A slope intelligent protection system and method

Abstract

This invention relates to the field of slope protection technology and discloses an intelligent slope protection system and method. The system includes: a shallow sensing subsystem, equipped with at least one plant and a sensing device embedded in the slope surface; a data transmission subsystem, used to transmit the sensing data acquired by the sensing device to a big data analysis and decision-making platform; a big data analysis and decision-making platform, including a risk assessment module that assesses the slope sliding risk based on the sensing data and generates a risk level; a response decision-making module that generates control commands based on the risk level and transmits the control commands to an application terminal; an application terminal that receives the control commands and generates command information based on the control commands and transmits it to a deep response subsystem; and a deep response subsystem equipped with at least one shape memory alloy anchor rod that penetrates the potential sliding surface of the slope horizontally and is anchored into the stable stratum. This invention solves the problems of poor timeliness and insufficient hierarchical decision-making capabilities in existing slope monitoring and protection systems.

Description

Technical Field

[0001] This invention relates to the field of slope protection technology, and in particular to an intelligent slope protection system and method. Background Technology

[0002] Slope instability is a common type of geological hazard in civil, water conservancy and transportation engineering. Its occurrence process usually goes through stages such as rainfall infiltration, reduction of matrix suction, and overall sliding of the soil mass. In order to avoid slope instability, it is often necessary to protect and monitor the slope. Currently, existing slope protection and monitoring technologies have the following shortcomings. First, traditional slope monitoring systems and slope reinforcement structures are two independent systems. Monitoring data is only used for alarms and cannot directly affect the response of the reinforcement structure, resulting in a time lag between monitoring and early warning and reinforcement measures. Second, existing anchor stress monitoring or surface displacement monitoring can only reflect the slope state in a single dimension, making it difficult to distinguish between shallow local disturbances and deep overall sliding. When anchor stress increases significantly, the sliding surface has often already undergone significant displacement, leading to insufficient early warning timeliness. In addition, conventional anchors are under stress from the time of installation and are affected by factors such as ground creep, groundwater corrosion, and stress relaxation over a long period, causing their anchoring force to gradually decrease over time. Finally, existing monitoring mainly relies on data display and threshold alarms, lacking intelligent decision-making and proactive control capabilities based on multi-source information fusion, making it difficult to meet the needs of important slope engineering projects for automated and intelligent protection. Therefore, existing slope monitoring and protection systems suffer from poor timeliness and insufficient hierarchical decision-making capabilities. Summary of the Invention

[0003] This invention provides an intelligent slope protection system and method to solve the problems of poor timeliness and insufficient hierarchical decision-making capabilities in existing slope monitoring and protection systems.

[0004] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides an intelligent slope protection system, the system comprising: The shallow sensing subsystem is equipped with at least one plant planted on the slope surface and a sensing device; the plant's roots grow along the slope surface in the opposite direction and are anchored in the shallow soil of the slope surface; the sensing device is deployed in the plant roots and / or the soil around the roots to acquire in real time the sensing data generated when the shallow soil sliding pulls on the plant roots. A data transmission subsystem is used to transmit the sensed data acquired by the sensing device to a big data analysis and decision-making platform. The big data analysis and decision-making platform is equipped with a risk assessment module and a response decision-making module. The risk assessment module is used to assess the slope sliding risk based on the perceived data and generate a risk level. The response decision-making module is used to generate control commands based on the risk level and transmit the control commands to the application terminal. The application terminal is used to receive control commands and generate command information based on the control commands, which is then transmitted to the deep response subsystem. The deep response subsystem is equipped with at least one shape memory alloy anchor rod that penetrates the potential sliding surface of the slope in the horizontal direction and is anchored into the stable stratum; the shape memory alloy anchor rod is provided with an openable and closable barb structure, which is used to control the opening and closing of the barb structure according to command information.

[0005] Optionally, the sensing device includes: an earth pressure sensor, a strain sensor, and a miniature displacement meter; The soil pressure sensor is deployed in the shallow soil around the plant roots to monitor the lateral pressure data of the soil around the roots. The strain sensor is deployed on the surface of the plant root system to monitor the axial strain data generated by the root system under the sliding and pulling action of the soil. The miniature displacement gauges are deployed on the plant roots and in the soil around the roots to monitor the relative displacement of the roots relative to the surrounding soil. The sensing data includes one or more of the following: the lateral pressure data, the axial strain data, and the relative displacement.

[0006] Optionally, the big data analysis and decision-making platform further includes: a data preprocessing module and a feature extraction module; The data preprocessing module is used to perform noise reduction, missing value imputation, outlier detection, and multi-source data time synchronization processing on the sensed data to obtain preprocessed data. The feature extraction module is used to extract feature parameters at multiple time scales from the preprocessed data.

[0007] Optionally, the risk assessment module has a built-in machine learning model, which is used to output a risk level probability value based on the feature parameters, and determine the risk level based on the risk level probability value; Among them, the machine learning model is a machine learning model based on gradient boosting decision tree or long short-term memory network, and the training data of the machine learning model comes from the perception data of normal working conditions and simulated sliding working conditions accumulated in history. The risk level probability value is the probability value corresponding to each risk level. The risk levels include: low risk level, attention level, warning level, and danger level. The risk level is determined based on the risk level probability value, and the risk level with the highest probability value is determined as the current slope risk level.

[0008] Optionally, the rod body of the shape memory alloy anchor is made of shape memory alloy material, which is one or more of nickel-titanium alloy, copper-zinc-aluminum alloy or iron-manganese-silicon alloy, and its austenitic phase transformation end temperature is set between 60°C and 120°C.

[0009] Optionally, a heating device is arranged on the shape memory alloy anchor rod to heat the barb structure according to command information; The heating device is a resistance heating wire or electric heating film arranged along the axial direction of the anchor rod. The power supply cable of the heating device is led out along the anchor rod to the slope and connected to the field controller or remote control system. The barbed structure is in close contact with the shape memory alloy anchor rod at room temperature and opens and closes when heated.

[0010] Optionally, the response decision module is equipped with a heating effect evaluation unit, which is used to evaluate the activation effect and anchoring force improvement effect of the barb structure by comparing the changes in sensor data before and after heating after the heating device is activated, and feed the evaluation results back to the risk assessment module.

[0011] Secondly, the present invention also provides a smart slope protection method, comprising: S1. Plant at least one plant on the slope surface, with the plant's roots growing in the opposite direction of the slope and anchored in the shallow soil of the slope surface. S2. Install sensing devices around the plant roots and obtain sensing data through the sensing devices. S3. Drill anchor holes in the horizontal direction and install shape memory alloy anchor rods containing heating devices so that they penetrate the potential sliding surface and anchor into the stable stratum. The shape memory alloy anchor rods are provided with barb structures that can be activated by heat. At room temperature, the barbs are in a retracted state. S4. Transmit the sensed data to the big data analysis and decision-making platform through the data transmission subsystem; S5. Perform feature extraction and risk level assessment on the perceived data through a big data analysis and decision-making platform; S6. Generate graded response control instructions based on risk level; S7. When it is necessary to activate the anchor bolt, the anchor bolt is heated by the heating device until its temperature reaches above the end temperature of the austenitic phase transformation of the shape memory alloy. The barb structure returns to the preset open state, embeds itself into the surrounding strata, and increases the anchoring force.

[0012] Beneficial effects: The intelligent slope protection system provided by this invention utilizes the characteristic of plant roots growing against the slope, causing the root growth direction to be opposite to the slope sliding direction. When sliding occurs, the roots bear axial pull-out force. Compared with traditional displacement sensors, plant roots respond more directly and sensitively to soil sliding. Even slight soil creep can generate measurable strain signals in the roots, enabling early displacement detection. Simultaneously, shape memory alloy anchors are used, remaining dormant before activation. The barbed structure is retracted onto the anchor surface, and the anti-corrosion coating is intact, avoiding additional ground creep stress and fundamentally preventing stress relaxation and accelerated corrosion problems caused by long-term stress on traditional anchors. Finally, the system achieves a complete closed loop from root sensing to risk assessment, graded decision-making, anchor heating activation, anchoring force enhancement, and effect feedback, significantly shortening the time delay from early warning to response and realizing proactive and intelligent slope protection. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the overall architecture of the intelligent slope protection system of the present invention; Figure 2 This is a schematic cross-sectional view of the arrangement of shape memory alloy anchors in a slope. Figure 3 Here is a flowchart illustrating the hierarchical early warning and response logic; Figure 4 Workflow diagrams for each module of the big data analytics and decision-making platform; Figure 5 A schematic diagram showing the arrangement of plants and sensing devices on a slope. In the diagram: 1. Shallow sensing subsystem; 11. Plant; 111. Root system growing on reverse slope; 12. Soil pressure sensor; 13. Strain sensor; 14. Miniature displacement gauge; 2. Deep response subsystem; 21. Shape memory alloy anchor; 211. Anchor body; 212. Barbed structure; 213. Heating device; 22. Potential sliding surface; 23. Stable strata; 3. Data transmission subsystem; 4. Big data analysis and decision-making platform; 5. Application terminal. Detailed Implementation

[0014] The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "an" or "a" and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms "connected" or "linked" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship also changes accordingly.

[0016] See Figure 1 and Figure 2 This embodiment provides an intelligent slope protection system, which includes a shallow sensing subsystem 1, a deep response subsystem 2, a data transmission subsystem 3, a big data analysis and decision-making platform 4, and an application terminal 5.

[0017] The core of the shallow sensing subsystem 1 is to use the characteristic of plant roots growing against the slope, that is, opposite to the potential sliding direction of the slope, to realize the sensing of shallow soil sliding on the slope.

[0018] like Figure 1 As shown, at least one plant 11 is planted on the slope surface. The reverse slope growth root system 111 of the plant 11 grows along the reverse slope direction and is anchored in the shallow soil of the slope surface.

[0019] In this embodiment, plant 11 is selected from plants with prominent taproots and root systems exhibiting reverse slope growth characteristics, such as ryegrass, tall fescue, and alfalfa. These plants have a natural tendency to extend their roots inwards towards the slope, forming a root system configuration that extends against the slope. Such deep-rooted plants demonstrate excellent soil stabilization and adaptability in slope ecological restoration.

[0020] This reverse slope configuration possesses unique mechanical sensing advantages: the potential sliding direction of the slope is downhill, while the plant roots grow uphill against the slope. When a small slip occurs in the shallow soil along the slope surface, the sliding soil exerts a pull-out force on the reverse slope roots along the root axis. Because the root growth direction is opposite to the sliding direction, the root system is extremely sensitive to soil slippage. Even a small soil slippage can generate measurable tensile force or axial strain signals in the root system. Compared to downhill growing roots, reverse slope roots are stretched rather than compressed during soil slippage; this tensile state significantly enhances sensing sensitivity.

[0021] Sensing devices include the following three categories, located as follows: Figure 5 As shown: First, the earth pressure sensor 12 is buried in the shallow soil around the plant roots, preferably symmetrically arranged on the slope-facing and slope-reverse sides of the root system. The earth pressure sensor 12 is a vibrating wire or resistance strain gauge miniature earth pressure cell used to monitor changes in lateral pressure on the soil around the roots. When the soil slides along the slope, the earth pressure on the slope-facing side increases, and the earth pressure on the slope-reverse side decreases. By comparing the difference between the two sides, the direction of soil sliding can be determined.

[0022] Second, strain sensors 13 are attached to the surface of the main root system. In this embodiment, flexible strain gauges or fiber optic strain sensors are used, arranged along the root axis, to monitor the axial strain generated by the roots under the pull-out force of soil sliding. When soil sliding applies a pull-out force to the roots on the reverse slope, the roots generate axial tensile strain, and the strain value is positively correlated with the magnitude of the pull-out force.

[0023] Third, a miniature displacement meter 14 is installed at the interface between the root system and the surrounding soil, as well as on the root system 111 growing on the reverse slope. In this embodiment, a miniature linear displacement sensor is used to monitor the minute relative displacement of the root system with respect to the surrounding soil, thereby determining whether slippage has occurred at the root-soil interface.

[0024] The data collected by the aforementioned sensing devices together constitute the sensing data, including parameters such as: earth pressure on the uphill side of the root, earth pressure on the downhill side of the root, axial strain of the root system, rate of change of axial strain of the root system, and relative displacement of the root-soil interface.

[0025] The deep response subsystem 2 includes at least one shape memory alloy anchor 21. For example... Figure 2 As shown, the shape memory alloy anchor 21 is drilled and installed horizontally, penetrating the potential sliding surface 22 of the slope and anchoring into the deep stable stratum 23. The length of the anchor and the length of the anchoring section are determined according to the slope engineering geological survey report to ensure that the anchoring section is completely located in the stable stratum 23.

[0026] See Figure 2 The shape memory alloy anchor rod 21 includes an anchor rod body 211, a barb structure 212, and a heating device 213.

[0027] The anchor body 211 is made of nickel-titanium alloy rods with a diameter of 25mm to 40mm. The austenitic phase transformation end temperature (Af) of the nickel-titanium alloy is set between 80℃ and 100℃. The basis for selecting this temperature range is: higher than the normal temperature of the stratum and the highest temperature of the slope caused by summer sun exposure, to avoid accidental triggering by natural high temperature; and lower than the boiling point of water, so as to facilitate reliable activation by a simple electric heating device.

[0028] The barb structure 212 is arranged in arrays at intervals along the axial direction of the anchor body 211, with each array of barbs evenly distributed on the circumference. The preset shape of the barb structure is a hook shape that opens outward at 30° to 45°. During the manufacturing process, the nickel-titanium alloy is first heated to above the Af temperature to completely transform it into the austenitic phase. The barb structure is then shaped into the preset outward opening form at high temperature. Subsequently, it is cooled to room temperature and an external force is applied to flatten and retract the barbs into a preset groove on the surface of the anchor body. Because the nickel-titanium alloy has good plastic deformation ability in the low-temperature martensitic phase state, the barbs can remain in the retracted state and will not spring open on their own.

[0029] The heating device 213 is a resistance heating wire that runs axially through the inside of the anchor bolt body 211, and is encased in an insulating heat-resistant sleeve. The leads at both ends of the heating wire extend along the free section of the anchor bolt to the outside of the slope and connect to the field controller. The heating power is designed to allow the anchor bolt to reach a temperature above Af within 10 to 20 minutes after being energized. The outer surface of the anchor bolt is covered with an anti-corrosion coating before heating activation; the coating remains intact when the barbs are retracted, protecting the anchor bolt from groundwater corrosion.

[0030] At room temperature, the barb structure 212 of the shape memory alloy anchor 21 is in a retracted state, and the overall appearance of the anchor is close to that of a smooth rod, allowing for drilling, installation, and grouting according to conventional anchor manufacturing processes. When the system determines that activation is required, electrical heating raises the anchor temperature above Af, and the barb structure 212 returns to its preset open state through the shape memory effect, flipping outward and embedding into the surrounding grout or stratum, significantly increasing the anchor's pull-out bearing capacity.

[0031] The data transmission subsystem 3 includes a field data acquisition unit and a wireless communication module. Earth pressure sensor 12, strain sensor 13, and miniature displacement gauge 14 are connected to the field data acquisition unit via cables. The acquisition unit has a built-in microprocessor and analog-to-digital converter module to preprocess and package the sensor signals. The wireless communication module uses 4G / 5G or LoRa communication to upload the sensed data to the big data analysis and decision-making platform 4 periodically or on demand.

[0032] See Figure 4 The big data analysis and decision-making platform 4 is deployed on a cloud server or a local server and includes a data preprocessing module, a feature extraction module, a risk assessment module, a response decision module, and a data storage and management module.

[0033] The data preprocessing module performs noise reduction, missing value imputation, outlier detection, and multi-source data time synchronization processing on the raw sensor data.

[0034] The feature extraction module extracts multi-timescale feature parameters from the preprocessed data, including: Short-term characteristics: rate of change of root axial strain, rate of change of earth pressure; Mid-term characteristics: Daily increase in root axial strain and daily average trend of earth pressure; Long-term characteristics: seasonal variation of root axial strain and long-term drift of earth pressure.

[0035] like Figure 3 As shown, the risk assessment module employs a machine learning model based on gradient boosting decision trees or long short-term memory networks. The model training data comes from historically accumulated perception data of normal operating conditions and simulated sliding operating conditions, along with corresponding risk level labels. The model input is the aforementioned multidimensional feature vector, and the output is the risk level probability value. Risk levels are divided into four levels: Low risk level (green): All perceived data are within the normal fluctuation range of long-term statistics, with no trend changes.

[0036] Attention Level (Yellow): The axial strain of the root system shows a trend of increasing, but the rate of change is lower than the first preset threshold; or the soil pressure at the root continues to rise but does not lead to relative displacement at the root-soil interface. This stage usually corresponds to the increase in water content of shallow soil and soil softening after rainfall, but overall sliding has not yet occurred.

[0037] Warning Level (Orange): The rate of change of axial strain in the root system exceeds the first preset threshold, and the micro displacement gauge 14 detects measurable relative displacement; or an asymmetrical distribution of soil pressure occurs at the root. This stage corresponds to measurable creep displacement in the shallow soil.

[0038] Danger Level (Red): The rate of change of axial strain in the root system exceeds the second preset threshold and shows an accelerating trend, or the relative displacement of the root-soil interface exceeds the preset limit value. This stage corresponds to the sliding surface about to enter or already entering the accelerated creep stage.

[0039] The response decision module generates tiered control instructions based on the risk assessment results: Green: No action taken; the system continues to monitor. Yellow: Generates an early warning message and pushes it to application terminal 5 to remind managers to strengthen patrols and pay attention to rainfall forecasts; Orange: A heating preparation command is generated and pushed to application terminal 5, prompting the administrator to confirm whether to activate the anchor heating. At the same time, the system automatically completes the heating circuit self-test. Red: Generates a heating execution command, which automatically or semi-automatically triggers the heating device 213 to heat the shape memory alloy anchor rod 21 after a preset authorization process.

[0040] The response decision module also includes a heating effect evaluation unit. After the anchor heating is activated, the evaluation unit continuously monitors changes in sensor data. Under normal circumstances, the anchoring force increases after the back stimulus is activated, further sliding of the shallow soil is inhibited, and the rate of change of root axial strain should decrease significantly. If the rate of change does not decrease after heating or even continues to increase, the system issues a supplementary response suggestion, indicating that more anchors may need to be activated or other engineering reinforcement measures may need to be taken.

[0041] The overall system workflow is as follows: Step 1: The shallow sensing subsystem 1 collects real-time data on root soil pressure, root axial strain, and root-soil interface displacement of plant 11.

[0042] Step 2: The data transmission subsystem 3 uploads the sensed data to the big data analysis and decision-making platform 4.

[0043] Step 3: The platform performs data preprocessing and feature extraction, and the risk assessment module outputs the current risk level.

[0044] Step 4: If the risk level is green, return to Step 1 to continue monitoring; if it is yellow, push a warning message; if it is orange, push a warning and prepare for heating; if it is red, generate a heating execution command.

[0045] Step 5: The heating command is sent to the field controller to heat the shape memory alloy anchor rod 21 to above 80°C.

[0046] Step Six: The barbed structure 212 restores its open state through the shape memory effect and embeds itself into the grouting body or stratum, significantly increasing the pull-out bearing capacity of the anchor and improving the slope safety factor.

[0047] Step 7: The heating effect evaluation unit continuously monitors the sensor data, confirms the response effect, and stores the data of this heating event in the platform database.

[0048] The data storage and management module of the Big Data Analysis and Decision Platform 4 stores all historical data in the long term, including complete perception data and response records under different seasons, different rainfall amounts, and different risk events.

[0049] Every quarter or semi-annually, the platform uses newly accumulated data to incrementally train and fine-tune the machine learning models in the risk assessment module. As the amount of data increases, the model's ability to distinguish between "normal fluctuations" and "abnormal signals" continues to improve, the false alarm rate gradually decreases, and the early warning time gradually increases.

[0050] Meanwhile, the heating response data accumulated by the platform can be used to train the heating decision optimization model, learn the optimal heating timing and heating strategy under different geological conditions and deformation characteristics, and achieve iterative upgrade from "rule-based response" to "data-driven optimization response".

[0051] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A smart slope protection system, characterized in that, The system includes: The shallow sensing subsystem is equipped with at least one plant planted on the slope surface and a sensing device; the plant's roots grow along the slope surface in the opposite direction and are anchored in the shallow soil of the slope surface; the sensing device is deployed in the plant roots and / or the soil around the roots to acquire in real time the sensing data generated when the shallow soil sliding pulls on the plant roots. A data transmission subsystem is used to transmit the sensed data acquired by the sensing device to a big data analysis and decision-making platform. The big data analysis and decision-making platform is equipped with a risk assessment module and a response decision-making module. The risk assessment module is used to assess the slope sliding risk based on the perceived data and generate a risk level. The response decision-making module is used to generate control commands based on the risk level and transmit the control commands to the application terminal. The application terminal is used to receive control commands and generate command information based on the control commands, which is then transmitted to the deep response subsystem. The deep response subsystem is equipped with at least one shape memory alloy anchor rod that penetrates the potential sliding surface of the slope in the horizontal direction and is anchored into the stable stratum; the shape memory alloy anchor rod is provided with an openable and closable barb structure, which is used to control the opening and closing of the barb structure according to command information.

2. The intelligent slope protection system according to claim 1, characterized in that, The sensing device includes: a soil pressure sensor, a strain sensor, and a miniature displacement meter; The soil pressure sensor is deployed in the shallow soil around the plant roots to monitor the lateral pressure data of the soil around the roots. The strain sensor is deployed on the surface of the plant root system to monitor the axial strain data generated by the root system under the sliding and pulling action of the soil. The miniature displacement gauges are deployed on the plant roots and in the soil around the roots to monitor the relative displacement of the roots relative to the surrounding soil. The sensing data includes one or more of the following: the lateral pressure data, the axial strain data, and the relative displacement.

3. The intelligent slope protection system according to claim 1, characterized in that, The big data analysis and decision-making platform also includes: a data preprocessing module and a feature extraction module; The data preprocessing module is used to perform noise reduction, missing value imputation, outlier detection, and multi-source data time synchronization processing on the sensed data to obtain preprocessed data. The feature extraction module is used to extract feature parameters at multiple time scales from the preprocessed data.

4. The intelligent slope protection system according to claim 3, characterized in that, The risk assessment module has a built-in machine learning model, which is used to output a risk level probability value based on the feature parameters, and determine the risk level based on the risk level probability value. The machine learning model is a machine learning model based on gradient boosting decision tree or long short-term memory network. The training data of the machine learning model comes from the perception data of normal working conditions and simulated sliding working conditions accumulated in history. The risk level probability value is the probability value corresponding to each risk level. The risk levels include: low risk level, attention level, warning level, and danger level. The risk level is determined based on the risk level probability value, and the risk level with the highest probability value is determined as the current slope risk level.

5. The intelligent slope protection system according to claim 1, characterized in that, The rod body of the shape memory alloy anchor is made of shape memory alloy material, which is one or more of nickel-titanium alloy, copper-zinc-aluminum alloy or iron-manganese-silicon alloy, and its austenitic phase transformation end temperature is set between 60°C and 120°C.

6. The intelligent slope protection system according to claim 5, characterized in that, The shape memory alloy anchor is equipped with a heating device for heating the barb structure according to command information; The heating device is a resistance heating wire or electric heating film arranged along the axial direction of the anchor rod. The power supply cable of the heating device is led out along the anchor rod to the slope and connected to the field controller or remote control system. The barbed structure is in close contact with the shape memory alloy anchor rod at room temperature and opens and closes when heated.

7. The intelligent slope protection system according to claim 6, characterized in that, The response decision module is equipped with a heating effect evaluation unit, which is used to evaluate the activation effect and anchoring force improvement effect of the barb structure by comparing the changes in sensor data before and after heating after the heating device is activated, and feeds back the evaluation results to the risk assessment module.

8. A smart slope protection method, characterized in that, include: S1. Plant at least one plant on the slope surface, with the plant's roots growing in the opposite direction of the slope and anchored in the shallow soil of the slope surface. S2. Install sensing devices around the plant roots and obtain sensing data through the sensing devices. S3. Drill anchor holes in the horizontal direction and install shape memory alloy anchor rods containing heating devices, so that they penetrate the potential sliding surface and anchor into the stable stratum. The shape memory alloy anchor rods are provided with barb structures that can be activated by heat. At room temperature, the barbs are in a retracted state. S4. Transmit the sensed data to the big data analysis and decision-making platform through the data transmission subsystem; S5. Perform feature extraction and risk level assessment on the perceived data through a big data analysis and decision-making platform; S6. Generate graded response control instructions based on risk level; S7. When it is necessary to activate the anchor bolt, the anchor bolt is heated by the heating device until its temperature reaches above the end temperature of the austenitic phase transformation of the shape memory alloy. The barb structure returns to the preset open state, embeds itself into the surrounding strata, and increases the anchoring force.

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