Geological drilling-based stratum deformation monitoring data processing method and system

Abstract

The application discloses a kind of stratum deformation monitoring data processing method and system based on geological drilling, it is related to data processing technical field, method includes: obtaining geological drilling, obtaining monitoring node, obtaining stratum position data sequence;Obtain similar index, if similar index exceeds similar threshold value then meet the condition, output the monitoring section set of geological drilling;The number of monitoring section in the monitoring section set of geological drilling and the difference amount of the number of monitoring section in the monitoring section set of adjacent geological drilling are obtained, the largest difference amount is taken as the typical difference amount of geological drilling, the adjustment parameter of geological drilling is obtained according to the typical difference amount of geological drilling;Obtain deformation threshold value, obtain the dynamic threshold value of geological drilling according to deformation threshold value and the adjustment parameter of geological drilling, obtain the monitoring result of geological drilling according to the dynamic threshold value of geological drilling and the stratum position data sequence of multiple monitoring nodes.The application has the advantages of dynamic self-adaptability, collaborative analysis and good monitoring effect.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for processing rock strata deformation monitoring data based on geological boreholes. Background Technology

[0002] In the fields of mining, tunnel engineering, and geological disaster prevention, rock deformation monitoring is a core component to ensure engineering safety. Existing technologies typically employ geological boreholes to deploy sensor arrays and periodically collect rock displacement data to perform deformation analysis. However, this method has certain limitations.

[0003] Specifically, existing methods uniformly set monitoring nodes along the borehole depth, but rock deformation is heterogeneous—some sections have weak deformation (such as stable bedrock), and the high similarity of adjacent node data leads to computational redundancy. In critical deformation areas (such as weak interlayers), local anomalies may be smoothly ignored due to insufficient node density, resulting in the risk of missed detection. At the same time, a uniform preset deformation threshold cannot adapt to the actual risk levels of different geological sections. For example, due to differences in geological structure (such as fault crossing), the deformation patterns of adjacent boreholes should have a strong correlation. If an anomaly occurs in a borehole (such as water inrush softening), the deformation amplitude of its internal nodes and the deformation coordination between adjacent boreholes will significantly deviate from the normal. Fixed thresholds are not only difficult to capture such cross-hole anomaly correlations, but also cannot implement stricter judgment criteria for high-risk sections. Finally, abnormal deformation often starts from local rock strata, but existing methods lack an intelligent mining mechanism for the spatiotemporal correlation of node data, making the local anomaly location ambiguous. The invalid calculation of a large number of similar nodes not only increases the processing burden, but may also mask key anomaly signals, making it impossible to accurately locate the risk source. Summary of the Invention

[0004] To address the technical problem in existing technologies that cannot accurately locate risk zones in rock strata and adaptively determine thresholds by dynamically identifying the correlation anomalies in deformation patterns between adjacent boreholes based on displacement time-series data from array-type geological boreholes, this invention provides a rock strata deformation monitoring data processing method and system based on geological boreholes.

[0005] A method for processing rock strata deformation monitoring data based on geological boreholes includes: acquiring multiple arrayed geological boreholes, acquiring multiple monitoring nodes uniformly arranged along the centerline of each target geological borehole, and acquiring the rock strata location data sequence of each monitoring node in the previous monitoring cycle at the current time; obtaining a similarity index based on the rock strata location data sequence of two adjacent monitoring nodes in the i-th geological borehole; if the similarity index exceeds a similarity threshold, both monitoring nodes corresponding to the similarity index are recorded as meeting the merging condition; and traversing all monitoring nodes of the i-th geological borehole from top to bottom, merging adjacent monitoring nodes that meet the merging condition into the same monitoring segment. Individual monitoring nodes that do not meet the merging conditions are treated as separate monitoring segments, and the monitoring segment set of the i-th geological borehole is output. The difference between the number of monitoring segments in the monitoring segment set of the i-th geological borehole and the number of monitoring segments in the monitoring segment sets of each adjacent geological borehole is obtained, and the largest difference is taken as the typical difference of the i-th geological borehole. The adjustment parameters of the i-th geological borehole are obtained based on the typical difference of the i-th geological borehole. The deformation threshold is obtained, and the dynamic threshold of each geological borehole is obtained based on the deformation threshold and the adjustment parameters of each geological borehole. The monitoring results of each geological borehole are obtained based on the dynamic threshold of each geological borehole and the rock strata location data sequence of multiple monitoring nodes.

[0006] Optionally, obtaining the adjustment parameters of the i-th geological borehole based on the typical difference of the i-th geological borehole includes: dividing the typical difference of the i-th geological borehole by the number of monitoring nodes in a single geological borehole to obtain the adjustment parameters of the i-th geological borehole.

[0007] Optionally, obtaining the deformation threshold and obtaining the dynamic threshold of each geological borehole based on the deformation threshold and the adjustment parameters of each geological borehole includes: subtracting the adjustment parameters of each geological borehole from 1 to obtain the adjustment ratio of each geological borehole; multiplying the adjustment ratio of each geological borehole by the deformation threshold to obtain the dynamic threshold of each geological borehole.

[0008] Optionally, obtaining the monitoring results of each geological borehole based on the dynamic threshold of each geological borehole and the rock stratum location data sequence of multiple monitoring nodes includes: for each geological borehole, traversing the rock stratum location data sequence of each monitoring node, and determining one by one whether the absolute variable between any two adjacent depth data in the rock stratum location data sequence exceeds the dynamic threshold of the geological borehole; if in the rock stratum location data sequence of any monitoring node, there is at least one case where the absolute variable between adjacent depth data exceeds the dynamic threshold, then the monitoring node is marked as a deformation risk node, and the deformation risk nodes of each geological borehole are output.

[0009] Optionally, obtaining similarity indicators based on the rock strata location data sequence of two adjacent monitoring nodes within the i-th geological borehole includes: obtaining adjacent data monitoring time points within the monitoring period, and obtaining the absolute difference of the relative changes in data of adjacent monitoring nodes at the adjacent data monitoring time points based on the rock strata location data sequence of adjacent monitoring nodes; obtaining similarity indicators of two adjacent monitoring nodes within the i-th geological borehole based on the absolute differences of multiple relative changes in data of two adjacent monitoring nodes within the i-th geological borehole.

[0010] Optionally, the similarity index between two adjacent monitoring nodes in the i-th geological borehole is obtained based on the absolute difference of the relative changes of multiple data points between two adjacent monitoring nodes in the i-th geological borehole, and is expressed as follows: ;in, This refers to the similarity index between the j-th monitoring node and the (j+1)-th monitoring node within the same geological borehole. The number of relative changes in the data of the monitoring nodes. Let be the relative change of the k-th data point at the j-th monitoring node within the geological borehole. This represents the relative change of the k-th data point at the (j+1)-th monitoring node within the geological borehole. The standard absolute difference, It is the absolute difference between the relative change of the kth data point at the j-th monitoring node and the relative change of the kth data point at the (j+1)-th monitoring node within the same geological borehole.

[0011] A data processing system for monitoring rock strata deformation based on geological boreholes is also provided. The system includes: a data acquisition module, used to acquire multiple arrayed geological boreholes, acquire multiple monitoring nodes uniformly set along the centerline of each target geological borehole, and acquire the rock strata location data sequence of each monitoring node in the previous monitoring cycle at the current time; and a first data processing module, used to acquire similarity indicators based on the rock strata location data sequences of two adjacent monitoring nodes in the i-th geological borehole. If the similarity indicator exceeds a similarity threshold, the two monitoring nodes corresponding to the similarity indicator are both recorded as meeting the merging condition. The system then traverses all monitoring nodes of the i-th geological borehole from top to bottom, merging adjacent monitoring nodes that meet the merging condition into the same monitoring node. The system consists of three modules: a first module that takes a single monitoring node that does not meet the merging conditions as a separate monitoring segment and outputs the monitoring segment set of the i-th geological borehole; a second data processing module that obtains the difference between the number of monitoring segments in the monitoring segment set of the i-th geological borehole and the number of monitoring segments in the monitoring segment sets of adjacent geological boreholes, takes the largest difference as the typical difference of the i-th geological borehole, and obtains the adjustment parameters of the i-th geological borehole based on the typical difference of the i-th geological borehole; and a data output module that obtains the deformation threshold and obtains the dynamic threshold of each geological borehole based on the deformation threshold and the adjustment parameters of each geological borehole, and obtains the monitoring results of each geological borehole based on the dynamic threshold of each geological borehole and the rock strata location data sequence of multiple monitoring nodes.

[0012] Optionally, the data output module is also used to: for each geological borehole, traverse the rock stratum location data sequence of each monitoring node, and determine one by one whether the absolute variable between any two adjacent depth data in the rock stratum location data sequence exceeds the dynamic threshold of the geological borehole; if in the rock stratum location data sequence of any monitoring node, there is at least one case where the absolute variable between adjacent depth data exceeds the dynamic threshold, then the monitoring node is marked as a deformation risk node, and the deformation risk nodes of each geological borehole are output.

[0013] Optionally, the data output module is also used to: subtract the adjustment parameters of each geological borehole from 1 to obtain the adjustment ratio of each geological borehole; multiply the adjustment ratio of each geological borehole by the deformation threshold to obtain the dynamic threshold of each geological borehole.

[0014] Optionally, the second data processing module is further configured to: divide the typical difference of the i-th geological borehole by the number of monitoring nodes in a single geological borehole, and obtain the adjustment parameters of the i-th geological borehole.

[0015] The beneficial effects of this invention are reflected in:

[0016] In the entire data processing method for rock strata deformation monitoring based on geological boreholes, firstly, based on the similarity analysis of adjacent nodes and adaptive zoning, monitoring nodes are merged in lithologically homogeneous areas (such as thick sandstone), while independent monitoring segments are retained in rock strata abrupt change areas (such as fault fracture zones), ensuring that local abnormal deformation is not smoothed out. Furthermore, by quantifying the difference in the number of monitoring segments of adjacent boreholes, a spatial coordination diagnostic mechanism is constructed, achieving for the first time the location of geological anomalies based on the overall deformation pattern of the borehole array, breaking through the limitation of existing methods that rely solely on single-hole data. Furthermore, the adjustment parameters are transformed into dynamic threshold compression coefficients, making the criteria for judging high-risk areas more stringent, avoiding false alarms in stable areas and improving the sensitivity of dangerous areas. At the same time, combined with the independent node location of S2 (such as fault interface nodes), a two-layer positioning of regional anomaly early warning and local precise locking is achieved. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a partial flowchart of S1 to S2 in the rock strata deformation monitoring data processing method based on geological boreholes of the present invention.

[0019] Figure 2 This is a schematic diagram of part S3 to S4 of the rock strata deformation monitoring data processing method based on geological boreholes in this invention;

[0020] Figure 3 This is a schematic diagram of part S4 in the rock strata deformation monitoring data processing method based on geological boreholes of the present invention;

[0021] Figure 4 This is a schematic diagram of the steps of the rock strata deformation monitoring data processing method based on geological boreholes of the present invention;

[0022] Figure 5 This is a schematic diagram of a portion of step S2 in the rock strata deformation monitoring data processing method based on geological boreholes of the present invention;

[0023] Figure 6 This is a schematic diagram of a portion of step S4 in the rock strata deformation monitoring data processing method based on geological boreholes of the present invention;

[0024] Figure 7 This is a schematic diagram of another part of step S4 in the rock stratum deformation monitoring data processing method based on geological boreholes of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] like Figures 1 to 4 As shown, a method for processing rock strata deformation monitoring data based on geological boreholes is provided. In one embodiment, the method includes:

[0029] S1. Obtain multiple arrayed geological boreholes, and obtain multiple monitoring nodes uniformly set along the centerline of each target geological borehole, and obtain the rock strata location data sequence of each monitoring node in the previous monitoring cycle at the current time.

[0030] S2. Obtain similarity indexes based on the rock stratum location data sequences of two adjacent monitoring nodes in the i-th geological borehole. If the similarity index exceeds the similarity threshold, both monitoring nodes corresponding to the similarity index are recorded as meeting the merging condition. Then, traverse all monitoring nodes of the i-th geological borehole from top to bottom, merge adjacent monitoring nodes that meet the merging condition into the same monitoring segment, and treat individual monitoring nodes that do not meet the merging condition as separate monitoring segments. Output the monitoring segment set of the i-th geological borehole.

[0031] S3. Obtain the difference between the number of monitoring segments in the monitoring segment set of the i-th geological borehole and the number of monitoring segments in the monitoring segment sets of each adjacent geological borehole, and take the largest difference as the typical difference of the i-th geological borehole, and obtain the adjustment parameters of the i-th geological borehole based on the typical difference of the i-th geological borehole.

[0032] S4. Obtain the deformation threshold and the dynamic threshold of each geological borehole based on the deformation threshold and the adjustment parameters of each geological borehole. Then, obtain the monitoring results of each geological borehole based on the dynamic threshold of each geological borehole and the rock strata location data sequence of multiple monitoring nodes.

[0033] In this embodiment, it should be noted that S1 is the initial data acquisition stage in the rock deformation monitoring scenario, and its process includes determining the spatial layout structure, node data attributes, and time series dynamic range.

[0034] First, a three-dimensional monitoring network covering the target rock mass is constructed using multiple arrayed geological boreholes (e.g., a grid-like distribution of boreholes at the tunnel face). Monitoring nodes are uniformly placed along the centerline of each borehole (e.g., one sensor every 1 meter), forming a vertical observation chain. This design solves the monitoring blind spot problem caused by the heterogeneity of the rock strata—for example, in hard bedrock sections, adjacent nodes may show convergent data due to slight deformation; while in jointed zones or weak interlayers, uniform nodes ensure that local deformations are fully captured, avoiding the omission of critical risk signals such as shear slip due to insufficient node density. The rock strata location data sequence for each node is essentially a set of timestamped depth values ​​(e.g., recording the absolute depth of the rock strata at that node at 0:00 each day), and its dynamic changes directly represent the deformation and displacement of the rock mass.

[0035] Secondly, the temporal characteristics of the data sequence define the observation window for deformation behavior. Depth data collected at a fixed frequency (e.g., hourly) within a monitoring period (e.g., the first 30 days) constitute a discrete temporal sequence, implicitly revealing the evolutionary patterns of rock mass creep, abrupt changes, and other modes. For example, at a node corresponding to a weak interlayer, the data sequence may exhibit a superposition of periodic small fluctuations (corresponding to changes in groundwater level) and a continuous subsidence trend (corresponding to plastic deformation of the rock strata); while the sequence of nodes in stable bedrock sections presents an approximately horizontal line. This temporal characteristic provides a basis for subsequent similarity analysis (step S2)—if the depth changes of adjacent nodes are consistent over a long period at the same time point, it indicates that they are both controlled by the same rock strata mechanical behavior; otherwise, it suggests a local anomaly. In addition, the setting of "the previous period at the current moment" ensures that the analysis focuses on the latest dynamics, avoiding interference from historical data while capturing short-term strong disturbance events (e.g., instantaneous displacement after blasting), and the complete period length can cover typical stages of rock deformation development (e.g., the accelerated deformation period from elastic deformation to failure).

[0036] In S2, the spatial analytical structure of the monitoring nodes is optimized, and adaptive partitioning is achieved through data similarity analysis of adjacent nodes. This process first compares each pair of monitoring nodes continuously deployed along the borehole depth within a single geological borehole. Specifically, based on the rock strata location data sequence obtained in S1 (i.e., depth value changes recorded at multiple time points for each node within the monitoring period), a similarity metric is calculated between any two adjacent nodes (e.g., nodes j and j+1 that are directly adjacent). This metric considers two dimensions: first, the synchronicity of the depth change direction of the two nodes throughout the monitoring period (e.g., whether they simultaneously subside or uplift); and second, the degree of difference in displacement changes between the two nodes in each adjacent time period. If the calculation result exceeds a preset similarity threshold (indicating a high degree of consistency in their deformation patterns), the two nodes are considered to be merged into the same monitoring segment; otherwise, they are retained as independent units.

[0037] After traversing and comparing all adjacent node pairs within the borehole, a merging operation is performed in order from the borehole opening to the bottom: multiple consecutive nodes that meet the merging criteria are aggregated into a single monitoring segment, while isolated nodes or boundary nodes form separate segments. A monitoring node meets the merging criteria when the similarity index between that monitoring node and any adjacent monitoring node exceeds a similarity threshold. The similarity threshold is determined by first extracting historical normal rock strata data (e.g., 1 year of monitoring records), then calculating the distribution of similarity indices between adjacent nodes in homogeneous rock strata (usually concentrated between 0.75 and 0.95), and finally taking the lower limit of the distribution: mean - 3 times the standard deviation. If the result is 0.58, the threshold is set to 0.6. For example, in a tunnel sandstone stratum, the mean similarity index of 300 node pairs is 0.82, and the standard deviation is 0.07; therefore, the threshold is 0.82 - 3 × 0.07 ≈ 0.61.

[0038] The final output set of monitoring segments reflects the deformation distribution characteristics of the borehole. In sections with uniform lithology and weak deformation (such as thick sandstone), most adjacent nodes are merged to form a small number of long monitoring segments. In areas with fractured strata or alternating layers of soft and hard rock (such as mudstone interlayers), the presence of multiple independent monitoring segments reveals the spatial location of local abnormal deformation.

[0039] Furthermore, during implementation, the calculation depth of similar indicators depends on the time-series data characteristics constructed by S1. For example, in a geological borehole traversing interbedded sandstone and mudstone, the data sequences of adjacent nodes A and B within the sandstone segment consistently show a slow and synchronous trend of increasing depth (uniform subsidence of 0.1 mm / day) throughout the entire monitoring period. The consistency of their change directions reaches over 95%, and the difference in change is consistently less than 0.05 mm. At this point, they are deemed to meet the merging criteria. However, between node C, located in the underlying mudstone layer, and node B in the sandstone layer, the sequence of node C experiences intermittent abrupt changes due to the softening of the mudstone upon contact with water (e.g., a single-day subsidence of 0.5 mm). The synchronicity of its change direction with that of node B drops sharply to 60%, and there are multiple instances of a change difference exceeding 0.3 mm. Therefore, merging is rejected, and node C is established as an independent segment. Ultimately, this borehole may form three monitoring segments: a merged sandstone segment at the top (including nodes AB), an independent transition segment in the middle (node ​​C), and a mudstone segment at the bottom (merged nodes DE). This structure provides a basis for cross-hole difference analysis in step S3—if neighboring boreholes are all continuous sandstone layers at this depth (manifested as a few segments merging), and this borehole has additional segments due to interlayer deformation, then the subsequent anomaly diagnosis mechanism is triggered.

[0040] In S3, geological deformation anomalies are diagnosed by the difference in the number of monitoring segments between adjacent boreholes, establishing a spatial correlation risk early warning mechanism. This process first calculates the absolute difference between the number of segments in the monitoring segment set generated in S2 for each geological borehole in the array (e.g., borehole i, numbered i) and the number of segments in all spatially adjacent boreholes (e.g., borehole A has 3 segments, borehole B has 4 segments), yielding multiple difference quantities. The largest difference quantity (e.g., |5-3|=2 in the example above) is determined as the typical difference quantity for that borehole. This design is based on the principle of continuity in geological deformation—the rock strata traversed by adjacent boreholes should have similar mechanical properties and deformation patterns when undisturbed, and their monitoring segment merging results should be close; if a borehole has a significantly higher number of segments than its neighbors (meaning that the borehole has more independent nodes), it reveals the possible existence of localized abnormal deformation areas (e.g., fault displacement leading to rock strata fracturing).

[0041] Subsequently, the typical difference is divided by the total number of original nodes in a single borehole (e.g., difference 2 divided by the 20 nodes in the borehole) to obtain an adjustment parameter ranging from 0 to 1. A larger parameter value indicates a higher degree of incompatibility between the borehole and its surrounding environment. For example, when a borehole crosses a concealed fault and has multiple independent monitoring segments (e.g., 15 segments), while the adjacent normal borehole has only 8 segments, the ratio of the typical difference 7 to the total number of nodes (20) yields an adjustment parameter of 0.35. This value implies two physical meanings: first, it directly quantifies the degree of deformation anomaly in the area where the borehole is located (0.35 > 0 indicates geological disturbance); second, it provides a risk weight for the subsequent dynamic threshold calculation of S4—a larger parameter indicates a higher risk level in the area, requiring stricter judgment criteria.

[0042] Furthermore, in practical applications, the execution of S3 relies on the structural characteristics of the borehole monitoring segments provided by S2. Assume that in a borehole array deployed along the tunnel sidewall, borehole 1 passes through a stable limestone layer (S2 steps merge it into 4 monitoring segments), the adjacent borehole 2, due to passing through a water-filled fault fracture zone, experiences abnormal deformation at multiple nodes in its weak interlayers, resulting in independent segments (ultimately forming 8 monitoring segments), and borehole 3 is located in a normal limestone area (merged into 3 segments). The adjacent differences of borehole 2 are calculated: the difference with borehole 1 is |8-4|=4, and the difference with borehole 3 is |8-3|=5. The maximum difference of 5 is selected as the typical difference. If the number of nodes per borehole is 20, the adjustment parameter is 5 / 20=0.25. In contrast, the maximum difference between borehole 1 and its adjacent boreholes is only |4-3|=1 (parameter 0.05), and the difference in borehole 3 is also minimal. This significant differentiation directly reflects the geological anomalies at the location of borehole 2.

[0043] It is worth noting that the adjustment parameters do not directly determine specific risk points (this responsibility lies with S4), but rather construct a regional risk assessment framework. The subsequent S4 step will use an adjustment parameter of 0.25 to perform threshold tightening on the borehole (e.g., reducing the deformation threshold by 25%), making the displacement monitoring of its internal nodes more sensitive, thereby prioritizing the exposure of high-risk signals in fault zones. The entire process forms a spatial collaborative diagnostic logic: differences in the number of monitoring segments in adjacent boreholes trigger risk warnings, the adjustment parameters quantify the risk level and transmit it downstream, ultimately achieving a progressive analysis of regional anomaly perception and precise local judgment.

[0044] In S4, dynamic risk assessment criteria for geological boreholes are generated and abnormal node location is performed, achieving adaptive precision in deformation monitoring. This step first sets a unified initial deformation threshold based on engineering experience (such as the upper limit of rock stratum depth variation allowed between adjacent monitoring time points), and then dynamically corrects it using adjustment parameters generated in S3.

[0045] Specifically, by subtracting the adjustment parameter from 1 to obtain the adjustment ratio (e.g., an adjustment parameter of 0.2 for a certain borehole corresponds to an adjustment ratio of 0.8), and then multiplying this ratio by the initial deformation threshold, a unique dynamic threshold for that borehole is generated. This design assigns differentiated judgment criteria to boreholes with different risk levels: the larger the adjustment parameter (indicating a higher abnormal risk in the borehole area), the smaller its dynamic threshold (e.g., an adjustment parameter of 0.3 reduces the threshold to 70% of its original value), thereby enabling more stringent monitoring of displacement changes in potential risk areas.

[0046] Subsequently, the time-series data sequences acquired by all monitoring nodes within the borehole in S1 are traversed. For each node, the depth change at adjacent time points in its stratum location data sequence is analyzed (e.g., the depth difference between hour t and hour t+1). If the absolute value of the change at any adjacent time point exceeds the dynamic threshold of the borehole, the node is marked as a deformation risk node. For example, in a node in a weak interlayer zone, if its depth sequence shows a single 0.4 mm jump (assuming the borehole has been dynamically compressed to 0.3 mm due to high risk), it will still be identified even if the initial threshold of 0.5 mm is not reached. Finally, the risk node set for each borehole is output, providing a precise location basis for engineering intervention.

[0047] Furthermore, taking borehole No. 2, which traverses a fault fracture zone, as an example: S3 assigns an adjustment parameter of 0.25 due to the significant difference in the number of monitoring segments between it and adjacent boreholes (maximum difference of 5 segments). Step S4 calculates the dynamic threshold accordingly (initial threshold 0.5 mm × (1-0.25) = 0.375 mm). When traversing this borehole node: the depth sequence of node A, located in stable rock strata, exhibits smooth fluctuations (maximum change between adjacent time points is 0.2 mm < 0.375 mm) and is not marked; however, the depth of node B at the fault boundary suddenly drops by 0.4 mm at an adjacent time point (exceeding the dynamic threshold), triggering a risk marker. Comparing this to the adjacent borehole No. 1 (adjustment parameter 0.05, dynamic threshold 0.475 mm): if node B is located in borehole No. 1, its 0.4 mm change will not be identified because it is below the threshold. This dynamic adjustment mechanism achieves risk sensitivity adaptation. Due to the reduced threshold, boreholes in fault zones are more likely to capture minute abnormal signals such as rock shear slip. At the same time, it improves computational efficiency. Stable rock nodes (such as node A) are less prone to false alarms due to the lenient judgment criteria. It also enables spatial positioning of anomalies, and the marking results are cross-verified with the monitoring segment structure of S2. Independent segments (such as node B) are often the areas where risk points are concentrated.

[0048] In summary, the entire data processing method for rock strata deformation monitoring based on geological boreholes firstly merges monitoring nodes in homogeneous lithology zones (such as thick sandstone) and retains independent monitoring segments in abrupt rock strata zones (such as fault fracture zones) based on similarity analysis of adjacent nodes and adaptive zoning, ensuring that local abnormal deformations are not smoothed out. Furthermore, by quantifying the differences in the number of monitoring segments in adjacent boreholes, a spatial coordination diagnostic mechanism is constructed, achieving for the first time the location of geological anomalies based on the overall deformation pattern of the borehole array, breaking through the limitations of existing methods that rely solely on single-hole data. Further, the adjustment parameters are transformed into dynamic threshold compression coefficients, making the criteria for judging high-risk areas more stringent, avoiding false alarms in stable areas and improving the sensitivity of dangerous areas. Simultaneously, combined with the independent node location of S2 (such as fault interface nodes), a two-layer positioning system of regional anomaly early warning and precise local locking is achieved.

[0049] like Figure 7 As shown, in one embodiment, S4, obtaining the monitoring results of each geological borehole based on the dynamic threshold of each geological borehole and the rock strata location data sequence of multiple monitoring nodes includes:

[0050] S43. For each geological borehole, traverse the rock stratum location data sequence of each monitoring node, and determine one by one whether the absolute variable between any two adjacent depth data in the rock stratum location data sequence exceeds the dynamic threshold of the geological borehole.

[0051] S44. If, in the rock strata location data sequence of any monitoring node, there is at least one case where the absolute variable between adjacent depth data exceeds the dynamic threshold, then the monitoring node is marked as a deformation risk node, and the deformation risk nodes of each geological borehole are output.

[0052] In this embodiment, it should be noted that in S43, deformation detection is performed on the time-series data sequence of all monitoring nodes in each borehole. The core operation is to traverse the rock strata location data sequence of each node (i.e., the depth value time sequence obtained in S1), calculate the absolute change of depth data at adjacent time points (such as the depth difference between the t-th hour and the t+1-th hour) for each pair, and compare it with the dynamic threshold generated in S42.

[0053] For example, if the depth sequence of a weak interlayer node over 24 hours is [d1, d2, ..., d24], then 23 changes in |d2-d1|, |d3-d2|, ..., |d24-d23| will be calculated sequentially. If any change exceeds the borehole's dynamic threshold, a subsequent marking mechanism will be triggered. This process, combined with the spatial differentiation characteristics of the dynamic threshold (e.g., a threshold of 0.375 mm in fault zones vs. 0.475 mm in stable zones), enables refined screening of anomalous signals—the same 0.4 mm displacement is identified in fault zones but ignored in stable zones.

[0054] In S44, the final risk node label is output based on the comparison results of S43: if the depth change of at least one adjacent time point in the time series data sequence of a node exceeds the dynamic threshold, it is labeled as a deformation risk node.

[0055] For example, in a borehole in a fault zone, the maximum change in depth sequence at node A is 0.2 mm (<0.375 mm) and is not marked, while a jump of 0.4 mm (>0.375 mm) occurs in the sequence at node B, triggering marking. The output is a set of risk nodes for each borehole (e.g., nodes B, F, and G are marked in borehole 2). This design overcomes the limitations of static judgment in existing methods: on the one hand, it supports the capture of instantaneous changes (e.g., single-point jumps caused by rockbursts); on the other hand, it is associated with the monitoring segment structure of S2 (nodes in independent monitoring segments are more easily marked), forming a location closed loop (e.g., node B is an independent segment in S2 that refuses to be merged).

[0056] like Figure 6 As shown, in one embodiment, obtaining the deformation threshold and obtaining the dynamic threshold of each geological borehole in S4 based on the deformation threshold and the adjustment parameters of each geological borehole includes:

[0057] S41. Subtract the adjustment parameters of each geological borehole from 1 to obtain the adjustment ratio of each geological borehole.

[0058] S42. Multiply the adjustment ratio of each geological borehole by the deformation threshold to obtain the dynamic threshold of each geological borehole.

[0059] In this embodiment, it should be noted that, firstly, a deformation threshold is obtained. When determining the deformation threshold, the average displacement of the first detected anomaly in historical disaster cases is used (e.g., an average displacement of 0.48 mm in the 24 hours before a water inrush accident). For example, in a coal mine, the initial value is set to 0.5 mm → the dynamic threshold in the fault zone (adjusted parameter 0.25) = 0.375 mm → capturing a sudden change of 0.4 mm. In engineering practice, the rock strata failure process can be simulated using finite element analysis, and then the critical displacement (e.g., 0.53 mm as the shear slip trigger point) can be extracted. Finally, the sensor accuracy is calibrated (±0.05 mm) to ensure that the threshold is greater than twice the measurement error.

[0060] In S41, a linear transformation of the adjustment parameters is performed, converting the borehole adjustment parameters (range 0-1) generated in S3 into the adjustment ratio required for dynamic threshold calculation. The core operation is to subtract the adjustment parameter from 1 (e.g., if the borehole adjustment parameter is 0.25, then the adjustment ratio = 1 - 0.25 = 0.75). This design follows the principle of a negative correlation between risk level and judgment severity—the larger the adjustment parameter (indicating a higher abnormal risk in the borehole area), the smaller the adjustment ratio.

[0061] For example, in boreholes traversing faults, if S3 detects significant spatial inconsistencies (such as a difference of 5 segments in the number of monitoring segments compared to adjacent boreholes), it assigns an adjustment parameter of 0.25. S41 then outputs an adjustment ratio of 0.75, laying the foundation for subsequent threshold compression. Essentially, this step transforms the spatial correlation quantification index (adjustment parameter) of geological anomalies into a mathematical operation factor (adjustment ratio), ensuring that high-risk areas receive stricter judgment criteria.

[0062] In S42, a dynamic threshold specific to geological boreholes is generated based on the adjustment ratio. This process multiplies the adjustment ratio output from S41 by a preset initial deformation threshold (engineering experience value) (e.g., initial threshold 0.5 mm × adjustment ratio 0.75 = 0.375 mm). This mechanism achieves spatial adaptive differentiation of the judgment criteria: the dynamic threshold for low-risk boreholes (adjustment parameter close to 0) is close to the initial value (e.g., 0.49 mm), while the threshold for high-risk boreholes (adjustment parameter 0.25) is significantly compressed (e.g., 0.375 mm).

[0063] For example, boreholes in stable limestone areas have a good spatial coordination (adjustment parameter 0.05), and their dynamic threshold remains at 0.475 mm, tolerating large deformation fluctuations; while boreholes in fault fracture zones have a threshold reduced to 0.375 mm due to the adjustment ratio of 0.75, making them more sensitive to small displacements.

[0064] In one implementation, obtaining the adjustment parameters for the i-th geological borehole based on the typical difference in the i-th geological borehole in S3 includes:

[0065] Divide the typical variation of the i-th geological borehole by the number of monitoring nodes in a single geological borehole to obtain the adjustment parameters for the i-th geological borehole.

[0066] In this embodiment, it should be noted that risk classification is achieved through spatial coordination quantification. The typical difference (the maximum difference in the number of monitoring segments between adjacent boreholes) of the i-th geological borehole is divided by the total number of original monitoring nodes for that borehole to generate an adjustment parameter ranging from 0 to 1. The typical difference reflects the degree of spatial anomaly between the borehole and its neighborhood (such as a fault causing a significant increase in the number of borehole segments). Dividing it by the total number of nodes achieves normalization, eliminating the influence of borehole size.

[0067] For example, a borehole with 8 monitoring segments due to rock fracture (only 3 segments in adjacent boreholes) has a typical difference of 5 (|8-3|). If the total number of nodes is 20, the adjustment parameter is 0.25. The adjustment parameter has two physical meanings: firstly, as a regional risk indicator (0.25>0 indicates the presence of geological disturbance); secondly, as a threshold adjustment weight—the larger the value, the higher the risk level, requiring more stringent judgment in step S4.

[0068] like Figure 5As shown, in one embodiment, obtaining similarity indicators in S2 based on the rock strata location data sequence of two adjacent monitoring nodes within the i-th geological borehole includes:

[0069] S21. Obtain adjacent data monitoring time points within the monitoring period, and obtain the absolute difference of the relative changes in data of adjacent monitoring nodes at the adjacent data monitoring time points based on the rock strata location data sequence of adjacent monitoring nodes.

[0070] S22. Obtain the similarity index of two adjacent monitoring nodes in the i-th geological borehole based on the absolute difference of the relative changes of multiple data of two adjacent monitoring nodes in the i-th geological borehole.

[0071] In this embodiment, it should be noted that in S21, relative measures of temporal change characteristics are extracted. First, all adjacent acquisition time points within the monitoring period are determined (e.g., t1→t2, t2→t3, ..., tn-1→tn). For nodes j and j+1 in borehole i, the depth change in each time period (depth at the later time point minus the depth at the previous time point) is calculated, resulting in two sets of relative change sequences. Then, the absolute difference in the changes of the two sequences in the same time period is calculated (e.g., the absolute value of the difference between the change of node j in t1→t2 and the change of node j+1 in t1→t2). For example, node j shows an accelerated settlement trend of 0.1 mm / day during the rainy season, while the adjacent node j+1 only changes by 0.02 mm. The cumulative difference between the two in multiple time periods forms evidence of difference, providing input for subsequent similarity determination.

[0072] In S22, a comprehensive evaluation index is constructed based on the absolute difference sequence of S21, integrating the dual-dimensional features of synchronicity of change direction and similarity of change magnitude. On the one hand, the consistency ratio of the depth change direction of the two nodes throughout the entire cycle (such as sinking or rising at the same time) is calculated, with a weight of 50%; on the other hand, the proportion of the difference sequence falling within the allowable error range is calculated (such as a preset tolerance a = 0.05 mm, if the difference ≤ a, a score is given).

[0073] For example, if nodes j and j+1 sink synchronously 8 times in 10 time periods (direction score 0.8), and the difference in change is ≤0.05 mm in 6 of these instances (amplitude score 0.6), the comprehensive index is 0.8×0.5+0.6×0.5=0.7. If the similarity threshold is set to 0.6, the two nodes can be merged. This design avoids misjudgment based on a single dimension: even if the difference in change is slightly large (e.g., a time difference of 0.1 mm), they may still be merged as long as the long-term trend is consistent; while sudden reverse changes (e.g., node j rising and j+1 sinking) significantly lower the index.

[0074] In one implementation, the similarity index obtained in S2 and S22 based on the absolute difference of multiple relative changes in data between two adjacent monitoring nodes in the i-th geological borehole is expressed as follows:

[0075] ;in,

[0076] This refers to the similarity index between the j-th monitoring node and the (j+1)-th monitoring node within the same geological borehole. The number of relative changes in the data of the monitoring nodes. Let be the relative change of the k-th data point at the j-th monitoring node within the geological borehole. This represents the relative change of the k-th data point at the (j+1)-th monitoring node within the geological borehole. The standard absolute difference, It is the absolute difference between the relative change of the kth data point at the j-th monitoring node and the relative change of the kth data point at the (j+1)-th monitoring node within the same geological borehole.

[0077] In this embodiment, it should be noted that the entire expression consists of two parts. The first part represents the quantization of deformation direction synchronization (weighted at 50%). Specifically, for each time point k, the depth change of adjacent nodes j and j+1 is extracted. and (Positive values ​​indicate sinking, negative values ​​indicate rising), and then through the sign function ( The direction of change is converted into a numerical value (+1, -1, or 0), and the absolute sum of the directional values ​​of the two nodes is calculated (e.g., when sinking in the same direction: |1+1|=2, when sinking in opposite directions: |1+(-1)|=0). Finally, the summation is performed over all time periods and standardized (divided by 2m) to obtain a directional synchronicity score (range 0~1). In this way, in rock deformation, if adjacent nodes are controlled by the same rock mass structure (such as intact bedrock), their deformation directions will inevitably be highly synchronized; while faults or weak interlayers will cause directional disharmony (e.g., when node j sinks, the adjacent node j+1 is uplifted due to rock mass fracturing); this calculation directly captures such anomalies through the directional consistency ratio, avoiding misjudgments caused by relying solely on displacement amplitude (e.g., when two nodes sink in the same direction but with a large difference in rate, existing methods may misjudge them as similar).

[0078] Part Two is This represents a quantification of the similarity of deformation magnitudes (weighted at 50%). Specifically, the absolute difference in the change of node j and node j+1 within the same time period k is first calculated. (Right now The acceptableness of the difference is determined by using a preset standard absolute difference 'a' (such as the allowable displacement error in the project): if... , then output 1 (indicating that the amplitudes in this period are similar); if , then output 0 (the amplitudes are not similar). Finally, the mean value is taken for all periods to obtain the amplitude similarity score. In this way, for the sudden change of local deformation gradient caused by the inhomogeneity of rock mass (such as the coexistence of a 0.5-mm displacement in a soft interlayer and a 0.1-mm displacement in the adjacent sandstone layer), the existing uniform threshold cannot distinguish such differences; a dynamic tolerance mechanism is introduced in this part, and points are only deducted when the difference in the change amounts of two nodes exceeds the geological safety limit (the standard absolute difference a), which not only allows the natural fluctuations of stable rock layers (< a), but also sensitively identifies significant differences (> a) in key deformation areas.

[0079] Furthermore, the equal weight distribution of direction synchrony (50%) + amplitude similarity (5%), avoids misjudgment dominated by a single index. For example, in a fault area, even if the displacement amplitudes of two nodes are close (meeting the second part), but the reverse deformation (getting 0 points in the first part) will significantly lower the total score and trigger the nodes to be independently segmented; on the contrary, in a uniform settlement layer, nodes with the same direction and amplitude difference < a can be safely merged to eliminate redundant calculations.

[0080] Furthermore, through the whole-process statistics of m periods (instead of single-point data), the interference of occasional differences caused by instantaneous disturbances (such as blasting vibrations) is solved. For example, the nodes of the mudstone layer sink synchronously with the nodes of the sandstone layer in most periods of the rainy season (full marks for the direction part), and only the amplitude difference exceeds the standard on heavy rain days - at this time, points are deducted for the amplitude part, but the total score may still be higher than the merging threshold, avoiding excessive splitting of stable rock layers.

[0081] Furthermore, the value of a can be related to the mechanical parameters of the rock mass (elastic modulus). For example, for intact granite, it is 0.03 mm, with low permeability and continuous deformation height; for sandstone-mudstone interbeds, it is 0.05 mm, and the lithological differences lead to local deformation gradients; for fault breccia, it is 0.10 mm with high fragmentation and disordered background displacements. At the same time, a can also be obtained through a quantification model, a = monitoring equipment calibration error * 2 + characteristic displacement of rock mass microfractures (such as taking 0.01 mm for intact limestone and 0 mm for fractured rock mass), where 2 can cover 95% of the noise. This geological adaptability enables the similarity index to directly reflect the mechanical continuity of the rock layer structure and fundamentally supports the engineering rationality of the S2 merging decision.

[0082] A data processing system for monitoring the deformation of rock layers based on geological boreholes is also provided. The system includes:

[0083] A data acquisition module, configured to acquire multiple geologically bored holes arranged in an array, acquire multiple monitoring nodes uniformly arranged along the center line direction of each target geological borehole, and acquire the data sequence of the rock layer positions of each monitoring node in the previous monitoring cycle before the current moment;

[0084] The first data processing module is used to obtain similarity indicators based on the rock stratum location data sequence of two adjacent monitoring nodes in the i-th geological borehole. If the similarity indicator exceeds the similarity threshold, the two monitoring nodes corresponding to the similarity indicator are recorded as meeting the merging condition. The module then traverses all monitoring nodes of the i-th geological borehole from top to bottom, merges adjacent monitoring nodes that meet the merging condition into the same monitoring segment, and treats a single monitoring node that does not meet the merging condition as a separate monitoring segment. Finally, the module outputs the monitoring segment set of the i-th geological borehole.

[0085] The second data processing module is used to obtain the difference between the number of monitoring segments in the monitoring segment set of the i-th geological borehole and the number of monitoring segments in the monitoring segment sets of each adjacent geological borehole, and to take the largest difference as the typical difference of the i-th geological borehole, and to obtain the adjustment parameters of the i-th geological borehole based on the typical difference of the i-th geological borehole.

[0086] The data output module is used to obtain the deformation threshold and the dynamic threshold of each geological borehole based on the deformation threshold and the adjustment parameters of each geological borehole. It also obtains the monitoring results of each geological borehole based on the dynamic threshold of each geological borehole and the rock strata location data sequence of multiple monitoring nodes.

[0087] In one implementation, the data output module is further configured to: for each geological borehole, traverse the rock stratum location data sequence of each monitoring node, and determine one by one whether the absolute variable between any two adjacent depth data in the rock stratum location data sequence exceeds the dynamic threshold of the geological borehole; if in the rock stratum location data sequence of any monitoring node, there is at least one case where the absolute variable between adjacent depth data exceeds the dynamic threshold, then the monitoring node is marked as a deformation risk node, and the deformation risk nodes of each geological borehole are output.

[0088] In one embodiment, the data output module is further configured to: subtract the adjustment parameters of each geological borehole from 1 to obtain the adjustment ratio of each geological borehole; and multiply the adjustment ratio of each geological borehole by the deformation threshold to obtain the dynamic threshold of each geological borehole.

[0089] In one embodiment, the second data processing module is further configured to: divide the typical difference of the i-th geological borehole by the number of monitoring nodes of a single geological borehole, and obtain the adjustment parameters of the i-th geological borehole.

[0090] In this embodiment, it should be noted that the specific method of performing the above-mentioned rock stratum deformation monitoring data processing system based on geological boreholes has been described in detail in the embodiments of the rock stratum deformation monitoring data processing method based on geological boreholes, and will not be elaborated here.

[0091] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0092] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0093] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for processing rock strata deformation monitoring data based on geological boreholes, characterized in that, include: Acquire multiple arrays of geological boreholes, acquire multiple monitoring nodes uniformly set along the centerline of each target geological borehole, and acquire the rock strata location data sequence of each monitoring node in the previous monitoring period at the current moment. Based on the rock stratum location data sequence of two adjacent monitoring nodes in the i-th geological borehole, similarity index is obtained. If the similarity index exceeds the similarity threshold, the two monitoring nodes corresponding to the similarity index are both recorded as meeting the merging condition. All monitoring nodes of the i-th geological borehole are traversed from top to bottom. Adjacent monitoring nodes that meet the merging condition are merged into the same monitoring segment, and single monitoring nodes that do not meet the merging condition are treated as separate monitoring segments. The monitoring segment set of the i-th geological borehole is then output. Obtain the difference between the number of monitoring segments in the monitoring segment set of the i-th geological borehole and the number of monitoring segments in the monitoring segment sets of each adjacent geological borehole, and take the largest difference as the typical difference of the i-th geological borehole, and obtain the adjustment parameters of the i-th geological borehole based on the typical difference of the i-th geological borehole. Obtain the deformation threshold and, based on the deformation threshold and the adjustment parameters of each geological borehole, obtain the dynamic threshold of each geological borehole. For each geological borehole, the rock stratum location data sequence of each monitoring node is traversed, and it is determined one by one whether the absolute variable between any two adjacent depth data in the rock stratum location data sequence exceeds the dynamic threshold of the geological borehole. If, in the rock strata location data sequence of any monitoring node, there exists at least one case where the absolute variable between adjacent depth data exceeds the dynamic threshold, then the monitoring node is marked as a deformation risk node, and the deformation risk nodes of each geological borehole are output.

2. The method for processing rock strata deformation monitoring data based on geological boreholes according to claim 1, characterized in that, The process of obtaining the adjustment parameters for the i-th geological borehole based on the typical difference of the i-th borehole includes: Divide the typical variation of the i-th geological borehole by the number of monitoring nodes in a single geological borehole to obtain the adjustment parameters for the i-th geological borehole.

3. The method for processing rock strata deformation monitoring data based on geological boreholes according to claim 1, characterized in that, The process of obtaining the deformation threshold and obtaining the dynamic threshold of each geological borehole based on the deformation threshold and the adjustment parameters of each geological borehole includes: Subtract the adjustment parameters of each geological borehole from 1 to obtain the adjustment ratio of each geological borehole. The adjustment ratio of each geological borehole is multiplied by the deformation threshold to obtain the dynamic threshold of each geological borehole.

4. The method for processing rock strata deformation monitoring data based on geological boreholes according to claim 1, characterized in that, The method of obtaining similarity indicators based on the rock strata location data sequence of two adjacent monitoring nodes within the i-th geological borehole includes: Obtain adjacent data monitoring time points within the monitoring period, and obtain the absolute difference of the relative changes in data of adjacent monitoring nodes at the adjacent data monitoring time points based on the rock stratum location data sequence of adjacent monitoring nodes; The similarity index of two adjacent monitoring nodes in the i-th geological borehole is obtained by using the absolute difference of the relative changes of multiple data from two adjacent monitoring nodes in the i-th geological borehole.

5. The method for processing rock strata deformation monitoring data based on geological boreholes according to claim 4, characterized in that, The similarity index obtained by calculating the absolute difference of the relative changes of multiple data from two adjacent monitoring nodes within the i-th geological borehole is expressed as follows: ;in, This refers to the similarity index between the j-th monitoring node and the (j+1)-th monitoring node within the same geological borehole. The number of relative changes in the data of the monitoring nodes. Let be the relative change of the k-th data point at the j-th monitoring node within the geological borehole. This represents the relative change of the k-th data point at the (j+1)-th monitoring node within the geological borehole. The standard absolute difference, It is the absolute difference between the relative change of the kth data point at the j-th monitoring node and the relative change of the kth data point at the (j+1)-th monitoring node within the same geological borehole.

6. A rock strata deformation monitoring data processing system based on geological boreholes, characterized in that, The system is used to implement the rock strata deformation monitoring data processing method based on geological boreholes as described in any one of claims 1 to 5, the system comprising: The data acquisition module is used to acquire multiple arrayed geological boreholes, acquire multiple monitoring nodes uniformly set along the center line of each target geological borehole, and acquire the rock strata location data sequence of each monitoring node in the previous monitoring cycle at the current moment. The first data processing module is used to obtain similarity indicators based on the rock stratum location data sequence of two adjacent monitoring nodes in the i-th geological borehole. If the similarity indicator exceeds the similarity threshold, the two monitoring nodes corresponding to the similarity indicator are recorded as meeting the merging condition. The module then traverses all monitoring nodes of the i-th geological borehole from top to bottom, merges adjacent monitoring nodes that meet the merging condition into the same monitoring segment, and treats a single monitoring node that does not meet the merging condition as a separate monitoring segment. Finally, the module outputs the monitoring segment set of the i-th geological borehole. The second data processing module is used to obtain the difference between the number of monitoring segments in the monitoring segment set of the i-th geological borehole and the number of monitoring segments in the monitoring segment sets of each adjacent geological borehole, and to take the largest difference as the typical difference of the i-th geological borehole, and to obtain the adjustment parameters of the i-th geological borehole based on the typical difference of the i-th geological borehole. The data output module is used to obtain the deformation threshold and the dynamic threshold of each geological borehole based on the deformation threshold and the adjustment parameters of each geological borehole. It also obtains the monitoring results of each geological borehole based on the dynamic threshold of each geological borehole and the rock strata location data sequence of multiple monitoring nodes.

7. The rock strata deformation monitoring data processing system based on geological boreholes according to claim 6, characterized in that, The data output module is also used for: For each geological borehole, the rock stratum location data sequence of each monitoring node is traversed, and it is determined one by one whether the absolute variable between any two adjacent depth data in the rock stratum location data sequence exceeds the dynamic threshold of the geological borehole. If, in the rock strata location data sequence of any monitoring node, there exists at least one case where the absolute variable between adjacent depth data exceeds the dynamic threshold, then the monitoring node is marked as a deformation risk node, and the deformation risk nodes of each geological borehole are output.

8. The rock strata deformation monitoring data processing system based on geological boreholes according to claim 6, characterized in that, The data output module is also used for: Subtract the adjustment parameters of each geological borehole from 1 to obtain the adjustment ratio of each geological borehole. The adjustment ratio of each geological borehole is multiplied by the deformation threshold to obtain the dynamic threshold of each geological borehole.

9. The rock strata deformation monitoring data processing system based on geological boreholes according to claim 6, characterized in that, The second data processing module is also used for: Divide the typical variation of the i-th geological borehole by the number of monitoring nodes in a single geological borehole to obtain the adjustment parameters for the i-th geological borehole.

Images