Mine roof risk identification and evaluation method based on space evolution of surrounding rock structural plane

Abstract

The invention discloses a mine roof risk identification and evaluation method based on surrounding rock structural plane spatial evolution. The method comprises the following steps: acquiring basic geometric and spatial data of a surrounding rock structural plane of a slope adjacent to a roof; structural plane space modeling is carried out, plane domain labels are marked, and structural plane candidate pairing preprocessing is completed under a unified space coordinate system; analyzing the incidence relation between the roof structural plane and the side structural plane in the spatial position and the extension direction, and determining a structural plane combination with spatial continuity; the roof surrounding rock internal structural plane distribution state and a confidence degree set are inversely output; constructing a roof turquoise body space model containing a hypothetical structural plane and a candidate set; extracting parameters to form a characteristic parameter set for instability analysis; analyzing the spatial relationship between the turquoise body and the free surface of the top plate, and determining the spatial range and the exposure index of the turquoise body; and calculating the instability probability of the turquoise body, and carrying out grading evaluation on the roof turquoise caving risk. According to the method, identification and caving risk grading of the potential turpentine body of the underground mine roof surrounding rock can be realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for underground rock masses, specifically relating to a method for identifying and assessing the risk of mine roof slabs based on the spatial evolution of surrounding rock structural surfaces. Background Technology

[0002] During underground mining operations, the roof rock in roadways or mining areas is subjected to high ground stress, high mining disturbance, and complex structural surfaces for extended periods, making it highly susceptible to localized loosening and even collapse accidents. Roof rock collapses not only threaten the lives of underground workers but can also lead to roadway instability, equipment damage, and production interruptions, making it one of the most critical types of hazards requiring strict control in underground mining safety. Therefore, accurate identification of potential loose rock masses in the roof rock and a reasonable assessment of their collapse risk have significant engineering implications and practical value.

[0003] From the perspective of rock mass structure characteristics, the surrounding rock in underground mines is not a continuous homogeneous medium, but rather a blocky or quasi-blocky structure formed by multiple sets of joints, fissures, and bedding planes. The formation process of turbidity in the roof is essentially the result of the spatial combination, cutting, and interaction of the surrounding rock structural planes with free surfaces. Especially in roadways or stopes, roof structural planes often have a certain degree of spatial continuity or correlation with adjacent sidewall structural planes. This continuous extension of structural planes in space directly affects the geometry, spatial extent, and instability mode of potential turbidity. However, under actual engineering conditions, most of the internal structural planes of the roof cannot be directly observed; only limited information on the exposed structural planes on the roof and sidewall surfaces can be obtained, leading to significant uncertainty in the understanding of the internal structural characteristics of the roof.

[0004] Currently, the identification and risk assessment of turquoise on the roof of underground mines mainly rely on manual inspection, experience-based judgment, or qualitative analysis methods based on local free surface features. While feasible to some extent, these methods are significantly affected by personnel experience levels, site conditions, and subjective judgment differences, making it difficult to achieve stable, objective, and repeatable risk assessment results. With the application of non-contact measurement technologies such as 3D laser scanning and photogrammetry in underground mines, the ability to acquire geometric information and structural surface exposure characteristics of the surrounding rock surface has been significantly improved, providing a new data foundation for the spatial analysis of turquoise bodies on the roof. However, existing research and applications based on 3D measurement technology mostly focus on the reconstruction of the surrounding rock surface morphology or the geometric analysis of a single free surface, often treating the roof and sidewall structural surface information separately, failing to fully explore their spatial correlation. Furthermore, while some methods attempt to geometrically segment the surrounding rock mass, they typically assume that structural surfaces exist in isolation in space, lacking a systematic consideration of the extension trend and spatial evolution process of structural surfaces, making it difficult to reasonably invert the distribution state of internal structural surfaces in the roof, thus limiting the accurate identification of the true morphology and spatial extent of potential turquoise bodies. Furthermore, in terms of roof rockfall risk assessment, existing methods mostly employ qualitative grading or simple judgments based on a few geometric indicators, failing to comprehensively consider the geometric morphological characteristics of the rock mass, structural surface constraints, and its spatial relationship with the free surface of the roof. This results in risk assessment results that are difficult to reflect the differences between different rock masses, and is also detrimental to the refined management and graded disposal of hazardous areas. In summary, existing technologies for identifying roof rockfall and assessing risk of collapse in underground mines still generally have the following shortcomings: First, the spatial correlation between the roof and sidewall structural surfaces is insufficient, making it difficult to infer the internal structural characteristics of the roof; second, the spatial construction of potential rock masses lacks a systematic method, and the geometric morphology and constraint conditions are not sufficiently represented; third, the quantification of rockfall risk assessment is limited, making it difficult to support scientific decision-making and refined management.

[0005] Therefore, there is an urgent need to provide a method for identifying and assessing the risk of turquoise collapse from the top of underground mines based on the spatial evolution of the surrounding rock structure. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method for identifying and assessing mine roof risks based on the spatial evolution of surrounding rock structural surfaces. This method is simple to implement and has low implementation costs. It can efficiently identify potential loose rock bodies in the surrounding rock of underground mine roofs and classify their risk of collapse, providing a scientific and reliable basis for on-site roof safety management and loose rock disposal decisions.

[0007] To achieve the above objectives, this invention provides a method for identifying and assessing mine roof risk based on the spatial evolution of surrounding rock structural surfaces, comprising the following steps: Step 1: Collect basic geometric and spatial data of the roof and the surrounding rock structure adjacent to the roof in underground tunnels or mining areas; Step 2: Based on the collected basic geometric and spatial data, perform spatial modeling of structural surfaces and label the surface regions. Complete the preprocessing of candidate pairing of structural surfaces under a unified spatial coordinate system. Step 3: Analyze the relationship between the top slab structural surface and the sidewall structural surface in terms of spatial location and extension direction, determine the combination of structural surfaces with spatial continuity, and output key information; Step 4: Based on the determined combination of structural surfaces, extend the sidewall structural surfaces into the interior of the roof along their extension direction, and invert and output the distribution state and confidence set of the internal structural surfaces of the roof surrounding rock. Step 5: Based on the location of the free surface of the top plate, construct and output the spatial model of the turquoise body of the top plate, including the hypothetical structural surface, and the candidate set; Step 6: Based on the constructed turquoise body spatial model, extract multidimensional parameters and normalize them to form a set of characteristic parameters for instability analysis, and output them in combination with confidence scores; Step 7: Analyze the spatial relationship between the turquoise body and the free surface of the top plate to determine the spatial range of the turquoise body and the exposure index of the turquoise body; Step 8: Calculate the probability of instability of the loose rock mass, and classify and assess the risk of loose rock falling from the roof based on the calculation results.

[0008] Furthermore, in order to lay a high-quality data foundation for subsequent structural surface inversion and risk assessment, the process of collecting basic geometric and spatial data of the roof and adjacent sidewall rock structural surfaces in the underground roadway or mining area in step one is as follows: S11: Basic data acquisition of surrounding rock structural surfaces; acquire visible outcrop traces of surrounding rock structural surfaces on the surface, outcrop locations expressed in three-dimensional coordinate point sets, and corresponding spatial coordinate information, preliminarily distinguish the structural surface data of the roof and sidewall areas, and form the original dataset. S12: Preprocessing and quality control of raw structural surface data; denoising and anomaly removal are performed on the collected point cloud or image data respectively: outlier deletion, point density equalization and normal estimation are performed in point cloud scenarios; distortion correction, illumination equalization and occlusion area identification are performed in image scenarios; unusable areas caused by dust, spraying, and reflection are masked and data integrity indicators are recorded.

[0009] Furthermore, in order to achieve the standardized and structured representation of structural surface data and provide accurate data support for subsequent spatial correlation analysis, the preprocessing of candidate structural surface pairing under a unified spatial coordinate system is completed in step two as follows: S21: Geometric parameterization and region labeling of structural surfaces; the exposed traces are discretized into point sets, and the trace direction, endpoints, and visible length are obtained through line segment fitting or curve fitting; combined with the local plane fitting of the point cloud or the image-point cloud registration results, each trace is assigned spatial normal, dip, and tilt parameters to form a complete structural surface object representation; finally, each structural surface is described by spatial location, geometric orientation, extension direction, visible length, and uncertainty parameters, and the top plate or sidewall region label to which each structural surface belongs is marked; S21: Preprocessing of candidate pairing of structural surfaces under a unified coordinate system; After completing the unified coordinate modeling, establish spatial grid indexes for the top plate and side plate structural surface sets respectively; Generate spatial extension regions along the extension direction of each structural surface; Filter structural surfaces that meet the preset spatial distance conditions or extension region overlap conditions, and output a list of candidate pairings of structural surfaces.

[0010] Furthermore, in order to achieve accurate determination and standardized integration of the spatial continuity of structural surfaces, and to provide a reliable correlation basis for subsequent structural surface distribution inversion, the process of determining the combination of structural surfaces with spatial continuity and outputting key information in step three is as follows: S31: Candidate association pair construction and consistency index calculation; Read the candidate pair list and establish a candidate association pair for each group of "top plate structural surface - side wall structural surface"; Calculate the three core indicators of positional consistency, orientation consistency and extension consistency of the association pair respectively, so as to provide a quantitative basis for subsequent structural surface continuity determination; S32: Screening of spatially continuous structural surface combinations; a combination of threshold judgment and comprehensive scoring is used to screen combinations of structural surfaces with spatial continuity: orientation threshold and position threshold are set as basic judgment conditions, and candidate association pairs that meet the basic judgment conditions are screened; the candidate association pairs are comprehensively evaluated based on the three calculated consistency indicators and sorted from high to low scores; for cases where the same roof structural surface corresponds to multiple side structural surfaces, the association relationship with the highest score and that meets the mutual exclusion constraint is retained; S33: Structural surface combination unit integration and information output; merge the filtered related pairs into independent structural surface combination units; group multiple structural surfaces that are in a chain-like continuous relationship in space into the same combination unit; finally output the key information of each combination unit, including the starting segment of the side wall, the corresponding segment of the top plate, the extension direction and the confidence level.

[0011] Furthermore, in order to achieve accurate inversion and reliable quantification of the distribution of internal structural surfaces of the roof, the process of inverting and outputting the distribution state and confidence set of the internal structural surfaces of the surrounding rock of the roof in step four is as follows: S41: Determine the top plate extension direction of the sidewall structural surface; for each structural surface combination, determine the extension direction into the top plate based on the relationship between the visible trace direction, local normal and gravity direction of the sidewall structural surface; if the extension direction is ambiguous, choose the direction that is more likely to intersect with the normal of the free surface of the top plate. S42: Spatial extrapolation generates imaginary structural surfaces inside the top plate; along the determined extension direction, spatial extrapolation is performed on the side wall structural surfaces to generate a set of "imaginary structural surfaces" corresponding to the internal region of the top plate; S43: Construct and optimize the internal structural surface field of the top plate; superimpose all hypothetical structural surfaces with the observed structural surfaces of the top plate to form a structural surface field of the internal structural surface distribution of the top plate; perform repeated merging and conflict elimination operations on the structural surface field: merge structural surfaces with consistent orientation and spatial overlap, and mark extrapolated structural surfaces with excessive orientation differences or contradictions with the observed structural surfaces of the top plate as low confidence and perform weight reduction processing. S44: Assign confidence scores and output the top plate structural surface field and confidence score set; combine four indicators, namely, original data integrity, candidate association reliability, extrapolation length, and consistency with the observed top plate structural surface, to assign confidence scores to each extrapolated top plate internal structural surface; finally output the integrated top plate internal structural surface field and its corresponding confidence score set.

[0012] Furthermore, in order to achieve accurate identification and structured extraction of potential turquoise bodies in the roof, the process of constructing and outputting a spatial model and candidate set of turquoise bodies in the roof containing hypothetical structural surfaces in step five is as follows: S51: Construct the free surface boundary and align it with the structural surface field; construct the free surface geometric boundary based on the range of the free surface of the top plate to achieve precise alignment with the structural surface field of the top plate, providing a benchmark for subsequent spatial cutting; S52: Cut the top plate space to form closed / semi-closed units; combine the free surface of the top plate, the observed structural surface and the internal extrapolated structural surface to cut the adjacent space of the top plate to form several closed or semi-closed space units; for units that are not completely closed, the free surface is used as the opening boundary to form a semi-closed unit. S53: Screen and determine candidates for turquoise bodies; set candidate screening conditions: intersecting with the free surface of the top plate or the distance is less than the threshold; constrained by at least two or more structural surfaces, or constrained by one structural surface and the free surface and having a separable tendency; the volume, thickness or projected area exceeds the minimum threshold to eliminate noisy units; screen spatial units as turquoise body candidates according to the set candidate screening conditions; S54: Record turquoise body information and output a set; construct the boundary for each turquoise body candidate record, and finally output the complete turquoise body set.

[0013] Furthermore, in order to provide comprehensive, accurate, and standardized data support for subsequent instability probability calculations, step six involves forming a set of characteristic parameters for instability analysis, and the process of combining this with confidence level output is as follows: S61: Extract the basic geometric and centroid parameters of the turquoise body; for each turquoise body, extract the core geometric parameters such as volume, surface area, maximum thickness, minimum thickness, centroid position, and principal axis direction from the geometric morphology parameters, and construct a basic feature dataset; S62: Extract the constraint feature parameters of free surface and structural surface; extract the proportion of exposed free surface, the number of structural surface constraint surfaces, and the proportion of extrapolated structural surfaces in the boundary from the geometric morphology parameters, and quantify the correlation features between turquoise body and free surface and structural surface; S63: Extract shape and instability-sensitive direction parameters; extract the shape slenderness ratio and bottom support area from the geometric morphology parameters, and determine the sensitive direction of potential slippage or toppling of the turquoise body, providing targeted parameters for subsequent instability analysis; S64: Parameter normalization and feature set output; All acquired parameters are normalized according to a preset range or statistical distribution to construct a set of geometric and spatial feature parameters for turquoise body instability analysis, which are then output together with the turquoise body confidence score.

[0014] Furthermore, in order to achieve a refined analysis of the spatial relationship between the turquoise body and the free surface of the roof, and to provide accurate exposure characteristics for risk assessment, the process of determining the spatial range and exposure characteristics of the turquoise body in step seven is as follows: S71: Determine exposed turquoise bodies; calculate the nearest distance between the turquoise body and the free surface of the top plate, and determine the turquoise bodies whose nearest distance is less than the preset range as exposed turquoise bodies; S72: Process the projection and overlapping areas of turquoise bodies; project the exposed turquoise bodies onto the free surface of the top plate to obtain the projected area and the boundary of the covered area; if multiple turquoise body projections overlap, merge the overlapping areas and record the number of overlapping layers to reflect the risk of local multiple turquoise body superposition. S73: Determine the exposure type and output the index; Based on the proportion of free surface exposure and the direction of the main axis of the turquoise body, the turquoise body exposure type is divided into three categories: local corner exposure, strip-shaped exposure, or planar exposure, and the corresponding exposure index is output.

[0015] Furthermore, in order to achieve quantitative assessment and graded management of the risk of roof rockfall and to identify key areas of concern, the graded assessment process for the risk of roof rockfall in step eight is as follows: S81: Construct a calculation model for the instability probability of turquoise; Based on the feature vectors and exposure indices of turquoise bodies, build an analytical model for calculating the instability probability of turquoise. S82: Calculate and correct the actual instability probability of the loose rock mass; calculate the initial instability probability for each loose rock mass, and correct the initial instability probability by combining the confidence level and data completeness to obtain the actual instability probability; if multiple loose rock masses cover the same roof area, the risk of the area is superimposed and corrected according to the number of overlapping layers. S83: Classify and label the risk levels of turquoise bodies; based on the actual instability probability of turquoise bodies, classify them into multiple discrete risk levels; the risk level classification is determined by referring to historical turquoise collapse records, on-site inspection results or expert experience, and assign a corresponding risk level label to each turquoise body; S84: Generate a list of risks of loose rock falling from the roof; sort the loose rock bodies from high to low risk level; for loose rock bodies of the same risk level, sort them from high to low actual instability probability, and finally form a list of risks of loose rock falling from the roof and the corresponding roof area level labels and key attention areas.

[0016] This invention provides a method for identifying and assessing mine roof risk based on the spatial evolution of surrounding rock structural surfaces. By acquiring spatial information on the structural surfaces of the roof and sidewalls of underground roadways or mining areas, it provides a complete data source for subsequent analysis of their spatial relationships. Modeling and preprocessing using a unified spatial coordinate system solves the problem of correlation analysis errors caused by inconsistent coordinates between roof and sidewall structural surface data, providing a standardized data carrier for subsequent determination of cross-location spatial relationships. Preprocessing with surface labeling and candidate pairing pre-screens potentially related structural surface combinations, avoiding the heavy computational load on the full dataset for subsequent analysis, significantly shortening the analysis cycle and reducing computational requirements. Simultaneously, surface labeling clarifies the structural surface's affiliation and attributes, providing a basis for accurate pairing and reducing invalid analysis processes. Analysis of the spatial extension patterns and locational correlations of structural surfaces effectively screens out structural surface combinations with continuous development characteristics, providing a reliable foundation for subsequent inversion of internal roof structural surfaces and avoiding analytical biases due to the omission of key correlation features. By combining the spatial correlation between the structural surfaces of the roof and sidewalls, the distribution characteristics of structural surfaces within the roof surrounding rock are inverted, ensuring that the inverted distribution of structural surfaces within the roof closely matches actual geological conditions and reducing errors caused by purely theoretical assumptions. A confidence set is simultaneously output to clarify the reliability of the inversion results, providing a quantitative reference for subsequent turbidity body model construction and risk assessment. Based on this, a spatial model of the roof turbidity body, including hypothetical structural surfaces, is constructed. This model accurately characterizes the geometric morphology and spatial extent of potential loosened areas in the roof surrounding rock, realistically reproducing the distribution morphology and boundary conditions of turbidity bodies within the roof surrounding rock. A candidate set is also output, providing clear analytical objects for subsequent multidimensional parameter extraction and instability analysis. Multidimensional feature parameters are extracted, comprehensively covering key influencing factors of turbidity body instability, ensuring the comprehensiveness of the analysis. Normalization eliminates the influence of dimensional differences and numerical ranges among different parameters, ensuring a balanced weight for each parameter in the instability analysis and improving the objectivity of the analysis results. By analyzing the shape characteristics, dimensional parameters, and spatial relationship of turquoise masses with free surfaces, the core triggering conditions for turquoise instability can be accurately identified. By determining the spatial range and exposure indicators, qualitative relationships are transformed into quantitative indicators, providing a quantifiable basis for subsequent instability probability calculations and risk classification. Criteria for identifying the instability probability of turquoise masses are established, and the instability probabilities of different turquoise masses are quantitatively calculated and classified. Ultimately, the risk level of roof turquoise collapse and key areas of concern are determined. This provides both accurate quantitative risk data and intuitive classification results, facilitating engineers to quickly assess risk levels and develop targeted prevention and control measures.

[0017] This invention enables the quantitative identification and assessment of the risk of roof rockfall in underground mines, providing a practical technical solution for the identification and handling of roof rockfall hazards. Its core advantages are as follows: 1. High inversion accuracy and more reliable identification of loose rock bodies: The innovative introduction of spatial correlation analysis method between the roof and sidewall structural surfaces fully explores and utilizes the limited structural surface exposure information of the surrounding rock surface to achieve accurate inversion of the distribution state of the internal structural surfaces of the roof, which greatly improves the reliability and accuracy of identifying potential loose rock bodies.

[0018] 2. Comprehensive assessment dimensions for more accurate risk assessment: By comprehensively considering the geometric morphological characteristics, structural constraint characteristics, and spatial relationship between the loose rock mass and the free surface of the roof, a multi-dimensional risk assessment system has been constructed, enabling quantitative identification and graded assessment of the risk of roof loose rock collapse.

[0019] This method is simple to implement and has low implementation costs. It can efficiently identify potential loose rock bodies in the surrounding rock of underground mine roofs and classify the risk of rockfall, providing a scientific and reliable basis for on-site roof safety management and loose rock disposal decisions. Attached Figure Description

[0020] Figure 1 This is a flowchart of the present invention; Figure 2 This is a spatial information acquisition diagram of the surrounding rock structure of the top plate and adjacent sidewalls according to the present invention; Figure 3 This is a diagram illustrating the spatial relationship between the top plate structural surface and the side wall structural surface of the present invention, along with a structural surface combination diagram. Figure 4 This is a spatial model diagram of the turquoise body on the top plate based on the spatial extension of the structural surface according to the present invention; Figure 5 This is a risk classification assessment diagram for the collapse of the turquoise body on the roof of the present invention. Detailed Implementation

[0021] The invention will now be further described with reference to the accompanying drawings.

[0022] like Figures 1 to 5 As shown, this invention provides a method for identifying and assessing mine roof risk based on the spatial evolution of surrounding rock structural surfaces, comprising the following steps: Step 1: Collect basic geometric and spatial data of the roof and adjacent sidewall rock structures in underground tunnels or mining areas to provide the original basis for subsequent analysis; In order to lay a high-quality data foundation for subsequent structural surface inversion and risk assessment, the specific implementation process is as follows: S11: Basic data acquisition of surrounding rock structural surfaces; In the mining area, a combination of manual geological logging, 3D laser scanning and photogrammetry is used to acquire visible outcrop traces of the surrounding rock structural surfaces on the surface, the outcrop positions expressed in 3D coordinate point sets, and the corresponding spatial coordinate information. The structural surface data of the roof and sidewall areas are initially distinguished to form the raw dataset. The raw data is saved in the form of point cloud, image, or fused data after registration of the two, and the station position, attitude and scale benchmark are recorded simultaneously.

[0023] S12: Preprocessing and quality control of raw structural surface data; denoising and anomaly removal are performed on the collected point cloud or image data respectively: outlier deletion, point density equalization and normal estimation are performed in point cloud scenarios; distortion correction, illumination equalization and occlusion area identification are performed in image scenarios; unusable areas caused by dust, spraying, and reflection are masked and data integrity indicators are recorded.

[0024] Step 2: Based on the collected basic geometric and spatial data, perform spatial modeling of structural surfaces and label the surface regions. Under a unified spatial coordinate system, complete the preprocessing of candidate pairing of structural surfaces to ensure that the spatial positions of all structural surfaces can be directly compared and analyzed. To achieve the standardized and structured representation of structural surface data, and to provide accurate data support for subsequent spatial correlation analysis, the specific implementation process is as follows: S21: Geometric Parametric Representation and Region Marking of Structural Surfaces; Based on the cleaned data, the visible outcrop traces and locations of structural surfaces on the surrounding rock surface are extracted, and each outcrop trace is discretized into a point set. The trace direction, endpoints, and visible length are obtained through line segment or curve fitting. Combining the local plane fitting of the point cloud or the image-point cloud registration results, spatial normal, dip, and dip angle parameters are assigned to each trace to form a complete structural surface object representation. Subsequently, the roof and sidewall structural surfaces are unified into the same spatial coordinate system to complete spatial modeling, such as... Figure 2 As shown; finally, each structural surface is described by parameters such as spatial location, geometric orientation, extension direction, visible length and uncertainty, and the top plate or sidewall surface region to which each structural surface belongs is labeled; S22: Preprocessing of candidate pairing of structural surfaces under a unified coordinate system; After completing the unified coordinate modeling, spatial grid indexes are established for the sets of roof and sidewall structural surfaces respectively; Spatial extension regions are generated along the extension direction of each structural surface, and the extension form adopts extrapolation rays or extrapolation strips. The extension length is determined according to the scale of the roadway / mining area or the proportion of the visible length of the structural surface, so as to realize the rapid retrieval of "roof structural surface - sidewall structural surface" in spatial proximity and extension overlap; Structural surfaces that meet the preset spatial distance conditions or extension area overlap conditions are further filtered, and a candidate pairing list of structural surfaces is output.

[0025] Step 3: Analyze the relationship between the top slab structural surface and the sidewall structural surface in terms of spatial location and extension direction, determine the combination of structural surfaces with spatial continuity, and output key information; To achieve accurate determination and standardized integration of the spatial continuity of structural surfaces, and to provide a reliable correlation basis for subsequent structural surface distribution inversion, the specific implementation process is as follows: S31: Candidate association pair construction and consistency index calculation; Read the candidate pair list and establish a candidate association pair for each "top plate structural surface - side wall structural surface" pair; Calculate the three core indicators of positional consistency, orientation consistency and extension consistency of the association pair respectively, and form a comparable score to provide a quantitative basis for subsequent structural surface continuity determination; Among them, positional consistency reflects the closest distance between two structural surfaces in a unified coordinate system, the degree of overlap of extrapolation zones, and the deviation of endpoint projection; orientation consistency reflects the normal angle, dip difference, and tilt angle difference between two structural surfaces; and extension consistency reflects whether the continuous trends of two structural surfaces are consistent in their respective extension directions. S32: Screening of Spatially Continuous Structural Surface Combinations; a combination of threshold judgment and comprehensive scoring is used to screen structural surface combinations with spatial continuity: an orientation threshold (normal angle) and a position threshold (nearest distance) are set as basic judgment conditions to screen candidate association pairs that meet the basic judgment conditions, thereby removing candidate pairs that are obviously impossible to be continuous; a comprehensive evaluation of candidate association pairs is carried out based on the three calculated consistency indicators and sorted from high to low scores; for cases where the same roof structural surface corresponds to multiple sidewall structural surfaces, the association relationship with the highest score and satisfying mutual exclusion constraints is retained; S33: Structural Surface Combination Unit Integration and Information Output; Merging filtered related pairs into independent structural surface combination units; grouping multiple structural surfaces that are spatially chain-like into the same combination unit; finally outputting key information for each combination unit, including the starting segment of the sidewall, the corresponding segment of the top plate, the extension direction, and the confidence level, such as... Figure 3 As shown.

[0026] Step 4: Based on the determined combination of structural surfaces, extend the sidewall structural surfaces into the interior of the roof along their extension direction, and invert and output the distribution state and confidence set of the internal structural surfaces of the roof surrounding rock. To achieve accurate inversion and reliable quantification of the internal structural surface distribution of the roof slab, the specific implementation process is as follows: S41: Determine the top plate extension direction of the sidewall structural surface; for each structural surface combination, determine the extension direction into the top plate based on the relationship between the visible trace direction, local normal and gravity direction of the sidewall structural surface; if the extension direction is ambiguous, give priority to the direction that is more likely to intersect with the normal of the free surface of the top plate. S42: Spatial extrapolation generates imaginary structural surfaces inside the top plate; along the determined extension direction, spatial extrapolation is performed on the side wall structural surfaces to generate a set of "imaginary structural surfaces" corresponding to the internal region of the top plate; S43: Construct and optimize the internal structural surface field of the top plate; superimpose all hypothetical structural surfaces with the observed structural surfaces of the top plate to form a structural surface field of the internal structural surface distribution of the top plate; perform repeated merging and conflict elimination operations on the structural surface field: merge structural surfaces with consistent orientation and spatial overlap, and mark extrapolated structural surfaces with excessive orientation differences or contradictions with the observed structural surfaces of the top plate as low confidence and perform weight reduction processing. S44: Assign confidence scores and output the top plate structural surface field and confidence score set; combine four indicators, namely, original data integrity, candidate association reliability, extrapolation length, and consistency with the observed top plate structural surface, to assign confidence scores to each extrapolated top plate internal structural surface; finally output the integrated top plate internal structural surface field and its corresponding confidence score set.

[0027] Step 5: Based on the location of the free surface of the top plate, construct and output the spatial model of the turquoise body of the top plate, including the hypothetical structural surface, and the candidate set; To achieve accurate identification and structured extraction of potential turquoise bodies in the roof, the specific implementation process is as follows: S51: Construct the free surface boundary and align it with the structural surface field; establish the geometric boundary of the free surface of the top plate in a unified coordinate system based on the range of the free surface of the top plate, and superimpose the "free surface of the top plate, the observed structural surface of the top plate, and the extrapolated structural surface inside the top plate" to achieve precise alignment with the structural surface field of the top plate, providing a benchmark for subsequent spatial cutting; S52: Cut the top plate space to form closed / semi-closed units; combine the free surface of the top plate, the observed structural surface and the internal extrapolated structural surface to cut the adjacent space of the top plate to form several closed or semi-closed space units; for units that are not completely closed, the free surface is used as the opening boundary to form a semi-closed unit, so that the potential separable rock mass can still be characterized. S53: Screen and determine candidates for turquoise bodies; set candidate screening conditions: have an intersection with the free surface of the top plate or a distance less than a threshold; be constrained by at least two or more structural surfaces, or be constrained by one structural surface and the free surface and have a separable tendency; the volume, thickness or projected area exceeds the minimum threshold to eliminate noisy units; screen spatial units as turquoise body candidates according to the set candidate screening conditions; S54: Record turquoise body information and output a set; record the boundary structure of each turquoise body candidate (including the composition of structural planes and free planes, the proportion of extrapolated structural planes, and the overall confidence level), and finally output the complete turquoise body set.

[0028] Step 6: Based on the constructed turquoise body spatial model, extract multidimensional parameters and normalize them to form a set of characteristic parameters for instability analysis, and output them in combination with confidence scores; To provide comprehensive, accurate, and standardized data support for subsequent instability probability calculations, the specific implementation process is as follows: S61: Extract the basic geometric and centroid parameters of the turquoise body; for each turquoise body, extract the core geometric parameters such as volume, surface area, maximum thickness, minimum thickness, centroid position, and principal axis direction from the geometric morphology parameters, and construct a basic feature dataset; S62: Extract the constraint feature parameters of free surface and structural surface; extract the free surface exposure ratio, the number of structural surface constraint surfaces in the geometric morphology parameters, and the proportion of extrapolated structural surfaces in the boundary (extrapolated surface ratio) in the geometric morphology parameters, and quantify the correlation features between turquoise body and free surface and structural surface; S63: Extract shape and instability-sensitive direction parameters; extract the shape slenderness ratio and bottom support area from the geometric parameters, and determine the sensitive direction of potential slippage or toppling of the turquoise body from the structural surface normal and gravity direction, providing targeted parameters for subsequent instability analysis; S64: Parameter Normalization and Feature Set Output; All parameters obtained in the preceding steps are normalized according to a preset range or statistical distribution to form a feature vector for risk calculation. A set of geometric and spatial feature parameters for turquoise body instability analysis is constructed and output together with the turquoise body confidence level, such as... Figure 4 As shown.

[0029] Step 7: Analyze the spatial relationship between the turquoise body and the free surface of the top plate to determine the spatial range of the turquoise body and the exposure index of the turquoise body; To achieve a refined analysis of the spatial relationship between the turquoise body and the free surface of the roof, and to provide accurate exposure characteristics for risk assessment, the specific implementation process is as follows: S71: Determine exposed turquoise bodies; calculate the nearest distance between the turquoise body and the free surface of the top plate, and determine the turquoise bodies whose nearest distance is less than the preset range as exposed turquoise bodies; S72: Process the projection and overlapping areas of turquoise bodies; project the exposed turquoise bodies onto the free surface of the top plate to obtain the projected area and the boundary of the covered area; if multiple turquoise body projections overlap, merge the overlapping areas and record the number of overlapping layers to reflect the risk of local multiple turquoise body superposition. S73: Determine the exposure type and output the index; Based on the proportion of free surface exposure and the direction of the main axis of the turquoise body, the turquoise body exposure type is divided into three categories: local corner exposure, strip-shaped exposure, or planar exposure, and the corresponding exposure index is output.

[0030] Step 8: Calculate the probability of instability of the loose rock mass, and classify and assess the risk of loose rock falling from the roof based on the calculation results.

[0031] To achieve quantitative assessment and tiered management of the risk of roof rockfall and to identify key areas of concern, the specific implementation process is as follows: S81: Construct a calculation model for the instability probability of turquoise; Based on the feature vectors and exposure indices of turquoise bodies, build an analytical model for calculating the instability probability of turquoise. S82: Calculate and correct the actual instability probability of the loose rock mass; calculate the initial instability probability for each loose rock mass, and correct the initial instability probability by combining the confidence level and data completeness to obtain the actual instability probability; if multiple loose rock masses cover the same roof area, the risk of the area is superimposed and corrected according to the number of overlapping layers, so as to obtain the risk intensity distribution in the spatial location of the roof. S83: Classify and label the risk levels of turquoise bodies; based on the actual instability probability of the turquoise body, map the actual instability probability to discrete risk levels, and classify them into multiple discrete risk levels (low / medium / high / extremely high); the classification of risk levels is determined by referring to historical turquoise collapse records, on-site inspection results or expert experience, and each turquoise body is assigned a corresponding risk level label, outputting the risk level, spatial location, coverage area and disposal priority; S84: Generate a list of risks of roof rockfall; in terms of output logic, first complete the "probability calculation and level determination of each loose rock body", then complete the "risk summary and sorting output for the roof area", sorting the loose rock bodies from high to low risk level; for loose rock bodies of the same risk level, sort them from high to low actual instability probability, so that the risk results can be used for individual hidden danger treatment, and finally form a list of roof rockfall risks and corresponding level labels and key attention areas for the roof area, such as... Figure 5 As shown.

[0032] Figure 5 (a) is a specific embodiment where the analysis focuses on the structural surface exposure information of the roof and adjacent sidewalls of an underground tunnel. Because this tunnel is located in a production area, the surrounding rock surface is obscured by dust, reflections, and shotcrete, and the exposed structural surfaces of the roof and sidewalls are densely distributed and have complex extension directions. This makes the traditional method of identifying loose rocks, which relies on local observation and experience, prone to omissions and misjudgments. Therefore, the method described in this invention is used to identify and assess the risk of loose rocks falling from the roof.

[0033] First, 3D laser scanning and photogrammetry were performed on the top plate and sidewalls to acquire point cloud and image data. This data was then cleaned, registered, and modeled using a unified coordinate system. Structural surface traces, spatial positions, and orientation parameters were extracted to obtain... Figure 5(a) shows the spatial information representation of the structural planes. Subsequently, based on the consistency of the spatial location, orientation, and extension trend of the roof structural planes and the sidewall structural planes, candidate associations were established and spatially continuous structural plane combinations were selected. The sidewall structural planes were then spatially extended into the roof along their extension direction, and the distribution of structural planes within the roof surrounding rock was obtained through inversion, forming… Figure 5 (b) shows the internal structural surface field of the roof slab. Further, the free surface of the roof slab and the aforementioned structural surface field are combined to cut the adjacent space of the roof slab, constructing a spatial model of the turquoise body of the roof slab containing the hypothetical structural surface. Geometric parameters such as volume, thickness, exposure ratio, supporting area, slenderness ratio, and sensitive instability direction are extracted to obtain... Figure 5 (c) shows the turquoise body model. Finally, based on the geometric parameters and exposure characteristics of the turquoise body, the instability probability is calculated. The probability is then corrected by considering confidence level and data completeness. Finally, the instability probability is mapped to discrete risk levels, outputting a risk list and key areas of concern, thus forming... Figure 5 The risk classification results shown in (d) provide a quantitative basis for on-site inspection deployment, support reinforcement and hazard management.

[0034] This invention provides a method for identifying and assessing mine roof risk based on the spatial evolution of surrounding rock structural surfaces. By acquiring spatial information on the structural surfaces of the roof and sidewalls of underground roadways or mining areas, it provides a complete data source for subsequent analysis of their spatial relationships. Modeling and preprocessing using a unified spatial coordinate system solves the problem of correlation analysis errors caused by inconsistent coordinates between roof and sidewall structural surface data, providing a standardized data carrier for subsequent determination of cross-location spatial relationships. Preprocessing with surface labeling and candidate pairing pre-screens potentially related structural surface combinations, avoiding the heavy computational load on the full dataset for subsequent analysis, significantly shortening the analysis cycle and reducing computational requirements. Simultaneously, surface labeling clarifies the structural surface's affiliation and attributes, providing a basis for accurate pairing and reducing invalid analysis processes. Analysis of the spatial extension patterns and locational correlations of structural surfaces effectively screens out structural surface combinations with continuous development characteristics, providing a reliable foundation for subsequent inversion of internal roof structural surfaces and avoiding analytical biases due to the omission of key correlation features. By combining the spatial correlation between the structural surfaces of the roof and sidewalls, the distribution characteristics of structural surfaces within the roof surrounding rock are inverted, ensuring that the inverted distribution of structural surfaces within the roof closely matches actual geological conditions and reducing errors caused by purely theoretical assumptions. A confidence set is simultaneously output to clarify the reliability of the inversion results, providing a quantitative reference for subsequent turbidity body model construction and risk assessment. Based on this, a spatial model of the roof turbidity body, including hypothetical structural surfaces, is constructed. This model accurately characterizes the geometric morphology and spatial extent of potential loosened areas in the roof surrounding rock, realistically reproducing the distribution morphology and boundary conditions of turbidity bodies within the roof surrounding rock. A candidate set is also output, providing clear analytical objects for subsequent multidimensional parameter extraction and instability analysis. Multidimensional feature parameters are extracted, comprehensively covering key influencing factors of turbidity body instability, ensuring the comprehensiveness of the analysis. Normalization eliminates the influence of dimensional differences and numerical ranges among different parameters, ensuring a balanced weight for each parameter in the instability analysis and improving the objectivity of the analysis results. By analyzing the shape characteristics, dimensional parameters, and spatial relationship of turquoise masses with free surfaces, the core triggering conditions for turquoise instability can be accurately identified. By determining the spatial range and exposure indicators, qualitative relationships are transformed into quantitative indicators, providing a quantifiable basis for subsequent instability probability calculations and risk classification. Criteria for identifying the instability probability of turquoise masses are established, and the instability probabilities of different turquoise masses are quantitatively calculated and classified. Ultimately, the risk level of roof turquoise collapse and key areas of concern are determined. This provides both accurate quantitative risk data and intuitive classification results, facilitating engineers to quickly assess risk levels and develop targeted prevention and control measures.

[0035] This method is simple to implement and has low implementation costs. It can efficiently identify potential loose rock bodies in the surrounding rock of underground mine roofs and classify the risk of rockfall, providing a scientific and reliable basis for on-site roof safety management and loose rock disposal decisions.

Claims

1. A method for identifying and assessing mine roof risk based on the spatial evolution of surrounding rock structural surfaces, characterized in that, Includes the following steps: Step 1: Collect basic geometric and spatial data of the roof and the surrounding rock structure adjacent to the roof in underground tunnels or mining areas; Step 2: Based on the collected basic geometric and spatial data, perform spatial modeling of structural surfaces and label the surface regions. Complete the preprocessing of candidate pairing of structural surfaces under a unified spatial coordinate system. Step 3: Analyze the relationship between the top slab structural surface and the sidewall structural surface in terms of spatial location and extension direction, determine the combination of structural surfaces with spatial continuity, and output key information; Step 4: Based on the determined combination of structural surfaces, extend the sidewall structural surfaces into the interior of the roof along their extension direction, and output the distribution state and confidence set of the internal structural surfaces of the roof surrounding rock. Step 5: Based on the location of the free surface of the top plate, construct and output the spatial model of the turquoise body of the top plate, including the hypothetical structural surface, and the candidate set; Step 6: Based on the constructed turquoise body spatial model, extract multidimensional parameters and normalize them to form a set of characteristic parameters for instability analysis, and output them in combination with confidence scores; Step 7: Analyze the spatial relationship between the turquoise body and the free surface of the top plate to determine the spatial range of the turquoise body and the exposure index of the turquoise body; Step 8: Calculate the probability of instability of the loose rock mass, and classify and assess the risk of loose rock falling from the roof based on the calculation results.

2. The method for risk identification and assessment of mine roof slabs based on the spatial evolution of surrounding rock structural surfaces according to claim 1, characterized in that, In step one, the process of collecting basic geometric and spatial data of the roof and adjacent sidewall rock structural surfaces in underground tunnels or mining areas is as follows: S11: Basic data acquisition of surrounding rock structural surfaces; acquire visible outcrop traces of surrounding rock structural surfaces on the surface, outcrop locations expressed in three-dimensional coordinate point sets, and corresponding spatial coordinate information, preliminarily distinguish the structural surface data of the roof and sidewall areas, and form the original dataset. S12: Preprocessing and quality control of raw structural surface data; Denoising and anomaly removal are performed on the collected point cloud or image data respectively: outlier removal, point density equalization and normal estimation are performed in point cloud scenarios; distortion correction, illumination equalization and occlusion region identification are performed in image scenarios. Masking is applied to unusable areas caused by dust, spraying, or reflection, and data integrity indicators are recorded.

3. A method for identifying and assessing mine roof risk based on the spatial evolution of surrounding rock structural surfaces, as described in claim 1 or 2, characterized in that... In step two, the preprocessing of candidate structural surface pairing under a unified spatial coordinate system is as follows: S21: Geometric parameterization and region labeling of structural surfaces; the exposed traces are discretized into point sets, and the trace direction, endpoints, and visible length are obtained through line segment fitting or curve fitting; combined with the local plane fitting of the point cloud or the image-point cloud registration results, each trace is assigned spatial normal, dip, and tilt parameters to form a complete structural surface object representation; finally, each structural surface is described by spatial location, geometric orientation, extension direction, visible length, and uncertainty parameters, and the top plate or sidewall region label to which each structural surface belongs is marked; S22: Preprocessing of candidate pairing of structural surfaces under a unified coordinate system; After completing the unified coordinate modeling, establish spatial grid indexes for the top plate and side plate structural surface sets respectively; Generate spatial extension regions along the extension direction of each structural surface; Filter structural surfaces that meet the preset spatial distance conditions or extension region overlap conditions, and output a list of candidate pairings of structural surfaces.

4. The method for risk identification and assessment of mine roof slabs based on the spatial evolution of surrounding rock structural surfaces according to claim 3, characterized in that, In step three, the process of determining the combination of structural surfaces with spatial continuity and outputting key information is as follows: S31: Candidate association pair construction and consistency index calculation; Read the candidate pair list and establish a candidate association pair for each "top plate structural surface - side wall structural surface" pair; Calculate the three core indicators of positional consistency, orientation consistency and extension consistency of the association pair respectively, so as to provide a quantitative basis for subsequent structural surface continuity determination; S32: Spatial Continuous Structural Surface Combination Screening; The method of "threshold judgment and comprehensive scoring" is used to screen structural surface combinations with spatial continuity: Orientation threshold and position threshold are set as basic judgment conditions, and candidate association pairs that meet the basic judgment conditions are screened; Based on the three calculated consistency indicators, the candidate association pairs are comprehensively evaluated and sorted from high to low scores; For the case where the same roof slab structural surface corresponds to multiple sidewall structural surfaces, the association relationship with the highest score and satisfying mutual exclusion constraints is retained; S33: Structural surface combination unit integration and information output; merge the filtered related pairs into independent structural surface combination units; group multiple structural surfaces that are in a chain-like continuous relationship in space into the same combination unit; finally output the key information of each combination unit, including the starting segment of the side wall, the corresponding segment of the top plate, the extension direction and the confidence level.

5. The method for risk identification and assessment of mine roof slab based on spatial evolution of surrounding rock structural planes according to claim 4, characterized in that, In step four, the process of inverting and outputting the distribution state of internal structural surfaces and the confidence set of the surrounding rock of the roof is as follows: S41: Determine the top plate extension direction of the sidewall structural surface; for each structural surface combination, determine the extension direction into the top plate based on the relationship between the visible trace direction, local normal and gravity direction of the sidewall structural surface; if the extension direction is ambiguous, choose the direction that is more likely to intersect with the normal of the free surface of the top plate. S42: Spatial extrapolation generates imaginary structural surfaces inside the top plate; along the determined extension direction, spatial extrapolation is performed on the side wall structural surfaces to generate a set of "imaginary structural surfaces" corresponding to the internal region of the top plate; S43: Construct and optimize the internal structural surface field of the top plate; superimpose all hypothetical structural surfaces with the observed structural surfaces of the top plate to form a structural surface field of the internal structural surface distribution of the top plate; perform repeated merging and conflict elimination operations on the structural surface field: merge structural surfaces with consistent orientation and spatial overlap, and mark extrapolated structural surfaces with excessive orientation differences or contradictions with the observed structural surfaces of the top plate as low confidence and perform weight reduction processing. S44: Assign confidence scores and output the top plate structural surface field and confidence score set; combine four indicators, namely, original data integrity, candidate association reliability, extrapolation length, and consistency with the observed top plate structural surface, to assign confidence scores to each extrapolated top plate internal structural surface; finally output the integrated top plate internal structural surface field and its corresponding confidence score set.

6. The method for risk identification and assessment of mine roof slab based on spatial evolution of surrounding rock structural planes according to claim 5, characterized in that, In step five, the process of constructing and outputting the spatial model and candidate set of the top turquoise body containing the hypothetical structural surface is as follows: S51: Construct the free surface boundary and align it with the structural surface field; construct the free surface geometric boundary based on the range of the free surface of the top plate to achieve precise alignment with the structural surface field of the top plate, providing a benchmark for subsequent spatial cutting; S52: Cut the top plate space to form closed / semi-closed units; combine the free surface of the top plate, the observed structural surface and the internal extrapolated structural surface to cut the adjacent space of the top plate to form several closed or semi-closed space units; for units that are not completely closed, the free surface is used as the opening boundary to form a semi-closed unit. S53: Screen and determine candidates for turquoise bodies; set candidate screening conditions: intersecting with the free surface of the top plate or the distance is less than the threshold; constrained by at least two or more structural surfaces, or constrained by one structural surface and the free surface and having a separable tendency; the volume, thickness or projected area exceeds the minimum threshold to eliminate noisy units; screen spatial units as turquoise body candidates according to the set candidate screening conditions; S54: Record turquoise body information and output a set; construct the boundary for each turquoise body candidate record, and finally output the complete turquoise body set.

7. The method for risk identification and assessment of mine roof slabs based on the spatial evolution of surrounding rock structural surfaces according to claim 6, characterized in that, In step six, the process of forming the set of characteristic parameters for instability analysis and combining it with the confidence score output is as follows: S61: Extract the basic geometric and centroid parameters of the turquoise body; for each turquoise body, extract the core geometric parameters such as volume, surface area, maximum thickness, minimum thickness, centroid position, and principal axis direction from the geometric morphology parameters, and construct a basic feature dataset; S62: Extract the constraint feature parameters of free surface and structural surface; extract the proportion of exposed free surface and the number of constraint surfaces of structural surface in the geometric morphology parameters, as well as the proportion of extrapolated structural surface in the boundary in the geometric morphology parameters, and quantify the correlation features between turquoise body and free surface and structural surface; S63: Extract shape and instability-sensitive direction parameters; extract the shape slenderness ratio and bottom support area from the geometric morphology parameters, and determine the sensitive direction of potential slippage or toppling of the turquoise body, providing targeted parameters for subsequent instability analysis; S64: Parameter normalization and feature set output; All acquired parameters are normalized according to a preset range or statistical distribution to construct a set of geometric and spatial characteristic parameters for turquoise body instability analysis, which are then output together with the turquoise body confidence score.

8. The method for risk identification and assessment of mine roof slabs based on the spatial evolution of surrounding rock structural surfaces according to claim 7, characterized in that, In step seven, the process of determining the spatial extent and exposure characteristics of the turquoise body is as follows: S71: Determine exposed turquoise bodies; calculate the nearest distance between the turquoise body and the free surface of the top plate, and determine the turquoise bodies whose nearest distance is less than the preset range as exposed turquoise bodies; S72: Processing turquoise body projections and overlapping areas; Project the exposed turquoise bodies onto the free surface of the top plate to obtain the projected area and the boundary of the covered area; if multiple turquoise body projections overlap, merge the overlapping areas and record the number of overlapping layers to reflect the risk of local multiple turquoise body superposition. S73: Determine the exposure type and output the index; Based on the proportion of free surface exposure and the direction of the main axis of the turquoise body, the turquoise body exposure type is divided into three categories: local corner exposure, strip-shaped exposure, or planar exposure, and the corresponding exposure index is output.

9. A method for identifying and assessing mine roof risk based on the spatial evolution of surrounding rock structural surfaces, as described in claim 7, is characterized in that... In step eight, the process of classifying and assessing the risk of loose rock falling from the roof is as follows: S81: Construct a calculation model for the instability probability of turquoise; Based on the feature vectors and exposure indices of turquoise bodies, build an analytical model for calculating the instability probability of turquoise. S82: Calculate and correct the actual instability probability of the turquoise body; calculate the initial instability probability for each turquoise body, and correct the initial instability probability by combining the confidence level and data completeness to obtain the actual instability probability; If multiple turquoise bodies cover the same roof area, the risk of that area is adjusted by superposition based on the number of overlapping layers. S83: Classify and label the risk levels of turquoise bodies; based on the actual instability probability of turquoise bodies, classify them into multiple discrete risk levels; the risk level classification is determined by referring to historical turquoise collapse records, on-site inspection results or expert experience, and assign a corresponding risk level label to each turquoise body; S84: Sort and generate a list of risks of turquoise collapse from the roof slab; The loose rock masses are sorted from highest to lowest risk level; for loose rock masses of the same risk level, they are further sorted from highest to lowest actual instability probability, ultimately forming a list of roof loose rockfall risks and corresponding roof area level labels and key areas of concern.

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