Method and system for identifying and evaluating deep unloading development zone of bank slope based on sliding window

Abstract

The present application relates to the technical field of geological data analysis, and discloses a bank slope deep unloading development zone identification and evaluation method and system based on a sliding window, aiming to solve the problems of low efficiency, strong subjectivity, discrete and non-quantifiable evaluation results of the existing method. The scheme mainly includes: obtaining the structured data of the fissures exposed along the exploration line; based on the set window parameters, performing spatial convolution calculation through the sliding window to generate a continuous characteristic signal sequence; using the preset background noise threshold to segment the sequence to identify the candidate section; matching and calibrating the boundary of the candidate section with the position of the known geological structure surface to delineate the deep unloading development zone; and calculating the comprehensive development intensity index of each deep unloading development zone to realize quantitative evaluation. The present application realizes the automatic, continuous spatial identification and quantitative characterization of the deep unloading development zone, improves the efficiency and accuracy, and is particularly suitable for the safe site selection and construction of major projects.

Description

A Method and System for Identifying and Evaluating Deep Unloading Development Zones on Bank Slopes Based on Sliding Window Technical Field

[0001] This invention relates to the field of geological data analysis technology, specifically to a method and system for identifying and evaluating deep unloading development zones on bank slopes based on a sliding window. Background Technology

[0002] The widespread development of "deep unloading" (also known as deep fracturing, deep cracks, or deep unloading) within riverbank slopes is a unique geological phenomenon closely related to the evolutionary history of river valleys. It is distributed within the slightly necrotic rock mass within the normal unloading zone, manifesting as tensile or tensile-shear fractures with opening widths ranging from a few millimeters to tens of centimeters, with alternating relaxed and relatively intact sections. This phenomenon is widely recognized as a key, hidden geological factor affecting the stability of rock masses in engineering projects such as dam foundations, caverns, and slopes. Therefore, the scientific and accurate identification and evaluation of deep unloading on riverbank slopes is of paramount importance for the safe site selection and construction of major engineering projects.

[0003] Currently, the identification and evaluation methods for deep unloading in engineering practice mainly rely on expert experience-based diagnostic methods based on multi-source testing. The main process of this method is as follows: at specific locations exposed in exploration adits or boreholes, intensive multi-source testing is carried out, including fracture geometry measurement, sonic velocity testing, in-hole CT scanning, and acquisition of rock mass integrity index and rock quality indicators. Subsequently, geological engineers comprehensively review all these heterogeneous data and, based on their personal experience and knowledge, qualitatively classify the degree of relaxation of the rock mass at the location, thereby achieving the identification and evaluation of deep unloading development zones.

[0004] However, the above methods have the following main drawbacks: First, they rely on the subjective comprehensive judgment of experts on multi-source heterogeneous information, which makes the identification process uncomputable and the results unrepeatable; second, the method can only make qualitative judgments on discrete test points and cannot characterize continuous defects in the exploration line space; third, the complex data model on which it is based is costly and inefficient; and fourth, the qualitative conclusions output lack accurate spatial boundaries and computable intensity quantification attributes. Summary of the Invention

[0005] This invention aims to address the problems of low efficiency, strong subjectivity, and discrete and unquantifiable evaluation results in existing deep unloading identification and evaluation methods. It proposes a sliding window-based method and system for identifying and evaluating deep unloading development zones on riverbanks.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0007] In a first aspect, the present invention provides a method for identifying and evaluating deep unloading development zones on riverbank slopes based on a sliding window, the method comprising:

[0008] Obtain structured data of fractures exposed along the exploration line in the target area. The structured data includes the starting mileage coordinates, ending mileage coordinates, and opening width of each fracture.

[0009] Based on the set window width and sliding step distance, the exploration line is slid in space in a sliding window manner; for each window, the cracks that intersect with the interval defined by the starting mileage coordinates and the ending mileage coordinates are selected, the deep unloading development intensity index at the center position of the window is calculated according to the cumulative opening width of the selected cracks, and a continuous feature signal sequence characterizing the deep unloading development intensity is generated according to the deep unloading development intensity index corresponding to each window.

[0010] The continuous feature signal sequence is segmented based on a preset background noise threshold to identify continuous segments with signal strength greater than the background noise threshold as candidate segments for concentrated development of deep unloading.

[0011] The boundary of the candidate section is matched and corrected with the position of the nearest known geological structure surface within a preset distance range, thereby delineating the deep unloading development zone;

[0012] For each delineated deep unloading development zone, a corresponding comprehensive development intensity index is calculated based on the cumulative opening width of all cracks within its boundary and its own length. The deep unloading development zone of the target area is then quantitatively evaluated based on the comprehensive development intensity index.

[0013] Furthermore, the calculation formula for the deep unloading development strength index is as follows:

[0014] ;

[0015] in, Indicates the center position of the window The intensity index of deep unloading development at the location, Indicates the window width. This represents the cumulative opening width of all cracks within the window.

[0016] Furthermore, the sliding step distance is less than or equal to half the width of the window.

[0017] Furthermore, threshold segmentation is performed on the continuous feature signal sequence based on a preset background noise threshold, specifically including:

[0018] The portion of the continuous feature signal sequence with an intensity value less than or equal to the background noise threshold is set to zero or marked as background; connected component analysis is performed on the continuous non-zero or non-background signal segments with an intensity value greater than the background noise threshold to identify and extract independent continuous segments, and each independent continuous segment is used as the candidate segment.

[0019] Furthermore, the method also includes:

[0020] After identifying the candidate segment, the continuous feature signal sequence within the candidate segment is traversed. If the relative change rate of the deep unloading development intensity index value corresponding to two adjacent positions exceeds a preset threshold, an internal segmentation point is marked between the two adjacent positions.

[0021] The candidate segment is divided at the corresponding position of each internal segmentation point to form multiple independent candidate sub-segments.

[0022] Furthermore, the boundary of the candidate segment is matched and corrected with the position of the nearest known geological structure surface within a preset distance range, specifically including:

[0023] For the boundary of each candidate segment or candidate sub-segment, perform boundary matching and correction: search for the existence of a known geological structure within the preset distance range of the boundary. If it exists, adjust the boundary to the position of the nearest known geological structure. If it does not exist, retain the original boundary. All candidate segments or candidate sub-segments that have undergone boundary matching and correction are the deep unloading development zone.

[0024] Furthermore, the formula for calculating the comprehensive developmental intensity index is as follows:

[0025] ;

[0026] in, Indicates the overall developmental intensity index, This represents the cumulative opening width of all fractures within the boundary of the deep unloading development zone. The length of the deep unloading development zone.

[0027] Furthermore, the method also includes:

[0028] The comprehensive development intensity index of each deep unloading development zone is compared with multiple preset intensity level threshold ranges to determine the development intensity level of the deep unloading development zone.

[0029] The developmental intensity levels include strong development, moderate development, slight development, and no development.

[0030] Furthermore, when the comprehensive development intensity index is greater than 10 mm / m, it is determined to be strong development; when the comprehensive development intensity index is greater than 5 mm / m and not greater than 10 mm / m, it is determined to be moderate development; when the comprehensive development intensity index is greater than 0 mm / m and not greater than 5 mm / m, it is determined to be slight development; when the comprehensive development intensity index is equal to 0 mm / m, it is determined to be no development.

[0031] Secondly, the present invention provides a sliding window-based system for identifying and evaluating deep unloading development zones on riverbanks, used to implement the sliding window-based method for identifying and evaluating deep unloading development zones on riverbanks as described in the first aspect, the system comprising:

[0032] The data acquisition module is used to acquire structured data of fractures exposed along the exploration line in the target area. The structured data includes the starting mileage coordinates, ending mileage coordinates, and opening width of each fracture.

[0033] The feature signal generation module is used to perform spatial sliding and statistics along the exploration line in a sliding window manner based on the set window width and sliding step distance; for each window, it filters out the fractures that intersect with the interval defined by the starting mileage coordinates and the ending mileage coordinates; it calculates the deep unloading development intensity index at the center position of the window based on the cumulative opening width of the filtered fractures; and it generates a continuous feature signal sequence characterizing the deep unloading development intensity based on the deep unloading development intensity index corresponding to each window.

[0034] The candidate segment identification module is used to perform threshold segmentation on the continuous feature signal sequence based on a preset background noise threshold, and identify continuous segments with signal intensity greater than the background noise threshold as candidate segments for concentrated development of deep unloading.

[0035] The development zone delineation module is used to match and correct the boundary of the candidate segment with the position of the nearest known geological structure surface within a preset distance range, thereby delineating the deep unloading development zone.

[0036] The quantitative evaluation module is used to calculate the corresponding comprehensive development intensity index for each delineated deep unloading development zone based on the cumulative opening width of all cracks within its boundary and its own length, and to perform a quantitative evaluation of the deep unloading development zone in the target area based on the comprehensive development intensity index.

[0037] The beneficial effects of this invention are as follows: The method and system for identifying and evaluating deep unloading development zones on bank slopes based on a sliding window, provided by this invention, automatically transforms discrete deep unloading geometric data into continuous development intensity characteristic signals through a sliding window algorithm. Based on objective threshold segmentation and geological structure surface calibration, it achieves automated, continuous spatial identification and precise delineation of deep unloading development zones within bank slopes, completely eliminating reliance on expert subjective experience and high-cost multi-source testing. Simultaneously, by calculating the comprehensive development intensity index of each deep unloading development zone, it achieves standardized quantitative evaluation of development intensity. The evaluation result is a structured geological object with both clear spatial boundaries and quantitative attributes, which can directly provide reliable input for digital zoning of rock mass quality and targeted engineering design, significantly improving evaluation efficiency, objectivity, and engineering application value. Attached Figure Description

[0038] Figure 1 is a flowchart illustrating the method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window, as provided in the embodiment.

[0039] Figure 2 is a schematic diagram of the continuous feature signal sequence provided in the embodiment;

[0040] Figure 3 is a schematic diagram of the structure of the sliding window-based identification and evaluation system for deep unloading development zones on riverbanks provided in the embodiment. Detailed Implementation

[0041] The purpose of this invention is to provide an integrated technical solution that can automatically perform continuous spatial intelligent scanning, precise boundary delineation, and quantitative strength evaluation of deep unloading development zones inside bank slopes based solely on basic geometric data of fractures through algorithms. This upgrades the deep unloading evaluation from a discrete, qualitative "point diagnosis" mode that relies on expert experience to a continuous, quantitative "zone identification" mode based on deterministic algorithms, providing core spatial data support for the digital and refined design and construction of geotechnical engineering.

[0042] Specifically, in this invention, a sliding window is first used to perform spatial convolution calculations on the discretely distributed deep unloading along the exploration line. By statistically analyzing the cumulative opening width of fractures within each window, a continuous sequence of deep unloading development intensity indicators is generated, thereby converting discrete point data into characteristic signals reflecting the degree of fracture concentration. Then, this characteristic signal is segmented using a preset background noise threshold to initially identify abnormally high value segments as candidate segments. Next, geological prior knowledge is introduced to forcibly match and calibrate the boundaries of candidate segments with the positions of known geological structural surfaces, ensuring that the boundaries of the delineated deep unloading development zones have both statistical anomalies and geological structural control rationality. Finally, the cumulative opening width per unit length of each delineated object is calculated as a comprehensive development intensity index, completing the transformation from raw data to engineering geological objects with quantitative attributes.

[0043] The technical solutions in this embodiment 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.

[0044] Figure 1 shows a flowchart of a method for identifying and evaluating deep unloading development zones on riverbank slopes based on a sliding window. Referring to Figure 1, the method includes the following steps:

[0045] Step 1: Obtain structured data of fractures exposed along the exploration line in the target area. The structured data includes the starting mileage coordinates, ending mileage coordinates, and opening width of each fracture.

[0046] This step serves as the data input and preprocessing stage. Its core function is to transform the raw, unstructured geological exploration data into a structured, ordered dataset that can be directly processed by subsequent algorithms.

[0047] The target area can be a micro-new rock mass area inside the conventional unloading development zone, and the exploration line can be a horizontal tunnel, borehole, etc.

[0048] Specifically, a standardized data input interface is first constructed. This interface receives and distinguishes two types of core input data: one type is the object data of the fracture, each fracture object is defined in the form of a feature vector, which includes at least the object identifier ID, the starting mileage coordinates, the ending mileage coordinates, and the opening width; the other type is the background data, including the starting coordinates and orientation of the exploration adit or borehole, as well as the type and precise mileage coordinates of known geological structural surfaces such as faults or rock strata interfaces.

[0049] Subsequently, a data cleaning and validation process is performed on the input data. This includes verifying the non-negativity of the fracture width and the rationality and validity of the mileage coordinates to ensure the accuracy and reliability of the data. After cleaning, all fracture objects are sorted in ascending order based on the starting mileage coordinates of each fracture, thereby generating a structured fracture dataset that is strictly arranged in spatial order along the exploration line.

[0050] Step 2: Based on the set window width and sliding step distance, slide the exploration line in a sliding window manner; for each window, select the fractures that intersect with the interval defined by the starting mileage coordinates and the ending mileage coordinates; calculate the deep unloading development intensity index at the center of the window based on the cumulative opening width of the selected fractures; and generate a continuous feature signal sequence characterizing the deep unloading development intensity based on the deep unloading development intensity index corresponding to each window.

[0051] Specifically, this step uses a sliding window algorithm to perform one-dimensional spatial convolution on the exploration line, transforming the discrete deep unloading data into a continuous feature signal sequence that characterizes the intensity of fracture concentration. Through mathematical operations, an objective and repeatable conversion from discrete point data to a continuous spatial intensity distribution is achieved, laying the data foundation for subsequent automatic identification.

[0052] In this embodiment, the calculation formula for the deep unloading development strength index is as follows:

[0053] ;

[0054] in, Indicates the center position of the window The intensity index of deep unloading development at the location, Indicates the window width. This represents the cumulative opening width of all cracks within the window.

[0055] In practical applications, the algorithm parameters are first initialized: a fixed window width is set. (Typically 1-5 meters, such as 2 meters) is used as the neighborhood range for spatial statistics, and a value less than or equal to is set. sliding step (Typically 1 meter) to ensure the continuity of the output signal. Then, starting from the beginning of the exploration line, a sliding step distance is used. Move the window gradually at intervals.

[0056] For each sliding window, perform the following calculation: automatically filter all fracture objects that meet the criteria from the preprocessed structured dataset, the criteria being the spatial interval defined by their starting and ending mileage coordinates, and the spatial range of the current window. There is an intersection. Then, calculate the opening width of all cracks falling within this window. The cumulative value, i.e. Finally, through the formula Calculate the current center position of the window. Deep unloading development intensity index .

[0057] After traversing the entire exploration line, a sequence of deep unloading development intensity indicators arranged in positional order will be output, which constitutes a continuous characteristic signal sequence. Each of them The value physically represents the position. The cumulative opening width of deep unloading per unit length corresponds directly to the spatially concentrated sections of fractures. See Figure 2; low-value areas represent relatively intact sections of the rock mass, while high-value areas and peaks clearly identify anomalous sections with concentrated deep unloading.

[0058] Step 3: Perform threshold segmentation on the continuous feature signal sequence based on a preset background noise threshold, and identify continuous segments with signal intensity greater than the background noise threshold as candidate segments for concentrated development of deep unloading.

[0059] In this embodiment, threshold segmentation of the continuous feature signal sequence is performed based on a preset background noise threshold, specifically including:

[0060] The portion of the continuous feature signal sequence with an intensity value less than or equal to the background noise threshold is set to zero or marked as background; connected component analysis is performed on the continuous non-zero or non-background signal segments with an intensity value greater than the background noise threshold to identify and extract independent continuous segments, and each independent continuous segment is used as the candidate segment.

[0061] The core task of this step is to automatically identify and extract potential concentrated deep unloading development zones from continuous deep unloading development intensity signals. This process is based on a preset background noise threshold. (Typically a positive decimal close to zero, such as 0.1 mm / m) for continuous characteristic signal sequences The sequence is then used for discrimination. Sections with intensity values ​​less than or equal to the background noise threshold are considered background noise zones, corresponding to intact or nearly intact rock mass sections; while sections with intensity values ​​greater than the background noise threshold are considered valid signals reflecting significant deep unloading development.

[0062] In practical applications, continuous characteristic signal sequences All The value is set to zero or a specific background marker is assigned. Then, the remaining... Connectivity analysis is performed on continuous non-zero signal segments. Each spatially continuous high-value signal segment that is not interrupted by background segments is identified and extracted as an independent candidate segment, and its start and end coordinates are recorded. This process realizes the transformation from continuous intensity signals to discretized spatially anomalous segments.

[0063] In this embodiment, the method further includes: after identifying the candidate segment, traversing the continuous feature signal sequence within the candidate segment; if the relative change rate of the deep unloading development intensity index value corresponding to two adjacent positions exceeds a preset threshold, then marking an internal segmentation point between the two adjacent positions; dividing the candidate segment at the position corresponding to each internal segmentation point to form multiple independent candidate sub-segments.

[0064] Specifically, after identifying candidate segments, the above-mentioned internal fine segmentation steps can be further included to address situations where multiple rupture development centers or different geological structures may exist within a single candidate segment.

[0065] In practical applications, traversing the continuous feature signal sequence within the candidate segment... By calculating two adjacent sampling positions and The relative rate of change of the deep unloading development intensity index (e.g., the ratio of the absolute difference between two deep unloading development intensity indices to the larger value of the deep unloading development intensity index) is compared with a preset threshold (e.g., 50%). When the relative rate of change between adjacent locations exceeds the preset threshold, it indicates a sudden change in signal intensity, which may correspond to a significant transition boundary in geological structure or deep unloading development intensity. In other words, an internal dividing point is marked between these two adjacent locations.

[0066] After marking all internal segmentation points, the original candidate segment is divided at its corresponding location based on the position of each internal segmentation point. This decomposes a potentially long continuous candidate segment into multiple shorter, independent candidate sub-segments with more uniform internal signal characteristics. The candidate sub-segments will serve as more refined processing units and will be input into the subsequent boundary calibration step to achieve a more accurate delineation of the spatial range of the deep unloading development zone.

[0067] Step 4: Match and correct the boundary of the candidate section with the position of the nearest known geological structure surface within its preset distance range, thereby delineating the deep unloading development zone.

[0068] In this embodiment, the boundary of the candidate segment is matched and corrected with the position of the nearest known geological structure surface within a preset distance range, specifically including:

[0069] For the boundary of each candidate segment or candidate sub-segment, perform boundary matching and correction: search for the existence of a known geological structure within the preset distance range of the boundary. If it exists, adjust the boundary to the position of the nearest known geological structure. If it does not exist, retain the original boundary. All candidate segments or candidate sub-segments that have undergone boundary matching and correction are the deep unloading development zone.

[0070] The core objective of this step is to integrate the initially identified candidate sections (or candidate sub-sections after internal subdivision) with the geological prior knowledge revealed by the engineering survey, thereby outputting a more geologically reasonable spatial object of the deep unloading development zone.

[0071] In practical applications, the start and end boundaries of each candidate segment or sub-segment are processed separately. For each boundary point, a search is conducted within a preset distance range (e.g., 1 to 5 meters) to determine if a known geological structure exists. If a known geological structure is found within this range, a boundary snapping operation is performed, forcibly adjusting the candidate boundary to the precise mileage position of the nearest known geological structure. If no known geological structure exists within the search range, the original position of the boundary is retained. This process ensures that the final boundary is controlled by a clearly defined geological structure or naturally demarcated by a point of significant change in statistical characteristics.

[0072] After the above matching and correction operations, each spatial interval with optimized new boundaries is defined and output as a deep unloading development zone object. The output deep unloading development zone object not only reveals the characteristics of fracture concentration, but its boundaries also have clear geological structural basis, thus realizing the transformation from statistical anomaly area to geological engineering unit, providing a directly locatable and geologically logical spatial entity for subsequent quantitative evaluation and engineering applications.

[0073] Step 5: For each delineated deep unloading development zone, calculate the corresponding comprehensive development intensity index based on the cumulative opening width of all cracks within its boundary and its own length, and quantitatively evaluate the deep unloading development zone of the target area according to the comprehensive development intensity index.

[0074] In this embodiment, the formula for calculating the comprehensive developmental intensity index is as follows:

[0075] ;

[0076] in, Indicates the overall developmental intensity index, This represents the cumulative opening width of all fractures within the boundary of the deep unloading development zone. The length of the deep unloading development zone.

[0077] The core task of this step is to assign a standardized quantitative intensity label to each precisely delineated deep unloading development zone, thereby transforming it from a spatial object into a calculable engineering attribute.

[0078] In practical applications, firstly, based on the optimized start and end boundaries of the unloading development zone, all fracture objects whose start and end mileage coordinates fall within the boundary are extracted from the original dataset; then, the cumulative opening width of all fractures within the boundary of the deep unloading development zone is calculated. The length of the deep unloading development zone is determined based on the optimized start and end boundaries; finally, the formula is used... The comprehensive development intensity index of the deep unloading development zone was calculated. Comprehensive developmental intensity index The physical meaning of is the cumulative opening width per unit length (unit: mm / m). It is a normalized intensity measure that eliminates the influence of the length of the development zone itself, allowing for an objective and direct comparison of the intensity between development zones of different sizes and locations.

[0079] Based on the calculated comprehensive developmental intensity index This allows for a quantitative evaluation of the deep unloading of the bank slope in the target area. Specifically, the comprehensive development intensity index... The larger the value, the higher the intensity of deep unloading development on the bank slope in the target area, and the more significant the cumulative damage effect experienced by the bank slope rock mass. The comprehensive development intensity index indicates... The smaller the value, the lower the intensity of deep unloading development on the target slope, and the weaker the cumulative damage effect. Therefore, the calculated comprehensive development intensity index can be used to determine the optimal index. The quantitative values ​​enable a quantitative evaluation of the development intensity of deep unloading on the bank slope in the target area, providing a clear and definite scientific basis for engineering decisions.

[0080] In this embodiment, the method further includes: comparing the comprehensive development intensity index of each deep unloading development zone with a preset range of multiple intensity level thresholds to determine the development intensity level of the deep unloading development zone; the development intensity level includes strong development, moderate development, slight development, and no development. Specifically, when the comprehensive development intensity index is greater than 10 mm / m, it is determined to be strongly developed; when the comprehensive development intensity index is greater than 5 mm / m and not greater than 10 mm / m, it is determined to be moderately developed; when the comprehensive development intensity index is greater than 0 mm / m and not greater than 5 mm / m, it is determined to be slightly developed; and when the comprehensive development intensity index is equal to 0 mm / m, it is determined to be no development.

[0081] Specifically, the comprehensive development intensity index of each deep unloading development zone will be used. The system automatically compares the data with a pre-defined threshold range determined through statistical analysis of extensive historical engineering data to objectively determine the development intensity level. Specifically, based on established rules: when... It was determined to be intense development at that time. At that time, it was determined to be of moderate development. It was initially determined to be mild development. If it is determined to be underdeveloped, it is then considered to be underdeveloped. In practical applications, this rule automatically outputs the corresponding strength level label, thereby transforming continuous quantitative indices into intuitive and definite engineering risk classification conclusions, providing a clear and operable qualitative judgment basis for macro-level decision-making.

[0082] In summary, the sliding window-based method for identifying and evaluating deep unloading development zones on riverbanks provided in this embodiment achieves fully automated, high-precision identification and quantitative evaluation of these zones by constructing a complete algorithm based on sliding window convolution, threshold segmentation, and geological knowledge fusion. This embodiment completely eliminates the reliance on expert subjective experience and costly multi-source testing found in traditional methods. It automatically completes continuous spatial scanning and anomaly detection along the entire exploration line using only basic fracture geometric data. By forcibly calibrating the data-driven preliminary identification results with known geological structural surfaces, it ensures that the delineated development zones possess both statistical significance and geological rationality. Finally, by calculating the comprehensive development intensity index of each development zone and mapping it to a standardized engineering risk level, a structured geological object with precise spatial boundaries (meter-level accuracy) and continuous quantitative attributes is generated. This result can be directly used by downstream BIM design platforms, numerical analysis software, and project management systems, thus achieving seamless integration from exploration data to intelligent design and significantly improving the objectivity, efficiency, and digital application value of engineering evaluation.

[0083] Based on the above technical solutions, this embodiment also proposes a sliding window-based system for identifying and evaluating deep unloading development zones on riverbanks, used to implement the sliding window-based method for identifying and evaluating deep unloading development zones on riverbanks as described in the embodiment. Please refer to Figure 3. The system includes:

[0084] The data acquisition module is used to acquire structured data of fractures exposed along the exploration line in the target area. The structured data includes the starting mileage coordinates, ending mileage coordinates, and opening width of each fracture.

[0085] The feature signal generation module is used to perform spatial sliding and statistics along the exploration line in a sliding window manner based on the set window width and sliding step distance; for each window, it filters out the fractures that intersect with the interval defined by the starting mileage coordinates and the ending mileage coordinates; it calculates the deep unloading development intensity index at the center position of the window based on the cumulative opening width of the filtered fractures; and it generates a continuous feature signal sequence characterizing the deep unloading development intensity based on the deep unloading development intensity index corresponding to each window.

[0086] The candidate segment identification module is used to perform threshold segmentation on the continuous feature signal sequence based on a preset background noise threshold, and identify continuous segments with signal intensity greater than the background noise threshold as candidate segments for concentrated development of deep unloading.

[0087] The development zone delineation module is used to match and correct the boundary of the candidate segment with the position of the nearest known geological structure surface within a preset distance range, thereby delineating the deep unloading development zone.

[0088] The quantitative evaluation module is used to calculate the corresponding comprehensive development intensity index for each delineated deep unloading development zone based on the cumulative opening width of all cracks within its boundary and its own length, and to perform a quantitative evaluation of the deep unloading development zone in the target area based on the comprehensive development intensity index.

[0089] It is understood that the sliding window-based deep unloading development zone identification and evaluation system for bank slopes described in this embodiment is a system for implementing the sliding window-based deep unloading development zone identification and evaluation method for bank slopes described in the embodiment. As the system disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant parts, please refer to the description of the method. It will not be repeated here.

Claims

1. A method for identifying and evaluating deep unloading development zones on riverbank slopes based on a sliding window, characterized in that, The method includes: acquiring structured data of fractures exposed along the exploration line in the target area, the structured data including the starting mileage coordinates, ending mileage coordinates, and opening width of each fracture; spatially sliding along the exploration line in a sliding window manner based on a set window width and sliding step distance; for each window, selecting fractures whose intervals defined by the starting and ending mileage coordinates intersect with the window; calculating the deep unloading development intensity index at the center of the window based on the cumulative opening width of the selected fractures; and generating a continuous feature characterizing the deep unloading development intensity based on the deep unloading development intensity index corresponding to each window. The continuous feature signal sequence is segmented based on a preset background noise threshold to identify continuous segments with signal intensities greater than the background noise threshold, which are then used as candidate segments for concentrated deep unloading development. The boundaries of the candidate segments are matched and corrected with the positions of the nearest known geological structural surfaces within a preset distance range to delineate deep unloading development zones. For each delineated deep unloading development zone, a corresponding comprehensive development intensity index is calculated based on the cumulative opening width of all fractures within its boundary and its own length. The deep unloading development zones in the target area are then quantitatively evaluated based on the comprehensive development intensity index.

2. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window, as described in claim 1, is characterized in that... The calculation formula for the deep unloading development strength index is as follows: ;in, Indicates the center position of the window The intensity index of deep unloading development at the location, Indicates the window width. This represents the cumulative opening width of all cracks within the window.

3. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window, as described in claim 1, is characterized in that... The sliding step distance is less than or equal to half the width of the window.

4. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window, as described in claim 1, is characterized in that... Threshold segmentation of the continuous feature signal sequence based on a preset background noise threshold specifically includes: setting the portion of the continuous feature signal sequence with an intensity value less than or equal to the background noise threshold to zero or marking it as background; performing connected component analysis on continuous non-zero or non-background signal segments with an intensity value greater than the background noise threshold, identifying and extracting independent continuous segments, and using each independent continuous segment as the candidate segment.

5. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window, as described in claim 1, is characterized in that... The method further includes: after identifying the candidate segment, traversing the continuous feature signal sequence within the candidate segment; if the relative change rate of the deep unloading development intensity index value corresponding to two adjacent positions exceeds a preset threshold, then marking an internal segmentation point between the two adjacent positions; dividing the candidate segment at the position corresponding to each internal segmentation point to form multiple independent candidate sub-segments.

6. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window, as described in claim 5, is characterized in that... The boundary of the candidate segment is matched and corrected with the position of the nearest known geological structure within a preset distance range. Specifically, this includes: performing boundary matching and correction for the boundary of each candidate segment or candidate sub-segment: searching for the existence of a known geological structure within the preset distance range of the boundary; if it exists, adjusting the boundary to the position of the nearest known geological structure; if it does not exist, retaining the original boundary; all candidate segments or candidate sub-segments that have undergone boundary matching and correction are the deep unloading development zone.

7. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window according to claim 1, characterized in that, The formula for calculating the comprehensive developmental intensity index is as follows: ;in, Indicates the overall developmental intensity index, This represents the cumulative opening width of all fractures within the boundary of the deep unloading development zone. The length of the deep unloading development zone.

8. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window according to claim 1, characterized in that, The method further includes: comparing the comprehensive development intensity index of each deep unloading development zone with multiple preset intensity level threshold ranges to determine the development intensity level of the deep unloading development zone; the development intensity level includes strong development, moderate development, slight development and no development.

9. The method for identifying and evaluating deep unloading development zones on riverbanks based on a sliding window, as described in claim 8, is characterized in that... When the comprehensive development intensity index is greater than 10 mm / m, it is determined to be strong development; when the comprehensive development intensity index is greater than 5 mm / m and not greater than 10 mm / m, it is determined to be moderate development; when the comprehensive development intensity index is greater than 0 mm / m and not greater than 5 mm / m, it is determined to be slight development; when the comprehensive development intensity index is equal to 0 mm / m, it is determined to be no development.

10. A sliding window-based system for identifying and evaluating deep unloading development zones on riverbank slopes, characterized in that, To implement the method for identifying and evaluating deep unloading development zones on bank slopes based on sliding windows as described in any one of claims 1 to 9, the system comprises: a data acquisition module for acquiring structured data of fractures exposed along the exploration line in the target area, the structured data including the starting mileage coordinates, ending mileage coordinates, and opening width of each fracture; a feature signal generation module for performing spatial sliding and statistical analysis along the exploration line in a sliding window manner based on a set window width and sliding step distance; for each window, selecting fractures whose intervals defined by the starting and ending mileage coordinates intersect with the window, calculating the deep unloading development intensity index at the center of the window based on the cumulative opening width of the selected fractures, and calculating the deep unloading development intensity index corresponding to each window. The system generates a continuous feature signal sequence characterizing the intensity of deep unloading development. A candidate segment identification module performs threshold segmentation on the continuous feature signal sequence based on a preset background noise threshold, identifying continuous segments with signal intensities greater than the background noise threshold as candidate segments for concentrated deep unloading development. A development zone delineation module matches and corrects the boundary of the candidate segment with the position of the nearest known geological structure within a preset distance range, thereby delineating the deep unloading development zone. A quantitative evaluation module calculates a comprehensive development intensity index for each delineated deep unloading development zone based on the cumulative opening width of all fractures within its boundary and its own length, and performs a quantitative evaluation of the deep unloading development zone in the target area based on the comprehensive development intensity index.

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