Drilling in-situ rock mass corrosion rate long-term monitoring system based on microspur scanning

Abstract

The invention provides a drilling in-situ rock mass corrosion rate long-term monitoring system based on microspur scanning, and relates to the technical field of geotechnical engineering monitoring. The system comprises a data acquisition module, a multi-parameter evaluation module and a self-adaptive monitoring module, the data acquisition module is integrated with a macro scanning probe and an in-hole multi-parameter sensor to obtain drill hole rock wall morphology, water chemical environment and rock geological data; the multi-parameter evaluation module is used for calculating a corrosion potential index and a corrosion activity index, and constructing a monitoring section association according to the underground water flow direction; the adaptive monitoring module dynamically adjusts the scanning frequency and the sampling strategy based on the potential and the activity index. Microscopic quantitative monitoring and multi-field coupling analysis of the drilling rock mass corrosion process are achieved, the problems that a traditional monitoring means is low in precision, high in hysteresis quality and incapable of achieving self-adaptive adjustment are solved, and the accuracy and efficiency of long-term safety assessment of karst area engineering are improved.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering monitoring technology, specifically to a long-term monitoring system for in-situ rock mass dissolution rate based on micro-scanning. Background Technology

[0002] In the construction of water conservancy and hydropower projects, transportation tunnels, and underground engineering projects, the dissolution of carbonate rocks (such as limestone and dolomite) is a key factor affecting the long-term stability of the projects. Continuous dissolution of rock masses by groundwater leads to increased porosity, reduced mechanical strength, and even induces geological disasters such as water inrush and landslides. Therefore, long-term and effective monitoring of the dissolution rate and development trend of rock masses is crucial.

[0003] Current karst monitoring technologies suffer from the following problems: Traditional engineering geological surveys rely primarily on pre-construction drilling and geophysical exploration (such as ground-penetrating radar and high-density electrical resistivity tomography). While these methods can identify macroscopic geological structures, they only provide a static "snapshot" at a specific moment. During project operation, there is a lack of intuitive and quantitative continuous monitoring data on how the internal dissolution channels of the rock mass expand and how the microscopic morphology of the borehole walls evolves over time. Existing displacement gauges or stress gauges can only monitor macroscopic deformation after rock mass failure, exhibiting significant lag. Rock mass dissolution is a physical-chemical coupled process, but current monitoring is often fragmented: either monitoring groundwater levels and flow rates separately, or periodically collecting water samples manually to analyze chemical composition. This discrete monitoring approach cannot capture the dynamic response relationship between fluctuations in water chemical parameters (such as pH and conductivity) and the microscopic dissolution rate on the rock mass surface in real time, making it difficult to accurately distinguish whether the rock mass is in a quiescent period of "potential dissolution" or an active period of "intense dissolution" (high activity). Existing monitoring systems typically employ a fixed-frequency data acquisition pattern (such as once daily or once monthly). During the dry season when erosion develops slowly, high-frequency acquisition generates a large amount of redundant data, resulting in wasted storage and energy consumption. Conversely, during the window of opportunity when heavy rain or sudden changes in groundwater levels accelerate erosion, low-frequency acquisition is prone to missing crucial information about these changes, leading to untimely warnings. Summary of the Invention

[0004] The purpose of this invention is to provide a long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning includes a data acquisition module, a multi-parameter evaluation module, and an adaptive monitoring module. The data acquisition module connects to a micro-scanning probe, a multi-parameter sensor inside the borehole, and a geological database to acquire topographic geometric data, borehole environmental data, and rock mass geological data for each monitoring section of the borehole. These data are then categorized into topographic geometric datasets, borehole environmental datasets, and rock mass geological datasets. The multi-parameter evaluation module includes a potential evaluation unit, an activity evaluation unit, and a monitoring section association unit. The potential evaluation unit assesses the comprehensive sensitivity of each monitoring section to dissolution after the superposition of the inherent properties of the rock mass and the long-term hydrochemical environment, based on the rock mass geological dataset and the borehole environment dataset, and generates the corresponding dissolution potential index. The monitoring section association unit generates a hydraulically upstream and downstream associated monitoring section group based on the groundwater flow direction in the borehole environment dataset. The activity evaluation unit is set with a fixed-duration sliding window, and combines the morphological geometry dataset and the borehole environment dataset to evaluate the dynamic trend of dissolution within the associated monitoring section group, and generates the corresponding dissolution activity index. The adaptive monitoring module is set with a fixed range of potential threshold range and active threshold range. Combined with the dissolution potential index and dissolution activity index, it determines the potential level of each monitoring section and the risk level of the associated monitoring section group, outputs the evaluation results and triggers the corresponding adaptive monitoring strategy.

[0006] Furthermore, the rock mass geological dataset includes lithological data, mineral composition content, fracture density, and rock mass integrity coefficient for each monitoring section.

[0007] Furthermore, the in-well environment dataset includes groundwater pH, conductivity, redox potential, temperature, pressure, and flow rate for each monitoring section.

[0008] Furthermore, the topographic geometry dataset includes a 3D point cloud model of each monitoring segment generated by macro scanning, surface roughness, and volume change calculated by differential calculation of the multidimensional point cloud model.

[0009] Furthermore, the calculation process for the dissolution potential index includes the following steps: S11. Based on the rock mass geological dataset and borehole environment dataset, extract the lithological data, mineral composition content, fracture density, rock mass integrity coefficient, and long-term average hydrochemical parameters of the i-th monitoring section. S12. Based on the mineral composition content of the i-th monitoring section and combined with the standard dissolution reaction rate, the lithological solubility coefficient of the monitoring section is calculated by weighting. S13. Based on the fracture density and rock mass integrity coefficient of the i-th monitoring section, the structural fragmentation degree of the monitoring section is calculated by weighting. S14. Based on the long-term average groundwater flow velocity and pH fluctuation range of the i-th monitoring section, the hydraulic disturbance degree of the monitoring section is calculated by weighting. S15. Based on S12-S14, calculate the dissolution potential index of the i-th monitoring section using a weighted method.

[0010] Furthermore, the monitoring segment association process includes the following steps: S21. Based on the borehole environment dataset, obtain the real-time groundwater flow direction of each monitoring section within the borehole; S22. Select the monitoring section with the highest dissolution potential index among all current monitoring sections as the core monitoring section; S23. Based on the real-time groundwater flow direction, determine the core monitoring section and its adjacent monitoring sections upstream or downstream of the hydraulic system, and form a group of related monitoring sections.

[0011] Furthermore, the calculation process for the dissolution activity index includes the following steps: S31. Given that within the sliding window, a group of associated monitoring sections consisting of the core monitoring section A and its hydraulically downstream adjacent monitoring section B has been determined, all data of this group of associated monitoring sections within the sliding window are extracted based on the topographic geometry dataset and the borehole environment dataset. S32. For the sliding window, based on the topographic geometry dataset of the core monitoring section A and the adjacent downstream hydraulic monitoring section B, calculate the rate of change of the volume of its three-dimensional model to obtain the geometric change rate. S33. For the sliding window, based on the in-hole environment dataset of the core monitoring section A and the hydraulically downstream adjacent monitoring section B, calculate the rate of change of its key parameters of pH and conductivity to obtain the hydrochemical driving degree. S34. For the sliding window, calculate the average difference of hydrochemical parameters between the core monitoring section A and the adjacent downstream monitoring section B to obtain the correlation difference degree. S35. Based on S32-S34, calculate the dissolution activity index of the associated monitoring section group within the sliding window using a weighted method.

[0012] Furthermore, the potential level assessment and monitoring strategy adjustment process includes the following steps: Let P be the upper limit of the potential threshold range. max Let P be the lower limit of the potential threshold interval. min If the dissolution potential index of the i-th monitoring section is < P min This indicates that the lithology of the monitoring section is stable and the structure is intact, with a potential level of 1. The response strategy is to execute a low-frequency benchmark scan; if P min ≤ Dissolution potential index of the i-th monitoring section ≤ P max This indicates that the monitoring section has a risk of dissolution, with a potential level of 2. The response strategy is to perform a medium-frequency routine scan. If the dissolution potential index of the i-th monitoring section is greater than P... maxThis indicates that the monitoring section is a high-risk area with a potential level of 3, and the response strategy is to perform high-frequency key scanning.

[0013] Furthermore, the risk level assessment and monitoring strategy adjustment process includes the following steps: Let A be the upper limit of the activity threshold range. max Let A be the lower limit of the activity threshold range. min Within the sliding window, if the dissolution activity index of the associated monitoring section group is <A min This indicates that the current dissolution process is gradual, the risk level is 1, and the response strategy is to maintain the baseline scanning frequency determined by the potential level. If A min ≤Dissolution Activity Index of Associated Monitoring Section Group ≤A max This indicates that the dissolution process is becoming more active, with a risk level of 2. The response strategy is to temporarily increase the scanning frequency by one level and increase the sampling frequency of the in-hole environment sensor. If the dissolution activity index of the associated monitoring section group is > A max This indicates that the dissolution process has accelerated significantly, posing a sudden risk. The risk level is 3. The response strategy is to immediately activate the highest frequency emergency scanning mode, conduct continuous key monitoring of the associated monitoring section group, and send an early warning signal to the management platform.

[0014] Furthermore, for the entire borehole, when there are three consecutive monitoring sections with a potential level of 2 or above, or when the risk level of any associated monitoring section group reaches level 3, the adaptive monitoring module will automatically generate a comprehensive risk assessment report and recommend initiating geophysical exploration of a larger area of ​​the borehole or carrying out grouting reinforcement engineering intervention.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention utilizes a potential assessment unit to comprehensively analyze rock mass geological datasets (lithology, fracture density, integrity coefficient) and borehole environmental datasets to calculate the Dissolution Potential Index (DPI) for each monitoring section. This unit can quantitatively assess the inherent tendency and theoretical sensitivity of rock masses to dissolution under current geological and hydrological conditions. Its beneficial effect lies in providing a static risk benchmark for the system, enabling the monitoring system to distinguish between stable and easily dissolvable rock strata within the borehole. This provides an initial grading basis for subsequent adaptive monitoring strategies, avoiding the resource waste caused by uniform monitoring of all sections and ensuring that monitoring focuses on high-potential areas with obvious geological defects. This invention utilizes real-time groundwater flow direction data to identify core monitoring sections and their hydraulically adjacent upstream or downstream sections through monitoring section association units, constructing them into a hydraulically connected group of associated monitoring sections. This unit establishes the spatial logical relationship between monitoring points, transforming previously isolated single-point monitoring into chain-like monitoring along the water flow path. Its beneficial effect lies in providing a spatial analysis basis for subsequent activity assessment, enabling the system to track the migration path of dissolution products by comparing changes in upstream and downstream hydrochemical parameters. This logically verifies the location of the dissolution reaction, eliminates misjudgments caused by local environmental fluctuations (such as rainfall), and improves the accuracy of dissolution source location. This invention, through an activity assessment unit, calculates the Dissolution Activity Index (DAI) of a associated monitoring section group by combining dynamic changes in morphological geometric data (volume change rate) and pore water chemistry data acquired through micro-scanning within a set sliding time window. This unit achieves real-time quantification of the dynamic evolution rate of the dissolution process. Its beneficial effect lies in its ability to keenly capture the process of dissolution from slow quantitative change to rapid abrupt change, particularly identifying the accelerated dissolution trend caused by abrupt changes in the water environment (such as increased acidity) or rock mass structure destruction. This provides a dynamic trigger signal for the adaptive monitoring module, enabling the system to adjust the sampling frequency in a timely manner during periods of active dissolution, ensuring the integrity and timeliness of data during critical change periods. Attached Figure Description

[0016] Figure 1 This is a schematic diagram illustrating the execution of the core logic flow nodes of the overall system of the present invention; Figure 2 This is a rendering of a three-dimensional point cloud model of a certain phase of borehole monitoring section 3 (20-30m) in an example embodiment of the present invention; Figure 3 This is a long-term monitoring curve of pH, EC, temperature, and flow rate in an example borehole monitoring section 2 (10-20m) in a specific embodiment of the ZK101 invention. Figure 4 This is a schematic diagram of the system process execution of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Please see Figures 1 to 4 The present invention provides a technical solution: A long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning includes a data acquisition module, a multi-parameter evaluation module, and an adaptive monitoring module. The data acquisition module connects to a micro-scanning probe, a multi-parameter sensor inside the borehole, and a geological database to acquire topographic geometric data, borehole environmental data, and rock mass geological data for each monitoring section of the borehole. These data are then categorized into topographic geometric datasets, borehole environmental datasets, and rock mass geological datasets. The multi-parameter evaluation module includes a potential evaluation unit, an activity evaluation unit, and a monitoring section association unit. The potential evaluation unit assesses the comprehensive sensitivity of each monitoring section to dissolution after the superposition of the inherent properties of the rock mass and the long-term hydrochemical environment, based on the rock mass geological dataset and the borehole environment dataset, and generates the corresponding dissolution potential index. The monitoring section association unit generates a hydraulically upstream and downstream associated monitoring section group based on the groundwater flow direction in the borehole environment dataset. The activity evaluation unit is set with a fixed-duration sliding window, and combines the morphological geometry dataset and the borehole environment dataset to evaluate the dynamic trend of dissolution within the associated monitoring section group, and generates the corresponding dissolution activity index. The adaptive monitoring module is set with a fixed range of potential threshold range and active threshold range. Combined with the dissolution potential index and dissolution activity index, it determines the potential level of each monitoring section and the risk level of the associated monitoring section group, outputs the evaluation results and triggers the corresponding adaptive monitoring strategy.

[0020] The specific application scenario is in Southwest China, particularly in Guizhou, Guangxi, and Yunnan provinces, which are typical karst landform areas. This region has active surface and groundwater circulation, and intense karst development, resulting in numerous geological structures such as caves, solution channels, and fissures. Constructing highway tunnels under such complex geological conditions presents significant challenges in both construction and operation due to karst problems. Karst tunnel construction often encounters disasters such as water inrush, mudslides, and collapses, while operation faces risks of tunnel structural instability, lining cracking and deformation, and even localized collapses due to long-term rock erosion.

[0021] For example, a highway traverses the Wumeng Mountains, where karst development is deep and complex. The tunnel project, from K15+200 to K16+500, crosses typical carbonate rock strata, containing numerous water-filled caves and dissolution fracture zones. Under the long-term influence of groundwater, these geologically weak zones experience continuous rock dissolution, posing a potential threat to the tunnel's structural safety. Traditional geological exploration methods (drilling and geophysical exploration) can only provide static geological information before construction. Long-term stability monitoring after construction relies on manual inspections, visual observation, or simple displacement monitoring. These methods are often lagging, subjective, and difficult to quantify, failing to effectively capture the dynamic changes in dissolution within the rock mass.

[0022] This embodiment focuses on a typical monitoring borehole (named ZK101) at location K16+050 of a highway tunnel in the Wumeng Mountains. It details the system's deployment, data acquisition, analysis and evaluation, and strategy adjustment process in this borehole. Specifically, it includes the following steps: In the karst development section from K15+200 to K16+500, a total of 15 long-term monitoring boreholes were installed in a grid pattern, spaced approximately 50 meters apart. These boreholes ranged in depth from 30 to 100 meters, penetrating the potential karst development zone above and to the sides of the tunnel. This monitoring system was installed in each borehole.

[0023] Micro-scanning probe: A customized high-resolution laser (or structured light) scanner for boreholes, capable of underwater operation, and able to acquire three-dimensional topographic data of the borehole wall.

[0024] In-hole multi-parameter sensor array: integrates pH meter, conductivity (EC) sensor, oxidation-reduction potential (ORP) sensor, temperature sensor, pressure sensor, micro flow meter, etc., encapsulated in pressure-resistant and corrosion-resistant probes and deployed along the borehole depth.

[0025] This embodiment will focus on one representative borehole: ZK101, located at K16+050, with a vertical depth of 60 meters, penetrating the overlying rock mass above the tunnel. The strata revealed by borehole ZK101, from top to bottom, are mainly: 0-5m: Quaternary residual slope deposits, mainly clay containing gravel.

[0026] 5-25m: Lower Carboniferous Maping Formation limestone, in medium to thick layers, locally interbedded with thin layers of argillaceous limestone, with well-developed fissures filled with calcite.

[0027] 25-50m: Lower Carboniferous Duyun Formation limestone, in the form of thick blocks, with relatively good rock mass integrity, but dissolution fissures and small caves are visible.

[0028] 50-60m: Lower Carboniferous Duyun Formation dolomite, thick-layered, with well-developed joints and localized weak dissolution.

[0029] The groundwater level remains stable at a depth of 15 meters year-round. Groundwater flows within the borehole, roughly parallel to the tunnel axis. Based on geological stratification and karst development characteristics, borehole ZK101 was divided into 6 monitoring sections, each 10 meters long. Monitoring section 1 (0-10m): The boundary between the clay layer and the upper limestone.

[0030] Monitoring section 2 (10-20m): Maping Formation limestone, below the groundwater level, with well-developed fissures.

[0031] Monitoring section 3 (20-30m): Maping Formation limestone, with relatively well-developed dissolution fissures.

[0032] Monitoring section 4 (30-40m): Duyun Formation limestone, the rock mass is relatively intact, but there are small karst caves.

[0033] Monitoring section 5 (40-50m): Duyun Formation limestone, the rock mass is intact and the dissolution phenomenon is not obvious.

[0034] Monitoring section 6 (50-60m): Duyun Formation dolomite, with well-developed joints and weak dissolution.

[0035] Each monitoring section is equipped with a micro-scanning probe (liftable) and an array of multi-parameter sensors inside the borehole.

[0036] The micro-scanning probe operates on the principle of laser triangulation, acquiring 3D point cloud data of the borehole wall by rotating the scanning head. The accuracy is 0.1mm at a scanning distance of 100mm, and the scanning speed is 100,000 points / second. The initial scanning frequency is set to a full-hole scan once a month. Based on instructions from the adaptive monitoring module, this can be adjusted to a weekly or daily scan of a specific monitoring section. The probe descends at a constant speed, pausing every 10 meters (one monitoring section) to perform a 360-degree surround scan, acquiring the high-density point cloud of that monitoring section.

[0037] The topographic geometry dataset includes a 3D point cloud model of each monitoring segment generated by macro scanning, surface roughness, and volume change calculated by multi-period point cloud model difference.

[0038] Raw point cloud data is stored in PCD or LAS format. A separate point cloud file is generated for each monitoring segment and each scanning cycle. It is stored in the object storage service of the cloud platform and organized by borehole ID, monitoring segment ID, and timestamp. Please refer to [link / reference]. Figure 2 , Figure 2 A three-dimensional point cloud model of section 3 (20-30m) of borehole monitoring in ZK101 was depicted after a certain scan (June 1, 2023). Figure 2 The distribution and geometry of dissolution pits and fissures on the borehole wall are visible. Fine registration, filtering, and surface reconstruction (using Poisson reconstruction or moving least squares method) are performed on the original point cloud to generate a high-precision 3D mesh model.

[0039] For each monitoring section's 3D mesh model, its surface roughness is calculated using a standard algorithm (setting the root mean square roughness Rq and the arithmetic mean roughness Ra). The specific formula is: ; in The height of the surface point. The average height is N, and N represents the total number of surface data points in each monitoring section.

[0040] The calculation of volume change is set up using a multi-period point cloud model difference method. The same monitoring segment is scanned at times t1 and t2, resulting in three-dimensional mesh models M1 and M2, respectively. M1 and M2 are then finely aligned. The volume increase or decrease of M2 relative to M1 is calculated by determining the spatial difference between the two mesh models, for example, using M1 as a reference. This can be achieved by calculating the distance between the two model surfaces and integrating the volume difference. The specific algorithm uses Boolean operations (using the VTK library) or calculations based on the nearest point distance within the mesh. Example: During the period from June 1, 2023 to July 1, 2023, point cloud difference calculations in monitoring segment 3 showed an increase of 5.2 cm³ in the volume of the solution pits. The in-well environmental dataset includes groundwater pH, electrical conductivity (EC), redox potential (ORP), temperature, pressure, and flow rate for each monitoring section.

[0041] The process of constructing the in-well environmental dataset involved deploying a set of sensor probes at the midpoint of each monitoring section (10 meters). Specifically, these included: a pH sensor (measurement range 0-14 pH, accuracy ±0.02 pH); an electrical conductivity (EC) sensor (measurement range 0-200 mS / cm, accuracy ±1% FS); a redox potential (ORP) sensor (measurement range -1500 mV to +1500 mV, accuracy ±2 mV); a temperature sensor (measurement range -20℃ to +80℃, accuracy ±0.1℃); a pressure sensor (measurement range 0-10 MPa, accuracy ±0.05% FS); and a microflow meter (measurement range 0.01-1 m / s, accuracy ±2% FS). Initially, data was collected once per hour. Based on instructions from the adaptive monitoring module, this was adjusted to once every 10 minutes (medium risk) or once per minute (high risk). Please refer to [reference needed]. Figure 3 , Figure 3 The graph shows the pH, EC, temperature, and flow rate changes in monitoring section 2 (10-20m) of borehole ZK101 from June 1, 2023 to September 30, 2023. The graph shows that the pH fluctuated between 7.5 and 8.2, the EC ranged from 300 to 450 μS / cm, and the flow rate showed a significant peak due to rainfall.

[0042] The rock mass geology dataset includes lithological data, mineral composition, fracture density, and rock mass integrity coefficient for each monitoring section. Example data: Lithology, mineral composition, fracture density, and integrity coefficient for each monitoring section of ZK101, as shown in Table 1.

[0043] Table 1: Example table of rock mass geological data sets for each monitoring section of ZK101

[0044] RQD is defined as the ratio, expressed as a percentage, of the sum of the lengths of hard core segments greater than or equal to 10 cm in a single drilling run to the total drilling length of that run. The specific formula is as follows: ; in, This represents the total length of all core segments with a length greater than or equal to 10 centimeters during a single drilling operation. This represents the total drilling length for that run. In Table 1, taking the ZK101 third section (depth 20-30m) as an example, the 50 indicates that only 50% of the rock mass in that section is relatively intact, while the remaining 50% is in a fractured or extremely jointed state. This corroborates the high "fracture density (10 fractures / m)" of that section in the table.

[0045] High RQD (set to 90-100) indicates an intact rock mass, making it difficult for water to penetrate, with dissolution mainly occurring on the surface, and a low DPI (dissolution potential index). Low RQD (set to <50) indicates a fractured rock mass, where groundwater can penetrate into the internal network of the rock mass, and due to the exponential increase in contact area, the DPI (dissolution potential index) is extremely high.

[0046] The multi-parameter assessment module includes a potential assessment unit, which evaluates the likelihood and susceptibility of dissolution in the rock mass based on its inherent properties and long-term hydrochemical environment. The specific steps for constructing the dissolution potential index are as follows: S11. Based on the rock mass geology dataset and borehole environment dataset, extract the lithology data, mineral composition content, fracture density, rock mass integrity coefficient, and long-term average hydrochemical parameters for the i-th monitoring section. Specifically, obtain the lithology, mineral composition, fracture density, and RQD values ​​for each monitoring section of ZK101 from the relational database (set in Table 1). Extract the average values ​​and fluctuation ranges of pH, EC, ORP, temperature, pressure, and flow velocity for each monitoring section over the past year (e.g., June 1, 2022 to May 31, 2023) from the time-series database. Calculate the long-term average pH value of monitoring section 2 as 7.8, average EC as 380 μS / cm, average flow velocity as 0.05 m / s, and pH fluctuation range as 0.7.

[0047] S12. Based on the mineral composition content (setting carbonate rock content) of the i-th monitoring section and combined with the standard dissolution reaction rate, the lithological solubility coefficient of the monitoring section is calculated using a weighted method and denoted as LDC. i The specific formula is as follows: ; Among them, C ij R represents the content (%) of the j-th soluble mineral in the i-th monitoring segment.j is the relative dissolution rate coefficient of the j-th mineral, M represents the number of major soluble mineral types in the monitoring section, and WLj is the weight coefficient of the j-th mineral in the LDC calculation, as shown in Table 2.

[0048] Table 2: Examples of mineral component weighting coefficients:

[0049] Example calculation (ZK101 monitoring section 3): Lithology: limestone, mineral composition: calcite 95%, quartz 2%, clay 3%; LDC3=(0.95×1.0×0.6)+(0.02×0.01×0)+(0.03×0.05×0)=0.57; S13. Based on the fracture density and rock mass integrity coefficient of the i-th monitoring section, the structural fragmentation degree of the monitoring section is calculated using a weighted method and denoted as SFI. i The principle is that the greater the fracture density and the worse the rock mass integrity, the larger the contact area between the rock mass and groundwater, and the easier it is for dissolution to occur. Specifically, SFI... i The calculation formula is: ; Among them, D i The fracture density (fractures / meter) of the i-th monitoring segment needs to be normalized (e.g., if the maximum fracture density is 15 fractures / meter, then the normalization factor is 1 / 15), RQD i W represents the rock mass integrity coefficient (%) for the i-th monitoring section. SD W is the weighting factor for fracture density in the SFI calculation (set to 0.6). SR Set the weighting factor of RQD in the SFI calculation (set to 0.4). Example calculation (ZK101 monitoring section 3): fracture density: 15 fractures / meter, RQD: 50%. Normalization factor is 1 / 15. SFI3=((10 / 15)×0.6)+((1−50 / 100)×0.4)=0.600; S14. Based on the long-term average groundwater flow velocity and pH fluctuation range of the i-th monitoring section, the hydraulic disturbance degree of the monitoring section is calculated using a weighted method and denoted as HDD. i The principle is that the faster the flow rate and the greater the pH fluctuation, the more favorable it is for the dissolution reaction to proceed and for the dissolution products to be carried away. Specifically, for HDDs... i The calculation formula is: ; Among them, V avg,i Let be the long-term average groundwater flow velocity (m / s) of the i-th monitoring section, which is the normalized value. ΔpH idenoted as W, represents the long-term pH fluctuation range (maximum pH - minimum pH) for the i-th monitoring segment; it is a normalized value. HV W is the weighting factor for flow rate in HDD calculations (set to 0.5). HP The weighting factor for pH fluctuation range in HDD calculation (set to 0.5). Example calculation (ZK101 monitoring section 3): Long-term average flow velocity: 0.08 m / s (normalized to 1 / 0.2 m / s). Long-term pH fluctuation range: 1.2 (normalized to 1 / 2.0).

[0050] HDD3=((0.08 / 0.2)×0.5)+((1.2 / 2.0)×0.5)=(0.4×0.5)+(0.6×0.5)=0.20+0.30=0.50; S15. Based on S12-S14, calculate the dissolution potential index (DPI) of the i-th monitoring section using a weighted method. i . Specific DPI i The calculation formula is: ; Among them, W L W S W H These are the weighting coefficients for lithological solubility coefficient, structural fragmentation degree, and hydraulic disturbance degree, respectively, which are collectively set to 1 and denoted as W. L =0.4; W S =0.3; W H =0.3; The section association unit is used to identify sections with close hydraulic connections within the borehole, construct associated section groups, and provide a basis for activity assessment.

[0051] The monitoring section association process includes the following steps: S21. Based on the borehole environment dataset, obtain the real-time groundwater flow direction of each monitoring section within the borehole; Real-time flow velocity data for a given section is acquired, assuming the groundwater flow direction within the borehole is unidirectional (from top to bottom or bottom to top). The relative readings of multiple flow velocity meters and the water pressure difference are used to assist in the determination. For example, if the flow velocity meter reading in the upper section is consistently higher than that in the lower section, and the water pressure decreases gradually, the flow direction is determined to be from top to bottom. In karst regions, there may be localized complex flow directions, and the system will perform a more detailed flow field simulation (based on water pressure, velocity, and porosity data). This embodiment assumes the flow direction is from top to bottom. S22. Select the monitoring section with the highest dissolution potential index among all current monitoring sections as the core monitoring section; Periodically (e.g., weekly), identify the segment with the highest current DPI value from the calculated DPI values ​​as the core monitoring segment; S23. Based on the real-time groundwater flow direction, determine the core monitoring section and its adjacent monitoring sections upstream or downstream of the hydraulic system, and form a group of related monitoring sections.

[0052] The specific logic for determining this is as follows: if the flow direction is from top to bottom, then the downstream adjacent segment of core monitoring segment A is segment B. If the flow direction is from bottom to top, then the upstream adjacent segment of core monitoring segment A is another segment.

[0053] Example: Core monitoring section A: ZK101 section 3 (depth 20-30m). Groundwater flow direction is from top to bottom. Hydraulically downstream adjacent monitoring section B: ZK101 section 4 (depth 30-40m). The resulting associated section group is (ZK101 section 3, ZK101 section 4).

[0054] The activity assessment unit is used to evaluate the activity level of the dissolution process by analyzing the dynamic changes in morphological geometry data and pore environment data within a specific time window, and to calculate the dissolution activity index, denoted as DAI. The dissolution activity index calculation process includes the following steps: S31. Given that within the sliding window, a group of associated monitoring sections consisting of the core monitoring section A and its hydraulically downstream adjacent monitoring section B has been determined, all data of this group of associated monitoring sections within the sliding window are extracted based on the topographic geometry dataset and the borehole environment dataset. The fixed time period is set to 30 days. This means that DAI calculation is based on data from the past 30 days. The system calculates DAI every 24 hours.

[0055] S32. For the sliding window, based on the morphological geometry datasets of the core monitoring section A and the adjacent downstream hydraulic monitoring section B, calculate the rate of volume change of its three-dimensional model to obtain the geometric change rate, denoted as GCR; extract the following data from the associated section group (ZK101 section 3, ZK101 section 4) over the past 30 days: Morphological geometry dataset: all micro-scan point cloud data and their calculated volume change and surface roughness. In-pore environment dataset: all pH, EC, ORP, temperature, pressure, and flow rate data.

[0056] The formula for the geometric rate of change (GCR) is: ; Where, ΔV A ,ΔV B These are the volume changes in the core monitoring section A and the adjacent downstream hydraulic monitoring section B within a sliding window (e.g., 30 days), respectively (usually negative values, indicating dissolution and loss; the absolute value represents the magnitude). Indicates the duration of the sliding window; (⋅) norm This indicates normalization, which maps numerical values ​​to the [0,1] interval; S33. For the sliding window, based on the in-hole environment datasets of the core monitoring section A and the adjacent downstream hydraulic monitoring section B, calculate the rate of change of key parameters such as pH and conductivity to obtain the hydrochemical driving degree. Specifically, hydrochemical drive reflects the drastic changes in groundwater chemical properties during the sliding window period, which are often precursors or consequences of accelerated dissolution. The calculation steps are as follows: For each segment in the associated monitoring segment group (core segment A and the adjacent downstream hydraulic monitoring segment B), its sliding window time Δt is extracted. window Time series data of pH and electrical conductivity (EC) within the cell.

[0057] The rate of change of pH over time was calculated using linear regression or the difference method. and the rate of change of conductivity over time .

[0058] The rate of change of the two sections is averaged and then weighted and normalized to obtain the hydrochemical driving force, denoted as . The calculation formula is as follows: ; Among them, |ΔpH k | represents the maximum pH fluctuation range within segment k of the sliding window; |ΔEC k | represents the maximum fluctuation range of conductivity in segment k within the sliding window; α1 and α2 are weighting coefficients, and α1 + α2 = 1 (preferred values ​​are α1 = 0.6, α2 = 0.4, because pH changes are more sensitive to carbonate rock dissolution). Hydrochemical driving force reflects the severity of fluctuations in the groundwater environment over time; for example, a sudden acid rain can cause a rapid drop in pH in that segment within a short period. This "temporal abrupt change" means that the driving force of dissolution has increased, and the risk has already increased even if the rock has not yet been dissolved. S34. For the sliding window, calculate the mean difference in hydrochemical parameters (set pH value, specific ion concentration) between the core monitoring section A and the adjacent downstream monitoring section B to obtain the correlation difference degree, denoted as SAD; this step aims to quantify the hydrochemical gradient between upstream and downstream sections. During strong dissolution, the chemical composition (such as Ca²⁺ concentration, pH value) of the water flow will change significantly after passing through the dissolution zone.

[0059] Calculate the downstream adjacent monitoring section B and the core monitoring section A. Since A is upstream of B, calculate the difference in pH value and specific ion concentration between them at time t. : ; ; The arithmetic mean method is used to sum and average the differences of all sampling points within the sliding window, followed by normalization to obtain the correlation dissimilarity (SAD). ; in, Represents the sliding window time Δt window The total number of data sampling points within the range, α3 and α4 are weighting coefficients, and α3+α4=1 (preferred values ​​are α3=0.5, α4=0.5). The correlation difference (SAD) reflects the spatial changes in water quality as groundwater flows from upstream (section A) to downstream (section B). It indicates how much rock composition is carried away by the water flowing from A to B. For example, if the pH is 6.5 when the water enters section A and becomes 7.0 when it exits section B, with an increased calcium ion concentration, this spatial difference directly proves that a chemical reaction (dissolution) occurred between A and B, consuming hydrogen ions and generating calcium ions.

[0060] S35. Based on S32-S34, calculate the dissolution activity index of the associated monitoring section group within the sliding window using a weighted method, and denot it as DAI.

[0061] ; Among them, W X W Y W Z The weighting coefficients for geometric change rate, hydrochemical driving force, and correlation difference are respectively set to 1. Considering that volume change from micro-scanning is the most direct evidence of dissolution, it is given the highest weight. Let W be... X =0.5; Water chemical parameters are relatively sensitive to changes but contain noise, let W Y =0.3; Association difference auxiliary verification, let W = 0.3; Z =0.2.

[0062] The adaptive monitoring module is set with a fixed range of potential threshold range and active threshold range. Combined with the dissolution potential index and dissolution activity index, it determines the potential level of each monitoring section and the risk level of the associated monitoring section group, outputs the evaluation results and triggers the corresponding adaptive monitoring strategy.

[0063] Figure 1 The left side shows the working scenario of the data acquisition module, where a macro scanning probe is placed inside the borehole to acquire morphological geometric datasets (such as 3D point clouds and volume changes of the borehole wall) of various monitoring sections through laser scanning technology. Simultaneously, it combines this with in-hole sensors to acquire borehole environmental datasets (such as groundwater pH value and flow velocity) and rock mass geological datasets. Secondly, Figure 1The flowchart indicated by the arrow represents the data processing and decision-making process: The multi-parameter evaluation module receives the above data, calculates the dissolution potential index through its internal potential evaluation unit, calculates the dissolution activity index by combining the geometric and environmental changes within the sliding window through the activity evaluation unit, and constructs the hydraulic upstream and downstream correlation through the monitoring section correlation unit; finally, the adaptive monitoring module comprehensively judges the risk level of each monitoring section based on the set potential threshold and activity threshold, and feeds back instructions to dynamically adjust the probe scanning frequency (such as switching from low-frequency reference scanning to high-frequency key scanning), thereby realizing a closed-loop adaptive monitoring strategy.

[0064] The potential level assessment and monitoring strategy adjustment process includes the following steps: Let P be the upper limit of the potential threshold range. max Let P be the lower limit of the potential threshold interval. min If the dissolution potential index of the i-th monitoring section is < P min This indicates that the lithology of the monitoring section is stable and the structure is intact, with a potential level of 1 (low potential). The response strategy is to perform a low-frequency (set to once a month) baseline scan; if P min ≤ Dissolution potential index of the i-th monitoring section ≤ P max This indicates that the monitoring section has a risk of dissolution, with a potential level of 2 (medium potential). The response strategy is to perform a medium-frequency (set to once a week) routine scan; if the dissolution potential index of the i-th monitoring section is greater than P... max This indicates that the monitoring section is a high-risk area with a potential level of 3 (high potential), and the response strategy is to perform high-frequency (set to once a day) key scanning.

[0065] The risk level assessment and monitoring strategy adjustment process includes the following steps: The adaptive monitoring module is set with a fixed range of potential threshold intervals [P] min ,P max ] and activity threshold range [A min A max Then, by combining the previously calculated dissolution potential index (DPI) and dissolution activity index (DAI), the potential level of each monitoring section and the risk level of the associated monitoring section group are determined, the evaluation results are output, and the corresponding adaptive monitoring strategy is triggered.

[0066] The process for potential level assessment and monitoring strategy adjustment is set as follows: Let P be the upper limit of the potential threshold range. max (Set as 0.7), the lower limit is denoted as P. min (For example, set to 0.3), the potential level is determined as follows: Low potential (Level 1): If the dissolution potential index of the i-th monitoring section is < P minThis indicates that the lithology of the monitored section is stable (e.g., intact dolomite) and dense, making it resistant to dissolution. A low-frequency reference scanning mode is executed. The micro-scanning probe is set to scan this section once a month, while the borehole environment sensor sampling frequency is maintained at once per hour to save system energy and data storage space.

[0067] Medium potential (level 2): ​​If P min ≤ Dissolution potential index of the i-th monitoring section ≤ P max This indicates that the monitored section has certain geological defects (such as limestone with well-developed fissures) and a moderate risk of dissolution. A medium-frequency conventional scanning mode is executed. The system automatically increases the micro-scanning frequency of this section to once a week, focusing on changes in the morphology of the fissure surfaces.

[0068] High potential (Level 3): If the dissolution potential index of the i-th monitoring section is greater than P max This indicates that the monitored section is located in a geologically highly erosive environment (such as a fractured zone or pure limestone section), making it a high-risk area. A high-frequency, focused scanning mode is then implemented. The system designates this section as a "key monitoring target," increasing the micro-scanning frequency to once daily and automatically activating the probe's high-precision mode (doubling the point cloud density) to capture even minute erosion progress.

[0069] The risk level assessment and monitoring strategy adjustment process is set as follows: The upper limit of the activity threshold range is denoted as A. max (Set to 0.6), the lower limit is denoted as A. min (Set to 0.2). The risk level results are as follows: Low risk (Level 1): For the sliding window, if the dissolution activity index of the associated monitoring section group is <A min This indicates that although there may be potential for dissolution, the actual dissolution process is gradual, the hydrochemical environment is stable, and the geometric volume shows no significant change. The baseline scanning frequency determined by the potential level is maintained without any additional intervention.

[0070] Medium risk (Level 2): ​​If A min ≤Dissolution Activity Index of Associated Monitoring Section Group ≤A max This indicates that the dissolution process is becoming more active, with a significant volume loss rate or strong hydrochemical fluctuations detected. "Alert Mode" is triggered. The micro-scanning frequency of the associated monitoring section group is temporarily increased by one level (e.g., from once a month to once a week, or from once a week to once a day). The sampling frequency of the in-well environment sensor is increased to once every 10 minutes to obtain more detailed hydrochemical change curves.

[0071] High risk (Level 3): If DAI > A maxThis indicates a significant acceleration in the dissolution process, potentially indicating sudden dissolution damage or channel breakthrough, posing a safety hazard to the project. Immediately activate the "Emergency Monitoring Mode." Conduct continuous, focused monitoring of the associated monitoring sections, with the micro-scanning probe performing real-time online scanning (or the highest frequency allowed by the system, set to once every 4 hours); adjust the sampling frequency of the borehole environment sensor to once every 1 minute. Immediately generate a red alert signal, push it to the cloud management platform via wireless network, and notify the project safety manager via SMS / email.

[0072] Comprehensive decision-making and intervention recommendations from the adaptive monitoring module: The adaptive monitoring module not only makes adjustments for a single section or a single associated group, but also has the ability to make comprehensive judgments for the entire borehole.

[0073] When the system detects that three consecutive monitoring sections (e.g., sections 2, 3, and 4) in the entire borehole all have a potential level of 2 or higher, or that any group of related monitoring sections has a risk level of 3 (high risk), a first report is generated. The first report includes a three-dimensional morphological comparison map of the risk section, a dissolution rate curve, a record of hydrochemical anomalies, and the basis for determining the risk level.

[0074] The system automatically provides specific engineering recommendations in the report, such as: "It is recommended to immediately initiate trans-hole resistivity tomography (ERT) and microseismic monitoring within a 20-meter radius around the borehole to determine the extent of the hidden karst cave; it is recommended to carry out grouting reinforcement treatment on the 30m-40m depth section and continuously monitor the restoration of rock mass integrity after grouting." The technical principle of this invention is as follows: The system utilizes a liftable micro-scanning probe and employs laser triangulation to acquire high-density three-dimensional point cloud data of the borehole wall. Through fine registration and differential algorithm (S32) of multi-period point cloud data, the volume change (GCR) of the rock mass surface is directly calculated. This process transforms the invisible micro-dissolution pits and fissure expansion in traditional monitoring into quantifiable geometric indicators, intuitively reflecting "how much rock has been lost" from a physical perspective. This system overcomes the limitations of single-parameter monitoring and innovatively introduces dual evaluation indicators: dissolution potential index and dissolution activity index. Dissolution Potential Index (DPI) probability is used to determine the baseline monitoring frequency (S11-S15).

[0075] Dynamic trend of Dissolution Activity Index (DAI). The system integrates information from three dimensions by calculating data from a group of monitored sections (upstream section A and downstream section B): Geometric rate of change (GCR) characterizes physical results (volume loss). Hydrochemical drive degree (HDD) characterizes environmental abrupt changes over time (such as acid rain causing a sudden drop in pH). The correlation difference degree (SAD) characterizes the chemical reaction gradient in the spatial dimension (an increase in ion concentration from upstream to downstream indicates that dissolution and consumption have occurred). By weighted fusion of these three dimensions, the DAI can accurately determine whether the dissolution process is in a "dormant" or "explosive" state.

[0076] The system determines the baseline scanning frequency based on DPI (low / medium / high potential corresponding to monthly / weekly / daily) to establish a routine monitoring rhythm; at the same time, it adjusts the temporary frequency in real time based on DAI (low / medium / high risk corresponding to maintain / elevate by one level / emergency real-time). This dual-track strategy constitutes the adaptive core of the system.

[0077] Compared with existing technologies, this invention, through micro-scanning and point cloud differential technology, can identify early dissolution pits and fracture propagation on the borehole wall with millimeter-level precision. This ability to "see the big picture from small details" allows engineers to take intervention measures such as grouting before substantial damage to the rock mass occurs, truly achieving "prevention before the event." This invention establishes the hydraulic connection between upstream and downstream sections by calculating the correlation units (S21-S23) and the correlation difference degree (SAD) (S34) of the monitoring section. It not only tells users "the rock is dissolving," but also explains "why dissolution is happening" (e.g., whether it's due to upstream acidic water intrusion or increased flow velocity) through the correlation difference degree (SAD) of the ion concentration difference between upstream and downstream and the hydrochemical driving degree (HDD) of environmental fluctuation. This mechanistic explanation provides a clear target basis for engineering remediation. Given the uneven development and sudden nature of dissolution in karst areas, fixed-frequency monitoring often falls into the dilemma of "redundant data in normal times and missing data in emergencies." The adaptive monitoring module of this invention can automatically adjust the sampling frequency according to the rock mass potential and real-time activity: it hibernates during the stable period of the rock mass (low DPI, low DAI) to save energy consumption and storage space; during the sudden dissolution period (high DAI), it instantly switches to high-frequency or even real-time monitoring mode (S35 and subsequent strategies). This not only ensures the integrity of data during critical window periods, but also effectively extends the maintenance cycle of field equipment, making it especially suitable for monitoring in remote mountainous areas with inconvenient transportation.

[0078] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization. The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning, characterized in that, It includes a data acquisition module, a multi-parameter evaluation module, and an adaptive monitoring module; The data acquisition module connects to a micro-scanning probe, a multi-parameter sensor inside the borehole, and a geological database to acquire topographic geometric data, borehole environmental data, and rock mass geological data for each monitoring section of the borehole. These data are then categorized into topographic geometric datasets, borehole environmental datasets, and rock mass geological datasets. The multi-parameter evaluation module includes a potential evaluation unit, an activity evaluation unit, and a monitoring section association unit. The potential evaluation unit evaluates the comprehensive sensitivity of each monitoring section to dissolution after the superposition of the inherent properties of the rock mass and the long-term hydrochemical environment, based on the rock mass geological dataset and the borehole environment dataset, and generates the corresponding dissolution potential index. The monitoring section association unit generates a hydraulically upstream and downstream associated monitoring section group based on the groundwater flow direction in the borehole environment dataset. The activity assessment unit is equipped with a sliding window of fixed duration. It combines the morphological geometry dataset and the pore environment dataset to assess the dynamic trend of dissolution within the associated monitoring section group and generate the corresponding dissolution activity index. The adaptive monitoring module is set with a fixed range of potential threshold range and active threshold range. Combined with the dissolution potential index and dissolution activity index, it determines the potential level of each monitoring section and the risk level of the associated monitoring section group, outputs the evaluation results and triggers the corresponding adaptive monitoring strategy.

2. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The rock mass geological dataset includes lithological data, mineral composition content, fracture density, and rock mass integrity coefficient for each monitoring section.

3. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The borehole environment dataset includes groundwater pH, conductivity, redox potential, temperature, pressure, and flow rate for each monitoring section.

4. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The topographic geometry dataset includes a 3D point cloud model of each monitoring segment generated by macro scanning, surface roughness, and volume change calculated by multi-period point cloud model difference.

5. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The calculation process for the dissolution potential index includes the following steps: S11. Based on the rock mass geological dataset and borehole environment dataset, extract the lithological data, mineral composition content, fracture density, rock mass integrity coefficient, and long-term average hydrochemical parameters of the i-th monitoring section. S12. Based on the mineral composition content of the i-th monitoring section and combined with the standard dissolution reaction rate, the lithological solubility coefficient of the monitoring section is calculated by weighting. S13. Based on the fracture density and rock mass integrity coefficient of the i-th monitoring section, the structural fragmentation degree of the monitoring section is calculated by weighting. S14. Based on the long-term average groundwater flow velocity and pH fluctuation range of the i-th monitoring section, the hydraulic disturbance degree of the monitoring section is calculated by weighting. S15. Based on S12-S14, calculate the dissolution potential index of the i-th monitoring section using a weighted method.

6. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The monitoring section association process includes the following steps: S21. Based on the borehole environment dataset, obtain the real-time groundwater flow direction of each monitoring section within the borehole; S22. Select the monitoring section with the highest dissolution potential index among all current monitoring sections as the core monitoring section; S23. Based on the real-time groundwater flow direction, determine the core monitoring section and its adjacent monitoring sections upstream or downstream of the hydraulic system, and form a group of related monitoring sections.

7. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The calculation process for the dissolution activity index includes the following steps: S31. Given that within the sliding window, a group of associated monitoring sections consisting of the core monitoring section A and its hydraulically downstream adjacent monitoring section B has been determined, all data of this group of associated monitoring sections within the sliding window are extracted based on the topographic geometry dataset and the borehole environment dataset. S32. For the sliding window, based on the topographic geometry dataset of the core monitoring section A and the adjacent downstream hydraulic monitoring section B, calculate the rate of change of the volume of its three-dimensional model to obtain the geometric change rate. S33. For the sliding window, based on the in-hole environment dataset of the core monitoring section A and the hydraulically downstream adjacent monitoring section B, calculate the rate of change of its key parameters of pH and conductivity to obtain the hydrochemical driving degree. S34. For the sliding window, calculate the average difference of hydrochemical parameters between the core monitoring section A and the adjacent downstream monitoring section B to obtain the correlation difference degree. S35. Based on S32-S34, calculate the dissolution activity index of the associated monitoring section group within the sliding window using a weighted method.

8. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The potential level assessment and monitoring strategy adjustment process includes the following steps: Let P be the upper limit of the potential threshold range. max Let P be the lower limit of the potential threshold interval. min If the dissolution potential index of the i-th monitoring section is < P min This indicates that the lithology of the monitoring section is stable and the structure is intact, with a potential level of 1. The response strategy is to execute a low-frequency benchmark scan; if P min ≤ Dissolution potential index of the i-th monitoring section ≤ P max This indicates that the monitoring section has a risk of dissolution, with a potential level of 2. The response strategy is to perform a medium-frequency routine scan. If the dissolution potential index of the i-th monitoring section is greater than P... max This indicates that the monitoring section is a high-risk area with a potential level of 3, and the response strategy is to perform high-frequency key scanning.

9. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The risk level assessment and monitoring strategy adjustment process includes the following steps: Let A be the upper limit of the activity threshold range. max Let A be the lower limit of the activity threshold range. min Within the sliding window, if the dissolution activity index of the associated monitoring section group is <A min This indicates that the current dissolution process is gradual, the risk level is 1, and the response strategy is to maintain the baseline scanning frequency determined by the potential level. If A min ≤Dissolution Activity Index of Associated Monitoring Section Group ≤A max This indicates that the dissolution process is becoming more active, with a risk level of 2. The response strategy is to temporarily increase the scanning frequency by one level and increase the sampling frequency of the in-hole environment sensor. If the dissolution activity index of the associated monitoring section group is > A max This indicates that the dissolution process has accelerated significantly, posing a sudden risk. The risk level is 3. The response strategy is to immediately activate the highest frequency emergency scanning mode, conduct continuous key monitoring of the associated monitoring section group, and send an early warning signal to the management platform.

10. The long-term monitoring system for in-situ rock dissolution rate in boreholes based on micro-scanning according to claim 1, characterized in that: The adaptive monitoring module targets the entire borehole. When three consecutive monitoring sections have a potential level of 2 or above, or when the risk level of any associated monitoring section group reaches level 3, the system will automatically generate a comprehensive risk assessment report and recommend initiating geophysical exploration of a wider area of ​​the borehole or carrying out grouting reinforcement engineering intervention.

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