Intelligent anchoring system for slow-bonding pressure type anchor rod and regulating and monitoring method thereof

Abstract

The invention relates to the technical field of geotechnical engineering anchoring, and provides a slow-bonding pressure type anchor rod intelligent anchoring system and a regulating and monitoring method thereof. Comprising a sensing unit, an analysis decision unit and an execution unit, wherein the sensing unit comprises a plurality of stress sensors which are integrated on a pile / anchor rod and are used for collecting stress and strain of the pile / anchor rod; the analysis and decision-making unit comprises an edge computing node arranged in the pile / anchor rod, and the edge computing node is used for performing real-time processing and feature extraction on the sensing data of the sensing unit to realize regulation and control parameter calculation; the execution unit comprises a hydraulic servo tensioning mechanism used for finely adjusting the prestress of the pile / anchor rod, and self-adaptive matching of the rigidity of the pile / anchor rod and deformation of a rock-soil body is achieved. The system has the advantages that sensing, analysis and decision making are integrated, and real-time sensing and dynamic optimization regulation and control of the anchoring performance are achieved.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering anchoring technology, specifically to an intelligent anchoring system for slow-bonding pressure type anchor bolts and its control and monitoring method. Background Technology

[0002] Traditional anchor bolt support systems cannot sense their own stress and damage status, and cannot adapt to time-varying behaviors such as creep and unloading relaxation of soil and rock, resulting in insufficient long-term reliability of anchor bolt support systems in complex environments.

[0003] Therefore, this invention is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a slow-bonding pressure type intelligent anchoring system for anchor bolts and its control and monitoring method, so as to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a slow-bonding pressure type intelligent anchoring system for anchor bolts, comprising: a sensing unit, an analysis and decision-making unit, and an execution unit; wherein,

[0006] The sensing unit includes multiple stress sensors integrated on the pile / anchor for collecting stress and strain of the pile / anchor;

[0007] The analysis and decision-making unit includes an edge computing node built into the pile / anchor. The edge computing node is used to process and extract features from the sensing data of the sensing unit in real time, and to calculate the control parameters.

[0008] The execution unit includes a hydraulic servo tensioning mechanism for fine-tuning the prestress of the pile / anchor, thereby achieving adaptive matching between the stiffness of the pile / anchor and the deformation of the soil and rock mass.

[0009] In an optional embodiment, the sensing unit further includes multiple acoustic emission sensors for collecting acoustic emission signals from microcracks in the pile / anchor and multiple environmental sensors for collecting information about the surrounding environment of the pile / anchor.

[0010] In an alternative embodiment, the plurality of stress sensors are replaced by a plurality of resistive strain gauges.

[0011] In an optional embodiment, the piles / anchors are arranged inward along multiple excavation surfaces of the soil and rock mass.

[0012] In an optional embodiment, a plurality of stress sensors, a plurality of acoustic emission sensors, a plurality of acoustic emission sensors, and a plurality of environmental sensors are arranged in an array on the pile / anchor and are connected to the edge computing node via optical fiber.

[0013] In an optional embodiment, the hydraulic servo tensioning mechanism is located at the top of the pile / anchor.

[0014] On the other hand, the present invention also provides a control and monitoring method using the intelligent anchoring system described above, comprising the following steps:

[0015] S1: Real-time acquisition of stress and strain distribution along the entire length of the pile / anchor using multiple stress sensors; capture of high-frequency stress waves generated when micro-fractures occur in the soil and rock mass and when the pile / anchor interface is damaged using multiple acoustic emission sensors;

[0016] S2: Send all the raw data collected in S1 to the system health scoring function in the edge computing node. The coupling state score of the system health scoring function is S = w1·f(σ) + w2·g(ΔL) + w3·h(DAE), where f(σ) is the stress distribution uniformity score calculated based on the data of the stress sensor, g(ΔL) is the deformation compatibility score calculated based on the strain data, and h(DAE) is the damage activity score calculated based on the high-frequency stress wave collected by the acoustic emission sensor; w1, w2, and w3 are weighting coefficients dynamically adjusted according to geological conditions.

[0017] S3: Complete the health diagnosis based on the coupling state score S of the system health scoring function;

[0018] When the coupling state score S is lower than the preset safety threshold S_safe or the score decrease rate V_s exceeds the warning rate V_warn, an optimization adjustment loop is triggered, and the decision optimization generates an adjustment instruction to minimize the relative displacement between the pile / anchor and the soil mass through the objective function.

[0019] S4: The adjustment command is immediately sent to the hydraulic servo tensioning mechanism, and the piston rod of the hydraulic servo tensioning mechanism performs secondary tensioning or slight decompression on the pile / anchor.

[0020] In an optional embodiment, in S2, the axial stress value σ_i at n equally spaced points i along the anchorage length L is measured using the stress sensor.

[0021] Calculate the mean stress: σ_avg=(Σσ_i) / n

[0022] Determine the peak stress: σ_max = max(σ_i)

[0023] Calculate the stress concentration factor: K_σ = σ_max / σ_avg

[0024] Scoring mapping: f(σ) is negatively correlated with this coefficient, and a piecewise linear mapping function is established: when K_σ ≤ [threshold A], f(σ) = 100; when K_σ ≥ [threshold B], f(σ) = 0; when [threshold A] < K_σ < [threshold B], f(σ) is linearly interpolated between 0 and 100.

[0025] In an optional embodiment, it is assumed that the shear stress τ and the shear displacement ΔL at the interface between the anchor solid and the rock and soil mass are linearly related (τ = G_s·ΔL). Combining the static equilibrium and physical equations, the control equation and the general solution are derived:

[0026]

[0027] Its solution is in the form of hyperbolic functions, that is, the explicit mathematical expression of the deformation coordination of the anchor bolt:

[0028]

[0029] where β = sqrt(4G_s / (πE_a)), G_s is the interface shear modulus, and E_a is the equivalent elastic modulus of the grout;

[0030] The current anchor bolt tension P_0, design parameters (L_a, D), material and interface parameters (E_a, G_s);

[0031] Substitute the above parameters into the analytical formula to calculate the theoretical shear displacement distribution curve ΔL_theory(x) along the anchorage length;

[0032] Scoring construction:

[0033] Actual measurement comparison: If the actual displacement distribution ΔL_measured(x) is measured by a distributed stress sensor, calculate the root mean square error (RMSE) or mean absolute percentage error (MAPE) between it and the theoretical curve;

[0034] Status evaluation: If there is no full-line actual measurement data, use the hole mouth displacement ΔL_0 as the key index; calculate the theoretically obtained hole mouth displacement ΔL_theory(0) under the current P_0, and compare it with the allowable displacement threshold [ΔL]_allow;

[0035] Scoring mapping: Define that g(ΔL) is negatively correlated with the error or positively correlated with the displacement satisfaction degree, and g(ΔL) = 100*(1 - min(1, ΔL_0 / [ΔL]_allow)).

[0036] In an optional embodiment,

[0037] Load ratio (LR): LR = the load at which acoustic emission events start to occur in the current loading cycle / the maximum load reached in the previous loading cycle, which reflects the stress threshold for the reactivation of damage;

[0038] Calmness Ratio (CR): CR = cumulative number of acoustic emission events in this unloading phase / cumulative number of acoustic emission events in the previous complete cycle. It reflects the activity level of irreversible damage during the unloading process.

[0039] Scoring Mapping: A two-dimensional evaluation map is established with LR as the x-axis and CR as the y-axis. Based on the region where the (LR, CR) coordinate point falls, it is directly mapped to a discrete damage activity score.

[0040] Low-activity region: The corresponding coordinate point falls into this region, and the score h(DAE) is assigned a high value; it indicates that the structure is in a state of slight damage or no damage.

[0041] Medium active region: The corresponding coordinate point falls into this region, and the score h(DAE) is assigned to the medium zone, indicating that the structure has moderate damage and needs to be monitored more closely;

[0042] High-activity zone: If the corresponding coordinate point falls into this zone, the score h(DAE) is assigned a low value, indicating that the structure is severely damaged and close to destruction, requiring immediate warning.

[0043] The beneficial effects of this invention are as follows: This intelligent anchoring system achieves a leap from "passive support" to "active sensing and intelligent control," significantly improving long-term safety and adaptability in complex and ever-changing environments. It should be noted that by introducing a coupled state scoring function from edge computing, the current support status can be accurately determined. In particular, the introduction of three weighted scoring functions—f(σ), g(ΔL), and h(DAE)—improves the timeliness and accuracy of monitoring, effectively solving the problem of progressive failure caused by stiffness mismatch and stress relaxation. This provides key technical support for slope safety in major infrastructure projects such as large-scale hydropower, mining, highways, and railways. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of a high slope in rock and soil with a gently bonded pressure anchor bolt arranged according to an embodiment of the present invention.

[0046] Figure 2 This is a structural schematic diagram of a slow-bonding pressure type intelligent anchoring system for anchor bolts provided in an embodiment of the present invention.

[0047] Figure 3The flowchart illustrates the control and monitoring process of a slow-bonding pressure-type intelligent anchoring system for anchor bolts, as provided in an embodiment of the present invention.

[0048] The attached diagram is labeled as follows: 1-Soil and rock mass, 2-Excavation face, 3-Excavated soil and rock mass, 4-Natural slope, 5-Pile / anchor, 6-Hydraulic servo tensioning mechanism, 7-Stress sensor, 8-Fiber optic cable, 9-Edge computing node, 10-Acoustic emission sensor, 11-Environmental sensor. Detailed Implementation

[0049] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0050] It should be noted that when a component is referred to as being "fixed to" or "attached" to another component, it can be located directly or indirectly on that other component. When a component is referred to as being "connected to" another component, it can be directly or indirectly connected to that other component. The terms "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate orientations or positions based on the accompanying drawings, and are for ease of description only, and should not be construed as limiting the technical solution. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features. "Multiple" means two or more, and "several" means any number including one, unless otherwise explicitly specified.

[0051] Example 1

[0052] Please see the appendix Figure 1-2 The purpose of this embodiment is to provide a slow-bonding pressure type intelligent anchoring system for anchor bolts, including: a sensing unit, an analysis and decision-making unit, and an execution unit; wherein, the sensing unit includes multiple resistance strain gauges integrated on the pile / anchor bolt 5 for collecting stress and strain of the pile / anchor bolt 5; the analysis and decision-making unit includes an edge computing node 9 built into the pile / anchor bolt 5, which is used to process and extract features from the sensing data of the sensing unit in real time to realize the calculation of control parameters; the execution unit includes a hydraulic servo tensioning mechanism 6 for fine-tuning the prestress of the pile / anchor bolt 5 to achieve adaptive matching between the stiffness of the pile / anchor bolt 5 and the deformation of the soil and rock mass.

[0053] In high-risk scenarios, multiple stress sensors 7 are used, and multiple acoustic emission sensors 10 for collecting acoustic emission signals from microcracks in the pile / anchor 5 and multiple environmental sensors 11 for collecting information about the surrounding environment of the pile / anchor 5 are set in the sensing unit. This system can achieve full-function control and monitoring, and those skilled in the art can flexibly configure it according to the engineering risk level and reasonably select an advanced system or a basic system with full functions.

[0054] Furthermore, the piles / anchors 5 are arranged inward along multiple excavation faces 2 of the soil and rock mass 1. Multiple stress sensors 7, multiple acoustic emission sensors 10, and multiple environmental sensors 11 are arranged in an array on the piles / anchors 5 and are communicatively connected to the edge computing nodes 9 via optical fibers 8. A hydraulic servo tensioning mechanism 6 is located on top of the piles / anchors 5.

[0055] Example 2

[0056] Please see the appendix Figure 1-3 Based on Embodiment 1, the purpose of this embodiment is to provide a control and monitoring method using the above-mentioned intelligent anchoring system, including the following steps:

[0057] S1: The stress and strain distribution along the entire length of the pile / anchor 5 is collected in real time by multiple stress sensors 7; and the high-frequency stress waves generated when the soil / rock mass 1 is micro-fractured and the pile / anchor 5 interface is damaged are captured by multiple acoustic emission sensors 10.

[0058] Specifically, the stress and strain distribution along the entire length of the pile / anchor rod 5 is collected in real time by multiple stress sensors 7. This is the most direct basis for judging whether the load transfer is uniform and whether there is stress concentration.

[0059] In an optional embodiment, even when using an advanced system, multiple stress sensors 7 are used, and high-frequency stress waves generated by micro-fractures in the soil mass 1 and damage at the pile / anchor 5 interface are captured by acoustic emission sensors 10 to identify early damage that is not visible to the naked eye; environmental changes such as temperature, humidity, and seepage pressure of the soil mass 1 are monitored and collected by multiple environmental sensors 11, which are key environmental drivers that lead to material performance degradation and soil creep.

[0060] S2: Feature Extraction and Health Diagnosis

[0061] Send all the raw data collected in S1 to the system health scoring function in the edge computing node 9. The coupled state score S of the system health scoring function is S = w1·f(σ) + w2·g(ΔL) + w3·h(DAE), where f(σ) is the stress distribution uniformity score calculated based on the data of the stress sensor 7, g(ΔL) is the deformation coordination score calculated based on the strain data, and h(DAE) is the damage activity score calculated based on the high-frequency stress waves collected by the acoustic emission sensor 10; w1, w2, and w3 are weight coefficients dynamically adjusted according to the geological conditions; the introduction of each function improves the accuracy and referenceability of the score, and the setting of the weight parameters further improves the accuracy of the score judgment.

[0062] Specifically:

[0063] f(σ): Stress distribution uniformity

[0064] f(σ) aims to quantify the concentration degree of the axial stress of the rod body along the length in the anchorage section. The more uniform the distribution, the more sufficient the load transfer, the better the system synergy, and the higher the score should be.

[0065] Core algorithm - "stress concentration coefficient" method based on measured data:

[0066] Input: The axial stress values σ_i at n equally spaced points i along the anchorage length L are measured by the distributed stress sensors 7 installed on the bolt body.

[0067] Calculation: Calculate the average stress: σ_avg = (Σσ_i) / n

[0068] Determine the peak stress: σ_max = max(σ_i)

[0069] Calculate the stress concentration coefficient: K_σ = σ_max / σ_avg

[0070] Score mapping: f(σ) is negatively correlated with this coefficient, and a piecewise linear mapping function is established: when K_σ ≤ [threshold A], f(σ) = 100; when K_σ ≥ [threshold B], f(σ) = 0; when [threshold A] < K_σ < [threshold B], f(σ) is linearly interpolated between 0 and 100.

[0071] g(ΔL): Deformation coordination score

[0072] g(ΔL) evaluates the load transfer efficiency and the coherence of the overall deformation when the bolt and the surrounding rock undergo a shear displacement ΔL at the interface. The higher the coordination, the smaller the relative displacement generated under the same load, or the smoother the displacement distribution.

[0073] Core algorithm (based on the linear elastic assumption model):

[0074] Mathematical Model: This is a commonly used classical analytical solution. Assuming a linear relationship between the shear stress τ at the interface between the anchor body and the soil / rock mass and the shear displacement ΔL (τ = G_s·ΔL), and combining static equilibrium and physical equations, the governing equations and general solution can be derived:

[0075]

[0076] Its solution is in hyperbolic function form, which is an explicit mathematical expression of the anchor bolt deformation compatibility:

[0077]

[0078] Where β = sqrt(4G_s / (πE_a)), G_s is the interfacial shear modulus, and E_a is the equivalent elastic modulus of the slurry.

[0079] Algorithm inputs: current anchor bolt tension P_0, design parameters (L_a, D), material and interface parameters (E_a, G_s).

[0080] Algorithm execution: Substituting the above parameters into the analytical expression, the theoretical shear displacement distribution curve ΔL_theory(x) along the anchorage length can be calculated.

[0081] Rating structure:

[0082] (1) Actual measurement comparison: If the actual displacement distribution ΔL_measured(x) is measured by the distributed stress sensor 7, the root mean square error (RMSE) or mean absolute percentage error (MAPE) between it and the theoretical curve can be calculated.

[0083] (2) Status assessment: If there is no full-line measured data, the orifice displacement ΔL_0 can be used as the key indicator. Calculate the theoretical orifice displacement ΔL_theory(0) under the current P_0 and compare it with the allowable displacement threshold [ΔL]_allow.

[0084] (3) Scoring mapping: Define g(ΔL) as negatively correlated with error or positively correlated with displacement satisfaction. For example: g(ΔL)=100*(1-min(1,ΔL_0 / [ΔL]_allow)).

[0085] Extended to nonlinear cases: For nonlinear interfaces (τ-ΔL is a hyperbolic relationship), a piecewise deformation coordination iterative algorithm can be used for numerical solution to obtain ΔL(x), and the scoring principle is the same as above.

[0086] h(DAE): Damage activity score

[0087] h(DAE) is used to quantify the activity level of acoustic emission signals generated inside the anchor bolt due to microscopic damage (such as steel yielding, grout cracking, and interface slip). Higher activity indicates that the damage is developing rapidly and the system health is lower.

[0088] Core algorithm (two-dimensional evaluation graph method based on load ratio and calm ratio):

[0089] Key parameter calculation:

[0090] (1) Load ratio (LR): LR = Load at which acoustic emission events begin to occur in the current loading cycle / Maximum load reached in the previous loading cycle. It reflects the stress threshold at which damage is reactivated.

[0091] (2) Calm Ratio (CR): CR = Total number of acoustic emission events in the current unloading phase / Total number of acoustic emission events in the previous complete (load-hold-unload) cycle. It reflects the activity level of irreversible damage during the unloading process.

[0092] (3) Scoring Mapping (State Partitioning Method): A two-dimensional assessment map is established with LR as the abscissa and CR as the ordinate. Based on the region where the (LR, CR) coordinate point falls, it is directly mapped to a discrete damage activity score:

[0093] Low-activity zone (healthy): The corresponding coordinate point falls into this zone, and the score h(DAE) is assigned a high value (e.g., 80-100). This indicates that the structure is in a state of slight damage or no damage.

[0094] Medium active zone (attention): If the corresponding coordinate point falls into this zone, the score h(DAE) is assigned to the medium zone (e.g., 40-79), indicating that the structure has moderate damage and needs to be monitored more closely.

[0095] High activity zone (warning): If the corresponding coordinate point falls into this zone, the score h(DAE) is assigned a low value (e.g., 0-39), indicating that the structure is severely damaged and close to destruction, requiring an immediate warning.

[0096] Algorithm integration and deployment:

[0097] At this point, all three functions have been clearly defined, from their physical definitions to their mathematical expressions or calculation rules:

[0098] f(σ) outputs a score of 0-100 based on the stress distribution concentration or fit.

[0099] g(ΔL) outputs a score of 0-100 based on the comparison between the deformation compatibility theoretical model and the actual displacement.

[0100] h(DAE) outputs a score of 0-100 based on the partitioning determination of the AE parameters in the 2D graph.

[0101] These three normalized scores provide a computable and repeatable algorithmic foundation for the subsequent weighted summation with weight coefficients w1, w2, and w3 to generate the final health score S. The algorithms for each function can be selected and adapted according to the types of sensor data available in the actual engineering project.

[0102] For example, under the current conditions of weak, water-bearing rock formations, the system may assign a higher weight to h(DAE), which characterizes interface damage. Assuming that w1=0.3, w2=0.3, w3=0.4 (satisfying w1+w2+w3=1).

[0103] S3: Complete the health diagnosis based on the coupling state score S of the system health scoring function;

[0104] When the coupling state score S is lower than the preset safety threshold S_safe or the score decrease rate V_s exceeds the warning rate V_warn, an optimization adjustment loop is triggered, and the decision optimization generates an adjustment instruction to minimize the relative displacement between the pile / anchor and the soil mass 1 through the objective function;

[0105] It should be noted that the coupled state scoring of the health scoring function in edge computing can accurately determine the current support status, effectively solving the problem of progressive failure caused by stiffness mismatch and stress relaxation, and providing key technical support for slope safety of major infrastructure such as large-scale hydropower, mining, highway and railway projects.

[0106] S4: The adjustment command is immediately sent to the hydraulic servo tensioning mechanism 6, and the piston rod of the hydraulic servo tensioning mechanism 6 performs secondary tensioning or slight decompression on the pile / anchor 5.

[0107] Specifically

[0108] Command received: The hydraulic servo system receives a digital command from edge computing node 9;

[0109] Precision actuation: The system's servo motor drives the hydraulic pump, controlling the piston rod of the hydraulic cylinder to perform precise extension and retraction at the millimeter or even micrometer level;

[0110] Dynamic adjustment: The piston rod drives the anchor to perform secondary tensioning on the pile / anchor 5 to increase the prestress or slightly depressurize to reduce the prestress, thereby accurately adjusting the prestress to the target value, such as 158kN.

[0111] It is important to note that the physical stiffness of the pile / anchor itself is fixed, determined by the elastic modulus and cross-sectional area of ​​the steel. The "stiffness adjustment" in this embodiment essentially refers to "adjusting the equivalent stiffness of the pile / anchor-soil composite system."

[0112] This intelligent anchoring system represents a leap from "passive support" to "active sensing and intelligent control," significantly improving long-term safety and adaptability in complex and ever-changing environments.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A slow-bonding pressure type intelligent anchoring system for anchor bolts, characterized in that, include: The unit comprises a perception unit, an analysis and decision-making unit, and an execution unit; among which, The sensing unit includes multiple stress sensors (7) integrated on the pile / anchor (5) for collecting stress and strain of the pile / anchor (5). The analysis and decision unit includes an edge computing node (9) built into the pile / anchor (5). The edge computing node (9) is used to process and extract features from the sensing data of the sensing unit in real time, and to calculate the control parameters. The execution unit includes a hydraulic servo tensioning mechanism (6) for fine-tuning the prestress of the pile / anchor (5) to achieve adaptive matching between the stiffness of the pile / anchor (5) and the deformation of the soil and rock mass.

2. The intelligent anchoring system for pressure-type anchor bolts with slow bonding as described in claim 1, characterized in that, The sensing unit also includes multiple acoustic emission sensors (10) for collecting acoustic emission signals from microcracks in the pile / anchor (5) and multiple environmental sensors (11) for collecting information about the surrounding environment of the pile / anchor (5).

3. The intelligent anchoring system for pressure-type anchor bolts with slow bonding as described in claim 2, characterized in that, Multiple stress sensors (7) are replaced with multiple resistive strain gauges.

4. The intelligent anchoring system for pressure-type anchor bolts with slow bonding as described in claim 2, characterized in that, The piles / anchors (5) are arranged inward along multiple excavation faces (2) of the rock and soil mass (1).

5. The intelligent anchoring system for slow-bonding pressure-type anchor bolts as described in claim 3, characterized in that, Multiple stress sensors (7), multiple acoustic emission sensors (10), multiple acoustic emission sensors (10) and multiple environmental sensors (11) are arranged in an array on the pile / anchor (5) and are connected to the edge computing node (9) via optical fiber (8).

6. The intelligent anchoring system for slow-bonding pressure-type anchor bolts as described in claim 1, characterized in that, The hydraulic servo tensioning mechanism (6) is located on top of the pile / anchor (5).

7. A method for regulating and monitoring the intelligent anchoring system as described in any one of claims 1-6, characterized in that, Includes the following steps: S1: The stress and strain distribution of the entire length of the pile / anchor (5) is collected in real time by multiple stress sensors (7); the high-frequency stress waves generated when the micro-fracture of the soil and rock mass (1) and the pile / anchor (5) interface are damaged are captured by multiple acoustic emission sensors (10); S2: Send all the raw data collected in S1 to the system health scoring function in the edge computing node (9). The coupling state score of the system health scoring function is S = w1·f(σ) + w2·g(ΔL) + w3·h(DAE), where f(σ) is the stress distribution uniformity score calculated based on the data of the stress sensor (7), g(ΔL) is the deformation compatibility score calculated based on the strain data, and h(DAE) is the damage activity score calculated based on the high-frequency stress wave collected by the acoustic emission sensor (10); w1, w2, and w3 are weighting coefficients dynamically adjusted according to geological conditions. S3: Complete the health diagnosis based on the coupling state score S of the system health scoring function; When the coupling state score S is lower than the preset safety threshold S_safe or the score decrease rate V_s exceeds the warning rate V_warn, the optimization adjustment loop is triggered, the decision optimization generates the adjustment instruction, and the relative displacement between the pile / anchor and the soil (1) is minimized through the objective function; S4: The adjustment command is immediately sent to the hydraulic servo tensioning mechanism (6), and the piston rod of the hydraulic servo tensioning mechanism (6) performs secondary tensioning or slight decompression on the pile / anchor (5).

8. The regulation and monitoring method as described in claim 7, characterized in that, In S2, through the stress sensor (7), the axial stress values σ_i at n equally spaced points i along the anchorage length L are measured; Calculate the average stress: σ_avg = (Σσ_i) / n Determine the peak stress: σ_max = max(σ_i) Calculate the stress concentration factor: K_σ = σ_max / σ_avg Scoring mapping: f(σ) is negatively correlated with this coefficient, and a piecewise linear mapping function is established: when K_σ ≤ [threshold A], f(σ) = 100; when K_σ ≥ [threshold B], f(σ) = 0; when [threshold A] < K_σ < [threshold B], f(σ) is linearly interpolated between 0 and 100.

9. The regulation and monitoring method as described in claim 8, characterized in that, Assume that the shear stress τ and the shear displacement ΔL at the interface between the anchor body and the rock and soil mass are linearly related (τ = G_s·ΔL). Combining the static equilibrium and physical equations, the control equation and the general solution are derived: ， Its solution is in the form of a hyperbolic function, that is, the explicit mathematical expression of the deformation coordination of the anchor bolt: ， where β = sqrt(4G_s / (πE_a)), G_s is the interface shear modulus, and E_a is the equivalent elastic modulus of the grout; The current anchor bolt tension P_0, design parameters (L_a, D), material and interface parameters (E_a, G_s); Substitute the above parameters into the analytical formula to calculate the theoretical shear displacement distribution curve ΔL_theory(x) along the anchorage length; Scoring construction: Actual measurement comparison: If the distributed stress sensor (7) measures the actual displacement distribution ΔL_measured(x), calculate the root mean square error (RMSE) or the mean absolute percentage error (MAPE) between it and the theoretical curve; Status evaluation: If there is no full-line actual measurement data, use the hole mouth displacement ΔL_0 as the key index; calculate the theoretically obtained hole mouth displacement ΔL_theory(0) under the current P_0, and compare it with the allowable displacement threshold [ΔL]_allow; Scoring mapping: Define that g(ΔL) is negatively correlated with the error or positively correlated with the displacement satisfaction degree, g(ΔL) = 100*(1 - min(1, ΔL_0 / [ΔL]_allow)).

10. The regulation and monitoring method according to claim 9, wherein Load ratio (LR): LR = the load at the start of the acoustic emission event in the current loading cycle / the maximum load reached in the previous loading cycle, which reflects the stress threshold for the reactivation of damage; Quiet ratio (CR): CR = the cumulative number of acoustic emission events in the current unloading stage / the cumulative number of acoustic emission events in the previous complete cycle, which reflects the activity degree of irreversible damage during the unloading process; Scoring mapping: Establish a two-dimensional evaluation diagram with LR as the abscissa and CR as the ordinate. According to the region where the coordinate point (LR, CR) falls, directly map it to a discrete damage activity score: Low activity area: When the corresponding coordinate point falls into this area, the score h(DAE) is assigned to the high score area; it indicates that the structure is in a slightly damaged or undamaged state; Medium activity area: When the corresponding coordinate point falls into this area, the score h(DAE) is assigned to the medium score area, indicating that the structure has medium damage and needs to strengthen monitoring; High-activity zone: If the corresponding coordinate point falls into this zone, the score h(DAE) is assigned a low value, indicating that the structure is severely damaged and close to destruction, requiring immediate warning.

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