A comprehensive management system for safety engineering

Abstract

The present invention relates to the field of risk analysis technology, specifically a comprehensive safety engineering management system, the system includes: a geological parameter dynamic monitoring module, a risk threshold warning module, a stability weight assessment module, and a risk comprehensive management module. In the present invention, lithology data is extracted through drilling profiles to calculate the layer thickness mutation rate and strength attenuation rate, the coefficient of variation method is used to locate lithology abnormal areas, spatial clustering analysis is used to analyze lithology mutation markers, sliding window mean comparison is used to capture dynamic trends, a digital elevation model is used to calculate slope amplitude and slope discreteness, the density of abnormal points is integrated to generate a terrain instability index, a multi-dimensional quantitative evaluation framework is established, a fuzzy comprehensive evaluation is used to cross-validate the instability index and engineering parameter trends, a dynamic priority ranking is generated, a pile number interval matching is used to construct a risk measure table, a hierarchical analysis method is used to dynamically allocate resources, support and drainage plans are implemented, real-time monitoring and prediction closed-loop iterations are performed, and management and control strategies are adaptively adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of risk analysis, and in particular to a safety engineering integrated management system. Background Art

[0002] The field of risk analysis technology encompasses the identification, assessment, and management of potential risk factors to reduce the probability and impact of accidents. The core of this technical field includes risk identification, risk assessment, risk control, and monitoring, systematically building a full-process management system focused on safety assurance. Risk analysis encompasses multiple stages, from initial risk screening to quantitative and qualitative assessments. Relying on multidisciplinary theoretical foundations such as statistical analysis, systems engineering, and human factors engineering, it is widely used in industries such as engineering construction, manufacturing, transportation, and energy development. Its overall technical system is supported by data collection and processing, combined with detailed methods such as fault tree analysis, event tree analysis, and probabilistic risk assessment to achieve comprehensive management of various systemic and incidental risks.

[0003] The "Safety Engineering Integrated Management System" addresses the safety risk management needs during the construction and operation of engineering projects, utilizing risk identification, hierarchical classification management, and dynamic assessment methods to complete the full-cycle safety management process. The technical matters covered by this patent subject include the development of risk identification standards based on project lifecycle nodes, the classification of risk levels based on different processes and environmental conditions, safety assurance management through the development of risk response plans and resource allocation programs, the continuous tracking and adjustment of risk changes through regular risk reviews and dynamic monitoring mechanisms, and the establishment of a risk event database using historical data archiving and big data analysis technology to support future risk prediction and decision-making.

[0004] Existing technologies rely on fixed-cycle screening and historical data attribution, making it difficult to capture gradual anomalies in lithologic layer thickness and strength in real time. This leads to delayed identification of sudden change areas, such as failure to trigger timely warnings when the thickness sudden change rate exceeds a threshold. The risk assessment process fails to establish a spatial correlation model between thickness sudden change rate and strength decay rate, analyzing single parameter anomalies in isolation and failing to quantify the cascading impact of local anomalies on the stability of adjacent areas. Terrain stability assessment lacks a framework that integrates slope amplitude, aspect dispersion, and elevation anomaly density. Risk grading is based solely on a single terrain parameter, which can easily overlook complex terrain instability factors. Risk control strategies fail to incorporate geological differences across pile number intervals and instead employ universal standards. This results in a poor match between support solutions and local terrain instability indices. The dynamic monitoring mechanism lacks parameter trend prediction capabilities and relies on fixed thresholds to trigger responses, making it difficult to generate timely predictive measures when encountering slowly changing risks. The lack of closed-loop verification between historical data and real-time monitoring data hinders the efficient iterative optimization of risk control plans during construction. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a comprehensive safety engineering management system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a safety engineering integrated management system comprising:

[0007] The risk threshold warning module is used to receive lithologic mutation markers and perform spatial cluster analysis. It compares the mean of the layer thickness mutation rate and strength attenuation rate based on the sliding window length dynamically adjusted based on the borehole spacing and the geological tectonic activity cycle. When the threshold is exceeded, a segmented engineering warning signal is generated. The sliding average model regression analysis is used to determine the window weight coefficient. The warning signal distribution map and parameter prediction trend are generated and transmitted to the comprehensive risk management module.

[0008] The stability weight assessment module is used to calculate the grid slope variation and slope aspect dispersion through the digital elevation model, generate the terrain instability index based on the density of elevation anomaly points, normalize the slope variation using the maximum and minimum value method, output the prevention and control recommendation priority and instability index distribution, and pass it to the comprehensive risk management module;

[0009] The comprehensive risk management and control module is used to receive the early warning signal distribution map matching the pile number interval, call the parameter prediction trend to determine the direction of risk evolution, combine the prevention and control recommendation priority with the instability index distribution to generate a pile number-measure mapping relationship table, use the hierarchical analysis method to construct the criterion layer weights of support, drainage, and monitoring measures, and output a segmented engineering management and control plan bound to the pile number.

[0010] As a further solution of the present invention, the segmented engineering early warning signal includes the early warning level, the early warning section range, and the early warning trigger type. The early warning signal distribution map includes the early warning pile number distribution, the spatial risk concentration area, and the early warning signal density. The parameter prediction trend includes the layer thickness change trend, the strength evolution trend, and the comprehensive risk change trend. The terrain instability index includes the slope change index, the slope dispersion index, and the elevation anomaly index. The prevention and control recommendation priorities are specifically support priority, drainage priority, and monitoring priority. The instability index distribution includes high-risk instability areas, medium-risk instability areas, and low-risk instability areas. The pile number-measure mapping relationship table includes pile number corresponding support measures, pile number corresponding drainage measures, and pile number corresponding monitoring measures. The segmented engineering management and control plan includes segmented support plan, segmented drainage plan, and segmented monitoring plan.

[0011] As a further solution of the present invention, the sliding window length dynamic adjustment rule is that when the borehole spacing is less than 10 meters and the tectonic activity cycle is less than 1 year, the window length is set to 5 layers;

[0012] The normalization formula of the maximum and minimum value method is: slope amplitude = (original value - minimum value) / (maximum value - minimum value);

[0013] The criterion layer weight includes a support measure weight that is dynamically allocated according to the product result of the instability index distribution and the parameter prediction trend.

[0014] As a further solution of the present invention, the risk threshold warning module includes:

[0015] The spatial clustering submodule calls the lithologic mutation markers, extracts the coordinate data of the marker points, sets a cluster radius threshold based on the inverse proportional relationship between the layer thickness mutation rate and the marker point density, calculates the Euclidean distance between the marker points, merges the marker points with a distance less than the threshold into the same group, and generates lithologic cluster partitions according to the spatial distribution of the groups;

[0016] The inverse proportional relationship sets the cluster radius threshold as cluster radius = 10 meters / (layer thickness mutation rate × 0.1 + 1);

[0017] The mean comparison submodule calls the lithologic cluster partition, the layer thickness mutation rate, and the strength attenuation rate, intercepts continuous data segments within the partition according to a preset window length, calculates the arithmetic mean of the mutation rate and the attenuation rate within the window, compares the mean with the engineering risk threshold set based on the regional geological stability classification, and generates an over-threshold interval;

[0018] The regional geological stability classification setting rules are as follows: the threshold value of the first-level stable area is 0.3, the threshold value of the second-level medium area is 0.5, and the threshold value of the third-level high-risk area is 0.7;

[0019] The early warning generation submodule calls the above-threshold interval, performs a sliding average calculation on the mutation rate and attenuation rate within the interval according to the time series, sets the window weight coefficient through historical data regression analysis to determine that the previous window is 0.7 times the current window, and generates an early warning signal distribution map and parameter prediction trend.

[0020] As a further solution of the present invention, the stability weight evaluation module includes:

[0021] The slope feature quantification submodule obtains digital elevation model data, calculates the slope angle difference between adjacent elevation points, extracts the maximum and minimum slope angle differences within the grid cell, generates slope amplitude, converts the aspect angle into polar coordinates, calculates the standard deviation to eliminate periodic effects, and outputs the aspect dispersion.

[0022] The instability index calculation submodule calls the slope variation value for normalization processing, converts the slope dispersion into a positive correlation coefficient using the inverse proportional function f(x)=1 / (x+1), superimposes the elevation anomaly point density data, and performs a weighted product operation according to the 4:3:3 weight based on the geological parameter sensitivity analysis to generate the terrain instability index of the grid cell center point;

[0023] The geological parameter sensitivity analysis determines the contribution weights of slope variation, slope aspect dispersion, and elevation anomaly point density through principal component analysis.

[0024] The prevention and control measures grading submodule divides the terrain instability index into four intervals based on the fuzzy comprehensive evaluation method, defines the vertex parameters of the triangle membership function as instability indices 0.2, 0.5, and 0.8, generates the prevention and control priority coefficient, and outputs the global instability index distribution.

[0025] As a further solution of the present invention, the comprehensive risk management module includes:

[0026] The pile number interval analysis submodule obtains the pixel coordinates and warning intensity values ​​of the warning signal distribution map, converts the horizontal and vertical coordinates into pile number segment identifiers based on the pile number coding rules, calculates the absolute value of the warning intensity difference between adjacent pile numbers, compares it with the warning fluctuation threshold set according to the historical disaster frequency of the region, and generates a pile number interval table;

[0027] The setting rule for the regional historical disaster frequency is: when the disaster frequency is ≥ 5 times / year, the fluctuation threshold = 0.5;

[0028] The measure mapping generation submodule, based on the pile number interval table, calls the parameter prediction trend to determine the direction of risk evolution, extracts the level value of the prevention and control recommendation priority and the grid unit value of the instability index distribution, divides the branch protection measures into anchor support, pile foundation support, and retaining wall support types according to the priority level, establishes a many-to-one mapping relationship between the pile number and the measure type and intensity, and generates a pile number-measure mapping table;

[0029] The measure priority ranking submodule uses the hierarchical analysis method to construct the criterion layer and scheme layer structure, sets the ratio of the support measure weight to the drainage measure weight to 3:2, calculates the weight value of each measure for pile number risk control, and generates a measure priority ranking by arranging the weight values ​​in descending order. The segmented engineering control plan is output by binding the pile number interval.

[0030] As a further embodiment of the present invention, the system further comprises:

[0031] The geological parameter dynamic monitoring module is used to extract lithologic distribution, layer thickness sequence, and shear strength sequence through drilling profiles, calculate the layer thickness mutation rate and strength attenuation rate of adjacent sections, and use the coefficient of variation method combined with the dynamic coupling relationship between the layer thickness mutation rate and the strength attenuation rate to optimize the threshold generation logic, identify abnormal lithologic mutation areas, generate lithologic mutation markers, and transmit them to the risk threshold warning module.

[0032] As a further solution of the present invention, the rock mutation markers are specifically mutation position identification, mutation area boundaries, and mutation type classification. The layer thickness mutation rate includes layer thickness variation, layer thickness variation degree, and local layer thickness difference. The strength attenuation rate includes shear strength variation, strength decline rate, and abnormal strength gradient.

[0033] As a further solution of the present invention, the dynamic coupling relationship optimization threshold generation logic is to adjust the coefficient of variation threshold according to the correlation coefficient between the layer thickness mutation rate and the intensity attenuation rate. When the correlation coefficient is greater than 0.5, the threshold is raised to 1.8 times the mean coefficient of variation in the current data set.

[0034] As a further solution of the present invention, the geological parameter dynamic monitoring module includes:

[0035] The lithologic sequence extraction submodule obtains drilling profile data, divides lithologic layers using the interval segmentation method based on the mineral composition and particle size parameters in the lithologic classification standard, extracts lithologic codes for multiple layers, and calculates layer thickness and shear strength data to generate layer thickness sequences and shear strength sequences.

[0036] The mutation rate calculation submodule calls the layer thickness sequence, calculates the absolute value of the difference between the current layer thickness and the previous layer thickness in the order of adjacent layers, performs the difference calculation after unifying the layer thickness unit into meters, divides the difference by the previous layer thickness, and obtains the layer thickness mutation rate of multiple layers; calls the shear strength sequence, calculates the absolute value of the difference between the current layer strength and the previous layer strength in the order of adjacent layers, performs the difference calculation after unifying the shear strength unit into megapascals, and divides the difference by the previous layer strength to obtain the strength attenuation rate of multiple layers;

[0037] The anomaly identification submodule calls the layer thickness mutation rate and the intensity attenuation rate, calculates the mean and standard deviation of the two sequences, divides the standard deviation by the mean to obtain the coefficient of variation, and based on the lithologic mutation threshold generation rule verified by the historical geological disaster case database, screens the layer numbers whose coefficient of variation exceeds the threshold to generate lithologic mutation markers;

[0038] The verification rule of the historical geological disaster case database is that when the layer thickness mutation rate and the intensity attenuation rate exceed the threshold at the same time, the coefficient of variation threshold is reduced to 1.2 times the mean.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are:

[0040] In the present invention, lithologic distribution data is extracted through drilling profiles and the thickness mutation rate and strength attenuation rate are calculated. The coefficient of variation method is combined to accurately locate lithologic anomaly areas, thereby reducing the identification bias of single static indicators. Spatial clustering analysis is used to regionally aggregate lithologic mutation markers, and sliding window mean comparison is combined to capture the dynamic change trend of parameters, thereby improving the sensitivity of gradual risk warning. A digital elevation model is introduced to calculate the slope amplitude and slope dispersion, and the density of elevation anomaly points is integrated to generate a terrain instability index, and a multi-dimensional quantitative assessment framework for terrain stability is established. Based on the fuzzy comprehensive evaluation method, the terrain instability index is cross-validated with the predicted trend of engineering parameters to generate a dynamic priority ranking, thereby enhancing the matching accuracy of prevention and control measures with the direction of risk evolution. A risk measure mapping relationship table is constructed by matching the pile number interval, and the hierarchical analysis method is combined to dynamically allocate resources for support and drainage schemes, thereby achieving coordinated optimization of measure combinations with geological characteristics and construction stages. Real-time monitoring data and prediction models form a closed-loop iteration, promoting adaptive adjustment of management and control strategies as the project progresses, and reducing response delays caused by sudden changes in environmental parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a system flow chart of the present invention;

[0042] Figure 2 This is a flow chart of the geological parameter dynamic monitoring module of the present invention;

[0043] Figure 3 This is a flow chart of the risk threshold warning module of the present invention;

[0044] Figure 4 This is a flow chart of the stability weight evaluation module of the present invention;

[0045] Figure 5 This is a flow chart of the comprehensive risk management module of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0048] For example 1, please refer to Figure 1 , a safety engineering integrated management system includes:

[0049] The geological parameter dynamic monitoring module is used to extract lithologic distribution, layer thickness sequence, and shear strength sequence from drilling profiles, calculate the thickness mutation rate and strength attenuation rate of adjacent sections, and optimize the threshold generation logic using the coefficient of variation method combined with the dynamic coupling relationship between the thickness mutation rate and strength attenuation rate. This identifies areas with abnormal lithologic mutations, generates lithologic mutation markers, and transmits them to the risk threshold warning module.

[0050] The risk threshold warning module is used to receive lithologic mutation markers and perform spatial cluster analysis. It compares the mean of the layer thickness mutation rate and strength attenuation rate based on the sliding window length dynamically adjusted based on the borehole spacing and the geological tectonic activity cycle. When the threshold is exceeded, a segmented engineering warning signal is generated. The sliding average model regression analysis is used to determine the window weight coefficient. The warning signal distribution map and parameter prediction trend are generated and transmitted to the comprehensive risk management module.

[0051] The stability weight assessment module is used to calculate the grid slope variation and slope aspect dispersion through the digital elevation model, generate the terrain instability index based on the density of elevation anomaly points, normalize the slope variation using the maximum and minimum value method, output the prevention and control recommendation priority and instability index distribution, and pass it to the comprehensive risk management module;

[0052] The comprehensive risk management and control module is used to receive the early warning signal distribution map to match the pile number interval, call the parameter prediction trend to determine the direction of risk evolution, combine the prevention and control recommendation priority with the instability index distribution to generate the pile number-measure mapping relationship table, use the hierarchical analysis method to construct the criterion layer weights of support, drainage, and monitoring measures, and output the segmented engineering control plan bound to the pile number.

[0053] The lithologic mutation markers specifically include mutation position identification, mutation area boundary, and mutation type classification. The layer thickness mutation rate includes layer thickness variation, layer thickness variation degree, and local layer thickness difference. The strength attenuation rate includes shear strength variation, strength decline rate, and abnormal strength gradient. The segmented engineering early warning signal includes warning level, warning section range, and warning trigger type. The warning signal distribution map includes warning pile number distribution, spatial risk concentration area, and warning signal density. The parameter prediction trend includes layer thickness change trend, strength evolution trend, and comprehensive risk change trend. The terrain instability index includes slope change index, slope aspect discrete index, and elevation anomaly index. The prevention and control recommendation priorities are specifically support priority, drainage priority, and monitoring priority. The instability index distribution includes high-risk instability area, medium-risk instability area, and low-risk instability area. The pile number-measure mapping relationship table includes pile number corresponding support measures, pile number corresponding drainage measures, and pile number corresponding monitoring measures. The segmented engineering management and control plan includes segmented support plan, segmented drainage plan, and segmented monitoring plan.

[0054] The logic for generating the threshold for dynamic coupling relationship optimization is to adjust the coefficient of variation threshold based on the correlation coefficient between the layer thickness mutation rate and the intensity attenuation rate. When the correlation coefficient is greater than 0.5, the threshold is raised to 1.8 times the mean coefficient of variation in the current dataset.

[0055] The dynamic adjustment rule of the sliding window length is that when the borehole spacing is less than 10 meters and the tectonic activity cycle is less than 1 year, the window length is set to 5 layers;

[0056] The normalization formula of the maximum and minimum value method is slope variation = (original value - minimum value) / (maximum value - minimum value);

[0057] The criterion layer weights include the weights of support measures, which are dynamically allocated according to the product of the instability index distribution and the parameter prediction trend.

[0058] See also Figure 2 , the geological parameter dynamic monitoring module includes:

[0059] The lithologic sequence extraction submodule obtains drilling profile data, divides lithologic layers using the interval segmentation method based on the mineral composition and particle size parameters in the lithologic classification standard, extracts lithologic codes for multiple layers, and calculates layer thickness and shear strength data to generate layer thickness sequences and shear strength sequences.

[0060] First, we obtained drill profile data for a borehole (numbered ZK01, located at stake K1+200) along a specific project (e.g., Section K1 of the G Highway). This is an engineering geology log that details the exposed rock and soil layers from the surface to a predetermined depth. The strata exposed in borehole ZK01 were classified according to the lithologic classification standards based on mineral composition, grain size, and structure, as specified in the "Code for Geological Exploration and Surveying for Construction Engineering."

[0061] In the ZK01 borehole, from 0 to 4.5 meters below the surface, the main mineral components of the rock and soil samples were identified as quartz and feldspar, and the content of fine particles with a particle size of less than 0.075 mm exceeded 50%. This layer was determined to be silty clay and was assigned the lithology code The thickness of this layer Direct measurement is 4.5 meters. Then, from 4.5 meters to 11.2 meters depth, the main component changes to quartz particles, with particle size concentrated in the range of 0.25 mm to 0.5 mm, which is consistent with the characteristics of medium sand, and the lithology code is determined. , its layer thickness It is calculated by depth difference, that is, Further down, from 11.2m to 20.0m depth, the cores taken out showed granite structure, but the mineral composition had undergone significant alteration, and the joints and fissures were very developed. It was comprehensively determined to be strongly weathered granite, and the lithology code was , layer thickness Through the systematic analysis of the entire drilling profile, the multi-layer lithologic coding sequence of ZK01 hole was extracted and recorded as .

[0062] At the same time, for each extracted lithologic layer, the corresponding physical and mechanical property data are collated. These data come from on-site in-situ tests (such as standard penetration tests (SPT)) or indoor geotechnical tests (such as direct shear tests). The shear strength of the silty clay layer measured by indoor direct shear tests is Kilopascals (kPa), the shear strength of the medium sand layer is converted from the standard penetration test results Kilopascals (kPa), shear strength of strongly weathered granite layer estimated by rock point load test Kilopascals (kPa). In this way, the shear strength sequence corresponding to the lithology code sequence and layer thickness sequence is obtained. , and its numerical unit is uniformly kilopascal.

[0063] Finally, the processing result of this submodule is to generate a layer thickness sequence reflecting the geological structure of ZK01 borehole. (Unit: meter) and shear strength series (Unit: kPa).

[0064] Table 1. Lithologic stratification and parameters of borehole ZK01

[0065]

[0066] As shown in Table 1, the table lists in detail the code, depth, thickness and corresponding shear strength measured or converted data of each lithologic layer in borehole ZK01.

[0067] The mutation rate calculation submodule calls the layer thickness sequence, calculates the absolute value of the difference between the current layer thickness and the previous layer thickness in the order of adjacent layers, unifies the layer thickness unit to meters, performs the difference calculation, divides the difference by the previous layer thickness, and obtains the layer thickness mutation rate of multiple layers. It calls the shear strength sequence, calculates the absolute value of the difference between the current layer strength and the previous layer strength in the order of adjacent layers, unifies the shear strength unit to megapascals, performs the difference calculation, and divides the difference by the previous layer strength to obtain the strength attenuation rate of multiple layers.

[0068] Call the layer thickness sequence generated by the previous submodule (Unit: meter) and shear strength series (Unit: kPa) The calculation process is carried out in the order of adjacent layers, starting from the second layer.

[0069] First, process the layer thickness sequence, whose unit is already in meters, which meets the calculation requirements. Calculate the absolute value of the layer thickness difference between the second layer (medium sand) and its previous layer (silty clay): Meters. Calculate the absolute difference in thickness between the third layer (strongly weathered granite) and the layer before it (medium sand): Then, these differences are divided by the thickness of the corresponding previous layer to obtain the layer thickness mutation rate. The layer thickness mutation rate of the second layer interface is The thickness mutation rate of the third layer interface . This generates a sequence of layer thickness mutation rates .

[0070] Next, we process the shear strength series , whose unit is kilopascals (kPa). In order to maintain consistency with other engineering parameters (usually megapascals MPa) in subsequent calculations and conduct a unified risk assessment, it is necessary to convert the shear strength unit to megapascals (MPa). The conversion rule is defined according to the International System of Units (SI) prefix. 1MPa is equal to 1000kPa, so the kPa value is divided by 1000 to get the MPa value. The converted shear strength sequence is MPa. Then, calculate the absolute value of the intensity difference after converting the unit according to the order of adjacent layers. The absolute value of the intensity difference between the second layer and the first layer is: MPa. The absolute value of the strength difference between the third layer and the second layer: MPa. Divide these differences by the corresponding previous layer strength (value after unit conversion) to obtain the intensity change rate (here called intensity attenuation rate, which more accurately reflects the relative change in intensity). The intensity attenuation rate of the second layer interface The intensity attenuation rate of the third layer interface . This generates a sequence of intensity decay rates .

[0071] Finally, two sequences are output: layer thickness mutation rate sequence and intensity decay rate series .

[0072] The anomaly identification submodule uses the layer thickness mutation rate and intensity attenuation rate to calculate the mean and standard deviation of the two sequences. The standard deviation is divided by the mean to obtain the coefficient of variation. Based on the lithologic mutation threshold generation rule verified by the historical geological disaster case database, the layer numbers whose coefficient of variation exceeds the threshold are screened to generate lithologic mutation markers.

[0073] The verification rule of the historical geological disaster case database is that when the layer thickness mutation rate and the strength attenuation rate exceed the threshold at the same time, the coefficient of variation threshold is reduced to 1.2 times the mean.

[0074] The submodule receives the layer thickness mutation rate sequence and intensity decay rate series . First, calculate the statistical characteristics of each of the two sequences:

[0075] Mean of the thickness mutation rate series ;

[0076] Standard deviation ;

[0077] Coefficient of variation of layer thickness mutation rate ;

[0078] Mean of the intensity decay rate series ;

[0079] Standard deviation ;

[0080] Coefficient of variation of intensity decay rate .

[0081] Next, we set the thresholds for lithologic mutations to identify anomalies. These thresholds are not set arbitrarily, but are based on a systematic analysis of a database of historical geological disasters (such as landslides and collapses) in the region. By statistically analyzing the distribution of layer thickness mutation rates and strength attenuation rates at locations where disasters occurred and did not occur, and using methods such as receiver operating characteristic (ROC) analysis, we determined the optimal segmentation points that can better distinguish between potentially unstable layer interfaces. In this project, we analyzed and determined the basic thresholds for layer thickness mutation rates. , the base threshold of the intensity decay rate Similarly, by analyzing the distribution of the coefficient of variation in historical data, a basic coefficient of variation threshold is determined. When the coefficient of variation exceeds this value, it indicates that the degree of dispersion of the parameter sequence has reached a statistically significant level.

[0082] Then, an adjustment rule based on the historical geological disaster case database is applied: if a certain layer interface Thickness mutation rate and intensity decay rate Exceeding their respective basic thresholds and , then for this layer interface, the determination threshold of its coefficient of variation is Need to be lowered and adjusted to Multiply the two coefficients of variation ( and , here refers to the mean of the coefficient of variation of the entire sequence, which is used to set the judgment standard for this specific layer).

[0083] Calculate this adjusted threshold:

[0084] ;

[0085] If the conditions of exceeding the threshold at the same time are not met, the coefficient of variation threshold of the layer interface still uses the basic value .

[0086] Now let’s screen the two horizon interfaces of ZK01 borehole: The second horizon interface (medium sand / silty clay interface): .because , the thickness mutation rate exceeds the threshold. However, .because , the intensity decay rate does not exceed the threshold. Both do not exceed the threshold at the same time, so this interface uses the basic coefficient of variation threshold Compare the coefficient of variation of the parameter sequence of the interface (represented here by the coefficient of variation of the entire sequence) with its threshold: and Both coefficients of variation do not exceed the threshold, so this interface is not considered abnormal. The third horizon interface (strongly weathered granite / medium sand interface): .because , the thickness mutation rate does not exceed the threshold. .because , the intensity decay rate exceeds the threshold. Both do not exceed the threshold at the same time, so this interface also uses the basic coefficient of variation threshold Comparing the coefficient of variation: and Both coefficients of variation do not exceed the threshold, and this interface is not judged as abnormal.

[0087] In order to demonstrate the identification of abnormal situations, the data of another borehole ZK02 (located at stake number K1+350) is introduced. Assume that at a certain layer interface k in ZK02, and . Compare to the base threshold: and . Both conditions are met at the same time. Therefore, the coefficient of variation threshold of this interface is adjusted to Assume that the coefficient of variation obtained by analyzing the ZK02 data is and . Compare the adjusted thresholds: and Both coefficients of variation exceed the adjusted thresholds. Therefore, the ZK02 horizon (assuming it is at a depth of 15.3 meters) is determined to be a lithologic abrupt change anomaly.

[0088] All the abnormal horizon interfaces (in this case, the 15.3-meter interface of ZK02) are screened out, and a lithologic mutation marker is generated for them. The marker contains information such as the location coordinates (ZK02, depth 15.3 meters), the corresponding layer thickness mutation rate, and the lithologic mutation rate. and intensity decay rate These tags and their associated data will be passed to the next module.

[0089] See also Figure 3 , the risk threshold warning module includes:

[0090] The spatial clustering submodule calls the lithologic mutation markers, extracts the coordinate data of the marker points, sets the cluster radius threshold based on the inverse proportional relationship between the layer thickness mutation rate and the marker point density, calculates the Euclidean distance between the marker points, merges the marker points with a distance less than the threshold into the same group, and generates lithologic cluster partitions based on the spatial distribution of the groups;

[0091] The inverse proportional relationship sets the cluster radius threshold as cluster radius = 10 m / (layer thickness mutation rate × 0.1 + 1);

[0092] The module processes the lithologic mutation markers from multiple boreholes (ZK01, ZK02, ZK03, etc.) in the region, which are screened by the anomaly identification submodule. The 3D coordinates of each marker are extracted. and the corresponding layer thickness mutation rate Assume that the following marker data have been obtained: Marker point A (from the 11.2m interface of ZK01 hole, not identified as an anomaly, introduced here to demonstrate the complete clustering process, assuming it is marked): coordinates (Unit: meter), layer thickness mutation rate Mark point B (from the ZK02 hole 15.3m interface, identified as anomaly): coordinates (Unit: meter), layer thickness mutation rate Mark point C (from the 9.5m interface of ZK03 hole, assumed to be identified as an anomaly): coordinates (Unit: meter), layer thickness mutation rate Mark point D (from the 12.0m interface of ZK04 hole, assumed to be identified as an anomaly): coordinates (Unit: meter), layer thickness mutation rate .

[0093] For each marker , according to its layer thickness mutation rate The cluster radius threshold is dynamically set using the following inverse proportional relationship formula : , the meaning of this formula is: layer thickness mutation rate The larger the value, the more dramatic the lithologic change, and the corresponding cluster radius The smaller the radius, the closer points must be to be considered as the same cluster. Conversely, the more gradual the change, the larger the radius. The constant of 10 meters is the base radius reference value, and 0.1 is the adjustment coefficient. These two values ​​are calibrated based on the geological characteristics of the region and engineering experience, aiming to adapt the clustering scale to the severity of geological changes.

[0094] Calculate the cluster radius of each marker point:

[0095] rice;

[0096] rice;

[0097] rice;

[0098] rice;

[0099] Next, calculate the Euclidean distance between all pairs of labeled points :

[0100] rice;

[0101] rice;

[0102] rice;

[0103] rice;

[0104] rice;

[0105] rice;

[0106] Clustering rule: any two marked points and distance and their respective cluster radii and Only when and When both conditions are met, the two points are merged into the same group. A and B: , greater than and , not merged. A and C: , greater than and , not merged. A and D: .because and , satisfying the conditions, A and D are merged into the same group, recorded as G1. B and C: , greater than and , not merged. B and D: , greater than , not merged (even if less than No, both parties must satisfy the requirement). C and D: , greater than and , not merged.

[0107] Cluster analysis revealed that markers A and D formed group G1, while markers B and C were isolated. Based on these clustering results (group G1) and the spatial distribution of the isolated points (B and C), lithologic clustering zones were defined. The area surrounding G1 was defined as zone 1, the area near point B as zone 2, and the area near point C as zone 3. These zones represent areas with similar lithologic variation characteristics or a high concentration of abrupt changes.

[0108] The mean comparison submodule uses lithologic clustering and partitioning, layer thickness mutation rate, and strength attenuation rate to intercept continuous data segments within the partition according to the preset window length, calculates the arithmetic mean of the mutation rate and attenuation rate within the window, and compares the mean with the engineering risk threshold set based on the regional geological stability classification to generate the over-threshold interval;

[0109] The regional geological stability classification setting rules are: the threshold value of the first-level stable area = 0.3, the threshold value of the second-level medium area = 0.5, and the threshold value of the third-level high-risk area = 0.7;

[0110] The module uses the lithologic clustering partitions (partition 1, partition 2, partition 3) divided in the previous step and the original layer thickness mutation rate of all boreholes in each partition (such as ZK01, ZK02, ZK03, ZK04) and intensity decay rate Data sequence. You need to set the length of an analysis window The setting rule is to consider the average borehole spacing in the region (20 meters) and the geological structure activity cycle (estimated to be 3 years). Since the borehole spacing is greater than 10 meters and the activity cycle is greater than 1 year, a larger window length is used. Layer data points.

[0111] In zone 1 (the area containing markers A and D, involving boreholes ZK01 and ZK04), select continuous The data of 7 consecutive layers in the ZK04 borehole with a depth of 25 to 48 meters are selected, and the layer thickness mutation rate sequence is: , the corresponding intensity decay rate sequence is .

[0112] Calculate the arithmetic mean of the data in this window:

[0113] Mean layer thickness mutation rate:

[0114] ;

[0115] Mean intensity decay rate:

[0116] ;

[0117] The two average values ​​calculated and Compare this with the engineering risk threshold. This threshold is set based on the regional geological stability classification. According to the latest regional geological survey report, the area where the project is located is rated as a "secondary medium stability area." Based on the preset rules: "first-level stable area threshold = 0.3, second-level medium area threshold = 0.5, third-level high-risk area threshold = 0.7," the engineering risk threshold is selected. For comparison: Within this data window, the average values ​​of both parameters did not exceed the risk threshold of 0.5. This indicates that the average rate of change of geological parameters in this depth range (25-48 meters) is within the acceptable level of the "Second Level Moderate Stability Zone" and does not meet the conditions for triggering an early warning.

[0118] The analysis window moves down one layer along the depth, selects 7 new data points, and repeats the above mean calculation and comparison. When the window moves to the depth range of 35m to 55m in ZK04 borehole, the intercepted layer thickness mutation rate sequence is , the intensity decay rate sequence is .

[0119] Calculate the mean:

[0120] ;

[0121] ;

[0122] Compare the thresholds again : The threshold is still not exceeded. Continue sliding the window.

[0123] When the window moves to the depth range of 40m to 60m in ZK04 borehole, the intercepted layer thickness mutation rate sequence is:

[0124] ;

[0125] The intensity decay rate sequence is:

[0126] ;

[0127] Calculate the mean:

[0128] ,

[0129] ;

[0130] Comparison threshold : Average value of intensity decay rate Exceeding the risk threshold Therefore, the depth interval covered by this window (40m to 60m) is recorded as the "over-threshold interval". All identified over-threshold intervals will be passed to the next module.

[0131] The early warning generation submodule calls the super-threshold interval, performs a sliding average calculation of the mutation rate and decay rate within the interval according to the time series, sets the window weight coefficient through historical data regression analysis to determine that the previous window is 0.7 times the current window, and generates an early warning signal distribution map and parameter prediction trend.

[0132] Receive the super-threshold interval information identified by the previous module. For example, it is determined that the ZK04 borehole depth range of 40 to 60 meters is a super-threshold interval based on the intensity attenuation rate. Extract the detailed intensity attenuation rate data sequence within this interval: (Added the following two points).

[0133] For this sequence A sliding average calculation is applied to smooth the data and reveal trends. The sliding window size is set to 5 data points. The weight coefficients in the calculation need to be set. These coefficients are determined by time series regression analysis of the historical geological hazard evolution data in the region. The analysis results show that recent data points (shallower layers or more recent times) are more predictive of future trends. Based on this, the weight of the previous window (representing the past state) on the current window prediction is determined to be , then the weight of the current data point is . Using exponential smoothing method to calculate, Smoothed prediction value of each point It is given by: ,in Is the first The actual intensity attenuation rate value of the point, Is the smoothed prediction value calculated at the previous point. Initial value Set to the first value of the sequence .

[0134] The calculation process is as follows:

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] The calculated smoothed series (Keep three decimal places.) Observing this sequence, its value shows a continuous upward trend, especially in the second half, exceeding the threshold of 0.5 and continuing to grow. This indicates that the intensity decay rate not only exceeds the threshold, but also has a worsening trend.

[0144] According to the degree of super-threshold (average Higher than the threshold of 0.5 ) and the predicted trend (smoothed value continues to rise) to determine the warning level for that interval. Alert level rules are set: if the threshold exceeds 0-10% and the trend is stable, it is a Level 1 (low risk) warning; if the threshold exceeds 0-10% but the trend is rising, or if the threshold exceeds 10%-30%, it is a Level 2 (medium risk) warning; if the threshold exceeds 30% or more, it is a Level 3 (high risk) warning. In this example, the threshold exceeds 3.8% and the trend is rising, resulting in a Level 2 (medium risk) warning.

[0145] This warning information is associated with the location. Borehole ZK04 is located near highway stake K1+800, exceeding the threshold at a depth of 40-60 meters. A warning signal distribution map is generated, marking stakes K1+780 to K1+820 (covering the impact range) and the area 40-60 meters deep as a Level 2 warning zone on the engineering drawing. The primary trigger parameter is noted as the intensity decay rate. A parameter prediction trend graph is also output, showing the smoothed intensity decay rate and a short-term forecast based on the last point (e.g., a predicted intensity decay rate of 0.65 within the next 5 meters).

[0146] See also Figure 4 , the stability weight evaluation module includes:

[0147] The slope feature quantification submodule obtains digital elevation model data, calculates the slope angle difference between adjacent elevation points, extracts the maximum and minimum slope angle differences within the grid cell, generates slope amplitude, converts the aspect angle into polar coordinates, calculates the standard deviation to eliminate periodic effects, and outputs the aspect dispersion.

[0148] This module processes the digital elevation model (DEM) data covering the project area. The selected one is a 5m spatial resolution 5-meter DEM grid data. For each grid cell (Located at row, Columns) for terrain feature calculations.

[0149] First, calculate the slope variation. , considering itself and the eight adjacent grids (forming a 3 3). Calculate this 3 3. The slope value of each grid in the window (slope is the angle of maximum rate of change of elevation on the horizontal plane, which can be calculated by dividing elevation difference and horizontal distance). Find the maximum value of these 9 slope values and minimum value The slope amplitude is defined as the difference between these two values: A specific grid cell , Part 3 The maximum slope calculated in the neighborhood is , the minimum slope is , then the slope variation of the grid .

[0150] Then calculate the slope dispersion. 3 Each grid in the window Slope (The projection direction of the slope vector on the horizontal plane is expressed in 0-360 degrees, with due north as 0 degrees). In order to avoid the 0 / 360 degree boundary problem and effectively calculate the discrete degree, each aspect angle Convert to a two-dimensional unit vector A grid with a slope of 45 degrees (northeast) has a vector of Calculation 3 The average vector of 9 such unit vectors in the 3-window ,in and Calculate the average slope direction based on the average vector , this function correctly handles angles in all quadrants. Then, the slope aspect is calculated for each grid cell in the window. With average slope The angle difference between them (it is necessary to pay attention to the case of spanning 0 / 360 degrees and take the acute angle difference). Finally, calculate the standard deviation of these 9 angle differences to get the center grid Slope dispersion For grid , the calculated slope dispersion is .

[0151] This module provides a grid cell for each Output two quantitative terrain characteristic values: slope variation value (like ) and aspect dispersion (like ).

[0152] The instability index calculation submodule uses the slope variation value for normalization processing, converts the slope dispersion into a positive correlation coefficient using the inverse proportional function f(x)=1 / (x+1), overlays the elevation anomaly density data, and performs a weighted product operation based on the 4:3:3 weighting based on the geological parameter sensitivity analysis to generate the terrain instability index of the grid cell center point;

[0153] The sensitivity analysis of geological parameters uses principal component analysis to determine the contribution weights of slope variation, aspect dispersion, and elevation anomaly density.

[0154] Receive the slope variation value calculated for each grid in the previous step and aspect dispersion A third terrain factor needs to be introduced: the density of elevation anomaly points. This data is obtained by analyzing the DEM, identifying points (outliers) whose elevations deviate significantly from the average elevation of their local neighborhood, and then The density of these abnormal points is obtained by counting the density of these abnormal points in the neighborhood of . The density of elevation anomaly points at per 100 square meters.

[0155] In order to comprehensively evaluate the risk of terrain instability, it is necessary to integrate these three heterogeneous factors ( , , ) are combined. First, the data is normalized to eliminate the differences in units and value ranges. Normalization is performed. It is necessary to first count the slope variation of all grids in the entire study area and obtain the minimum value. and maximum value After district-wide statistics, it was determined and . Normalization is performed using min-max scaling: , for the slope dispersion , the original text provides a conversion function , so that the greater the dispersion, the smaller the factor value. The converted factor is unitless. The density value range of the whole area is [0,0.5] per 100 square meters. Normalization:

[0156] ;

[0157] Next, determine the three standardized / transformed factors ( , , ) in the calculation of the terrain instability index. The weights are determined based on a sensitivity analysis of the relationship between the historical data of geological hazards in the region and these terrain factors. Principal component analysis (PCA) is used to identify the principal component with the strongest correlation with geological instability, and then the loads (contribution rates) of the three factors on the principal component are analyzed to determine the weights. The analysis results show that the contribution rate of normalized slope variation is 40%, the contribution rate of (converted) slope dispersion factor is 30%, and the contribution rate of normalized elevation anomaly point density is 30%. Therefore, the weights are set to , , .

[0158] Calculate the grid using weighted sum method Terrain instability index :

[0159] ;

[0160] ;

[0161] ;

[0162] , calculate the grid The terrain instability index is 0.287. Repeat this calculation for all grids in the study area to generate a terrain instability index distribution map.

[0163] The prevention and control measures classification submodule divides the terrain instability index into four categories based on the fuzzy comprehensive evaluation method, defines the vertex parameters of the triangle membership function as instability indices 0.2, 0.5, and 0.8, generates the prevention and control priority coefficient, and outputs the global instability index distribution.

[0164] This module uses the global terrain instability index generated in the previous module Distribution map. The fuzzy comprehensive evaluation method is used to classify the risk level of the terrain instability index of each grid. Four risk levels are set: low risk (L), medium-low risk (ML), medium-high risk (MH), and high risk (H). A membership function is defined for each level to describe a given index value. The degree of belonging to each level.

[0165] A triangular membership function is used, whose shape is determined by the vertex parameters. The vertex parameters are set to 0.2, 0.5, and 0.8. These vertices define where the membership of each risk level reaches its peak or begins / ends. The specific definitions are as follows: Low risk (L): Membership function exist When 1, When it drops to 0, =0. (This is a right-descending semi-trapezoidal shape or simplified as a linear decline in the interval [0,0.2]) - A more reasonable definition may be that the peak is at 0 and the support interval is [0,0.2]. Assuming the latter: Medium-low risk (ML): membership function is a triangle with vertices at , the support interval (non-zero range) is Calculation formula: If , ;like , Medium-high risk (MH): membership function is a triangle with vertices at , the support interval is Calculation formula: If , ;like , . High risk (H): membership function is a triangle with vertices at , the support interval is (Assume the upper limit is 1.0). Calculation formula: If , ;like , .

[0166] For the grid calculated by the previous module Terrain instability index , calculate its membership to these four risk levels: :because , apply the second formula, :because , apply the first formula, (because );

[0167] Compare the four membership values: The maximum value is According to the maximum membership principle, the grid is determined The risk level is medium-low risk (ML).

[0168] Set a prevention and control priority coefficient for each risk level to guide the formulation of subsequent measures. Setting rules: low risk (L) coefficient is 1, medium-low risk (ML) coefficient is 2, medium-high risk (MH) coefficient is 3, high risk (H) coefficient is 4. Therefore, the grid Prevention and control priority coefficient .

[0169] This fuzzy evaluation and priority coefficient allocation process is applied to all grid cells in the terrain instability index distribution map, and finally a risk level distribution map and a prevention and control priority coefficient distribution map covering the entire area are generated.

[0170] See also Figure 5 , the comprehensive risk management and control module includes:

[0171] The pile number interval analysis submodule obtains the pixel coordinates and warning intensity values ​​of the warning signal distribution map, converts the horizontal and vertical coordinates into pile number segment identifiers based on the pile number coding rules, calculates the absolute value of the warning intensity difference between adjacent pile numbers, compares it with the warning fluctuation threshold set according to the historical disaster frequency of the region, and generates a pile number interval table;

[0172] The setting rule for regional historical disaster frequency is: when the disaster frequency is ≥ 5 times / year, the fluctuation threshold = 0.5;

[0173] This module processes the warning signal distribution map generated by the risk threshold warning module. This map shows the warning intensity values ​​along the engineering lines (such as roads and railways) At the same time, the project's pile number coding rules are required. The starting pile number of the project is K0+000. A large pile number (K0+100, K0+200...K1+000...) is set every 100 meters along the line direction, and a small pile number mark is set every 10 meters.

[0174] First, the location information on the warning signal map (such as the distance along the line) ) is converted to a stake number. At 1535 meters from the starting point, the stake number is K1+535. The warning intensity values ​​are extracted every 10 meters (one small stake interval) along the line to obtain a sequence. The warning intensity value sequence for a section of the line is as follows:

[0175] ;

[0176] ;

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] ;

[0183] ;

[0184] Calculate the absolute value of the difference in warning intensity between adjacent stake numbers (10-meter interval):

[0185] K1+510vsK1+500: ;

[0186] K1+520vsK1+510: ;

[0187] K1+530vsK1+520: ;

[0188] K1+540vsK1+530: ;

[0189] K1+550vsK1+540: ;

[0190] K2+110vsK2+100: ;

[0191] K2+120vsK2+110: ;

[0192] Set an early warning fluctuation threshold Used to judge whether the change in warning intensity is drastic. The threshold is set based on the historical frequency of geological disasters in the study area. Query records to obtain the average annual occurrence of geological disasters in the area in the past ten years. times. According to the set rules: "If times / year, then ;like times / year, then ".because , select .

[0193] The absolute value of the calculated strength difference between adjacent pile numbers is compared with the threshold Comparison: From K1+500 to K1+550, all differences (0.05, 0.10, 0.05, 0.02, 0.02) are less than 0.5. From K2+100 to K2+110, the difference K2+110 to K2+120 segment, difference .

[0194] At the same time, define the risk level of the warning intensity itself: intensity value For high risk, For medium risk, Low risk. Volatility assessment: absolute value of difference For high volatility, For low volatility.

[0195] Based on the warning intensity risk level and volatility assessment results, consecutive, similar stake segments are merged to create different stake risk intervals. Stakes K1+500 to K1+510: Intensity 0.60, 0.65 (medium risk), volatility 0.05 (low). Stakes K1+510 to K1+520: Intensity 0.65, 0.75 (medium to high risk), volatility 0.10 (low). Stakes K1+520 to K1+530: Intensity 0.75, 0.70 (high risk), volatility 0.05 (low). Stakes K1+530 to K1+550: Intensity 0.70, 0.68, 0.66 (high to medium risk), volatility 0.02, 0.02 (low). Overall, Stakes K1+500 to K1+550: The risk level fluctuates between medium and high, but the intensity changes relatively smoothly (low volatility). These can be merged into interval A. Stakes K2+100 to K2+110: Intensity 0.40, 0.95 (low -> high risk), volatility 0.55 (high). Stakes K2+110 to K2+120: Intensity 0.95, 0.85 (high risk), volatility 0.10 (low). Overall, Stakes K2+100 to K2+120: The risk level jumps from low to high, with high volatility. These can be combined into Segment B.

[0196] Generate a pile number interval table and summarize the analysis results.

[0197] Table 2 Pile number risk interval division table

[0198]

[0199] As shown in Table 2, the table divides the line into intervals with different risk characteristics based on the warning intensity value and its severity of change along the line.

[0200] The measure mapping generation submodule uses the pile number interval table to call parameter prediction trends to determine the direction of risk evolution, extract the level value of the prevention and control recommendation priority and the grid unit value of the instability index distribution, and classify the branch protection measures into anchor support, pile foundation support, and retaining wall support types according to the priority level. A many-to-one mapping relationship between pile number and measure type and intensity is established to generate a pile number-measure mapping table;

[0201] The module performs subsequent processing based on the pile number interval table (see Table 2) generated by the previous module. We take interval B (pile numbers K2+100 to K2+120, high risk, high volatility) in Table 2 as an example.

[0202] First, call the parameter prediction trend for the interval (K2+100 to K2+120) output by the risk threshold warning module. Query to obtain the intensity attenuation rate of this interval The forecast trend is "it is expected to remain at a high level above 0.8 in the next monitoring cycle", indicating that the risk status is persistent.

[0203] Secondly, extract the topographic and geological information covering the pile number interval generated by the stability weight assessment module. Query the terrain instability index Distribution map and prevention and control priority coefficient The distribution map shows that the average value of the terrain instability index of the grid cells within the range of stake numbers K2+100 to K2+120 is , the corresponding prevention and control priority coefficient is mainly 4 (high risk).

[0204] Comprehensive Zone B information: high warning intensity, high volatility, predicted trend remains high, high terrain instability index (0.75), and high prevention and control priority coefficient (level 4). Based on this information, appropriate engineering measures need to be selected. A measure selection rule should be established that links the risk level and priority coefficient with the specific support measure type and intensity: * Priority coefficient 1 or 2 (low / medium-low risk): Slope protection (such as vegetation, lattice structures) and simple retaining (such as mortared stone) are generally selected. * Priority coefficient 3 (medium-high risk): System anchor / cable support or small anti-slide piles are usually used. * Priority coefficient 4 (high risk): Strong support measures are required, such as large-diameter anti-slide piles, pile-sheet walls, or gravity retaining walls.

[0205] For Section B, the comprehensive assessment indicated the highest risk level (priority 4), so the strongest support measures were selected. Taking into account the local terrain (assuming a high, steep slope) and geological conditions (assuming the presence of weak interlayers), the decision was made to use large-diameter reinforced concrete anti-slide piles (e.g., 1.5 meters in diameter) for support. Furthermore, based on the predicted trend (maintaining a high strength decay rate) and the composition of the terrain instability index (assuming a significant contribution from slope variation and the density of elevation anomalies), design parameters were refined, such as determining the pile length required to penetrate the sliding surface to reach stable bedrock and the pile spacing based on force calculations (e.g., 4 meters).

[0206] Map pile number interval B (K2+100 to K2+120) to the selected engineering measure (Type: Large Diameter Anti-Slide Piles, Strength: Reinforced Design, Parameters: Pile Diameter 1.5m, Pile Length XXm, Pile Spacing 4m). Repeat this process for all pile number intervals requiring measures to generate a final pile number-measure mapping table, which details the specific engineering measures and design considerations for each risk zone.

[0207] The measure priority ranking submodule uses the hierarchical analysis method to construct the criterion layer and scheme layer structure, sets the ratio of the support measure weight to the drainage measure weight to 3:2, calculates the weight value of each measure for pile number risk control, and generates a measure priority ranking by arranging the weight values ​​in descending order. The segmented engineering control plan is output by binding the pile number interval.

[0208] To ensure effective resource utilization and risk control, it is necessary to prioritize the multiple measures (such as support, drainage, and monitoring) that may be involved in the same pile number interval. The analytic hierarchy process (AHP) is used to construct a decision model.

[0209] The model structure is as follows: * Target layer (Level 0): Optimizes comprehensive risk control for each pile number interval. * Criteria layer (Level 1): Key factors influencing risk control effectiveness are defined as: support measure effectiveness (C1), drainage measure effectiveness (C2), and monitoring and early warning timeliness (C3). * Solution layer (Level 2): ​​Specific engineering measures proposed for a specific pile number interval (e.g., interval B), including: anti-slide pile support (S1), slope intercepting drainage ditches (S2), deep drainage holes (S3), and automated displacement monitoring (S4).

[0210] Construct a judgment matrix of the criterion layer relative to the target layer. Based on engineering experience and expert consensus, it is believed that for the high-risk, high-volatility interval B, support measures are the most critical, followed by drainage, and finally monitoring. In addition, the weight of support measures is clearly required. Weights with drainage measures The ratio is 3:2. This means that when constructing the judgment matrix, the importance scale of C1 to C2 is set to 3 / 2=1.5. By comparing with other criteria pairwise (e.g., support is extremely important to monitoring, scale 5; drainage is relatively important to monitoring, scale 3), the judgment matrix is ​​constructed, and the weight vector of the criterion layer is obtained by calculating the maximum eigenvalue of the matrix and the corresponding normalized eigenvector. The calculation result is This result satisfies requirements.

[0211] Next, for interval B, a judgment matrix was constructed for each scheme-level measure relative to each criterion. For example, relative to "Support Measures Effectiveness (C1)," anti-slide piles (S1) are the most directly effective and have the highest weight (e.g., 0.6). Other measures (drainage and monitoring) have an indirect impact on support effectiveness and have lower weights (e.g., S2 = 0.1, S3 = 0.2, S4 = 0.1). Relative to "Drainage Measures Effectiveness (C2)," deep drainage holes (S3) are the most effective (e.g., 0.5), followed by intercepting drainage ditches (S2) (e.g., 0.3). Support and monitoring have minimal impact (S1 = 0.1, S4 = 0.1). Relative to "Monitoring and Early Warning Timeliness (C3)," automated monitoring (S4) is the most relevant (e.g., 0.7), while other measures have low correlations (S1 = 0.1, S2 = 0.1, S3 = 0.1). Through calculation, the weight vectors for each criterion at the scheme level were obtained.

[0212] Finally, calculate the comprehensive weight of each plan (measure), which is the weighted sum of its weights under each criterion (the weight is the criterion-level weight):

[0213] Comprehensive weight (S1) = ;

[0214] Comprehensive weight (S2) = ;

[0215] Comprehensive weight (S3) = ;

[0216] Comprehensive weight (S4) = ;

[0217] The calculated comprehensive weights are arranged in descending order: S1 (0.350) > S3 (0.282) > S4 (0.202) > S2 (0.166). This indicates that for pile number interval B, the priority order of measures is: anti-slide pile support > deep drainage holes > automated displacement monitoring > slope intercepting drainage ditches.

[0218] Bind this ranking result to pile interval B. Repeat this ranking process for all pile intervals that require intervention, and ultimately output a detailed segmented engineering control plan that clearly defines the combination of measures to be taken for each risk section, their respective priorities, and key design parameters.

[0219] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A safety engineering integrated management system, characterized in that: The system comprises: The risk threshold warning module is used to receive lithologic mutation markers and perform spatial cluster analysis. It compares the mean of the layer thickness mutation rate and strength attenuation rate based on the sliding window length dynamically adjusted based on the borehole spacing and the geological tectonic activity cycle. When the threshold is exceeded, a segmented engineering warning signal is generated. The sliding average model regression analysis is used to determine the window weight coefficient. The warning signal distribution map and parameter prediction trend are generated and transmitted to the comprehensive risk management module. The stability weight assessment module is used to calculate the grid slope variation and slope aspect dispersion through the digital elevation model, generate the terrain instability index based on the density of elevation anomaly points, normalize the slope variation using the maximum and minimum value method, output the prevention and control recommendation priority and instability index distribution, and pass it to the comprehensive risk management module; The comprehensive risk management and control module is used to receive the early warning signal distribution map matching the pile number interval, call the parameter prediction trend to determine the direction of risk evolution, combine the prevention and control recommendation priority with the instability index distribution to generate a pile number-measure mapping relationship table, use the hierarchical analysis method to construct the criterion layer weights of support, drainage, and monitoring measures, and output a segmented engineering management and control plan bound to the pile number.

2. The safety engineering integrated management system according to claim 1, characterized in that: The segmented engineering warning signal includes the warning level, warning section range, and warning trigger type. The warning signal distribution map includes the warning pile number distribution, spatial risk concentration area, and warning signal density. The parameter prediction trend includes the layer thickness change trend, the strength evolution trend, and the comprehensive risk change trend. The terrain instability index includes the slope change index, the slope dispersion index, and the elevation anomaly index. The prevention and control recommendation priorities are specifically support priority, drainage priority, and monitoring priority. The instability index distribution includes high-risk instability areas, medium-risk instability areas, and low-risk instability areas. The pile number-measure mapping relationship table includes pile number corresponding support measures, pile number corresponding drainage measures, and pile number corresponding monitoring measures. The segmented engineering management and control plan includes segmented support plan, segmented drainage plan, and segmented monitoring plan.

3. The safety engineering integrated management system according to claim 2 is characterized in that: The dynamic adjustment rule of the sliding window length is that when the borehole spacing is less than 10 meters and the tectonic activity cycle is less than 1 year, the window length is set to 5 layers; The normalization formula of the maximum and minimum value method is: slope amplitude = (original value - minimum value) / (maximum value - minimum value); The criterion layer weight includes a support measure weight that is dynamically allocated according to the product result of the instability index distribution and the parameter prediction trend.

4. The safety engineering integrated management system according to claim 3 is characterized in that: The risk threshold warning module includes: The spatial clustering submodule calls the lithologic mutation markers, extracts the coordinate data of the marker points, sets a cluster radius threshold based on the inverse proportional relationship between the layer thickness mutation rate and the marker point density, calculates the Euclidean distance between the marker points, merges the marker points with a distance less than the threshold into the same group, and generates lithologic cluster partitions according to the spatial distribution of the groups; The inverse proportional relationship sets the cluster radius threshold as cluster radius = 10 meters / (layer thickness mutation rate × 0.1 + 1); The mean comparison submodule calls the lithologic cluster partition, the layer thickness mutation rate, and the strength attenuation rate, intercepts continuous data segments within the partition according to a preset window length, calculates the arithmetic mean of the mutation rate and the attenuation rate within the window, compares the mean with the engineering risk threshold set based on the regional geological stability classification, and generates an over-threshold interval; The regional geological stability classification setting rules are as follows: the threshold value of the first-level stable area is 0.3, the threshold value of the second-level medium area is 0.5, and the threshold value of the third-level high-risk area is 0.7; The early warning generation submodule calls the above-threshold interval, performs a sliding average calculation on the mutation rate and attenuation rate within the interval according to the time series, sets the window weight coefficient through historical data regression analysis to determine that the previous window is 0.7 times the current window, and generates an early warning signal distribution map and parameter prediction trend.

5. The safety engineering integrated management system according to claim 4 is characterized in that: The stability weight evaluation module includes: The slope feature quantification submodule obtains digital elevation model data, calculates the slope angle difference between adjacent elevation points, extracts the maximum and minimum slope angle differences within the grid cell, generates slope amplitude, converts the aspect angle into polar coordinates, calculates the standard deviation to eliminate periodic effects, and outputs the aspect dispersion. The instability index calculation submodule calls the slope variation value for normalization processing, converts the slope dispersion into a positive correlation coefficient using the inverse proportional function f(x)=1 / (x+1), superimposes the elevation anomaly point density data, and performs a weighted product operation according to the 4:3:3 weight based on the geological parameter sensitivity analysis to generate the terrain instability index of the grid cell center point; The geological parameter sensitivity analysis determines the contribution weights of slope variation, slope aspect dispersion, and elevation anomaly point density through principal component analysis. The prevention and control measures grading submodule divides the terrain instability index into four intervals based on the fuzzy comprehensive evaluation method, defines the vertex parameters of the triangle membership function as instability indices 0.2, 0.5, and 0.8, generates the prevention and control priority coefficient, and outputs the global instability index distribution.

6. The safety engineering integrated management system according to claim 5, characterized in that: The comprehensive risk management and control module includes: The pile number interval analysis submodule obtains the pixel coordinates and warning intensity values ​​of the warning signal distribution map, converts the horizontal and vertical coordinates into pile number segment identifiers based on the pile number coding rules, calculates the absolute value of the warning intensity difference between adjacent pile numbers, compares it with the warning fluctuation threshold set according to the historical disaster frequency of the region, and generates a pile number interval table; The setting rule for the regional historical disaster frequency is: when the disaster frequency is ≥ 5 times / year, the fluctuation threshold = 0.5; The measure mapping generation submodule, based on the pile number interval table, calls the parameter prediction trend to determine the direction of risk evolution, extracts the level value of the prevention and control recommendation priority and the grid unit value of the instability index distribution, divides the branch protection measures into anchor support, pile foundation support, and retaining wall support types according to the priority level, establishes a many-to-one mapping relationship between the pile number and the measure type and intensity, and generates a pile number-measure mapping table; The measure priority ranking submodule uses the hierarchical analysis method to construct the criterion layer and scheme layer structure, sets the ratio of the support measure weight to the drainage measure weight to 3:2, calculates the weight value of each measure for pile number risk control, and generates a measure priority ranking by arranging the weight values ​​in descending order. The segmented engineering control plan is output by binding the pile number interval.

7. The safety engineering integrated management system according to claim 6, characterized in that: The system further comprises: The geological parameter dynamic monitoring module is used to extract lithologic distribution, layer thickness sequence, and shear strength sequence through drilling profiles, calculate the layer thickness mutation rate and strength attenuation rate of adjacent sections, and use the coefficient of variation method combined with the dynamic coupling relationship between the layer thickness mutation rate and the strength attenuation rate to optimize the threshold generation logic, identify abnormal lithologic mutation areas, generate lithologic mutation markers, and transmit them to the risk threshold warning module.

8. The safety engineering integrated management system according to claim 7, characterized in that: The rock mutation markers specifically include mutation position identification, mutation area boundaries, and mutation type classification. The layer thickness mutation rate includes layer thickness variation, layer thickness variation degree, and local layer thickness difference. The strength attenuation rate includes shear strength variation, strength decline rate, and abnormal strength gradient.

9. The integrated safety engineering management system according to claim 8, characterized in that: The dynamic coupling relationship optimization threshold generation logic is to adjust the coefficient of variation threshold according to the correlation coefficient between the layer thickness mutation rate and the intensity attenuation rate. When the correlation coefficient is greater than 0.5, the threshold is increased to 1.8 times the mean coefficient of variation in the current data set.

10. The integrated safety engineering management system according to claim 9, characterized in that: The geological parameter dynamic monitoring module includes: The lithologic sequence extraction submodule obtains drilling profile data, divides lithologic layers using the interval segmentation method based on the mineral composition and particle size parameters in the lithologic classification standard, extracts lithologic codes for multiple layers, and calculates layer thickness and shear strength data to generate layer thickness sequences and shear strength sequences. The mutation rate calculation submodule calls the layer thickness sequence, calculates the absolute value of the difference between the current layer thickness and the previous layer thickness in the order of adjacent layers, performs the difference calculation after unifying the layer thickness unit into meters, divides the difference by the previous layer thickness, and obtains the layer thickness mutation rate of multiple layers; calls the shear strength sequence, calculates the absolute value of the difference between the current layer strength and the previous layer strength in the order of adjacent layers, performs the difference calculation after unifying the shear strength unit into megapascals, and divides the difference by the previous layer strength to obtain the strength attenuation rate of multiple layers; The anomaly identification submodule calls the layer thickness mutation rate and the intensity attenuation rate, calculates the mean and standard deviation of the two sequences, divides the standard deviation by the mean to obtain the coefficient of variation, and based on the lithologic mutation threshold generation rule verified by the historical geological disaster case database, screens the layer numbers whose coefficient of variation exceeds the threshold to generate lithologic mutation markers; The verification rule of the historical geological disaster case database is that when the layer thickness mutation rate and the intensity attenuation rate exceed the threshold at the same time, the coefficient of variation threshold is reduced to 1.2 times the mean.

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