Anchorless support system monitoring system based on multi-structure parameter monitoring

Abstract

The present application relates to the technical field of geotechnical engineering and underground structure monitoring, in particular to a monitoring system for anchorless support system based on multi-structure parameter monitoring, which, before monitoring the anchorless support system, builds a monitoring model of the anchorless support system in combination with general finite element software, fully analyzes the monitoring accuracy of various structure parameters of the anchorless support system, the repair ability of the entire sensor network and the cost under different grid spacings in combination with the natural redundancy advantage of regular hexagon topology, realizes topology structure innovation, algorithm optimization and multi-source data fusion, builds a complete technical chain of "high-precision modeling-intelligent network deployment-dynamic monitoring-accurate early warning", effectively solves the core problems of "difficulty in repairing data loss, lag in abnormal identification and imbalance between cost and efficiency" in monitoring the anchorless support system, and can be widely applied to tunnel, foundation pit, slope and other engineering, providing an intelligent, low-cost and high-robust new solution for safety control of geotechnical engineering.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering and underground structure monitoring technology, and more specifically, to a monitoring system for anchorless support systems based on multi-structural parameter monitoring. Background Technology

[0002] In the field of geotechnical engineering, anchorless support systems (such as soil nailing walls, pile support, and shotcrete support) are widely used in tunnel, foundation pit, and slope engineering due to their advantages of convenient construction and controllable cost. However, these support structures lack the mechanical anchoring effect of traditional anchors, and their stability highly depends on the synergistic effect between the support material and the soil and rock mass. Their mechanical behavior is complex and easily affected by geological conditions, environmental loads, and other factors.

[0003] Existing monitoring technologies generally suffer from the following bottlenecks: On the one hand, traditional monitoring schemes often employ uniform grid or single-point sensor layouts, lacking targeted monitoring of stress concentration areas in the support structure (such as pit corners and slope inflection points), resulting in missing data or insufficient accuracy in key areas. On the other hand, the sensor network topology is too simple (such as rectangular grids), and when local sensors fail, data repair relies on manual intervention or simple interpolation, resulting in low repair efficiency and large errors, making it difficult to meet real-time and reliability requirements. Furthermore, existing technologies often fail to achieve a synergistic optimization of monitoring accuracy, system robustness, and cost. Either the pursuit of high accuracy leads to a surge in costs due to dense sensor deployment, or the simplification of layout sacrifices monitoring reliability, making it difficult to achieve a balance in engineering practice.

[0004] Existing monitoring methods for anchorless support systems still face the technical shortcoming of "data-driven decision-making." On the one hand, single-parameter monitoring (such as measuring only displacement or stress) cannot fully reflect the coupled response between the structure and the soil mass, and early warning models are mostly based on empirical thresholds, failing to fully consider the spatiotemporal correlation characteristics of multiple parameters. On the other hand, sensor network design lacks mechanical model support, and grid spacing settings rely on engineering experience, making it impossible to verify the impact of different layouts on monitoring accuracy through quantitative analysis. For example, traditional methods struggle to simulate the interpolation repair effects of parameters such as displacement and cracks under different grid spacings, leading to highly arbitrary sensor layouts, delayed anomaly identification, and even safety hazards due to missing data. Therefore, how to construct a monitoring system that combines high precision, high robustness, and economy, achieving a leap from "experience-based network layout" to "intelligent optimization," has become a core issue that urgently needs to be addressed in the field of anchorless support system monitoring. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a monitoring system for anchorless support systems based on the monitoring of multiple structural parameters.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The monitoring system for anchorless support system based on multi-structural parameter monitoring includes a system monitoring model building module, an optimal grid spacing determination module, and a monitoring method execution module.

[0008] The system monitoring model building module constructs a monitoring model of the anchorless support system based on the actual anchorless support system. It defines the support structure type (which can be soil nail wall, pile support, shotcrete support, etc.) based on the geological survey report (soil layer distribution, groundwater level) and support structure design drawings (soil nail spacing, pile diameter). For soil nail walls, it requires creating wall surfaces (shell elements), soil nails (beam elements), and soil (solid elements); for pile support, it requires creating piles (beam / solid elements), capping beams (shell elements), and soil (solid elements); and for shotcrete support, it requires creating the shotcrete layer (shell elements), steel mesh (membrane elements), and surrounding rock (solid elements). The model is then built to a 1:1 scale according to the actual dimensions of the support structure type, with key areas (such as foundation pit corners) meshed. The model is encrypted to 0.5-1m, and relevant material properties are defined simultaneously (e.g., soil properties are defined using the Mohr-Coulomb model, with key properties including elastic modulus, Poisson's ratio, internal friction angle, cohesion, and unit weight; soil nails / anchors are defined using the linear elastic model, with key properties including elastic modulus, cross-sectional area, and yield strength). Boundary conditions and loads are applied (1. Boundary constraints: bottom of the model: fixed constraints (UX=UY=UZ=0); sides: normal displacement constraints (e.g., left side UX=0, right side UX=0, front and rear sides UY=0); top: free boundary (simulating the ground surface). 2. Initial stress field: self-weight stress: applied through the software's gravity load function (e.g., ABAQUS's GRAV) to activate the ground stress balance), thereby building a monitoring model for the anchorless support system.

[0009] The optimal grid spacing determination module lays out a regular hexagonal grid honeycomb within the monitoring model of the anchorless support system, determines the specified range of grid spacing, and further determines the optimal grid spacing.

[0010] The monitoring method execution module deploys sensors in the actual anchorless support system according to the optimal grid spacing and a regular hexagonal honeycomb grid, and monitors the anchorless support system based on the deployed sensors.

[0011] Further, the steps for determining the optimal grid spacing are as follows: generate multiple trial spacings according to the specified range of grid spacing, obtain the comprehensive monitoring and evaluation index of each trial spacing, and mark the trial spacing with the largest comprehensive monitoring and evaluation index value as the optimal grid spacing.

[0012] Further, the steps for obtaining the comprehensive monitoring and evaluation index of the trial spacing are as follows: Select a trial spacing, select each regular hexagon vertex in the monitoring model of the anchorless support system based on the selected trial spacing, obtain the interpolation correction deviation index of each regular hexagon vertex, further obtain the interpolation correction deviation average index, the number of neighboring point monitoring deviations, and the number of adjacent deviations, and calculate the comprehensive monitoring and evaluation index of the trial spacing based on the interpolation correction deviation average index, the number of neighboring point monitoring deviations, the number of adjacent deviations, and the total number of regular hexagon vertices.

[0013] Further steps for obtaining the interpolation correction deviation index: sum and average the interpolation correction deviation indices of all regular hexagon vertices to calculate the interpolation correction deviation index.

[0014] Further, the steps for obtaining the number of neighbor point monitoring deviations are as follows: compare every two adjacent regular hexagonal vertices, calculate the absolute difference between the interpolation correction deviation indices of the two compared regular hexagonal vertices, and calculate the neighbor point deviation gap index. When the neighbor point deviation gap index is higher than the deviation gap threshold index, the number of neighbor point monitoring deviations is increased by one.

[0015] Further, the steps for obtaining the number of adjacent deviations are as follows: when the interpolation correction deviation index of the regular hexagon vertex is higher than the correction deviation threshold index, the corresponding regular hexagon vertex is marked as a deviation vertex. Every two deviation vertices are compared. When the two deviation vertices being compared are directly adjacent vertex nodes, the number of adjacent deviations is increased by one.

[0016] Further, the steps for obtaining the interpolation repair deviation index of the regular hexagon vertex are as follows: Select a regular hexagon vertex in the monitoring model of the anchorless support system, further determine the first and second order deviation values ​​of various structural defect parameters for the regular hexagon vertex, sum and average the first and second order deviation values ​​of all types of structural defect parameters, and calculate the interpolation repair deviation index of the regular hexagon vertex.

[0017] Further, the steps for determining the first and second order deviation values ​​of a type of structural defect parameter at the vertex of the regular hexagon are as follows: select a type of structural defect parameter, add the type of structural defect parameter at the position of the vertex of the regular hexagon, determine the first and second order neighborhood interpolation of the regular hexagon vertex for the type of structural defect parameter, further obtain the interpolation accumulation value and the interpolation difference value, and calculate the first and second order deviation values ​​based on the interpolation accumulation value and the interpolation difference value.

[0018] Further, the steps for obtaining the interpolation accumulation value are as follows: the sum of the first-order neighborhood interpolation and the second-order neighborhood interpolation is calculated to obtain the interpolation accumulation value.

[0019] Further, the steps for obtaining the interpolation difference value are as follows: calculate the absolute difference between the first-order neighborhood interpolation and the second-order neighborhood interpolation to obtain the interpolation difference value.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] Before monitoring the anchorless support system, the system of this invention uses general-purpose finite element software to build a monitoring model of the anchorless support system. Leveraging the inherent redundancy of a regular hexagonal topology, it fully analyzes the monitoring accuracy, repair capability, and cost of various structural parameters of the anchorless support system under different grid spacings. This achieves topology innovation, algorithm optimization, and multi-source data fusion, constructing a complete technology chain of "high-precision modeling - intelligent network deployment - dynamic monitoring - accurate early warning." This effectively solves the core problems in anchorless support system monitoring, such as "difficult data repair, delayed anomaly identification, and cost-efficiency imbalance." It can be widely applied to tunnels, foundation pits, slopes, and other engineering projects, providing a new intelligent, low-cost, and highly robust solution for geotechnical engineering safety management. Attached Figure Description

[0022] Figure 1 A mind map of the structure of a monitoring system for an anchorless support system based on multi-structural parameter monitoring;

[0023] Figure 2 Flowchart for obtaining the comprehensive monitoring and evaluation index of the spacing test;

[0024] Figure 3 The flowchart shows the determination of first- and second-order deviation values. Detailed Implementation

[0025] Reference Figures 1 to 3 The monitoring system for anchorless support systems based on multi-structural parameter monitoring includes a system monitoring model building module, an optimal grid spacing determination module, and a monitoring method execution module.

[0026] The system monitoring model building module builds a monitoring model for the anchorless support system based on the actual anchorless support system.

[0027] Based on the geological survey report (soil layer distribution, groundwater level) and the support structure design drawings (soil nail spacing, pile diameter), define the support structure type (support structure type can be soil nail wall, pile support, shotcrete support, etc.; soil nail wall requires creating wall surface (shell element), soil nail (beam element), soil (solid element), etc.; pile support requires creating pile body (beam / solid element), capping beam (shell element), soil (solid element), etc.; shotcrete support requires creating shotcrete layer (shell element), steel mesh (membrane element), surrounding rock (solid element), etc.). Model according to the support structure type at a 1:1 actual size, with the mesh density increased to 0.5-1m in key areas (such as foundation pit corners), and define relevant material properties simultaneously. Properties (e.g., soil properties are defined using the Mohr-Coulomb model, with key properties including elastic modulus, Poisson's ratio, internal friction angle, cohesion, and unit weight; soil nails / anchors are defined using a linear elastic model, with key properties including elastic modulus, cross-sectional area, and yield strength), and boundary conditions and loads are applied (1. Boundary constraints: bottom of the model: fixed constraints (UX=UY=UZ=0); sides: normal displacement constraints (e.g., left side UX=0, right side UX=0, front and rear sides UY=0); top: free boundary (simulating the ground surface). 2. Initial stress field: self-weight stress: applied through the software's gravity load function (e.g., GRAV in ABAQUS) to activate the ground stress balance), thereby building a monitoring model for the anchorless support system.

[0028] The optimal grid spacing determination module deploys a regular hexagonal grid honeycomb within the anchorless support system monitoring model (generated using Python scripts within the anchorless support system monitoring model), determines the specified range of grid spacing (e.g., the "Standard for Distributed Fiber Optic Intelligent Monitoring Technology for Building Foundation Pit Engineering" recommends a sensor spacing of 1-3m, so the specified range of grid spacing is 1-3m; grid spacing refers to the straight-line distance between two adjacent nodes (such as sensors or monitoring points) in the regular hexagonal grid honeycomb, usually represented by the symbol d), and further determines the optimal grid spacing.

[0029] The steps for determining the optimal grid spacing are as follows: Generate multiple trial spacings based on the specified range of grid spacing (these can be selected from 1-3m using the golden ratio method, with four trial spacings of 1.24m, 1.76m, 2.24m, and 2.76m, or from 1-3m using the averaging method, with three trial spacings of 1m, 2m, and 3m, depending on the monitoring requirements). Obtain the comprehensive monitoring evaluation index for each trial spacing, and mark the trial spacing with the highest comprehensive monitoring evaluation index as the optimal grid spacing.

[0030] The steps for obtaining the comprehensive monitoring and evaluation index of the trial spacing are as follows: Select a trial spacing. Based on the selected trial spacing, select each regular hexagonal vertex in the anchorless support system monitoring model (in the sensor layout of the regular hexagonal grid honeycomb, sensors are only deployed at each regular hexagonal vertex, and sensors are usually not deployed at other positions. This is determined by the geometric characteristics of the regular hexagon, monitoring efficiency, and cost optimization strategy. After selecting a trial spacing, regenerate a regular hexagonal grid honeycomb in the anchorless support system monitoring model according to the selected trial spacing, and select each regular hexagonal vertex in the regular hexagonal grid honeycomb). Label the total number of regular hexagonal vertices as Vg (totalN). Obtain the interpolation correction deviation index of each regular hexagonal vertex. Sum and average the interpolation correction deviation indices of all regular hexagonal vertices to calculate the interpolation correction deviation average index, and label it as Ap (regularX). Compare every two adjacent regular hexagonal vertices (two adjacent regular hexagonal vertices...). A point refers to a directly adjacent vertex node in a regular hexagonal mesh, i.e., a first-order neighbor node. The absolute difference between the interpolation correction deviation indices of the two compared regular hexagonal vertices is calculated to determine the neighbor deviation gap index. When the neighbor deviation gap index is higher than the deviation gap threshold index, the neighbor deviation monitoring count is increased by one (no increase is needed if it is not higher; the deviation gap threshold index is set comprehensively based on monitoring accuracy, structural mechanical properties, and other dimensions). Finally, the neighbor deviation monitoring count is labeled Nb (MonitoringD). When the interpolation correction deviation index of a regular hexagonal vertex is higher than the correction deviation threshold index, the corresponding regular hexagonal vertex is labeled as a deviation vertex (no labeling is needed if it is not higher; the correction deviation threshold index is set comprehensively based on historical data). Every two deviation vertices are compared. When the two compared deviation vertices are directly adjacent vertex nodes, the adjacent deviation count is increased by one. Finally, the adjacent deviation count is labeled Lh (AdjacentD). The comprehensive monitoring and evaluation index of the trial interval was calculated, where vs1 is the first deviation coefficient, vs2 is the second deviation coefficient, vs3 is the third deviation coefficient, and vs4 is the quantity coefficient. The value of the first deviation coefficient is 0.61, the value of the second deviation coefficient is 0.72, the value of the third deviation coefficient is 0.69, and the value of the quantity coefficient is 1.08.

[0031] Before monitoring the anchorless support system, the above content describes the construction of a monitoring model of the anchorless support system using general-purpose finite element software. Taking advantage of the natural redundancy of the regular hexagonal topology, the monitoring accuracy, repair capability of the entire sensor network, and cost of various structural parameters of the anchorless support system under different grid spacings are fully analyzed.

[0032] Steps for obtaining the interpolation repair deviation index of a regular hexagon vertex: Select a regular hexagon vertex in the monitoring model of the anchorless support system, further determine the first and second order deviation values ​​of various structural defect parameters for this regular hexagon vertex (structural defect parameters include displacement parameters, crack parameters, etc.), sum and average the first and second order deviation values ​​of all types of structural defect parameters, and calculate the interpolation repair deviation index of this regular hexagon vertex.

[0033] The steps for determining the first and second order deviation values ​​of a type of structural defect parameter for a vertex of a regular hexagon are as follows: Select a type of structural defect parameter, add this type of structural defect parameter to the position of the vertex of the regular hexagon (taking displacement parameter as an example, adding a parameter of a sudden increase of 10mm in horizontal displacement in a single day to the position of the vertex of the regular hexagon), determine the first-order neighborhood interpolation and second-order neighborhood interpolation of the regular hexagon vertex for this type of structural defect parameter (the first-order neighborhood interpolation uses the data of the 6 directly adjacent vertices (first-order neighborhood) around the vertex of the regular hexagon to calculate the estimated value of the defect parameter of the selected regular hexagon vertex through an interpolation algorithm; the second-order neighborhood interpolation, based on the first-order neighborhood, further includes the adjacent nodes of the first-order neighborhood nodes, for a total of 12 vertices). The point is called the "second-order neighborhood". The defect parameter estimates of the selected hexagonal vertex are calculated using an interpolation algorithm with 12 vertices. Taking first-order neighborhood interpolation as an example, the selected hexagonal vertex is labeled A, and its six adjacent vertices are labeled B, C, D, E, F, and G. The actual horizontal displacement of A is 10mm, B is 5mm, C is 6mm, D is 7mm, E is 7mm, F is 6mm, and G is 5mm. The sum and average of the actual horizontal displacements of B, C, D, E, F, and G is calculated to be 6mm. Therefore, the first-order neighborhood interpolation value is |6-10| / 10. = 40%), sum the first-order and second-order neighborhood interpolations, calculate the interpolation summation value, and label it as RL (summation). Calculate the absolute difference between the first-order and second-order neighborhood interpolations, and label it as PE (difference). The first and second order bias values ​​are calculated, where pa1 is the first interpolation coefficient and pa2 is the second interpolation coefficient. The first interpolation coefficient is 0.92 and the second interpolation coefficient is 1.97.

[0034] The monitoring method execution module deploys sensors in the actual anchorless support system according to the optimal grid spacing and a regular hexagonal honeycomb grid, and monitors the anchorless support system based on the deployed sensors.

[0035] The above-mentioned content realizes topological structure innovation, algorithm optimization and multi-source data fusion, and constructs a complete technology chain of "high-precision modeling-intelligent network deployment-dynamic monitoring-precise early warning". It effectively solves the core problems of "difficult data repair, delayed anomaly identification and cost-efficiency imbalance" in the monitoring of anchorless support systems. It can be widely used in tunnel, foundation pit, slope and other projects, and provides a brand-new intelligent, low-cost and highly robust solution for the safety management of geotechnical engineering.

[0036] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0037] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0038] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0039] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0040] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0041] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0042] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A monitoring system for anchorless support systems based on multi-structural parameter monitoring, characterized in that, It includes a system monitoring model building module, which builds a monitoring model for the anchorless support system based on the actual anchorless support system; The optimal grid spacing determination module lays out a regular hexagonal grid honeycomb within the monitoring model of the anchorless support system, determines the specified range of grid spacing, and further determines the optimal grid spacing. The steps for determining the optimal grid spacing are as follows: generate multiple trial spacings based on the specified range of grid spacing, obtain the comprehensive monitoring evaluation index of each trial spacing, and mark the trial spacing with the largest comprehensive monitoring evaluation index value as the optimal grid spacing. The steps for obtaining the comprehensive monitoring and evaluation index of the trial spacing are as follows: Select a trial spacing, select each hexagon vertex in the monitoring model of the anchorless support system based on the selected trial spacing, obtain the interpolation correction deviation index of each hexagon vertex, further obtain the interpolation correction deviation average index, the number of neighboring point monitoring deviations, and the number of adjacent deviations, and calculate the comprehensive monitoring and evaluation index of the trial spacing based on the interpolation correction deviation average index, the number of neighboring point monitoring deviations, the number of adjacent deviations, and the total number of hexagon vertices; The monitoring method execution module deploys sensors in the actual anchorless support system according to the optimal grid spacing and a regular hexagonal honeycomb grid, and monitors the anchorless support system based on the deployed sensors.

2. The monitoring system for anchorless support systems based on multi-structural parameter monitoring according to claim 1, characterized in that, Steps to obtain the interpolation correction deviation index: Sum the interpolation correction deviation indices of all regular hexagon vertices and calculate the mean to obtain the interpolation correction deviation index.

3. The monitoring system for anchorless support systems based on multi-structural parameter monitoring according to claim 1, characterized in that, Steps for obtaining the number of neighbor point monitoring deviations: Compare every two adjacent regular hexagon vertices, calculate the absolute difference between the interpolation correction deviation indices of the two compared regular hexagon vertices, and calculate the neighbor point deviation gap index. When the neighbor point deviation gap index is higher than the deviation gap threshold index, increase the number of neighbor point monitoring deviations by one.

4. The monitoring system for anchorless support systems based on multi-structural parameter monitoring according to claim 1, characterized in that, Steps for obtaining the number of adjacent deviations: When the interpolation correction deviation index of the regular hexagon vertex is higher than the correction deviation threshold index, the corresponding regular hexagon vertex is marked as a deviation vertex. Every two deviation vertices are compared. When the two deviation vertices being compared are directly adjacent vertex nodes, the number of adjacent deviations is increased by one.

5. The monitoring system for anchorless support systems based on multi-structural parameter monitoring according to claim 1, characterized in that, Steps for obtaining the interpolation correction deviation index of a regular hexagon vertex: Select a regular hexagon vertex in the monitoring model of the anchorless support system, further determine the first and second order deviation values ​​of various structural defect parameters for the regular hexagon vertex, sum and average the first and second order deviation values ​​of all types of structural defect parameters, and calculate the interpolation correction deviation index of the regular hexagon vertex.

6. The monitoring system for anchorless support systems based on multi-structural parameter monitoring according to claim 1, characterized in that, The steps for determining the first and second order deviation values ​​of a type of structural defect parameter for a vertex of a regular hexagon are as follows: Select a type of structural defect parameter, add the type of structural defect parameter to the position of the vertex of the regular hexagon, determine the first and second order neighborhood interpolation of the regular hexagon vertex for the type of structural defect parameter, further obtain the interpolation accumulation value and the interpolation difference value, and calculate the first and second order deviation values ​​based on the interpolation accumulation value and the interpolation difference value.

7. The monitoring system for anchorless support systems based on multi-structural parameter monitoring according to claim 6, characterized in that, The steps to obtain the interpolation accumulation value are as follows: sum the first-order neighborhood interpolation and the second-order neighborhood interpolation to calculate the interpolation accumulation value.

8. The monitoring system for anchorless support systems based on multi-structural parameter monitoring according to claim 6, characterized in that, The steps to obtain the interpolation difference value are as follows: calculate the absolute difference between the first-order neighborhood interpolation and the second-order neighborhood interpolation to obtain the interpolation difference value.

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