the early warning thresholds at all levels are corrected in real time, and the normal fluctuation range is automatically updated to distinguish between environmental disturbances and unstable abnormal deformation. Step 6: Multi-parameter coupled weighted fusion calculation to construct a slope safety risk index calculation model. The weights of each feature parameter are adaptively allocated according to the slope type and working conditions. The real-time risk index that uniquely represents the overall safety status of the slope is obtained through weighted fusion calculation. Step 7: Slope condition classification and instability precursor determination. Based on the real-time risk index, the slope is classified into four levels: stable state, slightly abnormal state, warning state, and emergency avoidance state. Dual-feature precursor triggering rules are set to identify early creep instability signals of the slope. Step 8: Graded early warning output and data closed-loop management. The edge acquisition main control module (3) independently triggers the corresponding level of early warning locally, and at the same time uploads the monitoring data, risk judgment results and early warning information to the cloud platform through the Internet of Things wireless transmission module (4) to realize data storage, traceability and remote supervision.

3. The IoT slope signal acquisition device according to claim 1, characterized in that, The multi-source sensing module (2) specifically integrates a MEMS tilt sensor, a pull-string displacement sensor, a pore water pressure sensor, a soil moisture sensor, and a vibration acceleration sensor. All sensors are IP67 waterproof and dustproof industrial-grade packaged and are electrically connected to the high-precision ADC analog-to-digital conversion unit built into the edge acquisition main control module (3).

4. The IoT slope signal acquisition device according to claim 1, characterized in that, The IoT wireless transmission module (4) adopts a dual-mode parallel structure of 4G / 5G remote communication unit and LoRa local networking unit, and has the functions of monitoring data interruption resume transmission, offline local caching, multi-monitoring node synchronous networking, and low-power sleep wake-up transmission.

5. The IoT slope signal acquisition device according to claim 2, characterized in that, The layered wavelet threshold denoising algorithm adopts a three-layer wavelet decomposition structure and sets differentiated denoising thresholds for signals of different frequency bands, so as to completely preserve the early deformation signals of the small creep of the slope while removing noise.

6. The IoT slope signal acquisition device according to claim 2, characterized in that, The time and space references are uniformly aligned, with the system clock of the edge acquisition main control module (3) as the unified time reference and the main monitoring point of the slope as the spatial reference, to complete the time difference correction and point matching of all data.

7. The IoT slope signal acquisition device according to claim 2, characterized in that, The adaptive dynamic threshold correction automatically relaxes the normal fluctuation threshold under special meteorological conditions such as rainfall and sudden temperature changes, reducing the probability of false alarms caused by environmental factors.

8. The IoT slope signal acquisition device according to claim 2, characterized in that, The weights of each characteristic parameter are as follows: for soil slopes, the weights are adjusted to increase pore water pressure and soil moisture content; for rock slopes, the weights are adjusted to increase dip angle and deformation acceleration.

9. The IoT slope signal acquisition device according to claim 2, characterized in that, The dual-feature precursor triggering rule specifically means that when both conditions are met simultaneously—a continuous positive increase in slope deformation acceleration and a synchronous abrupt increase in pore water pressure—the warning level is directly raised to achieve early warning of slope instability.

10. The IoT slope signal acquisition device according to claim 2, characterized in that, The local independent early warning system, without cloud network communication, the edge acquisition main control module (3) can autonomously complete all detection calculations and trigger sound and light early warnings, ensuring uninterrupted monitoring and early warning.

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